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Response: HTTP/1.1 200 OK Content-Type: application/vnd.mendeley-document.1+json Date: Sat, 30 Sep 2023 00:18:05 GMT Vary: Accept-Encoding Vary: Accept-Encoding X-Mendeley-Trace-Id: O5iN5uSRCwg transfer-encoding: chunked Connection: keep-alive [{"title":"Siri , Siri , in my hand : Who ’ s the fairest in the land ? On the interpretations , illustrations , and implications of arti fi cial intelligence","type":"journal","authors":[{"first_name":"Andreas","last_name":"Kaplan"},{"first_name":"Michael","last_name":"Haenlein"}],"year":2019,"source":"Business Horizons","identifiers":{"doi":"10.1016/j.bushor.2018.08.004","issn":"0007-6813"},"keywords":["arti fi cial intelligence"],"pages":"15-25","volume":"62","issue":"1","websites":["https://doi.org/10.1016/j.bushor.2018.08.004"],"publisher":"\"Kelley School of Business, Indiana University\"","id":"19ff8474-f3c5-3e7c-8e65-4c81255b6793","created":"2020-07-26T20:11:46.534Z","file_attached":false,"profile_id":"3bf3b169-2b31-31f5-b60e-7285225335ce","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-07-27T13:10:40.802Z","read":false,"starred":false,"authored":false,"confirmed":true,"hidden":false,"citation_key":"Kaplan2019","private_publication":false},{"title":"Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems","type":"journal","authors":[{"first_name":"Jaimie","last_name":"Drozdal"},{"first_name":"Justin","last_name":"Weisz"},{"first_name":"Dakuo","last_name":"Wang"},{"first_name":"Gaurav","last_name":"Dass"},{"first_name":"Bingsheng","last_name":"Yao"},{"first_name":"Changruo","last_name":"Zhao"},{"first_name":"Michael","last_name":"Muller"},{"first_name":"Lin","last_name":"Ju"},{"first_name":"Hui","last_name":"Su"}],"year":2020,"identifiers":{"isbn":"9781450371186","doi":"10.1145/3377325.3377501","arxiv":"2001.06509"},"keywords":["AutoAI, AutoML, AutoDS, Automated Artificial Intel","acm reference format","auto-","autoai","autods","automated artificial intelligence","automated data science","automl","mated machine learning","trust"],"pages":"297-307","websites":["http://arxiv.org/abs/2001.06509%0Ahttp://dx.doi.org/10.1145/3377325.3377501"],"id":"46cdc3dc-0b77-3fc3-aea9-b5de69a6bab9","created":"2020-07-23T19:57:50.473Z","file_attached":false,"profile_id":"3bf3b169-2b31-31f5-b60e-7285225335ce","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-10-23T09:05:50.214Z","read":true,"starred":false,"authored":false,"confirmed":true,"hidden":false,"citation_key":"Drozdal2020","notes":"1","private_publication":false,"abstract":"We explore trust in a relatively new area of data science: Automated Machine Learning (AutoML). In AutoML, AI methods are used to generate and optimize machine learning models by automatically engineering features, selecting models, and optimizing hyperparameters. In this paper, we seek to understand what kinds of information influence data scientists' trust in the models produced by AutoML? We operationalize trust as a willingness to deploy a model produced using automated methods. We report results from three studies -- qualitative interviews, a controlled experiment, and a card-sorting task -- to understand the information needs of data scientists for establishing trust in AutoML systems. We find that including transparency features in an AutoML tool increased user trust and understandability in the tool; and out of all proposed features, model performance metrics and visualizations are the most important information to data scientists when establishing their trust with an AutoML tool."},{"title":"Measuring Human-Computer Trust","type":"journal","authors":[{"first_name":"Maria","last_name":"Madsen"},{"first_name":"Shirley","last_name":"Gregor"}],"year":2000,"source":"Proceedings of Eleventh Australasian Conference on Information Systems","keywords":["decision support","expert systems","human-computer trust","instrument development","survey research","systems"],"pages":"6-8","websites":["http://books.google.com/books?hl=en&lr=&id=b0yalwi1HDMC&oi=fnd&pg=PA102&dq=The+Big+Five+Trait+Taxonomy:+History,+measurement,+and+Theoretical+Perspectives&ots=758BNaTvOi&sig=L52e79TS6r0Fp2m6xQVESnGt8mw%5Cnhttp://citeseerx.ist.psu.edu/viewdoc/download?doi="],"id":"006a116f-a51b-322f-a8dc-3fe762df1f54","created":"2020-07-23T19:57:50.395Z","file_attached":false,"profile_id":"3bf3b169-2b31-31f5-b60e-7285225335ce","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-07-24T06:57:46.537Z","read":false,"starred":false,"authored":false,"confirmed":true,"hidden":false,"citation_key":"Madsen2000","private_publication":false,"abstract":"In this study a psychometric instrument specifically designed to measure human-computer trust (HCT) was developed and tested. A rigorous method similar to that described by Moore and Benbasat (1991) was adopted. It was found that both cognitive and affective components of trust could be measured and that, in this study, the affective components were the strongest indicators of trust. The reliability of the instrument, measured as Cronbach's alpha, was 0.94. This instrument is the first of its kind to be specifically designed to measure HCT and shown empirically to be valid and reliable. Keywords"},{"title":"AutoAIViz: Opening the blackbox of automated artificial intelligence with conditional parallel coordinates","type":"journal","authors":[{"first_name":"Daniel Karl I.","last_name":"Weidele"},{"first_name":"Justin D.","last_name":"Weisz"},{"first_name":"Erick","last_name":"Oduor"},{"first_name":"Michael","last_name":"Muller"},{"first_name":"Josh","last_name":"Andres"},{"first_name":"Alexander","last_name":"Gray"},{"first_name":"Dakuo","last_name":"Wang"}],"year":2020,"source":"International Conference on Intelligent User Interfaces, Proceedings IUI","identifiers":{"isbn":"9781450371186","doi":"10.1145/3377325.3377538"},"keywords":["AutoAI","AutoML","democratizing AI","human-AI collaboration","parallel coordinates","visualization"],"pages":"308-312","id":"5110e0f9-4dc7-3c17-89fd-91c4f996f10d","created":"2020-07-23T19:57:50.387Z","file_attached":false,"profile_id":"3bf3b169-2b31-31f5-b60e-7285225335ce","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-07-24T06:57:46.225Z","read":true,"starred":false,"authored":false,"confirmed":true,"hidden":false,"citation_key":"Weidele2020","private_publication":false,"abstract":"Artificial Intelligence (AI) can now automate the algorithm selection, feature engineering, and hyperparameter tuning steps in a machine learning workflow. Commonly known as AutoML or AutoAI, these technologies aim to relieve data scientists from the tedious manual work. However, today's AutoAI systems often present only limited to no information about the process of how they select and generate model results. Thus, users often do not understand the process, neither do they trust the outputs. In this short paper, we provide a first user evaluation by 10 data scientists of an experimental system, AutoAIViz, that aims to visualize AutoAI's model generation process. We find that the proposed system helps users to complete the data science tasks, and increases their understanding, toward the goal of increasing trust in the AutoAI system."},{"title":"Joint optimization of AI fairness and utility: A human-centered approach","type":"journal","authors":[{"first_name":"Yunfeng","last_name":"Zhang"},{"first_name":"Rachel K.E.","last_name":"Bellamy"},{"first_name":"Kush R.","last_name":"Varshney"}],"year":2020,"source":"AIES 2020 - Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","identifiers":{"isbn":"9781450371100","doi":"10.1145/3375627.3375862","arxiv":"2002.01621"},"pages":"400-406","id":"5e250ce8-adb6-3d75-9085-e91623cd783f","created":"2020-07-23T19:25:32.544Z","file_attached":false,"profile_id":"3bf3b169-2b31-31f5-b60e-7285225335ce","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-07-24T06:57:46.176Z","read":false,"starred":true,"authored":false,"confirmed":true,"hidden":false,"citation_key":"Zhang2020","private_publication":false,"abstract":"Today, AI is increasingly being used in many high-stakes decisionmaking applications in which fairness is an important concern. Already, there are many examples of AI being biased and making questionable and unfair decisions. The AI research community has proposed many methods to measure and mitigate unwanted biases, but few of them involve inputs from human policy makers. We argue that because different fairness criteria sometimes cannot be simultaneously satisfied, and because achieving fairness often requires sacrificing other objectives such as model accuracy, it is key to acquire and adhere to human policy makers' preferences on how to make the tradeoff among these objectives. In this paper, we propose a framework and some exemplar methods for eliciting such preferences and for optimizing an AI model according to these preferences."},{"title":"Does projection into use improve trust and exploration? An example with a cruise control system","type":"journal","authors":[{"first_name":"Béatrice","last_name":"Cahour"},{"first_name":"Jean François","last_name":"Forzy"}],"year":2009,"source":"Safety Science","identifiers":{"doi":"10.1016/j.ssci.2009.03.015","issn":"09257535"},"keywords":["Cruise control system","Driving activity","Exploration","System instruction","Trust","Use"],"pages":"1260-1270","volume":"47","issue":"9","websites":["http://dx.doi.org/10.1016/j.ssci.2009.03.015"],"publisher":"Elsevier Ltd","id":"c8ee7b52-8bf1-3f8c-aa46-422822425d90","created":"2020-07-23T19:14:07.938Z","file_attached":false,"profile_id":"3bf3b169-2b31-31f5-b60e-7285225335ce","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-07-24T06:57:46.488Z","read":true,"starred":false,"authored":false,"confirmed":true,"hidden":false,"citation_key":"Cahour2009","notes":"Good defintion of trust.
Focused on a cruise control system adn level fo trust
Instructions play an important role in warning subject","private_publication":false,"abstract":"We know that the systems which are trusted by the users are more often used, especially in a risky situation where they need to delegate control, but we still ignore largely the factors which improve trust in the systems. Our issue here was to explore whether the way we present the system to the users will have an effect on their confidence in it. In this study, we had nine subjects using for the first time a cruise control system on open road; before, we present the system to them in three different ways: (i) a function-oriented written presentation (G1), (ii) a use-oriented written presentation, \"augmented\" with difficult situations (G2) and (iii) a use-oriented film presentation (3). They evaluate their trust in the system on scales before the whole experiment, after the presentation and after the real use. At the end, they also have self-confrontation interviews, where they see the video of their driving and describe their activity, strategies and feelings. We then develop quantitative and qualitative analysis of trust, linked with specific situations of action. Our results indicate that the presentation of instructions lowers the evaluation of trust (and of efficiency) that conductors have a priori; they had constructed an a priori representation of a CCS that is rather idealistic, and realised, after reading the instructions, and above all after having watched a film, that its use is not so obvious as they had previously thought There is thenceforth a drop in trust that nevertheless goes up again after use of the system during driving We remark, from qualitative analyses of use experience of the regulator in real driving conditions, that this drop in trust in the system does not inhibit subjects in their use, and in particular, for subjects who have watched a film of projection into use They know more of the functions of the system in driving conditions, they produce less distorted reconstruction of the functioning, and they have a deeper level of understanding of the system. © 2009 Elsevier Ltd. All rights reserved."},{"title":"Deep Neural Networks Are More Accurate Than Humans at Detecting Sexual Orientation From Facial Images | Stanford Graduate School of Business","type":"journal","authors":[{"first_name":"Michal","last_name":"Kosinski"},{"first_name":"Wang","last_name":"Wang"}],"year":2017,"source":"Journal of Personality and Social Psychology (in press)","keywords":["computational social science","facial morphology","prenatal hormone theory","sexual orientation"],"pages":"246-257","volume":"114","issue":"2","websites":["https://www.gsb.stanford.edu/faculty-research/publications/deep-neural-networks-are-more-accurate-humans-detecting-sexual"],"id":"e77563a8-cfa6-35df-8c65-419ffe537efb","created":"2020-07-23T19:14:07.869Z","file_attached":false,"profile_id":"3bf3b169-2b31-31f5-b60e-7285225335ce","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-07-24T06:57:46.824Z","read":false,"starred":false,"authored":false,"confirmed":true,"hidden":false,"citation_key":"Kosinski2017","notes":"suggesting that artificial intelligence (AI) can detect whether people are gay or straight based on photos – said sexual orientation was just one of many characteristics that algorithms would be able to predict through facial recognition","private_publication":false},{"title":"AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias","type":"journal","authors":[{"first_name":"R. K.E.","last_name":"Bellamy"},{"first_name":"A.","last_name":"Mojsilovic"},{"first_name":"S.","last_name":"Nagar"},{"first_name":"K. Natesan","last_name":"Ramamurthy"},{"first_name":"J.","last_name":"Richards"},{"first_name":"D.","last_name":"Saha"},{"first_name":"P.","last_name":"Sattigeri"},{"first_name":"M.","last_name":"Singh"},{"first_name":"K. R.","last_name":"Varshney"},{"first_name":"Y.","last_name":"Zhang"},{"first_name":"K.","last_name":"Dey"},{"first_name":"M.","last_name":"Hind"},{"first_name":"S. C.","last_name":"Hoffman"},{"first_name":"S.","last_name":"Houde"},{"first_name":"K.","last_name":"Kannan"},{"first_name":"P.","last_name":"Lohia"},{"first_name":"J.","last_name":"Martino"},{"first_name":"S.","last_name":"Mehta"}],"year":2019,"source":"IBM Journal of Research and Development","identifiers":{"doi":"10.1147/JRD.2019.2942287","issn":"21518556"},"pages":"1-15","volume":"63","issue":"4-5","id":"f7e02df8-519f-393b-b831-9ec556b5c205","created":"2020-07-23T19:14:07.821Z","file_attached":false,"profile_id":"3bf3b169-2b31-31f5-b60e-7285225335ce","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-07-24T06:57:46.869Z","read":false,"starred":false,"authored":false,"confirmed":true,"hidden":false,"citation_key":"Bellamy2019","notes":"Fainess and the impossibility to meet it
AIF360: toolkit for detecting, understanding, and mitigating algorithmic biases","private_publication":false,"abstract":"Fairness is an increasingly important concern as machine learning models are used to support decision making in high-stakes applications such as mortgage lending, hiring, and prison sentencing. This article introduces a new open-source Python toolkit for algorithmic fairness, AI Fairness 360 (AIF360), released under an Apache v2.0 license (https://github.com/ibm/aif360). The main objectives of this toolkit are to help facilitate the transition of fairness research algorithms for use in an industrial setting and to provide a common framework for fairness researchers to share and evaluate algorithms. The package includes a comprehensive set of fairness metrics for datasets and models, explanations for these metrics, and algorithms to mitigate bias in datasets and models. It also includes an interactive Web experience that provides a gentle introduction to the concepts and capabilities for line-of-business users, researchers, and developers to extend the toolkit with their new algorithms and improvements and to use it for performance benchmarking. A built-in testing infrastructure maintains code quality."},{"title":"Fair, Transparent, and Accou","type":"generic","id":"b1c31fa8-ae94-33a9-9d25-6cb9337a42a1","created":"2020-07-23T19:14:07.812Z","file_attached":false,"profile_id":"3bf3b169-2b31-31f5-b60e-7285225335ce","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-07-23T19:14:07.812Z","read":false,"starred":false,"authored":false,"confirmed":false,"hidden":false,"private_publication":false},{"title":"It's only a computer: Virtual humans increase willingness to disclose","type":"journal","authors":[{"first_name":"Gale M.","last_name":"Lucas"},{"first_name":"Jonathan","last_name":"Gratch"},{"first_name":"Aisha","last_name":"King"},{"first_name":"Louis Philippe","last_name":"Morency"}],"year":2014,"source":"Computers in Human Behavior","identifiers":{"doi":"10.1016/j.chb.2014.04.043","issn":"07475632"},"keywords":["Clinical interviews","Computer-assisted assessment","Honest responding","Self-disclosure","Virtual humans"],"pages":"94-100","volume":"37","websites":["http://dx.doi.org/10.1016/j.chb.2014.04.043"],"publisher":"Elsevier Ltd","id":"c9b6c8b7-4e4d-392c-8277-6257c881ef19","created":"2020-07-23T19:14:07.766Z","file_attached":false,"profile_id":"3bf3b169-2b31-31f5-b60e-7285225335ce","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-07-24T06:57:45.860Z","read":true,"starred":false,"authored":false,"confirmed":true,"hidden":false,"citation_key":"Lucas2014","notes":"Computer assesement tools. human subjects more willign to disclose information with technology","private_publication":false,"abstract":"Research has begun to explore the use of virtual humans (VHs) in clinical interviews (Bickmore, Gruber, & Picard, 2005). When designed as supportive and \"safe\" interaction partners, VHs may improve such screenings by increasing willingness to disclose information (Gratch, Wang, Gerten, & Fast, 2007). In health and mental health contexts, patients are often reluctant to respond honestly. In the context of health-screening interviews, we report a study in which participants interacted with a VH interviewer and were led to believe that the VH was controlled by either humans or automation. As predicted, compared to those who believed they were interacting with a human operator, participants who believed they were interacting with a computer reported lower fear of self-disclosure, lower impression management, displayed their sadness more intensely, and were rated by observers as more willing to disclose. These results suggest that automated VHs can help overcome a significant barrier to obtaining truthful patient information. © 2014 Elsevier Ltd. All rights reserved."},{"title":"Fair, Transparent, and Accountable Algorithmic Decision-making Processes: The Premise, the Proposed Solutions, and the Open Challenges","type":"journal","authors":[{"first_name":"Bruno","last_name":"Lepri"},{"first_name":"Nuria","last_name":"Oliver"},{"first_name":"Emmanuel","last_name":"Letouzé"},{"first_name":"Alex","last_name":"Pentland"},{"first_name":"Patrick","last_name":"Vinck"}],"year":2018,"source":"Philosophy and Technology","identifiers":{"doi":"10.1007/s13347-017-0279-x","issn":"22105441"},"keywords":["Accountability","Algorithmic decision-making","Algorithmic transparency","Fairness","Social good"],"pages":"611-627","volume":"31","issue":"4","id":"f5605ac0-43ff-34eb-92d6-894f87311ed7","created":"2020-07-23T19:14:07.755Z","file_attached":false,"profile_id":"3bf3b169-2b31-31f5-b60e-7285225335ce","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-07-24T06:57:46.377Z","read":false,"starred":false,"authored":false,"confirmed":true,"hidden":false,"citation_key":"Lepri2018","private_publication":false,"abstract":"The combination of increased availability of large amounts of fine-grained human behavioral data and advances in machine learning is presiding over a growing reliance on algorithms to address complex societal problems. Algorithmic decision-making processes might lead to more objective and thus potentially fairer decisions than those made by humans who may be influenced by greed, prejudice, fatigue, or hunger. However, algorithmic decision-making has been criticized for its potential to enhance discrimination, information and power asymmetry, and opacity. In this paper, we provide an overview of available technical solutions to enhance fairness, accountability, and transparency in algorithmic decision-making. We also highlight the criticality and urgency to engage multi-disciplinary teams of researchers, practitioners, policy-makers, and citizens to co-develop, deploy, and evaluate in the real-world algorithmic decision-making processes designed to maximize fairness and transparency. In doing so, we describe the Open Algortihms (OPAL) project as a step towards realizing the vision of a world where data and algorithms are used as lenses and levers in support of democracy and development."},{"title":"Explaining models: An empirical study of how explanations impact fairness judgment","type":"journal","authors":[{"first_name":"Jonathan","last_name":"Dodge"},{"first_name":"Q.","last_name":"Vera Liao"},{"first_name":"Yunfeng","last_name":"Zhang"},{"first_name":"Rachel K.E.","last_name":"Bellamy"},{"first_name":"Casey","last_name":"Dugan"}],"year":2019,"source":"International Conference on Intelligent User Interfaces, Proceedings IUI","identifiers":{"isbn":"9781450362726","doi":"10.1145/3301275.3302310","arxiv":"1901.07694"},"keywords":["Empirical Studies","Explanation","Fairness","Machine Learning"],"pages":"275-285","volume":"Part F1476","id":"46f083cb-0a17-3500-975d-e068dc8f3980","created":"2020-07-23T19:14:07.720Z","file_attached":false,"profile_id":"3bf3b169-2b31-31f5-b60e-7285225335ce","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-07-24T06:57:46.205Z","read":true,"starred":false,"authored":false,"confirmed":true,"hidden":false,"citation_key":"Dodge2019","private_publication":false,"abstract":"Ensuring fairness of machine learning systems is a human-in-the-loop process. It relies on developers, users, and the general public to identify fairness problems and make improvements. To facilitate the process we need efective, unbiased, and user-friendly explanations that people can conidently rely on. Towards that end, we conducted an empirical study with four types of programmatically generated explanations to understand how they impact people's fairness judgments of ML systems. With an experiment involving more than 160 Mechanical Turk workers, we show that: 1) Certain explanations are considered inherently less fair, while others can enhance people's conidence in the fairness of the algorithm; 2) Diferent fairness problems-such as model-wide fairness issues versus case-speciic fairness discrepancies-may be more efectively exposed through diferent styles of explanation; 3) Individual diferences, including prior positions and judgment criteria of algorithmic fairness, impact how people react to diferent styles of explanation. We conclude with a discussion on providing personalized and adaptive explanations to support fairness judgments of ML systems."},{"title":"The New Science of Sentencing","type":"journal","authors":[{"first_name":"Anna Maria","last_name":"Barry-Jester"},{"first_name":"Ben","last_name":"Casselman"},{"first_name":"Dana","last_name":"Goldstein"}],"year":2015,"source":"The Marshall Project","pages":"1-9","id":"9c28f6e5-c9dd-3759-8497-252e2a6e1bdb","created":"2020-07-23T19:14:07.635Z","file_attached":false,"profile_id":"3bf3b169-2b31-31f5-b60e-7285225335ce","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-07-24T06:57:46.157Z","read":false,"starred":false,"authored":false,"confirmed":true,"hidden":false,"citation_key":"Barry-Jester2015","private_publication":false,"abstract":"Should prison sentences be based on crimes that haven’t been committed yet?"},{"title":"One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques","type":"journal","authors":[{"first_name":"Vijay","last_name":"Arya"},{"first_name":"Rachel K. E.","last_name":"Bellamy"},{"first_name":"Pin-Yu","last_name":"Chen"},{"first_name":"Amit","last_name":"Dhurandhar"},{"first_name":"Michael","last_name":"Hind"},{"first_name":"Samuel C.","last_name":"Hoffman"},{"first_name":"Stephanie","last_name":"Houde"},{"first_name":"Q. Vera","last_name":"Liao"},{"first_name":"Ronny","last_name":"Luss"},{"first_name":"Aleksandra","last_name":"Mojsilović"},{"first_name":"Sami","last_name":"Mourad"},{"first_name":"Pablo","last_name":"Pedemonte"},{"first_name":"Ramya","last_name":"Raghavendra"},{"first_name":"John","last_name":"Richards"},{"first_name":"Prasanna","last_name":"Sattigeri"},{"first_name":"Karthikeyan","last_name":"Shanmugam"},{"first_name":"Moninder","last_name":"Singh"},{"first_name":"Kush R.","last_name":"Varshney"},{"first_name":"Dennis","last_name":"Wei"},{"first_name":"Yunfeng","last_name":"Zhang"}],"year":2019,"identifiers":{"arxiv":"1909.03012"},"websites":["http://arxiv.org/abs/1909.03012"],"id":"b2e63c92-bd30-325d-bbc9-15fbdcf63726","created":"2020-07-23T19:14:07.551Z","file_attached":false,"profile_id":"3bf3b169-2b31-31f5-b60e-7285225335ce","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-07-24T06:57:45.891Z","read":false,"starred":false,"authored":false,"confirmed":true,"hidden":false,"citation_key":"Arya2019","notes":"Deep explanation of AI rules that sell IBMs system as the best","private_publication":false,"abstract":"As artificial intelligence and machine learning algorithms make further inroads into society, calls are increasing from multiple stakeholders for these algorithms to explain their outputs. At the same time, these stakeholders, whether they be affected citizens, government regulators, domain experts, or system developers, present different requirements for explanations. Toward addressing these needs, we introduce AI Explainability 360 (http://aix360.mybluemix.net/), an open-source software toolkit featuring eight diverse and state-of-the-art explainability methods and two evaluation metrics. Equally important, we provide a taxonomy to help entities requiring explanations to navigate the space of explanation methods, not only those in the toolkit but also in the broader literature on explainability. For data scientists and other users of the toolkit, we have implemented an extensible software architecture that organizes methods according to their place in the AI modeling pipeline. We also discuss enhancements to bring research innovations closer to consumers of explanations, ranging from simplified, more accessible versions of algorithms, to tutorials and an interactive web demo to introduce AI explainability to different audiences and application domains. Together, our toolkit and taxonomy can help identify gaps where more explainability methods are needed and provide a platform to incorporate them as they are developed."},{"title":"Self disclosure on computer forms: meta-analysis and implications","type":"journal","authors":[{"first_name":"Suzanne","last_name":"Weisband"},{"first_name":"Sara","last_name":"Kiesler"}],"year":1996,"source":"Conference on Human Factors in Computing Systems - Proceedings","identifiers":{"isbn":"0897917774"},"keywords":["computer forms","computer interviews","disclosure","electronic","electronic surveys","measurement","response bias"],"pages":"3-10","id":"1ed1c75e-090b-3fff-91db-7adb0c23016b","created":"2020-07-23T19:14:07.539Z","file_attached":false,"profile_id":"3bf3b169-2b31-31f5-b60e-7285225335ce","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-07-24T06:57:45.843Z","read":true,"starred":false,"authored":false,"confirmed":true,"hidden":false,"citation_key":"Weisband1996","private_publication":false,"abstract":"Do people disclose more on a computer form than they do in an interview or on a paper form? We report a statistical meta-analysis of the literature from 1969 to 1994. Across 39 studies using 100 measures, computer administration increased self-disclosure. Effect sizes were larger comparing computer administration with face-to-face interviews, when forms solicited sensitive information, and when medical or psychiatric patients were the subjects. Effect sizes were smaller but had not disappeared in recent studies, which we attribute in part to changes in computer interfaces. We discuss research, ethical, policy, and design implications."},{"title":"Prolegomena to a White Paper on an Ethical Framework for a Good AI Society","type":"journal","authors":[{"first_name":"Josh","last_name":"Cowls"},{"first_name":"Luciano","last_name":"Floridi"}],"year":2018,"source":"SSRN Electronic Journal","identifiers":{"doi":"10.2139/ssrn.3198732"},"id":"81e3f16f-91d2-3350-af8c-ece55acb4120","created":"2020-07-23T19:14:07.537Z","file_attached":false,"profile_id":"3bf3b169-2b31-31f5-b60e-7285225335ce","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-07-24T06:57:46.703Z","read":true,"starred":true,"authored":false,"confirmed":true,"hidden":false,"citation_key":"Cowls2018","notes":"How AI can be manipulated
Evils of AI
Opportunites versus risks","private_publication":false,"abstract":"That AI will have a major impact on society is no longer in question. Current debate turns instead on how far this impact will be positive or negative, for whom, in which ways, in which places, and on what timescale. In order to frame these questions in a more substantive way, in this prolegomena we introduce what we consider the four core opportunities for society offered by the use of AI, four associated risks which could emerge from its overuse or misuse, and the opportunity costs associated with its under use. We then offer a high-level view of the emerging advantages for organisations of taking an ethical approach to developing and deploying AI. Finally, we introduce a set of five principles which should guide the development and deployment of AI technologies. The development of laws, policies and best practices for seizing the opportunities and minimizing the risks posed by AI technologies would benefit from building on ethical frameworks such as the one offered here."},{"title":"Machine Bias","type":"journal","authors":[{"first_name":"Lauren","last_name":"Kirchner"},{"first_name":"Surya","last_name":"Mattu"},{"first_name":"Jeff","last_name":"Larson"},{"first_name":"Julia","last_name":"Angwin"}],"year":2016,"source":"Propublica","pages":"1-26","websites":["https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing"],"id":"0492dc4c-39fa-30ba-81e0-8e42f9353026","created":"2020-07-23T19:14:07.530Z","file_attached":false,"profile_id":"3bf3b169-2b31-31f5-b60e-7285225335ce","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-07-24T06:57:46.985Z","read":false,"starred":false,"authored":false,"confirmed":true,"hidden":false,"citation_key":"Kirchner2016","notes":"Sentnecing recomendation engines bias","private_publication":false,"abstract":"Scores like this — known as risk assessments — are increasingly common in courtrooms across the nation. They are used to inform decisions about who can be set free at every stage of the criminal justice system, from assigning bond amounts — as is the case in Fort Lauderdale — to even more fundamental decisions about defendants’ freedom. In Arizona, Colorado, Delaware, Kentucky, Louisiana, Oklahoma, Virginia, Washington and Wisconsin, the results of such assessments are given to judges during criminal sentencing"},{"title":"The Moral Machine experiment","type":"journal","authors":[{"first_name":"Edmond","last_name":"Awad"},{"first_name":"Sohan","last_name":"Dsouza"},{"first_name":"Richard","last_name":"Kim"},{"first_name":"Jonathan","last_name":"Schulz"},{"first_name":"Joseph","last_name":"Henrich"},{"first_name":"Azim","last_name":"Shariff"},{"first_name":"Jean François","last_name":"Bonnefon"},{"first_name":"Iyad","last_name":"Rahwan"}],"year":2018,"source":"Nature","identifiers":{"doi":"10.1038/s41586-018-0637-6","issn":"14764687","pmid":"30356211"},"pages":"59-64","volume":"563","issue":"7729","id":"e56a26d2-e6cb-39d7-9c86-e13b13e1ced3","created":"2020-07-23T19:14:07.513Z","file_attached":false,"profile_id":"3bf3b169-2b31-31f5-b60e-7285225335ce","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-07-24T06:57:46.503Z","read":false,"starred":false,"authored":false,"confirmed":true,"hidden":false,"citation_key":"Awad2018","private_publication":false,"abstract":"With the rapid development of artificial intelligence have come concerns about how machines will make moral decisions, and the major challenge of quantifying societal expectations about the ethical principles that should guide machine behaviour. To address this challenge, we deployed the Moral Machine, an online experimental platform designed to explore the moral dilemmas faced by autonomous vehicles. This platform gathered 40 million decisions in ten languages from millions of people in 233 countries and territories. Here we describe the results of this experiment. First, we summarize global moral preferences. Second, we document individual variations in preferences, based on respondents’ demographics. Third, we report cross-cultural ethical variation, and uncover three major clusters of countries. Fourth, we show that these differences correlate with modern institutions and deep cultural traits. We discuss how these preferences can contribute to developing global, socially acceptable principles for machine ethics. All data used in this article are publicly available."},{"title":"The ethical, legal and social implications of using artificial intelligence systems in breast cancer care","type":"journal","authors":[{"first_name":"Stacy M.","last_name":"Carter"},{"first_name":"Wendy","last_name":"Rogers"},{"first_name":"Khin Than","last_name":"Win"},{"first_name":"Helen","last_name":"Frazer"},{"first_name":"Bernadette","last_name":"Richards"},{"first_name":"Nehmat","last_name":"Houssami"}],"year":2020,"source":"Breast","identifiers":{"doi":"10.1016/j.breast.2019.10.001","issn":"15323080","pmid":"31677530"},"keywords":["AI (Artificial Intelligence)","Breast carcinoma","Ethical Issues","Social values","Technology Assessment, Biomedical"],"pages":"25-32","volume":"49","websites":["https://doi.org/10.1016/j.breast.2019.10.001"],"publisher":"Elsevier Ltd","id":"40db2725-8faa-3199-899b-b916727d49ea","created":"2020-07-23T19:14:07.340Z","file_attached":false,"profile_id":"3bf3b169-2b31-31f5-b60e-7285225335ce","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-07-24T06:57:47.071Z","read":true,"starred":false,"authored":false,"confirmed":true,"hidden":false,"citation_key":"Carter2020","private_publication":false,"abstract":"Breast cancer care is a leading area for development of artificial intelligence (AI), with applications including screening and diagnosis, risk calculation, prognostication and clinical decision-support, management planning, and precision medicine. We review the ethical, legal and social implications of these developments. We consider the values encoded in algorithms, the need to evaluate outcomes, and issues of bias and transferability, data ownership, confidentiality and consent, and legal, moral and professional responsibility. We consider potential effects for patients, including on trust in healthcare, and provide some social science explanations for the apparent rush to implement AI solutions. We conclude by anticipating future directions for AI in breast cancer care. Stakeholders in healthcare AI should acknowledge that their enterprise is an ethical, legal and social challenge, not just a technical challenge. Taking these challenges seriously will require broad engagement, imposition of conditions on implementation, and pre-emptive systems of oversight to ensure that development does not run ahead of evaluation and deliberation. Once artificial intelligence becomes institutionalised, it may be difficult to reverse: a proactive role for government, regulators and professional groups will help ensure introduction in robust research contexts, and the development of a sound evidence base regarding real-world effectiveness. Detailed public discussion is required to consider what kind of AI is acceptable rather than simply accepting what is offered, thus optimising outcomes for health systems, professionals, society and those receiving care."},{"title":"When eliminating bias isn't fair: Algorithmic reductionism and procedural justice in human resource decisions","type":"journal","authors":[{"first_name":"David T.","last_name":"Newman"},{"first_name":"Nathanael J.","last_name":"Fast"},{"first_name":"Derek J.","last_name":"Harmon"}],"year":2020,"source":"Organizational Behavior and Human Decision Processes","identifiers":{"doi":"10.1016/j.obhdp.2020.03.008","issn":"07495978"},"keywords":["Algorithms","Fairness","People analytics","Procedural justice"],"pages":"149-167","volume":"160","issue":"March","websites":["https://doi.org/10.1016/j.obhdp.2020.03.008"],"publisher":"Elsevier","id":"0c2d27b5-a3ec-37b5-9a97-42dfb834bb08","created":"2020-07-23T19:14:07.339Z","file_attached":false,"profile_id":"3bf3b169-2b31-31f5-b60e-7285225335ce","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-07-24T06:57:46.651Z","read":false,"starred":false,"authored":false,"confirmed":true,"hidden":false,"citation_key":"Newman2020","private_publication":false,"abstract":"The perceived fairness of decision-making procedures is a key concern for organizations, particularly when evaluating employees and determining personnel outcomes. Algorithms have created opportunities for increasing fairness by overcoming biases commonly displayed by human decision makers. However, while HR algorithms may remove human bias in decision making, we argue that those being evaluated may perceive the process as reductionistic, leading them to think that certain qualitative information or contextualization is not being taken into account. We argue that this can undermine their beliefs about the procedural fairness of using HR algorithms to evaluate performance by promoting the assumption that decisions made by algorithms are based on less accurate information than identical decisions made by humans. Results from four laboratory experiments (N = 798) and a large-scale randomized experiment in an organizational setting (N = 1654) confirm this hypothesis. Theoretical and practical implications for organizations using algorithms and data analytics are discussed."},{"title":"Intriguing properties of neural networks","type":"conference_proceedings","authors":[{"first_name":"Christian","last_name":"Szegedy"},{"first_name":"Wojciech","last_name":"Zaremba"},{"first_name":"Ilya","last_name":"Sutskever"},{"first_name":"Joan","last_name":"Bruna"},{"first_name":"Dumitru","last_name":"Erhan"},{"first_name":"Ian","last_name":"Goodfellow"},{"first_name":"Rob","last_name":"Fergus"}],"year":2014,"source":"2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings","identifiers":{"arxiv":"1312.6199"},"month":12,"publisher":"International Conference on Learning Representations, ICLR","day":21,"id":"b485c3ce-6739-3704-8b66-ad539c8a9d2f","created":"2020-07-23T19:14:07.338Z","accessed":"2020-05-26","file_attached":false,"profile_id":"3bf3b169-2b31-31f5-b60e-7285225335ce","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-07-24T06:57:46.540Z","read":false,"starred":false,"authored":false,"confirmed":true,"hidden":false,"citation_key":"Szegedy2014","private_publication":false,"abstract":"Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we report two such properties. First, we find that there is no distinction between individual high level units and random linear combinations of high level units, according to various methods of unit analysis. It suggests that it is the space, rather than the individual units, that contains the semantic information in the high layers of neural networks. Second, we find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extent. We can cause the network to misclassify an image by applying a certain hardly perceptible perturbation, which is found by maximizing the network’s prediction error. In addition, the specific nature of these perturbations is not a random artifact of learning: the same perturbation can cause a different network, that was trained on a different subset of the dataset, to misclassify the same input."},{"title":"Sustainable decision making: The role of decision support systems","type":"journal","authors":[{"first_name":"Marion A.","last_name":"Hersh"}],"year":1999,"source":"IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews","identifiers":{"doi":"10.1109/5326.777075","issn":"10946977"},"pages":"395-408","volume":"29","issue":"3","id":"40f7865d-53ff-39d3-898a-2840c5181947","created":"2020-07-23T19:14:07.337Z","file_attached":false,"profile_id":"3bf3b169-2b31-31f5-b60e-7285225335ce","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-07-24T06:57:46.876Z","read":false,"starred":false,"authored":false,"confirmed":true,"hidden":false,"citation_key":"Hersh1999","private_publication":false,"abstract":"Sustainable decision making stands for decision making which contributes to the transition to a sustainable society. It raises a number of challenging problems for which existing decision support systems (DSS) may not be equipped. In this work, the role of DSS in sustainable decision making is considered. The different models of decision making and their appropriateness in sustainable decision making are discussed. Examples from the areas of water resources and energy planning and management are presented to illustrate some of the issues in sustainable decision making and the role of DSS. Conclusions include a suggested research program for further development of models of decision making and the development of DSS for use in sustainable decision making."},{"title":"Trust in automated systems","type":"journal","authors":[{"first_name":"Barbara D","last_name":"Adams"},{"first_name":"Lora E","last_name":"Bruyn"}],"year":2003,"keywords":["Automated systems","Trust"],"pages":"136","issue":"June","id":"d2ec5af4-ea08-36cd-8b3f-757e27f6dcfe","created":"2020-07-23T19:14:07.335Z","file_attached":false,"profile_id":"3bf3b169-2b31-31f5-b60e-7285225335ce","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-07-24T06:57:45.848Z","read":false,"starred":false,"authored":false,"confirmed":true,"hidden":false,"citation_key":"Adams2003","private_publication":false,"abstract":"This report reviews research literature pertaining to trust in automated systems. Based on the review, we argue that trust in automation has many similarities with trust in the interpersonal domain, but also several unique dynamics and influences. Existing research has focused primarily on trust in automation that has an executive or control function, and to a lesser extent, has considered trust in automation that is designed to present information to operators (e.g. decision aids). We maintain that although there are many similarities between trust in automation and interpersonal trust, the dynamics of trust in automation also have some distinct qualities. Several models related to trust in automation have already been developed; in this report, a comprehensive -- although still preliminary -- model of trust in military automation is proposed. Several sets of factors are likely to impact on the development of trust in automation, including properties of the automation, properties of the operator, and properties of the context in which interaction with automation occurs. The consequences of trust in automation have yet to be fully explored. Based on this review, measures and methods to study trust in automation are considered, and a program of research to study trust in automated systems is described."},{"title":"Using Social Agents to Explore Theories of Rapport and Emotional Resonance","type":"journal","authors":[{"first_name":"Jonathan","last_name":"Gratch"}],"year":2013,"source":"Etica e Politica","identifiers":{"isbn":"9780199682676","doi":"10.1093/acprof","issn":"18255167"},"keywords":["Intuitionism","Moral education","Moral imagination"],"pages":"583-605","volume":"15","issue":"1","id":"c20a5566-d32e-38e7-91b5-8592c4b43b68","created":"2020-07-23T19:14:07.334Z","file_attached":false,"profile_id":"3bf3b169-2b31-31f5-b60e-7285225335ce","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-07-24T06:57:45.853Z","read":true,"starred":false,"authored":false,"confirmed":true,"hidden":false,"citation_key":"Gratch2013","private_publication":false,"abstract":"Temporary employment has become a focus of policy debate, theory, and research. This book addresses the relationship between temporary employment contracts and employee well-being. It does so within the analytic framework of the psychological contract, and advances theory and knowledge about the psychological contract by exploring it from a variety of perspectives. It also sets the psychological contract within the context of a range of other potential influences on work-related well-being including workload, job insecurity, employability, and organizational support. The book identifies the relative importance of these various potential influences on well-being, covering seven countries; Belgium, Germany, The Netherlands, Spain, Sweden, and the UK, as well as Israel as a comparator outside Europe. The book's conclusions are interesting and controversial. The central finding is that contrary to expectations, temporary workers report higher well-being than permanent workers. As expected, a range of factors help to explain variations in work-related well-being and the research highlights the important role of the psychological contract. However, even after taking into account alternative explanations, the significant influence of type of employment contract remains, with temporary workers reporting higher well-being. In addition to this core finding, by exploring several aspects of the psychological contract, and taking into account both employer and employee perspectives, the book sheds light on the nature and role of the psychological contract. It also raises some challenging policy questions and while acknowledging the potentially precarious nature of temporary jobs, highlights the need to consider the increasingly demanding nature of permanent jobs and their effects on the well-being of employees."},{"title":"K-Algorithm: A Modified Technique for Noise Removal in Handwritten Documents","type":"journal","authors":[{"first_name":"Kanika","last_name":"Bansal"},{"first_name":"Rajiv","last_name":"Kumar"}],"year":2013,"source":"International Journal of Information Sciences and Techniques","identifiers":{"doi":"10.5121/ijist.2013.3301","issn":"2319409X"},"pages":"1-8","volume":"3","issue":"3","month":5,"publisher":"Academy and Industry Research Collaboration Center (AIRCC)","day":31,"id":"4183535a-1fd4-3638-900a-9050c279267e","created":"2020-06-17T06:45:44.274Z","file_attached":true,"profile_id":"55ea2d84-4911-3c59-84b1-123f60c38cdb","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-06-17T06:45:44.360Z","read":false,"starred":false,"authored":false,"confirmed":false,"hidden":false,"private_publication":false,"abstract":"OCR has been an active research area since last few decades. OCR performs the recognition of the text in the scanned document image and converts it into editable form. The OCR process can have several stages like preprocessing, segmentation, recognition and post processing. The preprocessing stage is a crucial stage for the success of OCR, which mainly deals with noise removal. In the present paper, a modified technique for noise removal named as \" K-Algorithm \" has been proposed, which has two stages as filtering and binarization. The proposed technique shows improvised results in comparison to median filtering technique."},{"title":"The role of magnetic susceptibility in magnetic resonance imaging: MRI magnetic compatibility of the first and second kinds","type":"journal","authors":[{"first_name":"John F.","last_name":"Schenck"}],"year":1996,"source":"Medical Physics","identifiers":{"doi":"10.1118/1.597854","pmid":"8798169","sgr":"0030013529","issn":"00942405","scopus":"2-s2.0-0030013529","pui":"26192503"},"keywords":["electromagnetic units","image-guided therapy","magnetic field compatibility","magnetic susceptibility","positional accuracy","safety of MR imaging"],"pages":"815-850","volume":"23","issue":"6","month":6,"publisher":"John Wiley and Sons Ltd","day":1,"id":"c56009f0-d997-3610-945f-648fff47d72b","created":"2020-05-30T08:06:15.556Z","accessed":"2020-05-30","file_attached":false,"profile_id":"140dd142-2ab3-3826-aae7-8b51ce67581b","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-05-30T08:06:15.556Z","read":false,"starred":false,"authored":false,"confirmed":false,"hidden":false,"private_publication":false,"abstract":"The concept of magnetic susceptibility is central to many current research and development activities in magnetic resonance imaging (MRI); for example, the development of MR-guided surgery has created a need for surgical instruments and other devices with susceptibility tailored to the MR environment; susceptibility effects can lead to position errors of up to several millimeters in MR-guided stereotactic surgery; and the variation of magnetic susceptibility on a microscopic scale within tissues contributes to MR contrast and is the basis of functional MRI. The magnetic aspects of MR compatibility are discussed in terms of two levels of acceptability: Materials with the first kind of magnetic field compatibility are such that magnetic forces and torques do not interfere significantly when the materials are used within the magnetic field of the scanner; materials with the second kind of magnetic field compatibility meet the more demanding requirement that they produce only negligible artifacts within the MR image and their effect on the positional accuracy of features within the image is negligible or can readily be corrected. Several materials exhibiting magnetic field compatibility of the second kind have been studied and a group of materials that produce essentially no image distortion, even when located directly within the imaging field of view, is identified. Because of demagnetizing effects, the shape and orientation, as well as the susceptibility, of objects within and adjacent to the imaging region is important in MRI. The quantitative use of susceptibility data is important to MRI, but the use of literature values for the susceptibility of materials is often difficult because of inconsistent traditions in the definitions and units used for magnetic parameters-particularly susceptibility. The uniform use of SI units for magnetic susceptibility and related quantities would help to achieve consistency and avoid confusion in MRI."},{"title":"Adversarial Attacks and Defenses in Deep Learning","type":"journal","authors":[{"first_name":"Kui","last_name":"Ren"},{"first_name":"Tianhang","last_name":"Zheng"},{"first_name":"Zhan","last_name":"Qin"},{"first_name":"Xue","last_name":"Liu"}],"year":2020,"source":"Engineering","identifiers":{"doi":"10.1016/j.eng.2019.12.012","issn":"20958099"},"keywords":["Adversarial attack","Adversarial defense","Adversarial example","Deep neural network","Machine learning"],"pages":"346-360","volume":"6","issue":"3","month":3,"publisher":"Elsevier Ltd","day":1,"id":"7a9699d5-1f05-3ccb-9a01-b4bf4fa0bd64","created":"2020-05-26T08:16:58.562Z","accessed":"2020-05-26","file_attached":false,"profile_id":"140dd142-2ab3-3826-aae7-8b51ce67581b","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-06-03T14:52:49.389Z","read":false,"starred":false,"authored":false,"confirmed":true,"hidden":false,"citation_key":"Ren2020","private_publication":false,"abstract":"With the rapid developments of artificial intelligence (AI) and deep learning (DL) techniques, it is critical to ensure the security and robustness of the deployed algorithms. Recently, the security vulnerability of DL algorithms to adversarial samples has been widely recognized. The fabricated samples can lead to various misbehaviors of the DL models while being perceived as benign by humans. Successful implementations of adversarial attacks in real physical-world scenarios further demonstrate their practicality. Hence, adversarial attack and defense techniques have attracted increasing attention from both machine learning and security communities and have become a hot research topic in recent years. In this paper, we first introduce the theoretical foundations, algorithms, and applications of adversarial attack techniques. We then describe a few research efforts on the defense techniques, which cover the broad frontier in the field. Several open problems and challenges are subsequently discussed, which we hope will provoke further research efforts in this critical area."},{"title":"Guess First to Enable Better Compression and Adversarial Robustness","type":"journal","authors":[{"first_name":"Sicheng","last_name":"Zhu"},{"first_name":"Bang","last_name":"An"},{"first_name":"Shiyu","last_name":"Niu"}],"year":2020,"identifiers":{"arxiv":"2001.03311"},"websites":["http://arxiv.org/abs/2001.03311"],"month":1,"day":10,"id":"02af907c-2b68-3cdb-9977-0ccab646c66e","created":"2020-05-26T08:16:58.084Z","accessed":"2020-05-26","file_attached":false,"profile_id":"140dd142-2ab3-3826-aae7-8b51ce67581b","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-05-26T08:16:58.084Z","read":false,"starred":false,"authored":false,"confirmed":false,"hidden":false,"private_publication":false,"abstract":"Machine learning models are generally vulnerable to adversarial examples, which is in contrast to the robustness of humans. In this paper, we try to leverage one of the mechanisms in human recognition and propose a bio-inspired classification framework in which model inference is conditioned on label hypothesis. We provide a class of training objectives for this framework and an information bottleneck regularizer which utilizes the advantage that label information can be discarded during inference. This framework enables better compression of the mutual information between inputs and latent representations without loss of learning capacity, at the cost of tractable inference complexity. Better compression and elimination of label information further bring better adversarial robustness without loss of natural accuracy, which is demonstrated in the experiment."},{"title":"Automatic fetal brain extraction from 2D in utero fetal MRI slices using deep neural network","type":"journal","authors":[{"first_name":"Jinpeng","last_name":"Li"},{"first_name":"Yishan","last_name":"Luo"},{"first_name":"Lin","last_name":"Shi"},{"first_name":"Xin","last_name":"Zhang"},{"first_name":"Ming","last_name":"Li"},{"first_name":"Bing","last_name":"Zhang"},{"first_name":"Defeng","last_name":"Wang"}],"year":2020,"source":"Neurocomputing","identifiers":{"doi":"10.1016/j.neucom.2019.10.032","issn":"18728286"},"keywords":["Brain extraction","Deep learning","Fetal MRI","Fully connected network","Residual learning block","Segmentation"],"pages":"335-349","volume":"378","month":2,"publisher":"Elsevier B.V.","day":22,"id":"e1ab3f8c-50ee-31ee-a835-3b475de68d8e","created":"2020-05-26T08:16:57.888Z","accessed":"2020-05-26","file_attached":false,"profile_id":"140dd142-2ab3-3826-aae7-8b51ce67581b","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-05-26T08:16:57.888Z","read":false,"starred":false,"authored":false,"confirmed":false,"hidden":false,"private_publication":false,"abstract":"Background: In utero fetal MRI has been developing in common medical prenatal practice for nearly two decades. But the applications and research on fetal MRI still lag behind due to the lack of specialized image processing and analysis tools. Brain extraction, as an initial preprocessing step for many brain MRI-based processing methods, is an important basis for accurate fetal MRI analysis. However, it is very challenging to automatically extract fetal brains from fetal MRI due to the large variation in fetal brains across different gestational weeks and complex maternal tissues surrounding the fetal brains. Method: We proposed a novel two-step framework using the deep learning method for solving the challenging problem of automatic fetal brain extraction in 2D in utero fetal MRI slices. The proposed framework consisted of two fully convolutional network (FCN) models, i.e., a shallow FCN and an extra deep multi-scale FCN (M-FCN). The first shallow FCN rapidly located the fetal brain and extracted the region of interest (ROI) containing the brain. Then, within the brain ROI, the M-FCN further refined the segmentation and produced the final brain mask by leveraging the multi-scale information and residual learning blocks. Dilated convolutional layers were employed in both FCNs to control the size of feature maps and increase the field of view. Result: Eighty-eight 2D fetal MRIs were collected for experiments. We compared our method with the state-of-the-art methods on extracting fetal brains. It has been evaluated that our proposed framework outperformed the other methods in both fetal brain localization and segmentation tasks. With the proposed method, we located the fetal brain with an accuracy of 100%. The brain segmentation performance was measured based on the overlap between the automatic segmentations and the manual segmentations. Our proposed method achieved an average of 0.958 Dice score, 0.950 sensitivity rate, and 0.968 precision on the testing dataset, and it took an average of 6 s to process one fetal MRI stack on a workstation with TITAN X GPU and i7-6700 CPU. Conclusion: In this paper, we proposed an effective and efficient deep learning framework for automatic fetal brain extraction from fetal MRI. It has been validated with solid experiments that the proposed method can be used as a practical and useful tool in clinical practice and neuroscience research."},{"title":"Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images","type":"journal","authors":[{"first_name":"Navid","last_name":"Ghassemi"},{"first_name":"Afshin","last_name":"Shoeibi"},{"first_name":"Modjtaba","last_name":"Rouhani"}],"year":2020,"source":"Biomedical Signal Processing and Control","identifiers":{"doi":"10.1016/j.bspc.2019.101678","issn":"17468108"},"keywords":["Brain tumor classification","Deep neural networks","Generative adversarial network (GAN)","Magnetic resonance imaging (MRI)"],"pages":"101678","volume":"57","month":3,"publisher":"Elsevier Ltd","day":1,"id":"0293f821-c543-30c8-a3f0-bc203304b640","created":"2020-05-26T08:16:57.885Z","accessed":"2020-05-26","file_attached":false,"profile_id":"140dd142-2ab3-3826-aae7-8b51ce67581b","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-05-26T08:16:57.885Z","read":false,"starred":false,"authored":false,"confirmed":false,"hidden":false,"private_publication":false,"abstract":"In this paper, a new deep learning method for tumor classification in MR images is presented. A deep neural network is first pre-trained as a discriminator in a generative adversarial network (GAN) on different datasets of MR images to extract robust features and to learn the structure of MR images in its convolutional layers. Then the fully connected layers are replaced and the whole deep network is trained as a classifier to distinguish three tumor classes. The deep neural network classifier has six layers and about 1.7 million weight parameters. Pre-training as a discriminator of a GAN together with other techniques such as data augmentations (image rotation and mirroring) and dropout prevent the network from overtraining on a relatively small dataset. This method is applied to an MRI data set consists of 3064 T1-CE MR images from 233 patients, 13 images from each patient on average, with three different brain tumor types: meningioma (708 images), glioma (1426 images), and pituitary tumor (930 images). 5-Fold cross-validation is used to evaluate the performance of overall design, achieving the highest accuracy as compared to state-of-art methods."},{"title":"Prostate segmentation in MRI using a convolutional neural network architecture and training strategy based on statistical shape models","type":"journal","authors":[{"first_name":"Davood","last_name":"Karimi"},{"first_name":"Golnoosh","last_name":"Samei"},{"first_name":"Claudia","last_name":"Kesch"},{"first_name":"Guy","last_name":"Nir"},{"first_name":"Septimiu E.","last_name":"Salcudean"}],"year":2018,"source":"International Journal of Computer Assisted Radiology and Surgery","identifiers":{"doi":"10.1007/s11548-018-1785-8","issn":"18616429","pmid":"29766373"},"keywords":["Convolutional neural networks","Deep learning","Medical image segmentation","Prostate segmentation","Statistical shape models"],"pages":"1211-1219","volume":"13","issue":"8","month":8,"publisher":"Springer Verlag","day":1,"id":"72d0dd0d-9db8-3804-ae55-c66c1c7dbff3","created":"2020-05-26T08:16:57.880Z","accessed":"2020-05-26","file_attached":false,"profile_id":"140dd142-2ab3-3826-aae7-8b51ce67581b","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-05-26T08:16:57.880Z","read":false,"starred":false,"authored":false,"confirmed":false,"hidden":false,"private_publication":false,"abstract":"Purpose: Most of the existing convolutional neural network (CNN)-based medical image segmentation methods are based on methods that have originally been developed for segmentation of natural images. Therefore, they largely ignore the differences between the two domains, such as the smaller degree of variability in the shape and appearance of the target volume and the smaller amounts of training data in medical applications. We propose a CNN-based method for prostate segmentation in MRI that employs statistical shape models to address these issues. Methods: Our CNN predicts the location of the prostate center and the parameters of the shape model, which determine the position of prostate surface keypoints. To train such a large model for segmentation of 3D images using small data (1) we adopt a stage-wise training strategy by first training the network to predict the prostate center and subsequently adding modules for predicting the parameters of the shape model and prostate rotation, (2) we propose a data augmentation method whereby the training images and their prostate surface keypoints are deformed according to the displacements computed based on the shape model, and (3) we employ various regularization techniques. Results: Our proposed method achieves a Dice score of 0.88, which is obtained by using both elastic-net and spectral dropout for regularization. Compared with a standard CNN-based method, our method shows significantly better segmentation performance on the prostate base and apex. Our experiments also show that data augmentation using the shape model significantly improves the segmentation results. Conclusions: Prior knowledge about the shape of the target organ can improve the performance of CNN-based segmentation methods, especially where image features are not sufficient for a precise segmentation. Statistical shape models can also be employed to synthesize additional training data that can ease the training of large CNNs."},{"title":"Deep learning","type":"generic","authors":[{"first_name":"Yann","last_name":"Lecun"},{"first_name":"Yoshua","last_name":"Bengio"},{"first_name":"Geoffrey","last_name":"Hinton"}],"year":2015,"source":"Nature","identifiers":{"doi":"10.1038/nature14539","pmid":"26017442","sgr":"84930630277","issn":"14764687","scopus":"2-s2.0-84930630277","pui":"604536300"},"keywords":["Computer science","Mathematics and computing"],"pages":"436-444","volume":"521","issue":"7553","month":5,"publisher":"Nature Publishing Group","day":27,"id":"a0d7a723-9e81-35d4-88f6-c07a4774f0a3","created":"2020-05-26T08:11:26.259Z","accessed":"2020-05-26","file_attached":false,"profile_id":"140dd142-2ab3-3826-aae7-8b51ce67581b","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-05-26T08:11:26.259Z","read":false,"starred":false,"authored":false,"confirmed":false,"hidden":false,"private_publication":false,"abstract":"Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech."},{"title":"Explaining and harnessing adversarial examples","type":"conference_proceedings","authors":[{"first_name":"Ian J.","last_name":"Goodfellow"},{"first_name":"Jonathon","last_name":"Shlens"},{"first_name":"Christian","last_name":"Szegedy"}],"year":2015,"source":"3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings","identifiers":{"sgr":"85068958388","scopus":"2-s2.0-85068958388","arxiv":"1412.6572","pui":"628979431"},"month":12,"publisher":"International Conference on Learning Representations, ICLR","day":20,"id":"04d9b8b7-69cb-372b-b72d-8dd8dcc1c646","created":"2020-05-26T08:10:41.826Z","accessed":"2020-05-26","file_attached":true,"profile_id":"140dd142-2ab3-3826-aae7-8b51ce67581b","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-05-26T08:10:51.621Z","read":false,"starred":false,"authored":false,"confirmed":false,"hidden":false,"private_publication":false,"abstract":"Several machine learning models, including neural networks, consistently misclassify adversarial examples—inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence. Early attempts at explaining this phenomenon focused on nonlinearity and overfitting. We argue instead that the primary cause of neural networks’ vulnerability to adversarial perturbation is their linear nature. This explanation is supported by new quantitative results while giving the first explanation of the most intriguing fact about them: their generalization across architectures and training sets. Moreover, this view yields a simple and fast method of generating adversarial examples. Using this approach to provide examples for adversarial training, we reduce the test set error of a maxout network on the MNIST dataset."},{"title":"Hierarchical imitation and reinforcement learning","type":"conference_proceedings","authors":[{"first_name":"Hoang M.","last_name":"Le"},{"first_name":"Nan","last_name":"Jiang"},{"first_name":"Alekh","last_name":"Agarwal"},{"first_name":"Miroslav","last_name":"Dudík"},{"first_name":"Yisong","last_name":"Yue"},{"first_name":"Hal","last_name":"Daumé"}],"year":2018,"source":"35th International Conference on Machine Learning, ICML 2018","identifiers":{"isbn":"9781510867963","sgr":"85057287900","issn":"1938-7228","scopus":"2-s2.0-85057287900","arxiv":"1803.00590","pui":"625196436"},"pages":"4560-4573","volume":"7","month":5,"publisher":"International Machine Learning Society (IMLS)","day":24,"id":"8d08d1b7-f333-382f-9b6a-45e002c9eb5e","created":"2020-05-26T08:09:54.405Z","accessed":"2020-05-26","file_attached":false,"profile_id":"140dd142-2ab3-3826-aae7-8b51ce67581b","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-05-26T08:09:54.405Z","read":false,"starred":false,"authored":false,"confirmed":false,"hidden":false,"private_publication":false,"abstract":"We study how to effectively leverage expert feedback to learn sequential decision-making policies. We focus on problems with sparse rewards and long time horizons, which typically pose significant challenges in reinforcement learning. We propose an algorithmic framework, called hierarchical guidance, that leverages the hierarchical structure of the underlying problem to integrate different modes of expert interaction. Our framework can incorporate different combinations of imitation learning (IL) and reinforcement learning (RL) at different levels, leading to dramatic reductions in both expert effort and cost of exploration. Using long-horizon benchmarks, including Montezuma's Revenge, we demonstrate that our approach can learn significantly faster than hierarchical RL, and be significantly more label-efficient than standard IL. We also theoretically analyze labeling cost for certain instantiations of our framework."},{"title":"An Overview of Low-Rank Matrix Recovery from Incomplete Observations","type":"generic","authors":[{"first_name":"Mark A.","last_name":"Davenport"},{"first_name":"Justin","last_name":"Romberg"}],"year":2016,"source":"IEEE Journal on Selected Topics in Signal Processing","identifiers":{"doi":"10.1109/JSTSP.2016.2539100","sgr":"84970998198","issn":"19324553","scopus":"2-s2.0-84970998198","pui":"610479106"},"pages":"608-622","volume":"10","issue":"4","month":6,"publisher":"Institute of Electrical and Electronics Engineers Inc.","day":1,"id":"e557f154-8324-3f67-a5dc-0d908628bdfe","created":"2020-05-26T08:09:07.830Z","accessed":"2020-05-26","file_attached":false,"profile_id":"140dd142-2ab3-3826-aae7-8b51ce67581b","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-05-26T08:09:07.830Z","read":false,"starred":false,"authored":false,"confirmed":false,"hidden":false,"private_publication":false,"abstract":"Low-rank matrices play a fundamental role in modeling and computational methods for signal processing and machine learning. In many applications where low-rank matrices arise, these matrices cannot be fully sampled or directly observed, and one encounters the problem of recovering the matrix given only incomplete and indirect observations. This paper provides an overview of modern techniques for exploiting low-rank structure to perform matrix recovery in these settings, providing a survey of recent advances in this rapidly-developing field. Specific attention is paid to the algorithms most commonly used in practice, the existing theoretical guarantees for these algorithms, and representative practical applications of these techniques."},{"title":"Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples","type":"journal","authors":[{"first_name":"Nicolas","last_name":"Papernot"},{"first_name":"Patrick","last_name":"McDaniel"},{"first_name":"Ian","last_name":"Goodfellow"}],"year":2016,"identifiers":{"arxiv":"1605.07277"},"websites":["http://arxiv.org/abs/1605.07277"],"month":5,"day":23,"id":"19ae1a33-012a-3143-baba-bd19bfc69e26","created":"2020-05-26T08:08:34.240Z","accessed":"2020-05-26","file_attached":true,"profile_id":"140dd142-2ab3-3826-aae7-8b51ce67581b","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-05-26T08:08:43.434Z","read":false,"starred":false,"authored":false,"confirmed":false,"hidden":false,"private_publication":false,"abstract":"Many machine learning models are vulnerable to adversarial examples: inputs that are specially crafted to cause a machine learning model to produce an incorrect output. Adversarial examples that affect one model often affect another model, even if the two models have different architectures or were trained on different training sets, so long as both models were trained to perform the same task. An attacker may therefore train their own substitute model, craft adversarial examples against the substitute, and transfer them to a victim model, with very little information about the victim. Recent work has further developed a technique that uses the victim model as an oracle to label a synthetic training set for the substitute, so the attacker need not even collect a training set to mount the attack. We extend these recent techniques using reservoir sampling to greatly enhance the efficiency of the training procedure for the substitute model. We introduce new transferability attacks between previously unexplored (substitute, victim) pairs of machine learning model classes, most notably SVMs and decision trees. We demonstrate our attacks on two commercial machine learning classification systems from Amazon (96.19% misclassification rate) and Google (88.94%) using only 800 queries of the victim model, thereby showing that existing machine learning approaches are in general vulnerable to systematic black-box attacks regardless of their structure."},{"title":"Towards Deep Learning Models Resistant to Adversarial Attacks","type":"journal","authors":[{"first_name":"Aleksander","last_name":"Madry"},{"first_name":"Aleksandar","last_name":"Makelov"},{"first_name":"Ludwig","last_name":"Schmidt"},{"first_name":"Dimitris","last_name":"Tsipras"},{"first_name":"Adrian","last_name":"Vladu"}],"year":2017,"source":"6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings","identifiers":{"arxiv":"1706.06083"},"websites":["http://arxiv.org/abs/1706.06083"],"month":6,"publisher":"International Conference on Learning Representations, ICLR","day":19,"id":"45a51d0d-652e-34d1-b87e-0e478bcc9441","created":"2020-05-26T08:07:15.264Z","accessed":"2020-05-26","file_attached":true,"profile_id":"140dd142-2ab3-3826-aae7-8b51ce67581b","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-05-26T08:07:49.361Z","read":false,"starred":false,"authored":false,"confirmed":false,"hidden":false,"private_publication":false,"abstract":"Recent work has demonstrated that deep neural networks are vulnerable to adversarial examples---inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of deep learning models. To address this problem, we study the adversarial robustness of neural networks through the lens of robust optimization. This approach provides us with a broad and unifying view on much of the prior work on this topic. Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal. In particular, they specify a concrete security guarantee that would protect against any adversary. These methods let us train networks with significantly improved resistance to a wide range of adversarial attacks. They also suggest the notion of security against a first-order adversary as a natural and broad security guarantee. We believe that robustness against such well-defined classes of adversaries is an important stepping stone towards fully resistant deep learning models. Code and pre-trained models are available at https://github.com/MadryLab/mnist_challenge and https://github.com/MadryLab/cifar10_challenge."},{"title":"Harnessing structures in big data via guaranteed low-rank matrix estimation: Recent theory and fast algorithms via convex and nonconvex optimization","type":"journal","authors":[{"first_name":"Yudong","last_name":"Chen"},{"first_name":"Yuejie","last_name":"Chi"}],"year":2018,"source":"IEEE Signal Processing Magazine","identifiers":{"doi":"10.1109/MSP.2018.2821706","sgr":"85049366742","issn":"10535888","scopus":"2-s2.0-85049366742","pui":"622796230"},"pages":"14-31","volume":"35","issue":"4","month":7,"publisher":"Institute of Electrical and Electronics Engineers Inc.","day":1,"id":"fad326a1-be24-3b1c-9e15-fbef3c76b1fc","created":"2020-05-26T08:06:37.509Z","accessed":"2020-05-26","file_attached":false,"profile_id":"140dd142-2ab3-3826-aae7-8b51ce67581b","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-05-26T08:06:37.509Z","read":false,"starred":false,"authored":false,"confirmed":false,"hidden":false,"private_publication":false,"abstract":"Low-rank modeling plays a pivotal role in signal processing and machine learning, with applications ranging from collaborative filtering, video surveillance, and medical imaging to dimensionality reduction and adaptive filtering. Many modern high-dimensional data and interactions thereof can be modeled as lying approximately in a low-dimensional subspace or manifold, possibly with additional structures, and its proper exploitations lead to significant cost reduction in sensing, computation, and storage. In recent years, there has been a plethora of progress in understanding how to exploit low-rank structures using computationally efficient procedures in a provable manner, including both convex and nonconvex approaches. On one side, convex relaxations such as nuclear norm minimization often lead to statistically optimal procedures for estimating low-rank matrices, where first-order methods are developed to address the computational challenges; on the other side, there is emerging evidence that properly designed nonconvex procedures, such as projected gradient descent, often provide globally optimal solutions with a much lower computational cost in many problems. This survey article provides a unified overview of these recent advances in low-rank matrix estimation from incomplete measurements. Attention is paid to rigorous characterization of the performance of these algorithms and to problems where the lowrank matrix has additional structural properties that require new algorithmic designs and theoretical analysis."},{"title":"Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey","type":"generic","authors":[{"first_name":"Naveed","last_name":"Akhtar"},{"first_name":"Ajmal","last_name":"Mian"}],"year":2018,"source":"IEEE Access","identifiers":{"doi":"10.1109/ACCESS.2018.2807385","sgr":"85042198914","issn":"21693536","scopus":"2-s2.0-85042198914","arxiv":"1801.00553","pui":"620775110"},"keywords":["Deep learning","adversarial learning","adversarial perturbation","black-box attack","perturbation detection","white-box attack"],"pages":"14410-14430","volume":"6","month":2,"publisher":"Institute of Electrical and Electronics Engineers Inc.","day":16,"id":"9cb658fa-1871-32f6-9fb0-c0268c2c9d24","created":"2020-05-26T08:06:00.133Z","accessed":"2020-05-26","file_attached":false,"profile_id":"140dd142-2ab3-3826-aae7-8b51ce67581b","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-05-26T08:06:00.133Z","read":false,"starred":false,"authored":false,"confirmed":false,"hidden":false,"private_publication":false,"abstract":"Deep learning is at the heart of the current rise of artificial intelligence. In the field of computer vision, it has become the workhorse for applications ranging from self-driving cars to surveillance and security. Whereas, deep neural networks have demonstrated phenomenal success (often beyond human capabilities) in solving complex problems, recent studies show that they are vulnerable to adversarial attacks in the form of subtle perturbations to inputs that lead a model to predict incorrect outputs. For images, such perturbations are often too small to be perceptible, yet they completely fool the deep learning models. Adversarial attacks pose a serious threat to the success of deep learning in practice. This fact has recently led to a large influx of contributions in this direction. This paper presents the first comprehensive survey on adversarial attacks on deep learning in computer vision. We review the works that design adversarial attacks, analyze the existence of such attacks and propose defenses against them. To emphasize that adversarial attacks are possible in practical conditions, we separately review the contributions that evaluate adversarial attacks in the real-world scenarios. Finally, drawing on the reviewed literature, we provide a broader outlook of this research direction."},{"title":"Brain MRI Image Classification for Cancer Detection Using Deep Wavelet Autoencoder-Based Deep Neural Network","type":"journal","authors":[{"first_name":"Pradeep","last_name":"Kumar Mallick"},{"first_name":"Seuc Ho","last_name":"Ryu"},{"first_name":"Sandeep Kumar","last_name":"Satapathy"},{"first_name":"Shruti","last_name":"Mishra"},{"first_name":"Gia Nhu","last_name":"Nguyen"},{"first_name":"Prayag","last_name":"Tiwari"}],"year":2019,"source":"IEEE Access","identifiers":{"doi":"10.1109/ACCESS.2019.2902252","sgr":"85065174366","issn":"21693536","scopus":"2-s2.0-85065174366","pui":"627426740"},"keywords":["Neural network (NN)","autoencoder (AE)","deep neural network (DNN)","image classification"],"pages":"46278-46287","volume":"7","publisher":"Institute of Electrical and Electronics Engineers Inc.","id":"7b308d1d-174d-303c-a455-50225dd00ee3","created":"2020-05-26T08:04:56.433Z","accessed":"2020-05-26","file_attached":false,"profile_id":"140dd142-2ab3-3826-aae7-8b51ce67581b","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-05-26T08:04:56.433Z","read":false,"starred":false,"authored":false,"confirmed":false,"hidden":false,"private_publication":false,"abstract":"Technology and the rapid growth in the area of brain imaging technologies have forever made for a pivotal role in analyzing and focusing the new views of brain anatomy and functions. The mechanism of image processing has widespread usage in the area of medical science for improving the early detection and treatment phases. Deep neural networks (DNN), till date, have demonstrated wonderful performance in classification and segmentation task. Carrying this idea into consideration, in this paper, a technique for image compression using a deep wavelet autoencoder (DWA), which blends the basic feature reduction property of autoencoder along with the image decomposition property of wavelet transform is proposed. The combination of both has a tremendous effect on sinking the size of the feature set for enduring further classification task by using DNN. A brain image dataset was taken and the proposed DWA-DNN image classifier was considered. The performance criterion for the DWA-DNN classifier was compared with other existing classifiers such as autoencoder-DNN or DNN, and it was noted that the proposed method outshines the existing methods."},{"title":"A survey on Image Data Augmentation for Deep Learning","type":"journal","authors":[{"first_name":"Connor","last_name":"Shorten"},{"first_name":"Taghi M.","last_name":"Khoshgoftaar"}],"year":2019,"source":"Journal of Big Data","identifiers":{"doi":"10.1186/s40537-019-0197-0","issn":"21961115"},"keywords":["Big data","Data Augmentation","Deep Learning","GANs","Image data"],"pages":"1-48","volume":"6","issue":"1","month":12,"publisher":"SpringerOpen","day":1,"id":"f77f21dd-0d9c-35e9-973d-9c23fadce284","created":"2020-05-26T08:04:01.779Z","accessed":"2020-05-26","file_attached":true,"profile_id":"140dd142-2ab3-3826-aae7-8b51ce67581b","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-07-24T06:57:46.169Z","read":false,"starred":false,"authored":false,"confirmed":true,"hidden":false,"citation_key":"Shorten2019","private_publication":false,"abstract":"Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. Unfortunately, many application domains do not have access to big data, such as medical image analysis. This survey focuses on Data Augmentation, a data-space solution to the problem of limited data. Data Augmentation encompasses a suite of techniques that enhance the size and quality of training datasets such that better Deep Learning models can be built using them. The image augmentation algorithms discussed in this survey include geometric transformations, color space augmentations, kernel filters, mixing images, random erasing, feature space augmentation, adversarial training, generative adversarial networks, neural style transfer, and meta-learning. The application of augmentation methods based on GANs are heavily covered in this survey. In addition to augmentation techniques, this paper will briefly discuss other characteristics of Data Augmentation such as test-time augmentation, resolution impact, final dataset size, and curriculum learning. This survey will present existing methods for Data Augmentation, promising developments, and meta-level decisions for implementing Data Augmentation. Readers will understand how Data Augmentation can improve the performance of their models and expand limited datasets to take advantage of the capabilities of big data."},{"title":"Adversarial attacks on medical machine learning","type":"journal","authors":[{"first_name":"Samuel G.","last_name":"Finlayson"},{"first_name":"John D.","last_name":"Bowers"},{"first_name":"Joichi","last_name":"Ito"},{"first_name":"Jonathan L.","last_name":"Zittrain"},{"first_name":"Andrew L.","last_name":"Beam"},{"first_name":"Isaac S.","last_name":"Kohane"}],"year":2019,"source":"Science","identifiers":{"doi":"10.1126/science.aaw4399","issn":"0036-8075"},"pages":"1287-1289","volume":"363","issue":"6433","websites":["https://www.sciencemag.org/lookup/doi/10.1126/science.aaw4399"],"month":3,"publisher":"American Association for the Advancement of Science","day":22,"id":"be371528-2a7d-3223-b032-73b46cb7e8ba","created":"2020-05-26T08:03:23.141Z","accessed":"2020-05-26","file_attached":false,"profile_id":"140dd142-2ab3-3826-aae7-8b51ce67581b","group_id":"a22f1e83-37bb-3020-b4e0-8f4116a078cd","last_modified":"2020-05-26T08:03:23.141Z","read":false,"starred":false,"authored":false,"confirmed":false,"hidden":false,"private_publication":false}]

Tengo que crear un estilo CSL para formatear estas entradas, averiguar bien cómo rescatar la información exacta que quiero de Mendeley (folders, groups…) y sacarla de la mejor manera (con URLs y enlaces, etc.).

Lo que tenemos en el grupo de Narratech Laboratories:

Mendeley Plugin: groupby = , sortby = year, sortorder = desc, filter =

Mendeley Plugin: Unfiltered results count: 239 ()

Jim Drey Cubillos Tellez, Jim Drey Cubillos Tellez (2021) Desarrollo y evaluación de un videojuego serio para tratar la nictofobia

Jim Drey Cubillos Tellez, Jim Drey Cubillos Tellez (2021) Desarrollo y evaluación de un videojuego serio para tratar la nictofobia

Omar Naranjo Caprachi, Gonzalo Pérez Martín, Omar Naranjo Caprachi, Gonzalo Pérez Martín (2021) Diseño y Desarrollo de un Conjunto de Herramientas de Producción para Facilitar la Accesibilidad Auditiva en Videojuegos

Pedro Pinto Neves, Nelson Zagalo (2021) Game Design as an Autonomous Research Subject, Information 2021, Vol. 12, Page 367 12(9), p. 367, Multidisciplinary Digital Publishing Institute, url, doi:10.3390/INFO12090367

Manuel López Ibáñez, Maximiliano Miranda, Nahum Alvarez, Federico Peinado (2021) Using Gestural Emotions Recognised Through a Neural Network as Input for an Adaptive Music System in Virtual Reality, Entertainment Computing, p. 100404, Elsevier, url, doi:10.1016/j.entcom.2021.100404

Adriana del Castillo Espejo-Saavedra, Raúl Serrano Gómez, Adriana del Castillo Espejo-Saavedra, Raúl Serrano Gómez (2020) Diseño y desarrollo de personajes con presencia social en videojuegos de realidad virtual

Diego Romero-Hombrebueno Santos, Mario Sánchez Blanco, José Manuel Sierra Ramos, Diego Romero-Hombrebueno Santos, Mario Sánchez Blanco, José Manuel Sierra Ramos (2020) Planificación automática para el comportamiento de personajes de videojuegos como extensión de Unreal Engine

José Javier Cortés Tejada, José Javier Cortés Tejada (2020) Creación de videojuegos de rol táctico mediante herramientas de desarrollo para el usuario final

Adriana del Castillo Espejo-Saavedra, Raúl Serrano Gómez, Adriana del Castillo Espejo-Saavedra, Raúl Serrano Gómez (2020) Diseño y desarrollo de personajes con presencia social en videojuegos de realidad virtual

Diego Romero-Hombrebueno Santos, Mario Sánchez Blanco, José Manuel Sierra Ramos, Diego Romero-Hombrebueno Santos, Mario Sánchez Blanco, José Manuel Sierra Ramos (2020) Planificación automática para el comportamiento de personajes de videojuegos como extensión de Unreal Engine

José Javier Cortés Tejada, José Javier Cortés Tejada (2020) Creación de videojuegos de rol táctico mediante herramientas de desarrollo para el usuario final

Adriana del Castillo Espejo-Saavedra, Raúl Serrano Gómez, Adriana del Castillo Espejo-Saavedra, Raúl Serrano Gómez (2020) Diseño y desarrollo de personajes con presencia social en videojuegos de realidad virtual

Alfredo Hernández Burgos, Alfredo Hernández Burgos (2020) Formación de instructores de simuladores de vuelo basada en escenarios virtuales de entrenamiento

Diego Romero-Hombrebueno Santos, Mario Sánchez Blanco, José Manuel Sierra Ramos, Diego Romero-Hombrebueno Santos, Mario Sánchez Blanco, José Manuel Sierra Ramos (2020) Planificación automática para el comportamiento de personajes de videojuegos como extensión de Unreal Engine

Cheng Zhen Meng, Yu Tang, Chen Yao Wu, Di Lin (2020) Battle Prediction System in StarCraft Combined with Topographic Considerations, Lecture Notes in Electrical Engineering 572 LNEE, p. 122-130, Springer, doi:10.1007/978-981-15-0187-6_14

Adriana del Castillo Espejo-Saavedra, Raúl Serrano Gómez, Adriana del Castillo Espejo-Saavedra, Raúl Serrano Gómez (2020) Diseño y desarrollo de personajes con presencia social en videojuegos de realidad virtual

() Diseño y desarrollo de personajes con presencia social en videojuegos de realidad virtual - E-Prints Complutense, url

José Javier Cortés Tejada, José Javier Cortés Tejada (2020) Creación de videojuegos de rol táctico mediante herramientas de desarrollo para el usuario final

() Creación de videojuegos de rol táctico mediante herramientas de desarrollo para el usuario final Creation of Tactical Role-Playing Games using Tools for End-User Development - E-Prints Complutense, url

Diego Romero-Hombrebueno Santos, Mario Sánchez Blanco, José Manuel Sierra Ramos, Diego Romero-Hombrebueno Santos, Mario Sánchez Blanco, José Manuel Sierra Ramos (2020) Planificación automática para el comportamiento de personajes de videojuegos como extensión de Unreal Engine

() Planificación automática para el comportamiento de personajes de videojuegos como extensión de Unreal Engine Automatic planning for video game characters behavior as Unreal Engine plugin - E-Prints Complutense, url

José Javier Cortés, Federico Peinado (2020) Development of a User-Friendly Application for Creating Tactical Role-Playing Games, url

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Gabriel Peñas, Federico Peinado () Towards Basic Emotion Recognition using Players Body and Hands Pose in Virtual Reality Narrative Experiences, url

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Diego Romero, Mario Sánchez, José Manuel Sierra, Maximiliano Miranda, Federico Peinado (2020) Developing an Automated Planning Tool for Non-Player Character Behavior, url

() (No Title), pdf

Alfredo Hernández, Rubén Dapica, Federico Peinado (2020) Who Trains the Trainers? Towards a Flight Instructors Simulator based on Training Scenarios, url

() (No Title), pdf

Maximiliano Miranda, Antonio A Sánchez-Ruiz, Federico Peinado () Pac-Man or Pac-Bot? Exploring Subjective Perception of Players' Humanity in Ms. Pac-Man, url

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Clara Fernández-Vara, Bennett Foddy () WELL PLAYED, url

Maximiliano Miranda, Antonio A Sánchez-Ruiz, Federico Peinado () A Neuroevolution Approach to Imitating Human-Like Play in Ms. Pac-Man Video Game, url

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M. Miranda, A.A. Sánchez-Ruiz, F. Peinado (2020) Building Non-player Character Behaviors By Imitation Using Interactive Case-Based Reasoning, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 12311 LNAI, doi:10.1007/978-3-030-58342-2_17

Jorge Osorio González, Federico Peinado Gil (2019) Hacia una herramienta metodológica que optimice la eficacia del discurso. Un caso de estudio aplicado a usuarios de videojuegos, Communication & Methods 1(2), p. 63-76, Communication and Methods, doi:10.35951/V1I2.26

Jaume Esteve, Federico Peinado (2019) Retos para diseñar una metodología para el estudio de la historia del videojuego en España, Communication & Methods 1(2), p. 181-195, Communication and Methods, doi:10.35951/V1I2.35

Jaume Esteve, Federico Peinado (2019) Retos para diseñar una metodología para el estudio de la historia del videojuego en España, Communication & Methods 1(2), p. 181-195, Communication and Methods, doi:10.35951/V1I2.35

Jorge Osorio González, Federico Peinado Gil (2019) Hacia una herramienta metodológica que optimice la eficacia del discurso. Un caso de estudio aplicado a usuarios de videojuegos, Communication & Methods 1(2), p. 63-76, Communication and Methods, doi:10.35951/V1I2.26

Jorge Algaba Aranda, Manuel Hernández Nájera-Alesón, Jorge Algaba Aranda, Manuel Hernández Nájera-Alesón (2019) Desarrollo de una aplicación para analizar y evaluar el comportamiento de oradores en realidad virtual

Jaime María Bas Domínguez, Álvaro Cuevas Álvarez, Alejandro García Montero, Juan Gómez-Martinho González, Irene González Velasco, Jaime María Bas Domínguez, Álvaro Cuevas Álvarez, Alejandro García Montero, Juan Gómez-Martinho González, Irene González Velasco (2019) Diseño de una Arquitectura de Sistemas Multiagente para Videojuegos basada en el modelo de Creencias, Deseos e Intenciones en Unity

Dae Wook Kim, Sungyun Park, Seong Il Yang (2019) An Analysis of the Effects of Physical Abilities on RTS Game Outcome Using Machine Learning Approach, ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future, p. 929-933, Institute of Electrical and Electronics Engineers Inc., doi:10.1109/ICTC46691.2019.8939771

Jorge Algaba Aranda, Manuel Hernández Nájera-Alesón, Jorge Algaba Aranda, Manuel Hernández Nájera-Alesón (2019) Desarrollo de una aplicación para analizar y evaluar el comportamiento de oradores en realidad virtual

() Desarrollo de una aplicación para analizar y evaluar el comportamiento de oradores en realidad virtual - E-Prints Complutense, url

Jaime María Bas Domínguez, Álvaro Cuevas Álvarez, Alejandro García Montero, Juan Gómez-Martinho González, Irene González Velasco, Jaime María Bas Domínguez, Álvaro Cuevas Álvarez, Alejandro García Montero, Juan Gómez-Martinho González, Irene González Velasco (2019) Diseño de una Arquitectura de Sistemas Multiagente para Videojuegos basada en el modelo de Creencias, Deseos e Intenciones en Unity

() Diseño de una Arquitectura de Sistemas Multiagente para Videojuegos basada en el modelo de Creencias, Deseos e Intenciones en Unity - E-Prints Complutense, url

Jorge Osorio González, Federico Peinado Gil (2019) Hacia una herramienta metodológica que optimice la eficacia del discurso. Un caso de estudio aplicado a usuarios de videojuegos, Communication & Methods 1(2), p. 63-76, Communication and Methods, doi:10.35951/v1i2.26

() Hacia una herramienta metodológica que optimice la eficacia del discurso: un caso de estudio aplicado a usuarios de videojuegos Towards a methodological tool for optimizing the efficacy of discourse analysis: a case study with video gamers - E-Prints Complutense, url

() Retos para diseñar una metodología para el estudio de la historia del videojuego en España | Comunicación y Métodos, url

Jaume Esteve, Federico Peinado (2019) Retos para diseñar una metodología para el estudio de la historia del videojuego en España, Communication & Methods 1(2), p. 181-195, Communication and Methods, url, doi:10.35951/v1i2.35

Jorge Osorio González, Federico Peinado Gil (2019) Hacia una herramienta metodológica que optimice la eficacia del discurso. Un caso de estudio aplicado a usuarios de videojuegos, Communication & Methods 1(2), p. 63-76, Communication and Methods, url, doi:10.35951/v1i2.26

() Hacia una herramienta metodológica que optimice la eficacia del discurso | Comunicación y Métodos, url

Elisenda Estanyol, Mireia Montaña, Antonio José Planells (2019) Jóvenes y videojuegos. Percepciones sobre su tratamiento informativo en los medios de comunicación, Estudios sobre el Mensaje Periodístico 25(1), p. 129-145, url, doi:10.5209/ESMP.63720

Coursera (2019) Story and Narrative Development for Video Games, url

㻌 㞷, Yasuda Yuki () Toward a Network Analysis of Empathic and Exciting AI Conversation and Scenario

Kazuko Takahashi, Shizuka Yokohama () On a Formal Treatment of Deception in Argumentative Dialogues

Nguyen The Tung, Koichiro Yoshino, Sakriani Sakti, Satoshi Nakamura () Utilizing deception information for dialog management of doctor-patient conversations

Sergio González Jiménez, Sergio González Jiménez (2018) Desarrollo de un Sistema Multiagente basado en creencias, deseos e intenciones para modelar personajes autónomos en videojuegos utilizando Jason y Unity

Sergio González Jiménez, Sergio González Jiménez (2018) Desarrollo de un Sistema Multiagente basado en creencias, deseos e intenciones para modelar personajes autónomos en videojuegos utilizando Jason y Unity

Sergio González Jiménez, Sergio González Jiménez (2018) Desarrollo de un Sistema Multiagente basado en creencias, deseos e intenciones para modelar personajes autónomos en videojuegos utilizando Jason y Unity

Marcelino Pérez Durán, Juan José Prieto Escolar, Marcelino Pérez Durán, Juan José Prieto Escolar (2018) Desarrollo de una herramienta de autoría en Unity para la creación de Juegos de Rol con combate basado en turnos

Sergio González Jiménez, Sergio González Jiménez (2018) Desarrollo de un Sistema Multiagente basado en creencias, deseos e intenciones para modelar personajes autónomos en videojuegos utilizando Jason y Unity

Marcelino Pérez Durán, Juan José Prieto Escolar, Marcelino Pérez Durán, Juan José Prieto Escolar (2018) Desarrollo de una herramienta de autoría en Unity para la creación de Juegos de Rol con combate basado en turnos

Sergio González Jiménez, Sergio González Jiménez (2018) Desarrollo de un Sistema Multiagente basado en creencias, deseos e intenciones para modelar personajes autónomos en videojuegos utilizando Jason y Unity

Marcelino Pérez Durán, Juan José Prieto Escolar, Marcelino Pérez Durán, Juan José Prieto Escolar (2018) Desarrollo de una herramienta de autoría en Unity para la creación de Juegos de Rol con combate basado en turnos

Víctor García Rodríguez, Jesús Menéndez Montejo, Víctor García Rodríguez, Jesús Menéndez Montejo (2018) Desarrollo de una plataforma para creación e investigación en videojuegos multijugador de deducción

Víctor García Rodríguez, Jesús Menéndez Montejo, Víctor García Rodríguez, Jesús Menéndez Montejo (2018) Desarrollo de una plataforma para creación e investigación en videojuegos multijugador de deducción

() Desarrollo de una plataforma para creación e investigación en videojuegos multijugador de deducción - E-Prints Complutense, url

Sergio González Jiménez, Sergio González Jiménez (2018) Desarrollo de un Sistema Multiagente basado en creencias, deseos e intenciones para modelar personajes autónomos en videojuegos utilizando Jason y Unity

() Desarrollo de un Sistema Multiagente basado en creencias, deseos e intenciones para modelar personajes autónomos en videojuegos utilizando Jason y Unity - E-Prints Complutense, url

G. Peñas, F. Peinado (2018) An approach to basic emotion recognition through players body pose using virtual reality devices, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 10714 LNCS, doi:10.1007/978-3-319-76270-8_5

M. López Ibáñez, N. Álvarez, F. Peinado (2018) Towards an emotion-driven adaptive system for video game music, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 10714 LNCS, doi:10.1007/978-3-319-76270-8_25

Pablo Díez de la Puente, David Valdivia Martínez, Sheng Zhu, Pablo Díez de la Puente, David Valdivia Martínez, Sheng Zhu (2017) Desarrollo de una aplicación cliente-servidor para análisis de experiencias de usuario en realidad virtual con seguimiento de movimiento del visor y reconocimiento gestual de las manos

Javier Druet Honrubia, Luis Alfonso González de la Calzada, Javier Druet Honrubia, Luis Alfonso González de la Calzada (2017) Desarrollo de una herramienta de creación de videojuegos de rol táctico para escenarios isométricos compuestos por bloques

Pablo Díez de la Puente, David Valdivia Martínez, Sheng Zhu, Pablo Díez de la Puente, David Valdivia Martínez, Sheng Zhu (2017) Desarrollo de una aplicación cliente-servidor para análisis de experiencias de usuario en realidad virtual con seguimiento de movimiento del visor y reconocimiento gestual de las manos

() Desarrollo de una aplicación cliente-servidor para análisis de experiencias de usuario en realidad virtual con seguimiento de movimiento del visor y reconocimiento gestual de las manos Development of a client-server application for the analysis of user experiences in virtual reality with visor movement tracking and hand signal recognition - E-Prints Complutense, url

Pablo Díez de la Puente, David Valdivia Martínez, Sheng Zhu, Pablo Díez de la Puente, David Valdivia Martínez, Sheng Zhu (2017) Desarrollo de una aplicación cliente-servidor para análisis de experiencias de usuario en realidad virtual con seguimiento de movimiento del visor y reconocimiento gestual de las manos

Pablo Díez de la Puente, David Valdivia Martínez, Sheng Zhu, Pablo Díez de la Puente, David Valdivia Martínez, Sheng Zhu (2017) Desarrollo de una aplicación cliente-servidor para análisis de experiencias de usuario en realidad virtual con seguimiento de movimiento del visor y reconocimiento gestual de las manos

Yusuf Pisan, Nikitas Marinos Sgouros, Tim Marsh (2017) Editorial, Entertainment Computing 22, p. 1, Elsevier B.V., doi:10.1016/j.entcom.2017.05.003

Antonio J. Fernández-Leiva, Antonio M. Mora García (2017) Preface for the special issue on Science in Videogames, Entertainment Computing 21, p. 95-96, Elsevier B.V., doi:10.1016/j.entcom.2017.02.001

Karyn Riddle, Zhen Di, Sunghak Kim, Eunyoung Myung, Swee Kiat Tay, Fangxin Xu (2017) The unexpected comfort of wearing headphones: Emotional and cognitive effects of headphone use when playing a bloody video game, Entertainment Computing 19, p. 43-52, Elsevier B.V., doi:10.1016/j.entcom.2016.10.004

Lidson Jacob, Esteban Clua, Daniel de Oliveira (2017) Oh Gosh!! Why is this game so hard? Identifying cycle patterns in 2D platform games using provenance data, Entertainment Computing 19, p. 65-81, Elsevier B.V., doi:10.1016/j.entcom.2016.12.002

() A machine learning approach to predict the winner in StarCraft based on influence maps - ScienceDirect, url

Antonio A. Sánchez-Ruiz, Maximiliano Miranda (2017) A machine learning approach to predict the winner in StarCraft based on influence maps, Entertainment Computing 19, p. 29-41, Elsevier B.V., doi:10.1016/j.entcom.2016.11.005

Javier Druet Honrubia, Luis Alfonso González de la Calzada, Javier Druet Honrubia, Luis Alfonso González de la Calzada (2017) Desarrollo de una herramienta de creación de videojuegos de rol táctico para escenarios isométricos compuestos por bloques

() Desarrollo de una herramienta de creación de videojuegos de rol táctico para escenarios isométricos compuestos por bloques - E-Prints Complutense, url

G. Peñas, F. Peinado (2017) Towards basic emotion recognition using players body and hands pose in virtual reality narrative experiences, CEUR Workshop Proceedings 1957

J. Esteve, F. Peinado (2017) FX interactive: Growth and decline of the Spanish video games publisher, CEUR Workshop Proceedings 1957

M.L. Ibáñez, N. Álvarez, F. Peinado (2017) A study on an efficient spatialisation technique for near-field sound in video games, CEUR Workshop Proceedings 1957

M. Miranda, A.A. Sánchez-Ruiz, F. Peinado (2017) Pac-Man or Pac-Bot? Exploring subjective perception of players’ humanity in Ms. Pac-Man, CEUR Workshop Proceedings 1957

Raghav Bhardwaj (2017) The Ergonomic Development of Video Game Controllers, doi:10.4172/2165-7556.1000209

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Alejandro Sánchez López, Fernando Romero de la Morena, Hector Martín Solís, Alejandro Sánchez López, Fernando Romero de la Morena, Hector Martín Solís (2016) Un sistema de control autónomo para personajes de videojuegos basado en el modelo cognitivo Creencia-Deseo-Intención

Alejandro Sánchez López, Fernando Romero de la Morena, Hector Martín Solís, Alejandro Sánchez López, Fernando Romero de la Morena, Hector Martín Solís (2016) Un sistema de control autónomo para personajes de videojuegos basado en el modelo cognitivo Creencia-Deseo-Intención

() Un sistema de control autónomo para personajes de videojuegos basado en el modelo cognitivo Creencia-Deseo-Intención - E-Prints Complutense, url

() La realidad virtual y su aplicación en el tratamiento de la demencia: Una revisión de la literatura científica | La Ciencia al Servicio de la Salud, url

R. Hervás, J. Arroyo, V. Francisco, F. Peinado, P. Gervás (2016) Influence of personal choices on lexical variability in referring expressions, Natural Language Engineering 22(2), doi:10.1017/S1351324915000182

M. Miranda, A.A. Sánchez-Ruiz, F. Peinado (2016) A neuroevolution approach to imitating human-like play in Ms. Pac-man video game, CEUR Workshop Proceedings 1682

M.L. Ibáñez, F. Peinado (2016) Walking in VR: Measuring presence and simulator sickness in first-person virtual reality games, CEUR Workshop Proceedings 1682

Daniel Vella (2016) "Who Am 'I' in the Game?": A Typology of the Modes of Ludic Subjectivity

() (17) (PDF) Measuring Presence: The Temple Presence Inventory, url

() Protege4Pizzas10Minutes - Protege Wiki, url

Santiago Ontañón, Gabriel Synnaeve, Alberto Uriarte, Florian Richoux, David Churchill, Mike Preuss (2015) RTS AI Problems and Techniques, Encyclopedia of Computer Graphics and Games, p. 1-12, Springer International Publishing, doi:10.1007/978-3-319-08234-9_17-1

N. Alvarez, F. Peinado (2015) Modelling suspicion as a game mechanism for designing a computer-played investigation character, CEUR Workshop Proceedings 1394(January)

M. Miranda, F. Peinado (2015) Improving the performance of a computer-controlled player in a maze chase game using evolutionary Programming on a finite-state machine, CEUR Workshop Proceedings 1394(January)

Gary Klein (2015) A naturalistic decision making perspective on studying intuitive decision making, Journal of Applied Research in Memory and Cognition 4(3), p. 164-168, Elsevier Inc., doi:10.1016/j.jarmac.2015.07.001

Einir Einisson, Gísli Böðvar, Guðmundsson Steingrímur, Arnar Jónsson, Hannes Högni, Vilhjálmsson David, James Thue () Virtual Reality RPG Spoken Dialog System

() INVESTIGACIÓN SOBRE PSICOLOGIA DE LA MOTIVACIÓN EN LAS UNIVERSIDADES ESPAÑOLAS, url

Glen Robertson, Ian Watson (2014) A review of real-time strategy game AI, AI Magazine 35(4), p. 75-104, AI Access Foundation, doi:10.1609/aimag.v35i4.2478

Emese Domahidi, Ruth Festl, Thorsten Quandt (2014) To dwell among gamers: Investigating the relationship between social online game use and gaming-related friendships, Computers in Human Behavior 35, p. 107-115, url, doi:10.1016/j.chb.2014.02.023

Agustín C. Caminero, Antonio Robles-Gómez, Salvador Ros, Roberto Hernández, Llanos Tobarra (2013) P2P-based resource discovery in dynamic grids allowing multi-attribute and range queries, Parallel Computing 39(10), p. 615-637, doi:10.1016/j.parco.2013.08.003

Raul Lara-Cabrera, Carlos Cotta, Antonio J. Fernandez-Leiva (2013) A review of computational intelligence in RTS games, Proceedings of the 2013 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013, p. 114-121, doi:10.1109/FOCI.2013.6602463

Nick Redfern (2013) Film style and narration in Rashomon, Journal of Japanese and Korean Cinema 5(2), p. 21-36, url, doi:10.1386/jjkc.5.1-2.21_1

() Virtual storyteller: reader takes charge of story, url

() Generating Procedural Narrative - Nonadecimal Creative, url

Andrew J Reagan, Lewis Mitchell, Dilan Kiley, Christopher M Danforth, Peter Sheridan Dodds () The emotional arcs of stories are dominated by six basic shapes, url

José Luis Orihuela Colliva () El narrador en la ficción interactiva. El jardinero y el laberinto | Biblioteca Virtual Miguel de Cervantes, url

Brian McKernan (2013) The Morality of Play, Games and Culture 8(5), p. 307-329, url, doi:10.1177/1555412013493133

Richard J. Samworth (2012) Optimal weighted nearest neighbour classifiers, Annals of Statistics 40(5), p. 2733-2763, doi:10.1214/12-AOS1049

V. Francisco, R. Hervás, F. Peinado, P. Gervás (2012) EmoTales: Creating a corpus of folk tales with emotional annotations, Language Resources and Evaluation 46(3), doi:10.1007/s10579-011-9140-5

D. Oyarzun, F. Peinado, R.M. Young, A. Elizalde, G. Méndez (2012) Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7648 LNCS

N. Álvarez, F. Peinado (2012) Exploring body language as narrative interface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7648 LNCS, doi:10.1007/978-3-642-34851-8-19

Federico Peinado (2010) La evolución tecnológica de los generadores de historias, Congreso Internacional de Videojuegos UCM

Federico Peinado, Virginia Francisco, Raquel Hervás, Pablo Gervás, F Peinado, Á V Francisco Á R Hervás, V Francisco, R Hervás, P Gervás (2010) Assessing the Novelty of Computer-Generated Narratives Using Empirical Metrics 20, p. 565-588, doi:10.1007/s11023-010-9209-8

Federico Peinado, Virginia Francisco, Raquel Hervás, Pablo Gervás (2010) Assessing the novelty of computer-generated narratives using empirical metrics, Minds and Machines. Special Issue on Computational Creativity 20(4), p. 565–588

V. Francisco, F. Peinado, R. Hervás, P. Gervás (2010) Semantic web approaches to the extraction and representation of emotions in texts, Semantic Web: Standards, Tools and Ontologies

P. Lendvai, T. Declerck, S. Darányi, P. Gervás, R. Hervás, S. Malec, F. Peinado (2010) Integration of linguistic markup into semantic models of folk narratives: The fairy tale use case, Proceedings of the 7th International Conference on Language Resources and Evaluation, LREC 2010

Federico Peinado (2010) La evolución tecnológica de los generadores de historias, Congreso Internacional de Videojuegos UCM

Virginia Francisco, Pablo Gervás, Federico Peinado (2010) Ontological reasoning for improving the treatment of emotions in text, Knowledge and Information Systems 25(3), p. 421-443

Barbara Krahé, Ingrid Möller (2010) Longitudinal effects of media violence on aggression and empathy among German adolescents, Journal of Applied Developmental Psychology 31(5), p. 401-409, url, doi:10.1016/j.appdev.2010.07.003

Ben G. Weber, Michael Mateas (2009) A data mining approach to strategy prediction, CIG2009 - 2009 IEEE Symposium on Computational Intelligence and Games, p. 140-147, doi:10.1109/CIG.2009.5286483

Federico Peinado (2008) A Generic Interface for Controlling Virtual Environments in Interactive Applications, International Conference on Computing

Carlos León, Federico Peinado, Álvaro Navarro, Héctor Cortiguera (2008) An Intelligent Plot-Centric Interface for Mastering Computer Role-Playing Games, Joint International Conference on Interactive Digital Storytelling, url

Federico Peinado, Álvaro Navarro, Pablo Gervás (2008) A Testbed Environment for Interactive Storytellers, International Conference on Inteligent Technologies for Interactive Entertainment

Federico Peinado, Marc Cavazza, David Pizzi (2008) Revisiting Character-Based Affective Storytelling under a Narrative BDI Framework, Joint International Conference on Interactive Digital Storytelling, url

Federico Peinado (2008) Un Armazón para el Desarrollo de Aplicaciones de Narración Automática basado en Componentes Ontológicos Reutilizables, Departamento de Ingeniería del Software e Inteligencia Artificial

Federico Peinado, Álvaro Navarro, Pablo Gervás (2008) A Testbed Environment for Interactive Storytellers

Carlos León, Federico Peinado, Alvaro Navarro, Héctor Cortiguera () An Intelligent Plot-Centric Interface for Mastering Computer Role-Playing Games

Federico Peinado, Marc Cavazza, David Pizzi (2008) Revisiting Character-Based Affective Storytelling under a Narrative BDI Framework, LNCS 5334, p. 83-88

Federico Peinado, Michael Santorum () Juego Emergente: ¿Nuevas formas de contar historias en videojuegos? Emergent Game: New ways for storytelling in videogames?

Federico Peinado, Profesor José, García Santesmases () Narración Digital e Interactiva: Dirección automática de entornos virtuales, url

Federico Peinado, Michael Santorum () Nº 8-REVISTA DE COMUNICACIÓN Y NUEVAS, url

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Federico Peinado, Profesor José, García Santesmases () Interactive Digital Storytelling: Automatic direction of virtual environments, url

Federico Peinado, Pablo Gerv´as, Gerv´ Gerv´as, Profesor José, García Santesmases, Madrid Spain () Evaluation of Automatic Generation of Basic Stories, url

Presentada D por Federico Peinado Gil Dirigida por el D Pablo Gervás Gómez-Navarro (2008) Tesis doctoral

Holger Danielsiek, Raphael Sẗuer, Andreas Thom, Nicola Beume, Boris Naujoks, Mike Preuss (2008) Intelligent moving of groups in real-time strategy games, 2008 IEEE Symposium on Computational Intelligence and Games, CIG 2008, p. 71-78, doi:10.1109/CIG.2008.5035623

F. Peinado, A. Navarro, P. Gervás (2008) A testbed environment for interactive storytellers, INTETAIN 2008 - 2nd International Conference on INtelligent TEchnologies for Interactive EnterTAINment

F. Peinado, M. Cavazza, D. Pizzi (2008) Revisiting character-based affective storytelling under a narrative BDI framework, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5334 LNCS, doi:10.1007/978-3-540-89454-4_13

Federico Peinado, Marc Cavazza, David Pizzi (2008) Revisiting Character-Based Affective Storytelling under a Narrative BDI Framework, Joint International Conference on Interactive Digital Storytelling, url

C. León, F. Peinado, Á. Navarro, H. Cortiguera (2008) An intelligent plot-centric interface for mastering computer role-playing games, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5334 LNCS, doi:10.1007/978-3-540-89454-4_39

Federico Peinado, Álvaro Navarro, Pablo Gervás (2008) A Testbed Environment for Interactive Storytellers, International Conference on Inteligent Technologies for Interactive Entertainment

Carlos León, Federico Peinado, Álvaro Navarro, Héctor Cortiguera (2008) An Intelligent Plot-Centric Interface for Mastering Computer Role-Playing Games, Joint International Conference on Interactive Digital Storytelling, url

Federico Peinado (2008) A Generic Interface for Controlling Virtual Environments in Interactive Applications, International Conference on Computing

Federico Peinado (2008) Un Armazón para el Desarrollo de Aplicaciones de Narración Automática basado en Componentes Ontológicos Reutilizables, Departamento de Ingeniería del Software e Inteligencia Artificial

Federico Peinado, Pablo Gervás (2007) Automatic Direction of Interactive Storytelling: Formalizing the Game Master Paradigm, International Conference on Virtual Storytelling: Using Virtual Reality Technologies for Storytelling

Virginia Francisco, Pablo Gervás, Federico Peinado (2007) Ontological Reasoning to Configure Emotional Voice Synthesis, International Conference on Web Reasoning and Rule Systems, url

Federico Peinado, Álvaro Navarro (2007) RCEI: An API for Remote Control of Narrative Environments, International Conference on Virtual Storytelling: Using Virtual Reality Technologies for Storytelling

Federico Peinado (2007) The Role of Automatic Storytellers in Learning Environments, Online Working Conference on Narrative and Learning Environments

Federico Peinado, Álvaro Navarro (2007) RCEI: An API for Remote Control of Narrative Environments, LNCS 4871, p. 181-186

Virginia Francisco, Pablo Gervás, Federico Peinado (2007) Ontological Reasoning to Configure Emotional Voice Synthesis, International Conference on Web Reasoning and Rule Systems, url

Federico Peinado (2007) The Role of Automatic Storytellers in Learning Environments, Online Working Conference on Narrative and Learning Environments

Federico Peinado, Álvaro Navarro (2007) RCEI: An API for Remote Control of Narrative Environments, International Conference on Virtual Storytelling: Using Virtual Reality Technologies for Storytelling

V. Francisco, P. Gervás, F. Peinado (2007) Ontological reasoning to configure emotional voice synthesis, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4524 LNCS

Federico Peinado, Pablo Gervás (2007) Automatic Direction of Interactive Storytelling: Formalizing the Game Master Paradigm, International Conference on Virtual Storytelling: Using Virtual Reality Technologies for Storytelling

Federico Peinado, Michael Santorum (2006) De cómo la realidad puede tomar parte en juegos emergentes, La Revista Icono 14

Héctor Gómez-Gauchía, Federico Peinado (2006) Automatic Customization of Non-Player Characters Using Players Temperament, International Conference on Technologies for Interactive Digital Storytelling and Entertainment, url

Federico Peinado (2006) Interactive Digital Storytelling: Automatic Direction of Virtual Environments, Upgrade. Monograph: Virtual Environments

Federico Peinado, Pablo Gervás (2006) Minstrel Reloaded: From the Magic of Lisp to the Formal Semantics of OWL, International Conference on Technologies for Interactive Digital Storytelling and Entertainment, url

Federico Peinado (2006) Narración Digital e Interactiva: Dirección automática de entornos virtuales, Novática: Entornos Virtuales

Pablo Gervás, Birte Lönneker-Rodman, Jan Christoph Meister, Federico Peinado (2006) Narrative Models: Narratology Meets Artificial Intelligence, International Conference on Language Resources and Evaluation. Satellite Workshop: Toward Computational Models of Literary Analysis

Federico Peinado, Mateus Mendes (2006) Ontologies and Knowledge Bases: State-Of-The-Art Report. Informe Técnico 9/06

Federico Peinado, Mateus Mendes, Dpto Sistemas Informáticos Y Programación (2006) Ontologies and Knowledge Bases: State-Of-The-Art Report

Federico Peinado (2006) Interactive Digital Storytelling: Automatic Direction of Virtual Environments, Upgrade. Monograph: Virtual Environments

Federico Peinado, Mateus Mendes (2006) Ontologies and Knowledge Bases: State-Of-The-Art Report. Informe Técnico 9/06

Héctor Gómez-Gauchía, Federico Peinado (2006) Automatic Customization of Non-Player Characters Using Players Temperament, International Conference on Technologies for Interactive Digital Storytelling and Entertainment, url

Pablo Gervás, Birte Lönneker-Rodman, Jan Christoph Meister, Federico Peinado (2006) Narrative Models: Narratology Meets Artificial Intelligence, International Conference on Language Resources and Evaluation. Satellite Workshop: Toward Computational Models of Literary Analysis

Federico Peinado (2006) Narración Digital e Interactiva: Dirección automática de entornos virtuales, Novática: Entornos Virtuales

Federico Peinado, Pablo Gervás (2006) Evaluation of Automatic Generation of Basic Stories, New Generation Computing, Computational Paradigms and Computational Intelligence. Special issue: Computational Creativity 24(3), p. 289-302

Federico Peinado, Pablo Gervás (2006) Minstrel Reloaded: From the Magic of Lisp to the Formal Semantics of OWL, International Conference on Technologies for Interactive Digital Storytelling and Entertainment, url

H. Gómez-Gauchía, F. Peinado (2006) Automatic customization of non-player characters using players temperament, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4326 LNCS

Federico Peinado, Michael Santorum (2006) De cómo la realidad puede tomar parte en juegos emergentes, La Revista Icono 14

Thomas H. Apperley (2006) Genre and game studies: Toward a critical approach to video game genres, Simulation & Gaming 37(1), p. 6-23, url, doi:10.1177/1046878105282278

Federico Peinado, Pedro Pablo Gómez-Martín, Marco Antonio Gómez-Martín (2005) A Game Architecture for Emergent Story-Puzzles in a Persistent World, International Conference of the Digital Games Research Association

Federico Peinado, Pablo Gervás (2005) A Generative and Case-based Implementation of Proppian Morphology, Joint International Conference of the Association for Computers and the Humanities and the Association for Literary and Linguistic Computing

Federico Peinado, Pablo Gervás (2005) Creativity Issues in Plot Generation, International Joint Conference on Artificial Intelligence. Workshop on Computational Creativity

Federico Peinado, Pablo Gervás, Pablo Moreno-Ger (2005) Interactive Storytelling in Educational Environments, International Conference on Multimedia and ICT´s in Education: Recent Research Developments in Learning Technologies

Federico Peinado, Michael Santorum (2005) Juego Emergente: ¿Nuevas Formas de Contar Historias en Videojuegos?, La Revista Icono 14

Federico Peinado, Pablo Gervás (2005) KIIDSOnto: An OWL DL Ontology for Narrative Reasoning, International Protégé Conference.

Federico Peinado, Pablo Gervás, Pablo Moreno-Ger (2005) Interactive storytelling in educational environments, p. 1

Birte Lönneker, Jan Christoph Meister, Pablo Gervás, Federico Peinado, Uni Madrid, Michael Mateas, Jan Christoph () Story Generators: Models and Approaches for the Generation of Literary Artefacts "Dream on": Designing the ideal Story Generator Algorithm ACH/ALLC 2005

Federico Peinado (2005) A Game Architecture for Emergent Story-Puzzles in a Persistent World

Federico Peinado, Pablo Gervás, Dpto Sistemas Informáticos Y Programación () Creativity Issues in Plot Generation, url

Pablo Gervás, Birte Lönneker-Rodman, Jan Christoph Meister, Federico Peinado () Narrative Models: Narratology Meets Artificial Intelligence, url

Federico Peinado, Pablo Gervás () Minstrel Reloaded: From the Magic of Lisp to the Formal Semantics of OWL

Hector Gómez-Gauchía, Federico Peinado () Automatic Customization of Non-Player Characters using Players Temperament, url

Virginia Francisco, Pablo Gervás, Federico Peinado () Ontological Reasoning to Configure Emotional Voice Synthesis

Federico Peinado, Pablo Gervás () Automatic Direction of Interactive Storytelling: Formalizing the Game Master Paradigm

F. Peinado, P.P. Gómez-Martín, M.A. Gómez-Martín (2005) A game architecture for emergent story-puzzles in a persistent world, Proceedings of DiGRA 2005 Conference: Changing Views - Worlds in Play

Federico Peinado, Pablo Gervás (2005) A Generative and Case-based Implementation of Proppian Morphology, Joint International Conference of the Association for Computers and the Humanities and the Association for Literary and Linguistic Computing

Federico Peinado, Pablo Gervás (2005) Creativity Issues in Plot Generation, International Joint Conference on Artificial Intelligence. Workshop on Computational Creativity

Federico Peinado, Pablo Gervás (2005) KIIDSOnto: An OWL DL Ontology for Narrative Reasoning, International Protégé Conference.

Federico Peinado, Michael Santorum (2005) Juego Emergente: ¿Nuevas Formas de Contar Historias en Videojuegos?, La Revista Icono 14

Federico Peinado, Pablo Gervás, Pablo Moreno-Ger (2005) Interactive Storytelling in Educational Environments, International Conference on Multimedia and ICT´s in Education: Recent Research Developments in Learning Technologies

Pablo Gervás, Belén Díaz-Agudo, Federico Peinado, Raquel Hervás (2005) Story Plot Generation based on CBR, Knowledge-Based Systems. Special issue: AI-2004 18(4-5), p. 235-242

Federico Peinado, Pedro Pablo Gómez-Martín, Marco Antonio Gómez-Martín (2005) A Game Architecture for Emergent Story-Puzzles in a Persistent World, International Conference of the Digital Games Research Association

Belén Díaz-Agudo, Pablo Gervás, Federico Peinado (2004) A Case Based Reasoning Approach to Story Plot Generation, European Conference on Case Based Reasoning. Advances in Case Based Reasoning

Federico Peinado, Pablo Gervás, Belén Díaz-Agudo (2004) A Description Logic Ontology for Fairy Tale Generation, International Conference on Language Resources and Evaluation: Workshop on Language Resources for Linguistic Creativity

Federico Peinado, Miguel Ancochea, Pablo Gervás (2004) Automated Control of Interactions in Virtual Spaces: a Useful Task for Exploratory Creativity, European Conference on Case Based Reasoning. Joint Workshop on Computational Creativity

Federico Peinado (2004) Mediación Inteligente entre Autores e Interactores para Sistemas de Narración Digital Interactiva

Federico Peinado, Pablo Gervás (2004) Transferring Game Mastering Laws to Interactive Digital Storytelling, International Conference on Technologies for Interactive Digital Storytelling and Entertainment

Federico Peinado, Gil Tutor, Pablo Gervás Gómez-Navarro (2004) Mediación Inteligente entre Autores e Interactores para Sistemas de Narración Digital Interactiva

Federico Peinado, Pablo Gervás, Belén Díaz-Agudo () A Description Logic Ontology for Fairy Tale Generation, url

Federico Peinado, Pablo Gervás () Transferring Game Mastering Laws to Interactive Digital Storytelling

Federico Peinado, Miguel Ancochea, Pablo Gervás () Automated Control of Interactions in Virtual Spaces: a Useful Task for Exploratory Creativity

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Pablo Gervás, Belén Díaz-Agudo, Federico Peinado, Raquel Hervás () Story Plot Generation based on CBR, url

Federico Peinado, Pablo Gervás (2004) Transferring Game Mastering Laws to Interactive Digital Storytelling, International Conference on Technologies for Interactive Digital Storytelling and Entertainment

Belén Díaz-Agudo, Pablo Gervás, Federico Peinado (2004) A Case Based Reasoning Approach to Story Plot Generation, European Conference on Case Based Reasoning. Advances in Case Based Reasoning

Federico Peinado, Miguel Ancochea, Pablo Gervás (2004) Automated Control of Interactions in Virtual Spaces: a Useful Task for Exploratory Creativity, European Conference on Case Based Reasoning. Joint Workshop on Computational Creativity

Federico Peinado, Pablo Gervás, Belén Díaz-Agudo (2004) A Description Logic Ontology for Fairy Tale Generation, International Conference on Language Resources and Evaluation: Workshop on Language Resources for Linguistic Creativity

Federico Peinado, P Gervas (2004) Transferring game mastering laws to interactive digital storytelling, Lecture Notes in Computer Science, p. 48–54, Springer, pdf

Federico Peinado (2004) Mediación Inteligente entre Autores e Interactores para Sistemas de Narración Digital Interactiva

B. Díaz-Agudo, P. Gervás, F. Peinado (2004) A case based reasoning approach to story plot generation, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3155

Dmitri Williams (2003) The Video Game Lightning Rod, Information, Communication & Society 6(4), p. 523-550, url, doi:10.1080/1369118032000163240

Mary E. Ballard, J. Rose Wiest (1996) Mortal Kombat (tm): The Effects of Violent Videogame Play on Males' Hostility and Cardiovascular Responding1, Journal of Applied Social Psychology 26(8), p. 717-730, url, doi:10.1111/j.1559-1816.1996.tb02740.x

Corinna Cortes, Vladimir Vapnik (1995) Support-vector networks, Machine Learning 20(3), p. 273-297, Springer Science and Business Media LLC, doi:10.1007/bf00994018

B S Tufts University, ; M S University Of Washington (1995) Collective Artificial Intelligence: Simulated Role-Playing from Crowdsourced Data

() Leonid Berov - Institute of Cognitive Science, url

N. S. Altman (1992) An introduction to kernel and nearest-neighbor nonparametric regression, American Statistician 46(3), p. 175-185, doi:10.1080/00031305.1992.10475879

S. Fling, L. Smith, T. Rodriguez, D. Thornton, E. Atkins, K. Nixon (1992) VIDEOGAMES, AGGRESSION, AND SELF-ESTEEM: A SURVEY, Social Behavior and Personality: an international journal 20(1), p. 39-45, url, doi:10.2224/sbp.1992.20.1.39

Nicola S. Schutte, John M. Malouff, Joan C. Post-Gorden, Annette L. Rodasta (1988) Effects of Playing Videogames on Children's Aggressive and Other Behaviors1, Journal of Applied Social Psychology 18(5), p. 454-460, url, doi:10.1111/j.1559-1816.1988.tb00028.x

Mark Winkel, Dawn M. Novak, Helen Hopson (1987) Personality factors, subject gender, and the effects of aggressive video games on aggression in adolescents, Journal of Research in Personality 21(2), p. 211-223, url, doi:10.1016/0092-6566(87)90008-0

Steven B. Silvern, Peter A. Williamson (1987) The effects of video game play on young children's aggression, fantasy, and prosocial behavior, Journal of Applied Developmental Psychology 8(4), p. 453-462, url, doi:10.1016/0193-3973(87)90033-5

Joel Cooper, Diane Mackie (1986) Video Games and Aggression in Children1, Journal of Applied Social Psychology 16(8), p. 726-744, url, doi:10.1111/j.1559-1816.1986.tb01755.x

Albert L. Zobrist (1969) A model of visual organization for the game of GO, Proceedings of the May 14-16, 1969, spring joint computer conference on XX - AFIPS '69 (Spring), p. 103, New York, New York, USA: ACM Press, url, doi:10.1145/1476793.1476819

R. A. FISHER (1936) THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS, Annals of Eugenics 7(2), p. 179-188, Wiley, doi:10.1111/j.1469-1809.1936.tb02137.x

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Las mías, own:

Mendeley Plugin: groupby = year, sortby = , sortorder = asc, filter =

Mendeley Plugin: Unfiltered results count: 59 ()

2021

M. López Ibáñez, M. Miranda, N. Alvarez, F. Peinado (2021) Using gestural emotions recognised through a neural network as input for an adaptive music system in virtual reality, Entertainment Computing 38, doi:10.1016/j.entcom.2021.100404

2020

M. Miranda, A.A. Sánchez-Ruiz, F. Peinado (2020) Building Non-player Character Behaviors By Imitation Using Interactive Case-Based Reasoning, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 12311 LNAI, doi:10.1007/978-3-030-58342-2_17

2019

M. Miranda, A.A. Sánchez-Ruiz, F. Peinado (2019) Towards Human-Like Bots Using Online Interactive Case-Based Reasoning, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11680 LNAI, doi:10.1007/978-3-030-29249-2_21

2018

M. López Ibáñez, N. Álvarez, F. Peinado (2018) Towards an emotion-driven adaptive system for video game music, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 10714 LNCS, doi:10.1007/978-3-319-76270-8_25

G. Peñas, F. Peinado (2018) An approach to basic emotion recognition through players body pose using virtual reality devices, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 10714 LNCS, doi:10.1007/978-3-319-76270-8_5

2017

M. Miranda, A.A. Sánchez-Ruiz, F. Peinado (2017) Pac-Man or Pac-Bot? Exploring subjective perception of players’ humanity in Ms. Pac-Man, CEUR Workshop Proceedings 1957

M.L. Ibáñez, N. Álvarez, F. Peinado (2017) A study on an efficient spatialisation technique for near-field sound in video games, CEUR Workshop Proceedings 1957

J. Esteve, F. Peinado (2017) FX interactive: Growth and decline of the Spanish video games publisher, CEUR Workshop Proceedings 1957

G. Peñas, F. Peinado (2017) Towards basic emotion recognition using players body and hands pose in virtual reality narrative experiences, CEUR Workshop Proceedings 1957

2016

R. Hervás, J. Arroyo, V. Francisco, F. Peinado, P. Gervás (2016) Influence of personal choices on lexical variability in referring expressions, Natural Language Engineering 22(2), doi:10.1017/S1351324915000182

M. Miranda, A.A. Sánchez-Ruiz, F. Peinado (2016) A neuroevolution approach to imitating human-like play in Ms. Pac-man video game, CEUR Workshop Proceedings 1682

M.L. Ibáñez, F. Peinado (2016) Walking in VR: Measuring presence and simulator sickness in first-person virtual reality games, CEUR Workshop Proceedings 1682

2015

M. Miranda, F. Peinado (2015) Improving the performance of a computer-controlled player in a maze chase game using evolutionary Programming on a finite-state machine, CEUR Workshop Proceedings 1394(January)

N. Alvarez, F. Peinado (2015) Modelling suspicion as a game mechanism for designing a computer-played investigation character, CEUR Workshop Proceedings 1394(January)

2012

V. Francisco, R. Hervás, F. Peinado, P. Gervás (2012) EmoTales: Creating a corpus of folk tales with emotional annotations, Language Resources and Evaluation 46(3), doi:10.1007/s10579-011-9140-5

D. Oyarzun, F. Peinado, R.M. Young, A. Elizalde, G. Méndez (2012) Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7648 LNCS

N. Álvarez, F. Peinado (2012) Exploring body language as narrative interface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7648 LNCS, doi:10.1007/978-3-642-34851-8-19

2010

P. Lendvai, T. Declerck, S. Darányi, P. Gervás, R. Hervás, S. Malec, F. Peinado (2010) Integration of linguistic markup into semantic models of folk narratives: The fairy tale use case, Proceedings of the 7th International Conference on Language Resources and Evaluation, LREC 2010

V. Francisco, F. Peinado, R. Hervás, P. Gervás (2010) Semantic web approaches to the extraction and representation of emotions in texts, Semantic Web: Standards, Tools and Ontologies

Federico Peinado (2010) La evolución tecnológica de los generadores de historias, Congreso Internacional de Videojuegos UCM

Federico Peinado, Virginia Francisco, Raquel Hervás, Pablo Gervás (2010) Assessing the novelty of computer-generated narratives using empirical metrics, Minds and Machines. Special Issue on Computational Creativity 20(4), p. 565–588

Virginia Francisco, Pablo Gervás, Federico Peinado (2010) Ontological reasoning for improving the treatment of emotions in text, Knowledge and Information Systems 25(3), p. 421-443

2008

F. Peinado, M. Cavazza, D. Pizzi (2008) Revisiting character-based affective storytelling under a narrative BDI framework, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5334 LNCS, doi:10.1007/978-3-540-89454-4_13

F. Peinado, A. Navarro, P. Gervás (2008) A testbed environment for interactive storytellers, INTETAIN 2008 - 2nd International Conference on INtelligent TEchnologies for Interactive EnterTAINment

C. León, F. Peinado, Á. Navarro, H. Cortiguera (2008) An intelligent plot-centric interface for mastering computer role-playing games, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5334 LNCS, doi:10.1007/978-3-540-89454-4_39

Federico Peinado (2008) Un Armazón para el Desarrollo de Aplicaciones de Narración Automática basado en Componentes Ontológicos Reutilizables, Departamento de Ingeniería del Software e Inteligencia Artificial

Federico Peinado, Marc Cavazza, David Pizzi (2008) Revisiting Character-Based Affective Storytelling under a Narrative BDI Framework, Joint International Conference on Interactive Digital Storytelling, url

Federico Peinado, Álvaro Navarro, Pablo Gervás (2008) A Testbed Environment for Interactive Storytellers, International Conference on Inteligent Technologies for Interactive Entertainment

Carlos León, Federico Peinado, Álvaro Navarro, Héctor Cortiguera (2008) An Intelligent Plot-Centric Interface for Mastering Computer Role-Playing Games, Joint International Conference on Interactive Digital Storytelling, url

Federico Peinado (2008) A Generic Interface for Controlling Virtual Environments in Interactive Applications, International Conference on Computing

2007

V. Francisco, P. Gervás, F. Peinado (2007) Ontological reasoning to configure emotional voice synthesis, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4524 LNCS

Federico Peinado (2007) The Role of Automatic Storytellers in Learning Environments, Online Working Conference on Narrative and Learning Environments

Federico Peinado, Álvaro Navarro (2007) RCEI: An API for Remote Control of Narrative Environments, International Conference on Virtual Storytelling: Using Virtual Reality Technologies for Storytelling

Virginia Francisco, Pablo Gervás, Federico Peinado (2007) Ontological Reasoning to Configure Emotional Voice Synthesis, International Conference on Web Reasoning and Rule Systems, url

Federico Peinado, Pablo Gervás (2007) Automatic Direction of Interactive Storytelling: Formalizing the Game Master Paradigm, International Conference on Virtual Storytelling: Using Virtual Reality Technologies for Storytelling

2006

H. Gómez-Gauchía, F. Peinado (2006) Automatic customization of non-player characters using players temperament, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4326 LNCS

Federico Peinado, Mateus Mendes (2006) Ontologies and Knowledge Bases: State-Of-The-Art Report. Informe Técnico 9/06

Pablo Gervás, Birte Lönneker-Rodman, Jan Christoph Meister, Federico Peinado (2006) Narrative Models: Narratology Meets Artificial Intelligence, International Conference on Language Resources and Evaluation. Satellite Workshop: Toward Computational Models of Literary Analysis

Federico Peinado (2006) Narración Digital e Interactiva: Dirección automática de entornos virtuales, Novática: Entornos Virtuales

Federico Peinado, Pablo Gervás (2006) Minstrel Reloaded: From the Magic of Lisp to the Formal Semantics of OWL, International Conference on Technologies for Interactive Digital Storytelling and Entertainment, url

Federico Peinado (2006) Interactive Digital Storytelling: Automatic Direction of Virtual Environments, Upgrade. Monograph: Virtual Environments

Héctor Gómez-Gauchía, Federico Peinado (2006) Automatic Customization of Non-Player Characters Using Players Temperament, International Conference on Technologies for Interactive Digital Storytelling and Entertainment, url

Federico Peinado, Michael Santorum (2006) De cómo la realidad puede tomar parte en juegos emergentes, La Revista Icono 14

Federico Peinado, Pablo Gervás (2006) Evaluation of Automatic Generation of Basic Stories, New Generation Computing, Computational Paradigms and Computational Intelligence. Special issue: Computational Creativity 24(3), p. 289-302

2005

F. Peinado, P.P. Gómez-Martín, M.A. Gómez-Martín (2005) A game architecture for emergent story-puzzles in a persistent world, Proceedings of DiGRA 2005 Conference: Changing Views - Worlds in Play

Federico Peinado, Pablo Gervás (2005) KIIDSOnto: An OWL DL Ontology for Narrative Reasoning, International Protégé Conference.

Federico Peinado, Michael Santorum (2005) Juego Emergente: ¿Nuevas Formas de Contar Historias en Videojuegos?, La Revista Icono 14

Federico Peinado, Pablo Gervás, Pablo Moreno-Ger (2005) Interactive Storytelling in Educational Environments, International Conference on Multimedia and ICT´s in Education: Recent Research Developments in Learning Technologies

Federico Peinado, Pablo Gervás (2005) Creativity Issues in Plot Generation, International Joint Conference on Artificial Intelligence. Workshop on Computational Creativity

Federico Peinado, Pablo Gervás (2005) A Generative and Case-based Implementation of Proppian Morphology, Joint International Conference of the Association for Computers and the Humanities and the Association for Literary and Linguistic Computing

Federico Peinado, Pedro Pablo Gómez-Martín, Marco Antonio Gómez-Martín (2005) A Game Architecture for Emergent Story-Puzzles in a Persistent World, International Conference of the Digital Games Research Association

Pablo Gervás, Belén Díaz-Agudo, Federico Peinado, Raquel Hervás (2005) Story Plot Generation based on CBR, Knowledge-Based Systems. Special issue: AI-2004 18(4-5), p. 235-242

2004

B. Díaz-Agudo, P. Gervás, F. Peinado (2004) A case based reasoning approach to story plot generation, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3155

Federico Peinado, Pablo Gervás (2004) Transferring Game Mastering Laws to Interactive Digital Storytelling, International Conference on Technologies for Interactive Digital Storytelling and Entertainment

Federico Peinado (2004) Mediación Inteligente entre Autores e Interactores para Sistemas de Narración Digital Interactiva

Federico Peinado, Miguel Ancochea, Pablo Gervás (2004) Automated Control of Interactions in Virtual Spaces: a Useful Task for Exploratory Creativity, European Conference on Case Based Reasoning. Joint Workshop on Computational Creativity

Federico Peinado, Pablo Gervás, Belén Díaz-Agudo (2004) A Description Logic Ontology for Fairy Tale Generation, International Conference on Language Resources and Evaluation: Workshop on Language Resources for Linguistic Creativity

Belén Díaz-Agudo, Pablo Gervás, Federico Peinado (2004) A Case Based Reasoning Approach to Story Plot Generation, European Conference on Case Based Reasoning. Advances in Case Based Reasoning

Federico Peinado, P Gervas (2004) Transferring game mastering laws to interactive digital storytelling, Lecture Notes in Computer Science, p. 48–54, Springer, pdf

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El grupo de Artificial Intelligence

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