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International Conference on Machine Learning and Applications (ICMLA)

作者： Charalampos Karyotis Tomasz Maniak Faiyaz Doctor Rahat Iqbal Vasile Palade Raymond Tang Interactive Coventry Ltd. Coventry UK School of Computer Science and Electronic Engineering University of Essex UK Faculty of Engineering Environment and Computing Coventry University UK Metrobinaya Sdn Bhd Bhd Kuala Lumpur Malaysia

This paper describes the core computational mechanisms used by an urban flood forecasting and monitoring platform developed as part of a UK Newton Fund project in Malaysia. FLUD-FLood monitoring and forecasting platform for Urban Deployment - is a novel system aiming to deliver an effective and low cost urban flood forecasting solution, which is able to accurately forecast flood risk at street level, and deliver optimized recommendations to the relevant authorities as well as an early warning alerts to members of the public. This platform is based on a hybrid Deep Learning and Fuzzy Logic based architecture. As demonstrated by the experimental results and the analysis presented in this paper, this architecture enables the proposed system to account for factors that are not included in other modern flood forecasting systems, and simultaneously process high volumes of data originating from diverse data sources, in order to deliver accurate predictions concerning urban flood events.

关键词： Floods Forecasting Fuzzy logic Machine learning Ocean temperature Monitoring Soil

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Identifying and planning for individualized change: Patient-provider collaboration using lightweight food diaries in healthy eating and irritable bowel syndrome

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Proceedings of the ACM on interactive, Mobile, Wearable and Ubiquitous Technologies 2019年第1期3卷 1–27页

作者： Chung, Chia-Fang Wang, Qiaosi Schroeder, Jessica Cole, Allison Zia, Jasmine Fogarty, James Munson, Sean A. Informatics Indiana University Bloomington BloomingtonIN47405 United States School of Interactive Computing Georgia Institute of Technology AtlantaGA30332 United States Paul G. Allen School of Computer Science and Engineering DUB Group University of Washington SeattleWA98195 United States Family Medicine University of Washington SeattleWA98195 United States Division of Gastroenterology University of Washington SeattleWA98195 United States Human Centered Design and Engineering DUB Group University of Washington SeattleWA98195 United States

Identifying and planning strategies that support a healthy lifestyle or manage a chronic disease often require patient-provider collaboration. For example, people with healthy eating goals often share everyday food, exercise, or sleep data with health coaches or nutritionists to find opportunities for change, and patients with irritable bowel syndrome (IBS) often gather food and symptom data as part of working with providers to diagnose and manage symptoms. However, a lack of effective support often prevents health experts from reviewing large amounts of data in time-constrained visits, prevents focusing on individual goals, and prevents generating correct, individualized, and actionable recommendations. To examine how to design photo-based diaries to help people and health experts exchange knowledge and focus on collaboration goals when reviewing the data together, we designed and developed Foodprint, a photo-based food diary. Foodprint includes three components: (1) A mobile app supporting lightweight data collection, (2) a web app with photo-based visualization and quantitative visualizations supporting collaborative reflection, and (3) a pre-visit note communicating an individual's expectations and questions to experts. We deployed Foodprint in two studies: (1) with 17 people with healthy eating goals and 7 health experts, and (2) with 16 IBS patients and 8 health experts. Building upon the lens of boundary negotiating artifacts and findings from two field studies, our research contributes design principles to (1) prepare individuals to collect data relevant to their health goals and for collaboration, (2) help health experts focus on an individual's eating context, experiences, and goals in collaborative review, and (3) support individuals and experts to develop individualized, actionable plans and strategies. © 2019 Association for computing Machinery.

关键词： Data acquisition

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How to evaluate trading strategies: Single agent market replay or multiple agent interactive simulation?

arXiv

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arXiv 2019年

作者： Balch, Tucker Hybinette Mahfouz, Mahmoud Lockhart, Joshua Hybinette, Maria Byrd, David Artificial Intelligence Research New York United States Artificial Intelligence Research London United Kingdom Department of Electrical and Electronic Engineering Imperial College London Department of Computer Science University of Georgia Athens United States School of Interactive Computing College of Computing Georgia Institute of Technology

We show how a multi-agent simulator can support two important but distinct methods for assessing a trading strategy: Market Replay and interactive Agent-Based Simulation (IABS). Our solution is important because each method offers strengths and weaknesses that expose or conceal flaws in the subject strategy. A key weakness of Market Replay is that the simulated market does not substantially adapt to or respond to the presence of the experimental strategy. IABS methods provide an artificial market for the experimental strategy using a population of background trading agents. Because the background agents attend to market conditions and current price as part of their strategy, the overall market is responsive to the presence of the experimental strategy. Even so, IABS methods have their own weaknesses, primarily that it is unclear if the market environment they provide is realistic. We describe our approach in detail, and illustrate its use in an example application: The evaluation of market impact for various size orders. Copyright © 2019, The Authors. All rights reserved.

关键词： Multi agent systems

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Not Just Black and Not Just a Woman: Black Women Belonging in computing

Not Just Black and Not Just a Woman: Black Women Belonging i...

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Research in Equity and Sustained Participation in Engineering, computing, and Technology (RESPECT)

作者： Amber Solomon DeKita Moon Armisha L. Roberts Juan E. Gilbert School of Interactive Computing Georgia Institute of Technology Computer and Information Science and Engineering University of Florida

ISBN: (纸本)9781538613641

A sense of belonging is key to pursuing and persisting in computing. It is also one of the reasons for the low representation of women and Black people in computing. Research has attempted to understand why these underrepresented groups feel like they do not belong in computing. These initiatives tend to analyze race and gender separately as if people experience these aspects of identity separately. For Black women who are seeking to belong in computing, these sorts of analyses are problematic. Black women are not just “women plus color, or Black plus gender” [1]. Intersections of their race and gender yield unique experiences for them. Thus, analyses that only focus on women's experiences or Blacks' experiences leaves out the many nuances that result from one being both a woman and Black. This paper is an exploration of “what it means to belong” in computing. By analyzing Black womens multiple identities through an intersectional lens, we show that most research for Black people or women is not inclusive of Black womens unique perspectives. We make visible a gap in computing education research that does not address intersectionality.

关键词： Education Lenses Color TV Interviews Face Software

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The 13th AAAI conference on artificial intelligence and interactive digital entertainment

The 13th AAAI conference on artificial intelligence and inte...

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作者： Magerko, Brian Bahamón, Julio César Buro, Michael Damiano, Rossana Mazeika, Jo Ontañón, Santiago Robertson, Justus Ryan, James Siu, Kristin ADAM Lab. Georgia Institute of Technology AtlantaGA United States Department of Computer Science University of North Carolina CharlotteNC United States Computing Science Department University of Alberta EdmontonAB Canada Department of Computer Science University of Turin Italy Department of Computer Science University of California Santa Cruz in Santa CruzCA United States Department of Computer Science Drexel University PhiladelphiaPA United States Department of Computer Science University of California Santa Cruz Santa CruzCA United States School of Interactive Computing Georgia Institute of Technology AtlantaGA United States

The 13th AAAI Conference on Artificial Intelligence and interactive Digital Entertainment (AIIDE 2017) was held at the Snowbird Ski and Summer Resort in Little Cottonwood Canyon in the Wasatch Range of the Rocky Mountains near Salt Lake City, Utah. Along with the main conference presentations, the meeting included two tutorials, three workshops, and invited keynote talks. This report summarizes the main conference. It also includes contributions from the organizers of the three workshops. Copyright © 2018, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Artificial intelligence

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Controllable Neural Story Plot Generation via Reward Shaping

arXiv

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arXiv 2018年

作者： Tambwekar, Pradyumna Dhuliawala, Murtaza Martin, Lara J. Mehta, Animesh Harrison, Brent Riedl, Mark O. School of Interactive Computing Georgia Institute of Technology United States Department of Computer Science University of Kentucky United States

Language-modeling-based approaches to story plot generation attempt to construct a plot by sampling from a language model (LM) to predict the next character, word, or sentence to add to the story. LM techniques lack the ability to receive guidance from the user to achieve a specific goal, resulting in stories that don't have a clear sense of progression and lack coherence. We present a reward-shaping technique that analyzes a story corpus and produces intermediate rewards that are backpropagated into a pre-trained LM in order to guide the model towards a given goal. Automated evaluations show our technique can create a model that generates story plots which consistently achieve a specified goal. Human-subject studies show that the generated stories have more plausible event ordering than baseline plot generation techniques. Copyright © 2018, The Authors. All rights reserved.

关键词： Modeling languages

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Self-Awareness for Autonomous Systems

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PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

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RealityMedia: An Experimental Digital Book in WebXR

RealityMedia: An Experimental Digital Book in WebXR

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IEEE International Symposium on Mixed and Augmented Reality Workshops (ISMARW)

作者： Moria Engberg Jay David Bolter Blair Maclntyre Dept. of Computer Science and Media Technology Malmö University Georgia Tech School of Literature Communication and Culture Emerging Technologies Mozilla School of Interactive Computing

ISBN: (纸本)9781538675939;9781538675922

This paper presents an ongoing experiment using WebXR to create something analogous to a non-fiction book in AR and VR; an immersive, interactive experience that stands on its own, rather than merely complementing a traditional book. The book introduces the reader/user to AR and VR both as technologies and as media. The printed book is one of the more influential communicative interfaces in history. AR and VR have the potential to remediate several genres of printed books, but somewhat different conventions may need to be developed for different combinations of genre and modality. The lessons learned through this experiment should contribute to the establishment of guidelines for this new form of multimedia, in particular conventions that facilitate the reader/user's transition from discursive to immersive modes and back.

关键词： Media Portals Three-dimensional displays History Two dimensional displays Augmented reality Writing

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Does removing stereotype priming remove bias? A pilot human-robot interaction study

arXiv

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arXiv 2018年

作者： Ogunyale, Tobi Bryant, De'Aira Howard, Ayanna Department of Computer Science Georgia State University AtlantaGA United States School of Interactive Computing Georgia Institute of Technology AtlantaGA United States

Robots capable of participating in complex social interactions have shown great potential in a variety of applications. As these robots grow more popular, it is essential to continuously evaluate the dynamics of the human-robot relationship. One factor shown to have potential impacts on this critical relationship is the human projection of stereotypes onto social robots, a practice that is implicitly known to effect both developers and users of this technology. As such, in this research, we wished to investigate the difference in participants' perceptions of the robot interaction if we removed stereotype priming. This has not yet been a common practice in similar studies. Given the stereotypes of emotions among ethnic groups, especially in the U.S., this study specifically sought to investigate the impact that robot "skin color" could potentially have on the human perception of a robot's emotional expressive behavior. A between-subject experiment with 198 individuals was conducted. The results showed no significant differences in the overall emotion classification or intensity ratings for the different robot skin colors. These results lend credence to our hypothesis that when individuals are not primed with information related to human stereotypes, robots are evaluated based on functional attributes versus stereotypical attributes. This provides some confidence that robots, if designed correctly, can potentially be used as a tool to override stereotype-based biases associated with human behavior. Copyright © 2018, The Authors. All rights reserved.

关键词： Human robot interaction

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Incremental Task Modification via Corrective Demonstrations

Incremental Task Modification via Corrective Demonstrations

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IEEE International Conference on Robotics and Automation

作者： Reymundo A. Gutierrez Vivian Chu Andrea L. Thomaz Scott Niekum Department of Computer Science University of Texas at Austin Austin Texas School of Interactive Computing Georgia Institute of Technology Atlanta Georgia Department of Electrical and Computer Engineering University of Texas at Austin Austin Texas

In realistic environments, fully specifying a task model such that a robot can perform a task in all situations is impractical. In this work, we present Incremental Task Modification via Corrective Demonstrations (ITMCD), a novel algorithm that allows a robot to update a learned model by making use of corrective demonstrations from an end-user in its environment. We propose three different types of model updates that make structural changes to a finite state automaton (FSA) representation of the task by first converting the FSA into a state transition auto-regressive hidden Markov model (STARHMM). The STARHMM's probabilistic properties are then used to perform approximate Bayesian model selection to choose the best model update, if any. We evaluate ITMCD Model Selection in a simulated block sorting domain and the full algorithm on a real-world pouring task. The simulation results show our approach can choose new task models that sufficiently incorporate new demonstrations while remaining as simple as possible. The results from the pouring task show that ITMCD performs well when the modeled segments of the corrective demonstrations closely comply with the original task model.

关键词： Hidden Markov models Task analysis Robots Computational modeling Adaptation models Probabilistic logic Bayes methods

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