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2020 AAAI Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice, AAAI-MAKE 2020

作者： Mensio, Martino Bastianelli, Emanuele Tiddi, Ilaria Rizzo, Giuseppe Knowledge Media Institute Open University United Kingdom Interaction Lab Heriot-Watt University United Kingdom Department of Computer Science Vrije Universiteit Amsterdam Netherlands LINKS Foundation Italy

In this paper, we tackle the problem of lack of understandability of deep learning systems by integrating heterogeneous knowledge sources, and in the specific we present how we used FrameNet to guarantee the correct learning for an LSTM-based semantic parser in the task of Spoken Language Understanding for robots. The problem of the explainability of Artificial Intelligence (AI) systems, i.e. their ability to explain decisions to both experts and end users, has attracted growing attention in the latest years, affecting their credibility and trustworthiness. Trusting these systems is fundamental in the context of AI-based robotic companions interacting in natural language, as the users’ acceptance of the robot also relies on the ability to explain the reasons behind its actions. Following similar approaches, we first use the values of the neural attention layers employed in the semantic parser as a clue to analyze and interpret the model’s behavior and reveal the intrinsic bias induced by the training data. We then show how the integration of knowledge from external resources such as FrameNet can help minimizing, or mitigating, such bias, and consequently guarantee the model to provide the correct interpretations. Our preliminary, but promising results suggest that (i) attention layers can improve the model understandability;(ii) the integration of different knowledge bases can help overcoming the limitations of machine learning models;and (iii) an approach combining the strengths of both knowledge engineering and machine learning can foster the development of more transparent, understandable intelligent systems. Copyright © 2020 held by the author(s).

关键词： Semantics

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Transfer learning to improve streamflow forecasts in data sparse regions

arXiv

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arXiv 2021年

作者： Oruche, Roland Egede, Lisa Baker, Tracy O'Donncha, Fearghal Department of Electrical Engineering & Computer Science University of Missouri Columbia United States Human Computer Interaction Institute Carnegie Mellon University United States The Nature Conservancy New YorkNY United States IBM Research Europe IE Switzerland

Effective water resource management requires information on water availability - both in terms of quality and quantity - spatially and temporally. In this paper, we study the methodology behind Transfer Learning (TL) through fine-tuning and parameter transferring for better generalization performance of streamflow prediction in data-sparse regions. We propose a standard recurrent neural network in the form of Long Short-Term Memory (LSTM) to fit on a sufficiently large source domain dataset and repurpose the learned weights to a significantly smaller, yet similar target domain datasets. We present a methodology to implement transfer learning approaches for spatiotemporal applications by separating the spatial and temporal components of the model and training the model to generalize based on categorical datasets representing spatial variability. The framework is developed on a rich benchmark dataset from the US and evaluated on a smaller dataset collected by The Nature Conservancy in Kenya. The LSTM model exhibits generalization performance through our TL technique. Results from this current experiment demonstrate the effective predictive skill of forecasting streamflow responses when knowledge transferring and static descriptors are used to improve hydrologic model generalization in data-sparse regions. © 2021, CC BY.

关键词： Long short-term memory

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Reconsidering Accountability in the Present and Future of Work

Reconsidering Accountability in the Present and Future of Wo...

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Companion Publication of the 2022 Conference on computer Supported Cooperative Work and Social Computing

作者： Sarah Elizabeth Fox Daniel Marcus Greene Karen Gregory Mohammad Hossein Jarrahi Vera Khovanskaya Human-Computer Interaction Institute Carnegie Mellon University United States College of Information Studies University of Maryland United States School of Social and Political Science University of Edinburgh Scotland School of Information and Library Science University of North Carolina at Chapel Hill United States Feminist Labor Lab University of California San Diego United States

ISBN: (纸本)9781450391900

There is a growing effort within CSCW and related fields to understand the effects of artificial intelligence, algorithmic management, and other contemporary and near-future transformations of digitally-mediated work. This panel builds on this effort by asking three guiding questions: how should academics more fully account for the consequences of new transformations in the workplace, how should we engage with workers who are already accounting for the ways that these systems impact their labor conditions and experience of work, and how should we be made accountable to workers for the design interventions we propose or deploy. The panelists will discuss topics including the impact of AI technology on frontline and essential workers, the transformative effect of algorithmic management on organizational practices within and beyond platform work, and how demands and efforts to attain algorithmic transparency by platform/gig-workers both reveal power imbalances within the workplaces and point to avenues for worker advocacy. We will also draw on scholarship that analyzes the shifting labor conditions of CSCW to situate contemporary concerns within the field's disciplinary history. We aim to generate conversation about the ways that these forms of accountability have already been unfolding, identify shared themes and concerns across parallel topics, and identify future directions for researchers moving forward in this space.

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When Does an Individual Accept Misinformation? 43

When Does an Individual Accept Misinformation?

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43rd Annual Meeting of the Cognitive science Society: Comparative Cognition: Animal Minds, CogSci 2021

作者： Borukhson, David Lorenz-Spreen, Philipp Ragni, Marco Department for Computer Science University of Freiburg Germany Center for Adaptive Rationality Max Planck Institute for Human Development Berlin Germany Danish Institute for Advanced Studies South Denmark University Odense Denmark Cognitive Computation Lab Technical Faculty University of Freiburg Germany

A new phenomenon is the spread and acceptance of "fake news" on an individual user level, facilitated by social media such as Twitter. So far, state of the art socio–psychological theories and cognitive models focus on explaining how the accuracy of fake news is judged on average, with little consideration of the individual. This paper takes it to a new level: A breadth of core models are comparatively assessed on their predictive accuracy for the individual decision maker, i.e., how well can models predict an individual’s decision before the decision is made. To conduct this analysis, it requires the raw responses of each individual and the implementation and adaption of theories to predict the individual’s response. We used two previously collected large data sets with a total of 3309 participants and searched for, analyzed and refined existing classical and heuristic modeling approaches. The results suggest that classical reasoning, sentiment analysis models and heuristic approaches can best predict the "Accept" or "Reject" response of a person. A hybrid model that combines those models outperformed the prediction of all individual models pointing to an adaptive tool-box. © Cognitive science Society: Comparative Cognition: Animal Minds, CogSci *** rights reserved.

关键词： Sentiment analysis

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A Q-learning-based smart clustering routing method in flying Ad Hoc networks

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Journal of King Saud University - computer and Information sciences 2024年第1期36卷

作者： Hosseinzadeh, Mehdi Tanveer, Jawad Rahmani, Amir Masoud Aurangzeb, Khursheed Yousefpoor, Efat Yousefpoor, Mohammad Sadegh Darwesh, Aso Lee, Sang-Woong Fazlali, Mahmood Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Computer Science and Engineering Sejong University Seoul 05006 South Korea Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Engineering College of Computer and Information Sciences King Saud University P.O. Box 51178 Riyadh 11543 Saudi Arabia Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Information Technology University of Human Development Sulaymaniyah Iraq Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea Cybersecurity and Computing Systems Research Group School of Physics Engineering and Computer Science University of Hertfordshire Hertfordshire AL10 9AB United Kingdom

Flying ad hoc networks (FANETs) have particular importance in various military and civilian applications due to their specific features, including frequent topological changes, the movement of drones in a three-dimensional space, and their restricted energy. These features have created challenges for designing cluster-based routing protocols. In this paper, a Q-learning-based smart clustering routing method (QSCR) is suggested in FANETs. In QSCR, each node discovers its neighbors through the periodic exchange of hello messages. The hello time interval is different in each cluster, and cluster leaders determine this interval based on the average speed similarity. Next, an adaptive clustering process is presented for categorizing drones in the clusters. In this step, the cluster leader is selected based on a new parameter called merit value, which includes residual energy, centrality, neighbor degree, speed similarity, and link validity time. Then, a centralized Q-learning model is presented to tune weight coefficients related to merit parameters dynamically. In the last step, the routing process is done using a greedy forwarding technique. Finally, QSCR is run on NS2, and the simulation results of QSCR are compared with those of ICRA, WCA, and DCA. These results show that QSCR carries out the clustering process rapidly but has less cluster stability than ICRA. QSCR gets energy efficiency and improves network lifetime. In the routing process, QSCR has a high packet delivery rate compared to other schemes. Also, the number of isolated clusters created in QSCR is less than other clustering methods. However, the proposed scheme has a higher end-to-end delay than ICRA. Also, this scheme experiences more communication overhead than ICRA slightly. © 2024 The Author(s)

关键词： Clustering Flying ad hoc networks (FANETs) Machine learning (ML) Reinforcement learning (RL) Unmanned aerial vehicles (UAVs)

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Improving paraphrase detection with the adversarial paraphrasing task

arXiv

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arXiv 2021年

作者： Nighojkar, Animesh Licato, John Advancing Machine and Human Reasoning Lab Department of Computer Science and Engineering University of South Florida TampaFL United States

If two sentences have the same meaning, it should follow that they are equivalent in their inferential properties, i.e., each sentence should textually entail the other. However, many paraphrase datasets currently in widespread use rely on a sense of paraphrase based on word overlap and syntax. Can we teach them instead to identify paraphrases in a way that draws on the inferential properties of the sentences, and is not over-reliant on lexical and syntactic similarities of a sentence pair? We apply the adversarial paradigm to this question, and introduce a new adversarial method of dataset creation for paraphrase identification: the Adversarial Paraphrasing Task (APT), which asks participants to generate semantically equivalent (in the sense of mutually implicative) but lexically and syntactically disparate paraphrases. These sentence pairs can then be used both to test paraphrase identification models (which get barely random accuracy) and then improve their performance. To accelerate dataset generation, we explore automation of APT using T5, and show that the resulting dataset also improves accuracy. We discuss implications for paraphrase detection and release our dataset in the hope of making paraphrase detection models better able to detect sentence-level meaning equivalence. © 2021, CC BY.

关键词： Syntactics

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An alternate statistical lens to look at collaboration data: Extreme value theory 13

An alternate statistical lens to look at collaboration data:...

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13th International Conference on computer Supported Collaborative Learning - A Wide Lens: Combining Embodied, Enactive, Extended, and Embedded Learning in Collaborative Settings, CSCL 2019

作者： Sharma, Kshitij Olsen, Jennifer Department of Computer Science Norwegian University of Science and Technology Norway Computer Human Interaction Lab in Learning and Instruction École Polytechnique Fédérale de Lausanne Switzerland

ISBN: (纸本)9781732467231

To provide beneficial feedback to students during their collaboration, it is important to identify behaviors that are indicative of good collaboration. However, in a collaborative learning session, students engage in a range of behaviors and it can be difficult to indicate which of those behaviors correlate with higher outcomes. In this paper, we propose using Extreme Value Theory (EVT), a method that considers the data points in the tail (upper or lower) of the distribution, to analyse the relationship between collaborative process variables and outcome measures through insights derived from high impact, low-frequency events. Specifically, in this paper, we analyse the relationship between dual gaze patterns and outcome measures across two different datasets. In both datasets we found that students with lower outcomes had lower focus during the collaborative session. This paper provides a contribution by both introducing EVT as a viable method for analysing CSCL data as well as demonstrating the effectiveness of eyetracking as a collaborative indicator to use to adapt to in real-time. © ISLS.

关键词： Students

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The understanding of congruent and incongruent referential gaze in 17-month-old infants: an eye-tracking study comparing human and robot (vol 10, pg 11918, 2020)

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SCIENTIFIC REPORTS 2020年第1期10卷 1-10页

作者： Manzi, F. Ishikawa, M. Di Dio, C. Itakura, S. Kanda, T. Ishiguro, H. Massaro, D. Marchetti, A. Research Unit on Theory of Mind Department of Psychology Università Cattolica del Sacro Cuore Milan Italy School of Graduated Letter Department of Psychology Kyoto University Kyoto Japan Centre for Baby Science Doshisha University Kyoto Japan Human-Robot Interaction Laboratory Department of Computer Science Kyoto University Kyoto Japan Advanced Telecommunications Research Institute International IRC/HIL Keihanna Science City Kyoto Japan Department of Systems Innovation Osaka University Toyonaka Japan

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Theory-based habit modeling for enhancing behavior prediction

arXiv

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arXiv 2021年

作者： Zhang, Chao Vanschoren, Joaquin van Wissen, Arlette Lakens, Daniël de Ruyter, Boris IJsselsteijn, Wijnand A. Human-Technology Interaction Group Eindhoven University of Technology Eindhoven Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Brain Behavior and Cognition Philips Research Eindhoven Netherlands

Psychological theories of habit posit that when a strong habit is formed through behavioral repetition, it can trigger behavior automatically in the same environment. Given the reciprocal relationship between habit and behavior, changing lifestyle behaviors (e.g., toothbrushing) is largely a task of breaking old habits and creating new and healthy ones. Thus, representing users’ habit strengths can be very useful for behavior change support systems (BCSS), for example, to predict behavior or to decide when an intervention reaches its intended effect. However, habit strength is not directly observable and existing self-report measures are taxing for users. In this paper, built on recent computational models of habit formation, we propose a method to enable intelligent systems to compute habit strength based on observable behavior. The hypothesized advantage of using computed habit strength for behavior prediction was tested using data from two intervention studies, where we trained participants to brush their teeth twice a day for three weeks and monitored their behaviors using accelerometers. Through hierarchical cross-validation, we found that for the task of predicting future brushing behavior, computed habit strength clearly outperformed self-reported habit strength (in both studies) and was also superior to models based on past behavior frequency (in the larger second study). Our findings provide initial support for our theory-based approach of modeling user habits and encourages the use of habit computation to deliver personalized and adaptive interventions. © 2021, CC BY.

关键词： Intelligent systems

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Queer in HCI: Strengthening the Community of LGBTQIA+ Researchers and Research

Queer in HCI: Strengthening the Community of LGBTQIA+ Resear...

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2021 CHI Conference on human Factors in Computing Systems: Making Waves, Combining Strengths, CHI EA 2021

作者： Devito, Michael Ann Lustig, Caitlin Simpson, Ellen Allison, Kimberley Chuanromanee, Tya Spiel, Katta Ko, Amy Rode, Jennifer Dym, Brianna Muller, Michael Klaus Scheuerman, Morgan Marie Walker, Ashley Brubaker, Jed Ahmed, Alex Department of Information Science University of Colorado United States Human Centered Design and Engineering University of Washington United States School of Information Studies Syracuse University United States Department of Psychology Macquarie University Australia University of Notre Dame United States Human-Computer Interaction Group Tu Wien Austria The Information School University of Washington United States Knowledge Lab University College London United Kingdom Department of Information Science University of Colorado Boulder United States Ai Interactions Ibm Research United States Information Science University of Colorado Boulder United States Pitch Lab Northwestern University United States University of Colorado Boulder United States Carnegie Mellon University United States

ISBN: (纸本)9781450380959

As Queer human-computer interaction (HCI) becomes an established part of the larger field, both in terms of research on and with queer populations and in terms of employing queering theories and methods, the role of queer researchers has become a timely topic of discussion. However, these discussions have largely centered around member-researcher status and positionality when working with queer populations. Based on insights gathered at multiple ACM events over the past two years, we identified two pressing issues: (1) we need to better support queer people doing HCI research not specific to queer populations, and (2) we need to identify how to best support member-researchers in leading Queer HCI while including collaborators beyond the queer community. This Special Interest Group (SIG) aims to directly address these challenges by convening a broad community of queer researchers and allies, working not only on explicitly-queer topics but across a broad range of HCI topics. © 2021 Owner/Author.

关键词： Computation theory

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