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The Goofus & Gallant Story Corpus for Practical Value Alignment

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The Goofus & Gallant Story Corpus for Practical Value Alignm...

International Conference on Machine Learning and Applications (ICMLA)

作者： Md Sultan Al Nahian Tasmia Tasrin Spencer Frazier Mark Riedl Brent Harrison Institute for Biomedical Informatics University of Kentucky Lexington KY USA School of Interactive Computing Georgia Institute of Technology Atlanta GA USA Department of Computer Science University of Kentucky Lexington KY USA

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Values or principles are key elements of human society that influence people to behave and function according to an accepted standard set of social rules to maintain social order. As AI systems are becoming ubiquitous in human society, it is a major concern that they could violate these norms or values and potentially cause harm. Thus, to prevent intentional or unintentional harm, AI systems are expected to take actions that align with these principles. Training systems to exhibit this type of behavior is difficult and often requires a specialized dataset. This work presents a multi-modal dataset illustrating normative and non-normative behavior in real-life situations described through natural language and artistic images. This training set contains curated sets of images that are designed to teach young children about social principles. We argue that this is an ideal dataset to use for training socially normative agents given this fact.

关键词： Training Ethics Natural languages Machine learning Standards

The Goofus & Gallant Story Corpus for Practical Value Alignment

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arXiv 2025年

作者： Nahian, Md Sultan Al Tasrin, Tasmia Frazier, Spencer Riedl, Mark Harrison, Brent Institute for Biomedical Informatics University of Kentucky LexingtonKY United States Department of Computer Science University of Kentucky LexingtonKY United States School of Interactive Computing Georgia Institute of Technology AtlantaGA United States

关键词： Adversarial machine learning

Advancing a Consent-Forward Paradigm for Digital Mental Health Data

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arXiv 2024年

作者： Pendse, Sachin R. Stapleton, Logan Kumar, Neha De Choudhury, Munmun Chancellor, Stevie School of Interactive Computing Georgia Institute of Technology AtlantaGA30309 United States GroupLens Lab. Department of Computer Science & Engineering University of Minnesota Twin Cities MinneapolisMN55455 United States

The field of digital mental health is advancing at a rapid pace. Passively collected data from user engagements with digital tools and services continue to contribute new insights into mental health and illness. As the field of digital mental health grows, a concerning norm has been established—digital service users are given little say over how their data is collected, shared, or used to generate revenue for private companies. Given a long history of service user exclusion from data collection practices, we propose an alternative approach that is attentive to this history: the consent-forward paradigm. This paradigm embeds principles of affirmative consent in the design of digital mental health tools and services, strengthening trust through designing around individual choices and needs, and proactively protecting users from unexpected harm. In this perspective, we outline practical steps to implement this paradigm, toward ensuring that people searching for care have the safest experiences possible. © 2024, CC BY.

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SwCS: Section-Wise Content Similarity Approach to Exploit Scientific Big Data

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computers, Materials & Continua 2021年第4期67卷 877-894页

作者： Kashif Irshad Muhammad Tanvir Afzal Sanam Shahla Rizvi Abdul Shahid Rabia Riaz Tae-Sun Chung Department of Computer Science Capital University of Science and TechnologyIslamabadPakistan Department of Computer Science NAMAL InstituteMianwali42250Pakistan Raptor Interactive(Pty)Ltd. Eco BoulevardWitch Hazel AveCenturion0157South Africa Institute of Computing Kohat University of Science and TechnologyPakistan Department of CS&IT University of Azad Jammu and KashmirMuzaffarabad13100Pakistan Department of Artificial Intelligence Ajou UniversityKorea

The growing collection of scientific data in various web repositories is referred to as Scientific Big Data,as it fulfills the four“V’s”of Big Data—volume,variety,velocity,and *** phenomenon has created new opportunities for startups;for instance,the extraction of pertinent research papers from enormous knowledge repositories using certain innovative methods has become an important task for researchers and ***,the content of the papers are compared to list the relevant papers from a *** conventional method results in a long list of papers that is often impossible to interpret ***,the need for a novel approach that intelligently utilizes the available data is ***,the primary element of the scientific knowledge base is a research article,which consists of various logical sections such as the Abstract,Introduction,Related Work,Methodology,Results,and ***,this study utilizes these logical sections of research articles,because they hold significant potential in finding relevant *** this study,comprehensive experiments were performed to determine the role of the logical sections-based terms indexing method in improving the quality of results(i.e.,retrieving relevant papers).Therefore,we proposed,implemented,and evaluated the logical sections-based content comparisons method to address the research objective with a standard method of indexing *** section-based approach outperformed the standard content-based approach in identifying relevant documents from all classified topics of computer ***,the proposed approach extracted 14%more relevant results from the entire *** the experimental results suggested that employing a finer content similarity technique improved the quality of results,the proposed approach has led the foundation of knowledge-based startups.

关键词： Scientific big data ACM classification term indexing content similarity cosine similarity

What Makes a Good Theory? Interdisciplinary Perspectives

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Computational Brain and Behavior 2024年第4期7卷 503-507页

作者： van Rooij, Iris Devezer, Berna Skewes, Joshua Varma, Sashank Wareham, Todd Department of Cognitive Science and Artificial Intelligence Radboud University Nijmegen Netherlands Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen Netherlands Department of Linguistics Cognitive Science and Semiotics & amp Interacting Minds Centre Aarhus University Aarhus Denmark Department of Business University of Idaho Moscow United States Department of Mathematics and Statistical Science University of Idaho Moscow United States School of Interactive Computing Georgia Institute of Technology Atlanta United States School of Psychology Georgia Institute of Technology Atlanta United States Department of Computer Science Memorial University of Newfoundland St. John’s Canada

Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models

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arXiv 2025年

作者： Chen, Qiguang Qin, Libo Liu, Jinhao Peng, Dengyun Guan, Jiannan Wang, Peng Hu, Mengkang Zhou, Yuhang Gao, Te Che, Wanxiang LARG China Research Center for Social Computing and Interactive Robotics China Harbin Institute of Technology China School of Computer Science and Engineering Central South University China The University of Hong Kong Hong Kong Fudan University China

Recent advancements in reasoning with large language models (RLLMs), such as OpenAI-O1 and DeepSeek-R1, have demonstrated their impressive capabilities in complex domains like mathematics and coding. A central factor in their success lies in the application of long chain-of-thought (Long CoT) characteristics, which enhance reasoning abilities and enable the solution of intricate problems. However, despite these developments, a comprehensive survey on Long CoT is still lacking, limiting our understanding of its distinctions from traditional short chain-of-thought (Short CoT) and complicating ongoing debates on issues like "overthinking" and "test-time scaling." This survey seeks to fill this gap by offering a unified perspective on Long CoT. (1) We first distinguish Long CoT from Short CoT and introduce a novel taxonomy to categorize current reasoning paradigms. (2) Next, we explore the key characteristics of Long CoT: deep reasoning, extensive exploration, and feasible reflection, which enable models to handle more complex tasks and produce more efficient, coherent outcomes compared to the shallower Short CoT. (3) We then investigate key phenomena such as the emergence of Long CoT with these characteristics, including overthinking, and test-time scaling, offering insights into how these processes manifest in practice. (4) Finally, we identify significant research gaps and highlight promising future directions, including the integration of multi-modal reasoning, efficiency improvements, and enhanced knowledge frameworks. By providing a structured overview, this survey aims to inspire future research and further the development of logical reasoning in artificial intelligence1 © 2025, CC BY-SA.

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Enhancing Cyclone Center Identification in Radar Images through Deep Learning and Match Recognition

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Enhancing Cyclone Center Identification in Radar Images thro...

International Conference on computing and Networking Technology (ICCNT)

作者： Thirumurugan Shanmugam Chirag Chandrashekar Maheswari Subburaj Arun Kumar Sivaraman Ajmery Sultana Senthil Kumar Thangavel College of Computing and Information Sciences University of Technology and Applied Sciences Suhar Oman School of Computer Science and Engineering Vellore Institute of Technology Chennai India Digital Engineering Solution Center Photon Interactive Inc Ontario Canada School of Computer Science and Technology Algoma University Brampton Ontario Canada Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This research focuses on improving the identification of cyclone centers using deep learning and match recognition applied to radar images. Accurately pinpointing the cyclone’s center is vital for predicting its intensity and trajectory. However, challenges persist in automatically locating the center due to the diverse nature of cyclone morphology and structure. To address this, the deep convolutional network’s capability is leveraged to capture various structural features in images by proposing two-step approach for cyclone center localization. Initially, a pre-trained EfficientDet model is employed using transfer learning to obtain weights. Subsequently, these weights, along with the data, are utilized in a deep learning model to provide precise coordinates of the cyclone center in the respective image. The effectiveness of existing deep learning and machine learning models show that the cyclone prediction systems have an accuracy ranging from $86 \%$ to $92 \%$ or better, and cyclone eye detection accuracy surpassing 87%. Experiment outcomes indicate that the proposed methodology outperforms conventional methods and existing works, showcasing its potential for enhancing cyclone monitoring and forecasting.

关键词： Deep learning YOLO Training Adaptation models Accuracy Machine learning algorithms Transfer learning Radar Radar imaging Cyclones

“User AR” — Leveraging AI Based Augmented Reality for User Manuals

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“User AR” — Leveraging AI Based Augmented Reality for Use...

International Conference on computing and Networking Technology (ICCNT)

作者： Thirumurugan Shanmugam Chirag Chandrashekar Vipin Bhati Maheswari Subburaj Arun Kumar Sivaraman Ajmery Sultana College of Computing and Information Sciences University of Technology and Applied Sciences Suhar Oman School of Computer Science and Engineering Vellore Institute of Technology Chennai India Digital Engineering Solution Center Photon Interactive Inc Toronto Ontario Canada School of Computer Science and Technology Algoma University Brampton Ontario Canada

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The objective of this research is to propose an AI based novel augmented reality (AR) software to assist users in operating a wide range of technical and non-technical devices. The project’s aim is to make this AR application, which provides detailed explanations of all the device’s functions, more widely used than traditional user manuals. Augmented reality, which overlays digital information onto the real world, can be employed to develop an AR-based user manual for products or devices. Mobile applications that use a camera to recognize a product or device and display dynamic graphics, such as 3 D models, animations, or text, are one example of AR user manuals. These augmented reality user manuals enable consumers to quickly and easily learn how to operate, assemble, repair, or troubleshoot their purchased products. Experiments show that the proposed algorithm was able to recognize the object accurately with a percentage of 95.2%.

关键词： Three-dimensional displays Biological system modeling Manuals Transforms Animation Software User experience Artificial intelligence Usability Augmented reality

ConvBoost: Boosting ConvNets for Sensor-based Activity Recognition

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arXiv 2023年

作者： Shao, Shuai Guan, Yu Zhai, Bing Missier, Paolo Plötz, Thomas Department of Computer Science University of Warwick Coventry United Kingdom Computer and Information Sciences Northumbria University Newcastle upon Tyne United Kingdom School of Computing Newcastle University Newcastle upon Tyne United Kingdom School of Interactive Computing Georgia Institute of Technology Atlanta United States

Human activity recognition (HAR) is one of the core research themes in ubiquitous and wearable computing. With the shift to deep learning (DL) based analysis approaches, it has become possible to extract high-level features and perform classification in an end-to-end manner. Despite their promising overall capabilities, DL-based HAR may suffer from overfitting due to the notoriously small, often inadequate, amounts of labeled sample data that are available for typical HAR applications. In response to such challenges, we propose ConvBoost – a novel, three-layer, structured model architecture and boosting framework for convolutional network based HAR. Our framework generates additional training data from three different perspectives for improved HAR, aiming to alleviate the shortness of labeled training data in the field. Specifically, with the introduction of three conceptual layers–Sampling Layer, Data Augmentation Layer, and Resilient Layer–we develop three "boosters"–R-Frame, Mix-up, and C-Drop–to enrich the per-epoch training data by dense-sampling, synthesizing, and simulating, respectively. These new conceptual layers and boosters, that are universally applicable for any kind of convolutional network, have been designed based on the characteristics of the sensor data and the concept of frame-wise HAR. In our experimental evaluation on three standard benchmarks (Opportunity, PAMAP2, GOTOV) we demonstrate the effectiveness of our ConvBoost framework for HAR applications based on variants of convolutional networks: vanilla CNN, ConvLSTM, and Attention Models. We achieved substantial performance gains for all of them, which suggests that the proposed approach is generic and can serve as a practical solution for boosting the performance of existing ConvNet-based HAR models. This is an open-source project, and the code can be found at https://***/sshao2013/ConvBoost © 2023, CC BY.

关键词： Deep learning