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arXiv 2023年

作者： Ma, Xinbei Xu, Yi Zhao, Hai Zhang, Zhuosheng The Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

Dialogue related Machine Reading Comprehension requires language models to effectively decoupling and modeling on multi-turn dialogue passages. As a dialogue development goes after the intentions of participants, its topic may not keep constant through the whole passage. Hence, it is non-trivial to detect and leverage the topic shift in dialogue modeling. Topic modeling, although has been widely studied in plain text, deserves far more utilization in dialogue reading comprehension. This paper proposes to model multi-turn dialogues from a topic-aware perspective. We start with a dialogue segmentation algorithm to split a dialogue passage into topic-concentrated fragments in an unsupervised way. Then we use these fragments as topic-aware language processing units in further dialogue comprehension. On one hand, the split segments indict specific topic rather than mixed intention, thus show convenient on in-domain topic detection and location. For this task, we design a clustering system with a self-training auto-encoder, and we build two constructed datasets for evaluation. On the other hand, the split segments are appropriate element of multi-turn dialogue response selection. For this purpose, we further present a novel model, Topic-Aware Dual-Attention Matching (TADAM) Network, which takes topic segments as processing element and matches response candidates with a dual cross-attention. Empirical studies on three public benchmarks show great improvements over baselines. Our work continues the previous studies on document topic, and brings the dialogue modeling to a novel topic-aware perspective with exhaustive experiments and analyses. Copyright © 2023, The Authors. All rights reserved.

关键词： Modeling languages

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Markerless Sagittal Gait Analysis of Cerebral Palsy in Children Using Pose Estimation Techniques

Markerless Sagittal Gait Analysis of Cerebral Palsy in Child...

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Innovation in Computing and Engineering (ICE), International Conference on

作者： Vinay Kumar Vats Chandra Prakash Amandeep Kaur Rajesh Kumar Computer Science and Engineering National Institute of Technology Delhi India Computational Intelligence and Smart Motion Research (CISMR) Lab SVNIT Surat India Malaviya National Institute of Technology Jaipur India Department of Human Anatomy and Physiology Faculty of Health Sciences University of Johannesburg Johannesburg South Africa

ISBN: (数字)9798350380460

ISBN: (纸本)9798350380477

Cerebral Palsy (CP) is a neurological disorder affecting children’s motor function and posture. Early detection and monitoring of Cerebral palsy is crucial for effective intervention and improved quality of life. Gait analysis is a critical tool for assessing motor function in children with cerebral palsy, often requiring controlled environments and markers for accuracy. Markerless gait analysis has emerged as a promising tool for objectively evaluating gait abnormalities in children with CP. In this study, we propose a markerless methodology utilising pose estimation to analyse gait patterns from sagittal video recordings captured in unconstrained environments. The dataset consists of 49 children, including 10 typically developing children and 39 with cerebral palsy, focusing on side-view videos for the analysis. Given the motor disabilities of these children, conducting gait analysis in a constrained environment can alter their natural walking patterns, making our approach particularly advantageous. Then we identify the key gait events, heel strike and toe-off, to distinguish gait between cycles by tracing knee joint movements. This technique demonstrates the potential for practical, non-invasive gait assessment in natural settings, offering insights into the distinct gait characteristics of children with cerebral palsy compared to their typically developing peers.

关键词： Legged locomotion Neurological diseases Cerebral palsy Technological innovation Pose estimation Motors Motion control Gait recognition Video recording Monitoring

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LBAA: A novel load balancing mechanism in cloud environments using ant colony optimization and artificial bee colony algorithms

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International Journal of Communication Systems 2023年第9期36卷 e5481-e5481页

作者： Mohammadian, Vahid Navimipour, Nima Jafari Hosseinzadeh, Mehdi Darwesh, Aso Department of Computer Engineering Qeshm Branch Islamic Azad University Qeshm Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan Pattern Recognition and Machine Learning Lab Gachon University Seongnam Korea Republic of Information Technology Departments University of Human Development Sulaimaniyah Iraq

Recently, cloud computing has been recognized as an effective paradigm for offering an on-demand platform, software services, and an efficient infrastructure to cloud clients. Due to the exponential growth of cloud tasks and the rapidly increasing number of cloud users, scheduling and balancing these tasks among involved heterogeneous virtual machines becomes an Non-deterministic Polynomial hard (NP-hard) optimization problem considering significant constraints, such as high rate of resource usage, low scheduling time, and low implementation cost. Therefore, various meta-heuristic algorithms have been widely used to tackle the issue. The current paper proposes a novel load balancing mechanism using the ant colony optimization and artificial bee colony algorithms, called LBAA, which aims to balance the load division among systems in data centers. The simulation outcomes confirm that our algorithm outperforms previous works regarding response time, imbalance degree, makespan, and resource utilization up to 25%, 15%, 12%, and 10%, respectively. © 2023 John Wiley & Sons Ltd.

关键词： Ant colony optimization

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Channel-aware Decoupling Network for Multi-turn Dialogue Comprehension

arXiv

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arXiv 2023年

作者： Zhang, Zhuosheng Zhao, Hai Liu, Longxiang The Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China China

Training machines to understand natural language and interact with humans is one of the major goals of artificial intelligence. Recent years have witnessed an evolution from matching networks to pre-trained language models (PrLMs). In contrast to the plain-text modeling as the focus of the PrLMs, dialogue texts involve multiple speakers and reflect special characteristics such as topic transitions and structure dependencies between distant utterances. However, the related PrLM models commonly represent dialogues sequentially by processing the pairwise dialogue history as a whole. Thus the hierarchical information on either utterance interrelation or speaker roles coupled in such representations is not well addressed. In this work, we propose compositional learning for holistic interaction across the utterances beyond the sequential contextualization from PrLMs, in order to capture the utterance-aware and speaker-aware representations entailed in a dialogue history. We decouple the contextualized word representations by masking mechanisms in Transformer-based PrLM, making each word only focus on the words in current utterance, other utterances, and two speaker roles (i.e., utterances of sender and utterances of the receiver), respectively. In addition, we employ domain-adaptive training strategies to help the model adapt to the dialogue domains. Experimental results show that our method substantially boosts the strong PrLM baselines in four public benchmark datasets, achieving new state-of-the-art performance over previous methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Constrained maximum weighted bipartite matching:a novel approach to radio broadcast scheduling

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science China(Information sciences) 2019年第7期62卷 156-169页

作者： Shaojiang WANG Tianyong WU Yuan YAO Dongbo BU Shaowei CAI State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences Beijing Key Lab of Human-Computer Interaction Institute of SoftwareChinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences

Given a set of radio broadcast programs, the radio broadcast scheduling problem is to allocate a set of devices to transmit the programs to achieve the optimal sound quality. In this article, we propose a complete algorithm to solve the problem, which is based on a branch-and-bound(BnB) algorithm. We formulate the problem with a new model, called constrained maximum weighted bipartite matching(CMBM),i.e., the maximum matching problem on a weighted bipartite graph with constraints. For the reduced matching problem, we propose a novel BnB algorithm by introducing three new strategies, including the highest quality first, the least conflict first and the more edge first. We also establish an upper bound estimating function for pruning the search space of the algorithm. The experimental results show that our new algorithm can quickly find the optimal solution for the radio broadcast scheduling problem at small scales, and has higher scalability for the problems at large scales than the existing complete algorithm.

关键词： radio broadcast scheduling branch-and-bound algorithm constrained maximum weighted bipartite matching Kuhn-Munkres algorithm strategy combinations

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Evaluating the Benefits of the Immersive Space to Think

Evaluating the Benefits of the Immersive Space to Think

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Lee Lisle Xiaoyu Chen J.K. Edward Gitre Chris North Doug A. Bowman Department of Computer Science Center for Human-Computer Interaction Grado Department of Industrial and Systems Engineering Virginia Tech Department of History Virginia Tech Center for Human-Computer Interaction

ISBN: (数字)9781728165325

ISBN: (纸本)9781728165332

Sensemaking with large multimedia dataset is a cognitively intensive task that requires analysts to understand the underlying stories that the dataset tells. Often, analysts use tools in order to offload cognition as well as convey their new understanding of the dataset; however, existing tools are limited by their underlying technologies. We have proposed a novel virtual reality tool to support sensemaking called the Immersive Space to Think (IST). IST can aid the process of analyzing multimedia data, but it remains unproven whether IST improves sensemaking performance over a traditional desktop setting. In a study performed over six weeks, one participant used both IST and traditional methods of sensemaking with a dataset of 100 text documents of transcribed survey responses from World War 2 soldiers to perform historical analysis. The participant was asked guided questions that produced three essays with their understanding of the data. After conducting a blind evaluation of the participant’s interpretation of the data, a team of three experts in historical analysis concluded that the essays written with IST displayed a better understanding of the dataset. Furthermore, the participant gave positive feedback on IST, and also suggested possible improvements.

关键词： Tools Visualization Task analysis human computer interaction Virtual reality Three-dimensional displays Information analysis

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The Disconnect between Engineering Students' Desire to Discuss Racial Injustice in the Classroom and Faculty Anxieties

The Disconnect between Engineering Students' Desire to Discu...

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2021 ASEE Virtual Annual Conference, ASEE 2021

作者： Hammond, Tracy Ray, Samantha Taele, Paul Thomas, Shawna Watson, Karan Stanley, Christine Polsley, Seth Institute for Engineering Education & Innovation Engineering Education Faculty Sketch Recognition Lab Department of Computer Science & Engineering Texas A&M University United States Engineering Education Faculty Department of Computer Science & Engineering Texas A&M University United States Engineering Education Faculty Department of Electrical and Computer Engineering Texas A&M University United States Engineering Education Faculty Department of Educational Administration & Human Development Texas A&M University United States

Protests against racial injustice have been increasing in the United States. Universities often rapidly respond to acts of injustice through public statements about their position to uphold the equality of all people. To gauge the desires and concerns around discussing events causing social unrest in engineering classrooms specifically, the engineering education faculty chair of a large university conducted discussions with both students and faculty regarding its place in their classrooms. This paper describes the emerging themes from survey responses using coding and grounded theory. Reactions from students and faculty were decidedly different. Most students stressed the importance of discussing such topics in class with their engineering faculty, while most faculty emphasized their concerns with doing so due to their lack of training to effectively handle such topics. This paper describes the evaluation of student and faculty responses and its implications for supporting diversity and inclusion in the engineering classroom. © American Society for Engineering Education, 2021

关键词： Students

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Tensor Embedding: A Supervised Framework for human Behavioral Data Mining and Prediction

Tensor Embedding: A Supervised Framework for Human Behaviora...

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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Homa Hosseinmardi Amir Ghasemian Kristina Lerman Shrikanth Narayanan Emilio Ferrara Department of Computer and Information Science University of Pennsylvania Philadelphia Pennsylvania USA Human Nature Lab Yale University New Haven Conneticut USA Information Sciences Institute University of Southern California Marina Del Rey California USA

Today’s densely instrumented world offers tremendous opportunities for continuous acquisition and analysis of multimodal sensor data, providing temporal characterization of individual behaviors. Is it possible to efficiently couple such rich sensor data with predictive modeling techniques to provide contextual and insightful assessments of individual behavior? Such data is noisy, incomplete, and collected from multiple sources, each with a different temporal resolution and dimensionality. In addition, longitudinal studies tend to examine a number of aspects of human behavior, such as well being, performance, or personality, which poses a challenge for handcrafted features engineering. To address these challenges, we propose supervised tensor embedding (STE), an algorithm for the joint decomposition of input and target variables in high-dimensional multimodal data. Latent features obtained from STE can be fed into any regression model for the estimation of the target variable(s). Following the embedding of higher-order data, features from different sources can be coupled. Additionally, we demonstrate that feature selection on higher-order data can enhance performance. The efficiency of our method is tested on two real-world datasets consisting of 29 target variables. Compared to the state of the art, we find that (i) our method outperforms others in 21 prediction tasks, and (ii) there are a few aspects of human behavior that are unpredictable regardless of the method, which can be the limitation of multilinear methods or lack of proper independent variables.

关键词：

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ToP-ToM: Trust-aware Robot Policy with Theory of Mind

arXiv

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arXiv 2023年

作者： Yu, Chuang Serhan, Baris Cangelosi, Angelo UCL Interaction Centre Computer Science Department University College London LondonWC1E 6BT United Kingdom Cognitive Robotics Lab Manchester Centre for Robotics and AI The University of Manchester ManchesterM13 9PL United Kingdom

Theory of Mind (ToM) is a fundamental cognitive architecture that endows humans with the ability to attribute mental states to others. humans infer the desires, beliefs, and intentions of others by observing their behavior and, in turn, adjust their actions to facilitate better interpersonal communication and team collaboration. In this paper, we investigated trust-aware robot policy with the theory of mind in a multiagent setting where a human collaborates with a robot against another human opponent. We show that by only focusing on team performance, the robot may resort to the reverse psychology trick, which poses a significant threat to trust maintenance. The human's trust in the robot will collapse when they discover deceptive behavior by the robot. To mitigate this problem, we adopt the robot theory of mind model to infer the human's trust beliefs, including true belief and false belief (an essential element of ToM). We designed a dynamic trust-aware reward function based on different trust beliefs to guide the robot policy learning, which aims to balance between avoiding human trust collapse due to robot reverse psychology. The experimental results demonstrate the importance of the ToM-based robot policy for human-robot trust and the effectiveness of our robot ToM-based robot policy in multiagent interaction settings. © 2023, CC BY.

关键词： Multi agent systems

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Algorithmic Mirror: Designing an Interactive Tool to Promote Self-Reflection for YouTube Recommendations

arXiv

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arXiv 2025年

作者： Kondo, Yui Dunnell, Kevin Xiao, Qing Zhao, Jun Rocher, Luc Oxford Internet Institute University of Oxford United Kingdom MIT Media Lab Massachusetts Institute of Technology United States Human-Computer Interaction Institute Carnegie Mellon University United States Department of Computer Science University of Oxford United Kingdom

Big Data analytics and Artificial Intelligence systems derive non-intuitive and often unverifiable inferences about individuals’ behaviors, preferences, and private lives. Drawing on diverse, feature-rich datasets of unpredictable value, these systems erode the intuitive connection between our actions and how we are perceived, diminishing control over our digital identities. While Explainable Artificial Intelligence scholars have attempted to explain the inner workings of algorithms, their visualizations frequently overwhelm end-users with complexity. This research introduces ‘hypothetical inference’—a novel approach that uses language models to simulate how algorithms might interpret users’ digital footprints and infer personal characteristics without requiring access to proprietary platform algorithms. Through empirical studies with fourteen adult participants, we identified three key design opportunities to foster critical algorithmic literacy: (1) reassembling scattered digital footprints into a unified map, (2) simulating algorithmic inference through LLM-generated interpretations, and (3) incorporating temporal dimensions to visualize evolving patterns. This research lays the groundwork for tools that can help users recognize the influence of data on platforms and develop greater autonomy in increasingly algorithm-mediated digital environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Video analysis

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