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IEEE-RAS International Conference on humanoid Robots

作者： Henri-Jacques Geiβ Firas Al-Hafez Andre Seyfarth Jan Peters Davide Tateo Computer Science Department Technical University Darmstadt Computer Science Department University of Innsbruck Human Sciences Department Technical University Darmstadt German Research Center for AI (DFKI) Centre for Cognitive Science Hessian.AI

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

Learning a locomotion controller for a musculoskeletal system is challenging due to over-actuation and high-dimensional action space. While many reinforcement learning methods attempt to address this issue, they often struggle to learn human-like gaits because of the complexity involved in engineering an effective reward function. In this paper, we demonstrate that adversarial imitation learning can address this issue by analyzing key problems and providing solutions using both current literature and novel techniques. We validate our methodology by learning walking and running gaits on a simulated humanoid model with 16 degrees of freedom and 92 Muscle-Tendon Units, achieving natural-looking gaits with only a few demonstrations. Code is available at https://***/henriTUD/musculoco_learning.

关键词： Legged locomotion Biomechanics Codes Imitation learning humanoid robots Reinforcement learning Muscles Motion capture Complexity theory Assistive devices

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LabelEase: A Semi-Automatic Tool for Efficient and Accurate Trace Labeling in Microservices

LabelEase: A Semi-Automatic Tool for Efficient and Accurate ...

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International Symposium on Software Reliability Engineering (ISSRE)

作者： Shenglin Zhang Zeyu Che Zhongjie Pan Xiaohui Nie Yongqian Sun Lemeng Pan Dan Pei Nankai University Haihe Laboratory of Information Technology Application Innovation Computer Network Information Center Chinese Academy of Sciences Tianjin Key Laboratory of Software Experience and Human Computer Interaction Huawei Beijing National Research Center for Information Science and Technology Tsinghua University

ISBN: (数字)9798350353884

ISBN: (纸本)9798350353891

Trace data is crucial for system observability and maintainability within microservices architectures, and many operation algorithms depend heavily on trace data, including anomaly detection, root cause analysis, etc. However, the actual performance of these algorithms might be unsatisfactory due to the absence of high-quality labeled datasets for effective training and evaluation. Since billions of traces could be generated daily for large-scale microservices, labeling overhead is the main hurdle to obtaining high-quality trace *** this paper, we propose LabelEase, a novel semi-automatic trace labeling tool, which uses active learning techniques to achieve efficient and accurate trace labeling. For anomaly trace labeling, LabelEase clusters similar traces with a graph-based trace representation technique and selects a few representative traces for human labeling, avoiding labeling most of the traces. For root cause labeling, LabelEase aggregates the labeled anomalous traces and identifies the service’s failures for operators to label. Our systematic experiments on two large-scale datasets show that LabelEase achieves over 0.98 F 1 -score in anomaly trace labeling and 0.89 precision of failure detection in root cause labeling, LabelEase can reduce operators’ labeling overhead by more than 99.9%. To the best of our knowledge, we are the first to propose a semi-automatic trace labeling tool capable of achieving efficient and accurate trace labeling.

关键词： Training Location awareness Accuracy Costs Transfer learning Microservice architectures Manuals Data models Labeling Anomaly detection

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Do Text-to-Text Multi-Task Learners Suffer from Task Conflict?

Do Text-to-Text Multi-Task Learners Suffer from Task Conflic...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Mueller, David Andrews, Nicholas Dredze, Mark Department of Computer Science Johns Hopkins University United States Human Language Technology Center of Excellence Johns Hopkins University United States

Traditional multi-task learning architectures learn a single model across multiple tasks through a shared encoder followed by task-specific decoders. Learning these models often requires specialized training algorithms that address task-conflict in the shared parameter updates, which otherwise can lead to negative transfer. A new type of multi-task learning within NLP homogenizes multi-task architectures as a shared encoder and language model decoder, which does surprisingly well across a range of diverse tasks (Raffel et al., 2020). Does this new architecture suffer from task-conflicts that require specialized training algorithms? We study how certain factors in the shift towards text-to-text models affects multitask conflict and negative transfer, finding that both directional conflict and transfer are surprisingly constant across architectures. © 2022 Association for Computational Linguistics.

关键词： Decoding

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Can I Trust You? Exploring the Impact of Misleading AI Suggestions on User's Trust

Can I Trust You? Exploring the Impact of Misleading AI Sugge...

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Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), IEEE International Conference on

作者： Daniele Pretolesi Olivia Zechner Helmut Schrom-Feiertag Manfred Tscheligi Center for Technology Experience Austrian Institute of Technology Vienna Austria Department of Artificial Intelligence and Human Interfaces University of Salzburg Salzburg Austria Department of Artificial Intelligence and Human Interfaces Human-Computer Interaction Division University of Salzburg Salzburg Austria

ISBN: (数字)9798350378009

ISBN: (纸本)9798350378016

Extended Reality (XR) offers immersive and interactive training experiences across fields like policing and medicine. However, tailoring training difficulty to individual needs remains a challenge. AI-driven training assistants could be a promising solution to personalize XR training by proposing adjustments based on user data. Yet, the effectiveness of such suggestions and the user's ability to discern accurate recommendations are not fully understood. This study investigates the effectiveness of suggestions within an AI-driven interface for proposing scenario changes in an XR training environment. We employ a $2\times 2$ experimental design, manipulating both the presence of stress during training (stressful versus neutral scenarios) and the biosignal information provided by the trainees during training (congruent versus incongruent). This work aims to determine whether trainers can reject misleading suggestions made by the system and how this affects their trust in the system. A sample of N=17 medical first responder trainers was recruited from three European countries. Our results show that the majority of the participants did not reject incorrect suggestions and the expected decrease in trust and perceived transparency towards the misleading interface was not observed. These results underscore the complex interplay between user perception, trust, and technological interface design, highlighting avenues for further research and refinement in the development of AI-driven suggestion systems.

关键词： Training Extended reality Decision making Neural engineering Metrology Reliability Artificial intelligence Usability Stress Optimization

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Surface defect identification using Bayesian filtering on a 3D mesh

Surface defect identification using Bayesian filtering on a ...

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作者： Dalle Vedove, Matteo Bonetto, Matteo Lamon, Edoardo Palopoli, Luigi Saveriano, Matteo Fontanelli, Daniele Department of Industrial Engineering Universitá di Trento Trento Italy DRIM Ph.D. of National Interest in Robotics and Intelligent Machines Italy Department of Information Engineering and Computer Science Universitá di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

This paper presents a CAD-based approach for automated surface defect detection. We leverage the a-priori knowledge embedded in a CAD model and integrate it with point cloud data acquired from commercially available stereo and depth cameras. The proposed method first transforms the CAD model into a high-density polygonal mesh, where each vertex represents a state variable in 3D space. Subsequently, a weighted least squares algorithm is employed to iteratively estimate the state of the scanned workpiece based on the captured point cloud measurements. This framework offers the potential to incorporate information from diverse sensors into the CAD domain, facilitating a more comprehensive analysis. Preliminary results demonstrate promising performance, with the algorithm achieving convergence to a sub-millimeter standard deviation in the region of interest using only approximately 50 point cloud samples. This highlights the potential of utilising commercially available stereo cameras for high-precision quality control applications. © 2025

关键词： Mesh generation

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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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Forebrain excitatory neuron-specific loss of Brpf1 attenuates excitatory synaptic transmission and impairs spatial and fear memory

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Neural Regeneration Research 2024年第5期19卷 1133-1141页

作者： Baicheng Zhao Hang Zhang Ying Liu Gaoyu Zu Yuxiao Zhang Jiayi Hu Shuai Liu Linya You Department of Human Anatomy&Histoembryology School of Basic Medical SciencesFudan UniversityShanghaiChina Shanghai Key Laboratory of Brain Functional Genomics(Ministry of Education) Affiliated Mental Health Center(ECNU)School of Psychology and Cognitive ScienceEast China Normal UniversityShanghaiChina Shanghai Changning Mental Health Center ShanghaiChina NYU-ECNU Institute of Brain and Cognitive Science at NYU Shanghai ShanghaiChina Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai ShanghaiChina

Bromodomain and plant homeodomain(PHD)finger containing protein 1(Brpf1)is an activator and scaffold protein of a multiunit complex that includes other components involving lysine acetyltransferase(KAT)6A/6B/***1,KAT6A,and KAT6B mutations were identified as the causal genes of neurodevelopmental disorders leading to intellectual *** previous work revealed strong and specific expression of Brpf1 in both the postnatal and adult forebrain,especially the hippocampus,which has essential roles in learning and ***,we hypothesized that Brpf1 plays critical roles in the function of forebrain excitatory neurons,and that its deficiency leads to learning and memory *** test this,we knocked out Brpf1 in forebrain excitatory neurons using *** found that Brpf1 deficiency reduced the frequency of miniature excitatory postsynaptic currents and downregulated the expression of genes Pcdhgb1,Slc16a7,Robo3,and Rho,which are related to neural development,synapse function,and memory,thereby damaging spatial and fear memory in *** findings help explain the mechanisms of intellectual impairment in patients with BRPF1 mutation.

关键词： behavioral test Brpf1 CAMKIIa-Cre intellectual disability miniature excitatory postsynaptic current mRNA-Seq

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What makes IM users (un)responsive: An empirical investigation for understanding IM responsiveness

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International Journal of human computer Studies 2023年 172卷

作者： Lee, Hao-Ping [Hank] Chiang, Yi-Shyuan Chou, Yu-Ling Lin, Kung-Pai Chang, Yung-Ju Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Human–Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Department of Computer Science University of Illinois Urbana-Champaign UrbanaIL United States

Instant messaging (IM) communication has been widely studied due to its prevalence in our everyday communication. Numerous factors that contribute to (un)responsiveness have been identified. Yet an integrated view of the factors that influence IM responsiveness remains absent. This paper reports qualitative findings from interviews with 46 IM users, and identifies five main elements underlying IM users’ response decisions: response habits, need fulfillment, perceived obligation, perceived readiness/suitability, and pace/rhythm coordination. To the best of our knowledge, this is the first integrated view of the key elements underlying IM responsiveness. Among these elements, we particularly highlight that regarding the influence of contextual factors on IM responsiveness, what may matters more is IM users’ perceptions of their readiness and suitability of the contexts, rather than the contexts’ objective properties. We also uncovered pace/rhythm coordination as a crucial factor behind IM responsiveness, which has been little discussed in the literature. Responsiveness, as our findings show, was often not a consequence, but a manifestation, of users’ pursuit of specific responsiveness-related aims such as ongoing shaping and sustaining of a dyad's pace and rhythm. © 2022 Elsevier Ltd

关键词： Message passing

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Exploring Culturally Informed AI Assistants: A Comparative Study of ChatBlackGPT and ChatGPT

Exploring Culturally Informed AI Assistants: A Comparative S...

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2025 CHI Conference on human Factors in Computing Systems, CHI EA 2025

作者： Egede, Lisa Al Haque, Ebtesam Thompson, Gabriella Boyd, Alicia Smith, Angela D.R. Johnson, Brittany Human Computer Interaction Institute Carnegie Mellon University Pittsburgh PA United States Department of Computer Science George Mason University Fairfax VA United States The University of Texas at Austin Austin TX United States School of Public Health Yale University New Haven CT United States School of Information University of Texas at Austin Austin TX United States

ISBN: (纸本)9798400713958

In recent years, we have seen an influx in reliance on AI assistants for information seeking. Given this widespread use and the known challenges AI poses for Black users, recent efforts have emerged to identify key considerations needed to provide meaningful support. One notable effort is the development of ChatBlackGPT, a culturally informed AI assistant designed to provide culturally relevant responses. Despite the existence of ChatBlackGPT, there is no research on when and how Black communities might engage with culturally informed AI assistants and the distinctions between engagement with general purpose tools like ChatGPT. To fill this gap, we propose a research agenda grounded in results from a preliminary comparative analysis of outputs provided by ChatGPT and ChatBlackGPT for travel-related inquiries. Our efforts thus far emphasize the need to consider Black communities’ values, perceptions, and experiences when designing AI assistants that acknowledge the Black lived experience. © 2025 Copyright held by the owner/author(s).

关键词： AI Black community comparative AI culturally informed AI

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Body Politics: Unpacking Tensions and Future Perspectives for Body-Centric Design Research in HCI

Body Politics: Unpacking Tensions and Future Perspectives fo...

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2025 CHI Conference on human Factors in Computing Systems, CHI EA 2025

作者： Tsaknaki, Vasiliki Alaoui, Sarah Fdili Homewood, Sarah Fritsch, Jonas Brynskov, Anna Núñez-Pacheco, Claudia Carlson, Kristin Spiel, Katta Gillies, Marco Fyfe Pietro Harrington, Christina IT University of Copenhagen Copenhagen Denmark Creative Computing Institute University of the Arts London London United Kingdom Department of Computer Science University of Copenhagen Copenhagen Denmark Department of Computer Science and Media Technology Malmö University Malmö Sweden Creative Technologies Program Illinois State University Normal IL United States Crip Collective Human-Computer Interaction Group TU Wien Vienna Austria Computing Goldsmiths University of London London United Kingdom HCI Institute Carnegie Mellon University Pittsburgh PA United States

ISBN: (纸本)9798400713958

human bodies are deeply political as they carry historical and social meanings, including race, gender, sexuality, ethnicity, class, and abilities. The expanding body-centric research in HCI can be traced in the plurality of methods, theories and domains that take bodies as a central point of departure, when designing or studying interaction with technologies. This one-day workshop will bring together researchers and practitioners within the CHI community to discuss, map, and unpack emerging tensions and challenges on the topic of body politics for HCI. Interested participants are invited to submit examples from their own research, which, in the workshop, will be used as a point of departure to critically reflect on and expand body-centric methods, theories and domains through the lens of body politics. Workshop outcomes will include charting future directions for body-centric research to address challenges and opportunities of acknowledging that bodies are always political in design research. © 2025 Copyright held by the owner/author(s).

关键词： Bodies design research politics

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