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26th ACM Conference on computer-Supported Cooperative Work and Social Computing, CSCW 2023

作者： Hansson, Karin Bardzell, Shaowen Bhandari, Aparajita Boulicault, Marion Doyle, Dylan Thomas Erete, Sheena Cerratto Pargman, Teresa Lazem, Shaimaa Muller, Michael Normark, Maria Petterson, Adrian Rosén, Anton Poikolainen Taylor, Alex S. Thomas, Jakita O. Watson, Julia Södertörn University The School of Natural Sciences Technology and Environmental Studies Sweden Georgia Institute of Technology The School of Interactive Computing United States Cornell University Department of Communication United States University of Edinburgh Department of Philosophy United States University of Colorado Boulder Department of Information Science United States University of Maryland College of Information United States Stockholm University The Department of Computer and Systems Sciences Sweden City for Scientific Research and Technological Applications The Informatics Research Institute Egypt Ibm Research Human Centered Ai United States Uppsala University Department of Informatics and Media Sweden University of Toronto Faculty of Information Canada Aalto University The School of Arts Design and Architecture Finland City University of London The Centre for Human Computer Interaction Design United Kingdom Auburn University Department of Computer Science and Software Engineering United States University of Toronto Department of Computer Science Canada

ISBN: (纸本)9798400701290

This one-day hybrid workshop builds on previous feminist CSCW workshops to explore feminist theoretical and methodological approaches that have provided us with useful tools to see things differently and make space for change. Since its inception over a decade ago, feminist HCI has progressed from the margins to mainstream HCI, with numerous references in the literature. Feminist HCI has also evolved to incorporate other critical HCI practices such as Queer HCI, participatory design, and speculative design. While feminist approaches have grown in popularity and become mainstream, it is getting more difficult to distinguish the feminist emancipatory core from other attempts of developing and improving society in various ways. In this workshop, we therefore want to revisit our feminist roots, where theory is a liberatory and creative practice, motivated by affect, curiosity, and wonder. From this standpoint, we consider which of our feminist tools can make a significant difference today, in a highly datafied world. The goal of this workshop is to;1) create an inventory of feminist theories and concepts that have had an impact on our work as designers, educators, researchers, and activists;2) develop a feminist toolbox for the CSCW community to strengthen our feminist literacy. © 2023 Owner/Author.

关键词： Computation theory

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Over-the-air Wireless Federated Learning Model for Generative AI

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IEEE Network 2025年

作者： Zheng, Jie Niyato, Dusit Zhang, Haijun Du, Hongyang Wang, Jiacheng Kang, Jiawen Xiong, Zehui Northwest University State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Shaanxi 710127 China Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore University of Science and Technology Beijing Institute of Artificial Intelligence Beijing100083 China University of Hong Kong Department of electrical and electronic engineering Hong Kong Hong Kong Guangdong University of Technology Automation of School Guangzhou510006 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore

Generative artificial intelligence (GenAI) technologies represent an important advancement in the field of AI, particularly for their capabilities in text and image generation. Over-the-air wireless federated learning (OA-WFL) can utilize the wireless waveform superposition property to achieve efficient model aggregation, thereby providing support for GenAI training and deployment. Therefore, this article investigates the support of OA-WFL for GenAI, focusing on potential applications and specific examples. We first discuss the OA-WFL and GenAI models, emphasizing their functionalities and the potential benefits arising from their interaction. We then explore its application in various GenAI scenarios, including large-scale edge device content generation, efficient distributed training of GenAI models, and reduction of bandwidth requirements and device load. Next, a framework is proposed to apply OA-WFL to diffusion models, such as those used in image generation, and validate its effectiveness through simulation results. Finally, we discuss prospective research directions for the application of OA-WFL for GenAI. © 2025 IEEE.

关键词： Federated learning

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User-Centric Interactive AI for Distributed Diffusion Model-based AI-Generated Content

arXiv

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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Shen, Xuemin Kim, Dong In The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Chinese University of Hong Kong Shenzhen China The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Distributed Artificial Intelligence-Generated Content (AIGC) has attracted increasing attention. However, it faces two significant challenges: how to maximize the subjective Quality of Experience (QoE) and how to enhance the energy efficiency, which are particularly pronounced in widely adopted Generative Diffusion Model (GDM)-based AIGC services for image generation. In this paper, we propose a novel user-centric Interactive AI (IAI) approach for service management, with a distributed GDM-based AIGC framework, prioritizing efficient and collaborative GDM deployment. Specifically, we restructure the GDM's inference process, i.e., the denoising chain, to enable users' semantically similar prompts to share a portion of diffusion steps. Furthermore, to maximize the users' subjective QoE, we propose a IAI approach, i.e., Reinforcement Learning With Large Language Models Interaction (RLLI), which utilizes Large Language Model (LLM)-empowered generative agents to replicate users interaction, providing real-time and subjective QoE feedback that reflects a spectrum of user personalities. Lastly, we present the GDM-based Deep Deterministic Policy Gradient (G-DDPG) algorithm, adapted to the proposed RLLI framework, for effective communication and computing resource allocation while considering user subjective personalities and dynamic wireless environments in decision-making. Simulation results show that G-DDPG can increase the sum QoE by 15%, compared with the conventional DDPG algorithm. © 2023, CC BY.

关键词： Reinforcement learning

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Generalized Simultaneous Perturbation-based Gradient Search with Reduced Estimator Bias

arXiv

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arXiv 2022年

作者： Pachal, Soumen Bhatnagar, Shalabh Prashanth, L.A. Department of Computer Science and Engineering Indian Institute of Technology Madras TCS Research Chennai India Department of Computer Science and Automation the Robert Bosch Centre for Cyber Physical Systems Indian Institute of Science Bangalore India Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai India

We present in this paper a family of generalized simultaneous perturbation-based gradient search (GSPGS) estimators that use noisy function measurements. The number of function measurements required by each estimator is guided by the desired level of accuracy. We first present in detail unbalanced generalized simultaneous perturbation stochastic approximation (GSPSA) estimators and later present the balanced versions (B-GSPSA) of these. We extend this idea further and present the generalized smoothed functional (GSF) and generalized random directions stochastic approximation (GRDSA) estimators, respectively, as well as their balanced variants. We show that estimators within any specified class requiring more number of function measurements result in lower estimator bias. We present a detailed analysis of both the asymptotic and non-asymptotic convergence of the resulting stochastic approximation schemes. We further present a series of experimental results with the various GSPGS estimators on the Rastrigin and quadratic function objectives. Our experiments are seen to validate our theoretical findings. Copyright © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

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SKU-Patch: Towards Efficient Instance Segmentation for Unseen Objects in Auto-Store

arXiv

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arXiv 2023年

作者： Yang, Biqi Tang, Weiliang Gao, Xiaojie Li, Xianzhi Liu, Yun-Hui Fu, Chi-Wing Heng, Pheng-Ann The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China The School of Computer Science and Technology Huazhong University of Science and Technology China The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong

In large-scale storehouses, precise instance masks are crucial for robotic bin picking but are challenging to obtain. Existing instance segmentation methods typically rely on a tedious process of scene collection, mask annotation, and network fine-tuning for every single Stock Keeping Unit (SKU). This paper presents SKU-Patch, a new patch-guided instance segmentation solution, leveraging only a few image patches for each incoming new SKU to predict accurate and robust masks, without tedious manual effort and model re-training. Technical-wise, we design a novel transformer-based network with (i) a patch-image correlation encoder to capture multi-level image features calibrated by patch information and (ii) a patch-aware transformer decoder with parallel task heads to generate instance masks. Extensive experiments on four storehouse benchmarks manifest that SKU-Patch is able to achieve the best performance over the state-of-the-art methods. Also, SKU-Patch yields an average of nearly 100% grasping success rate on more than 50 unseen SKUs in a robot-aided auto-store logistic pipeline, showing its effectiveness and practicality. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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CEBRA Method: Decoding Brain Activity for Advanced Brain-computer Interface technology 46

CEBRA Method: Decoding Brain Activity for Advanced Brain-Com...

引用

46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Yang, Jingcheng Kulwa, Frank Liu, Xuanwei Lu, Yiqing Fu, Yufa Li, Guanglin Huai, Yaping Zhang, Xin Li, Yongcheng Shenzhen Dapeng New District Nanao People's Hospital Guangdong Shenzhen518000 China Shenzhen Longhua District Central Hospital Department of Rehabilitation Medicine Guangdong Shenzhen518000 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Kunming University of Science and Technology Brain Cognition and Brain-Computer Intelligence Integration Group Kunming650500 China Cas Key Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen518055 China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Shenzhen518055 China Shandong Zhongke Advanced Technology Co. Ltd. Jinan250014 China

ISBN: (纸本)9798350371499

The emerging neurorehabilitation technology, Brain-computer Interface (BCI), provides a novel prospect for stroke recovery. However, decoding brain activity during the movement present substantial challenges, and feature extraction is crutial to build a better decoder. In this study the CEBRA method were employed to extract features first based on electroencephalogram(EEG) data during Motor Execution(ME) and Motor lmagery (Ml) tasks for 20 participants (including 10 stroke patients). The results revealed that, in MI tasks, CEBRA-RF (Random Forests) achieved an average classification accuracy of 91.33%, with an average F1-score of 91.19%, and CEBRA-SVM (Support Vector Machine) achieved an average classification accuracy of 91.32%, with an average F1-score of 90.83%. Compared to other conventional feature extraction methods, CEBRA shows significantly higher accuracy (t-test, p © 2024 IEEE.

关键词： Patient rehabilitation

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GSLB: the graph structure learning benchmark 23

GSLB: the graph structure learning benchmark

引用

Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Zhixun Li Liang Wang Xin Sun Yifan Luo Yanqiao Zhu Dingshuo Chen Yingtao Luo Xiangxin Zhou Qiang Liu Shu Wu Jeffrey Xu Yu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences and Department of Automation University of Science and Technology of China Department of Automation University of Science and Technology of China School of Cyberspace Security Beijing University of Posts and Telecommunications Department of Computer Science University of California Los Angeles Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB.

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Optimizing Generative AI Networking: A Dual Perspective with Multi-Agent systems and Mixture of Experts

arXiv

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arXiv 2024年

作者： Zhang, Ruichen Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Zhang, Ping Kim, Dong In The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

In the continued development of next-generation networking and artificial intelligence content generation (AIGC) services, the integration of multi-agent systems (MAS) and the mixture of experts (MoE) frameworks is becoming increasingly important. Motivated by this, this article studies the contrasting and converging of MAS and MoE in AIGC-enabled networking. First, we discuss the architectural designs, operational procedures, and inherent advantages of using MAS and MoE in generative AI to explore its functionality and applications fully. Next, we review the applications of MAS and MoE frameworks in content generation and resource allocation, emphasizing their impact on networking operations. Subsequently, we propose a novel multi-agent-enabled MoE-proximal policy optimization (MoE-PPO) framework for 3D object generation and data transfer scenarios. The framework uses MAS for dynamic task coordination of each network service provider agent and MoE for expert-driven execution of respective tasks, thereby improving overall system efficiency and adaptability. The simulation results demonstrate the effectiveness of our proposed framework and significantly improve the performance indicators under different network conditions. Finally, we outline potential future research directions. © 2024, CC BY.

关键词： Multi agent systems

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An Evolutionary Algorithm Based on Fully Connected Weight Networks for Mixed-Variable Multiobjective Optimization

SSRN

引用

SSRN 2023年

作者： Dong, Nanjiang Zhang, Tao Wang, Rui Liao, Xiangke Wang, Ling College of Systems Engineering National University of Defense Technology Changsha410073 China Xiangjiang Laboratory Changsha410205 China College of Computer Science National University of Defense Technology Changsha410073 China Department of Automation Tsinghua University Beijing100084 China

Multiobjective optimization research has mostly focused on continuous-variable problems. However, real-world optimization problems often involve multiple types of variables (real, integer, and discrete) and multiple conflicting optimization objectives, called mixed-variable multiobjective optimization problems (MVMOPs). Discrete variables make the decision space of the problem discrete, while different types of variables need different treatments by the evolutionary algorithm, which poses a challenge to the efficient search of the evolutionary algorithm. To address MVMOPs, we propose a fully connected weight network-based evolutionary algorithm (FCWNEA) in this paper. The fully connected network structure characterizes the entire decision space, the node access count records the frequency of visits to the node, and the weights of connections and the activity of variables estimate the distribution of the decision space. This information assists in generating offspring solutions. Furthermore, since the fully connected network discretizes the continuous variables, we propose a strategy to dynamically adjust the node values to perform a fine-grained global search for continuous variables. The results show that the proposed algorithm has a significant advantage in mixed-variable multiobjective problems. Moreover, the proposed algorithm is also quite competitive in continuous problems and can better handle the correlation between variables in optimization problems. © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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S2KAN-SLAM: Elastic Neural LiDAR SLAM with SDF Submaps and Kolmogorov-Arnold Networks

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IEEE Transactions on Circuits and systems for Video technology 2025年

作者： Wang, Zhong Zhang, Lin Wang, Hesheng Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Tongji University School of Computer Science and Technology National Pilot Software Engineering School with Chinese Characteristics Shanghai201804 China

Traditional LiDAR SLAM approaches prioritize localization over mapping, yet high-precision dense maps are essential for numerous applications involving intelligent agents. Recent advancements have introduced methods leveraging neural fields to enhance mapping capabilities;however, these approaches still face several limitations. Firstly, concerning scene representation, they typically employ neural fields with high-dimensional features and multi-layer perceptron decoders utilizing non-continuous activation functions. This results in low learning efficiency and challenges in capturing high-frequency signals. Secondly, in terms of scene organization, these methods often treat the entire scene as a singular neural field, leading to inefficiencies, inflexibility, and difficulties in rectifying accumulated errors when mapping large-scale environments over extended periods. To tackle the first issue, we propose a lightweight continuous SDF regression approach by encoding the scene in single-valued embeddings and decoding SDF values from a Kolmogorov-Arnold Network. By minimizing discrepancies in measuring range, sampling distance, and decoded SDF values, we facilitate iterative frame-to-model tracking and bundle adjustment neural mapping. To mitigate the second challenge, we propose structuring the whole scene into multiple neural SDF submaps. By establishing node-node, node-submap, and loop closure constraints into a global pose graph, the system can create dense neural maps with global consistency across large-scale scenes. Experimental evaluations in both real-world and simulated settings indicate that our system achieves superior mapping completeness and accuracy, enhanced learning efficiency, reduced memory consumption, and greater flexibility compared to its counterparts. © 2025 IEEE.

关键词： Mapping

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