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arXiv 2024年

作者： Li, Jiapeng Shao, Xiaodan Chen, Feng Wan, Shaohua Liu, Chang Wei, Zhiqiang Ng, Derrick Wing Kwan The College of Artificial Intelligence Southwest University Chongqing400715 China The Brain-Inspired Computing and Intelligent Control Key Laboratory Southwest University Chongqing400715 China The Key Laboratory of Luminescence Analysis and Molecular Sensing Southwest University Chongqing400715 China The Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China The School of Mathematics and Statistics Xi'an Jiaotong University Xi'An710049 China The Peng Cheng Laboratory Guangdong Shenzhen518055 China Guangdong Guangzhou510555 China The Department of Computer Science and Information Technology La Trobe University MelbourneVIC3150 Australia The School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia

Integrated sensing and communication (ISAC) is envisioned as a key pillar for enabling the upcoming sixth generation (6G) communication systems, requiring not only reliable communication functionalities but also highly accurate environmental sensing capabilities. In this paper, we design a novel networked ISAC framework to explore the collaboration among multiple users for environmental sensing. Specifically, multiple users can serve as powerful sensors, capturing back scattered signals from a target at various angles to facilitate reliable computational imaging. Centralized sensing approaches are extremely sensitive to the capability of the leader node because it requires the leader node to process the signals sent by all the users. To this end, we propose a two-step distributed cooperative sensing algorithm that allows low-dimensional intermediate estimate exchange among neighboring users, thus eliminating the reliance on the centralized leader node and improving the robustness of sensing. This way, multiple users can cooperatively sense a target by exploiting the block-wise environment sparsity and the interference cancellation technique. Furthermore, we analyze the mean square error of the proposed distributed algorithm as a networked sensing performance metric and propose a beamforming design for the proposed network ISAC scheme to maximize the networked sensing accuracy and communication performance subject to a transmit power constraint. Simulation results validate the effectiveness of the proposed algorithm compared with the state-of-the-art algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

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Digital Mahjong System: Towards Precise Cognitive Assessment with IoT Technologies 1

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24th International Conference on Human-Computer Interaction, HCII 2022

作者： An, Ning Hu, Enze Guo, Yanrui Yang, Jiaoyun Au, Rhoda Ding, Huitong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Shenzhen Corecloud Innovation Technology Co. Ltd. Shenzhen China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China Department of Anatomy and Neurobiology Neurology and Framingham Heart Study Boston University School of Medicine Boston United States Department of Epidemiology Boston University School of Public Health Boston United States

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

To the best of our knowledge, this paper is the first to apply IoT technologies to transform the popular Mahjong game into a Digital Mahjong System (DMS) for digitally performing cognitive assessments. People have started exploring digital cognitive assessment tools for better objectivity and simplification. However, most of these tools are not friendly for older adults. This paper aims to address this issue by integrating IoT technologies with Mahjong to make cognitive assessment simpler and more engaging for older adults. DMS has the following features: 1) integrating motion tracking devices into Mahjong tiles and transferring them into Digital Mahjong Tiles (DMTs), which can precisely capture their moving trajectory during the assessment, 2) using a Near Field Communication reader to configure DMT’s primary data, 3) supporting Over-The-Air Device Firmware Update that can be done remotely via Bluetooth, and 4) developing a charging base that can charge 16 DMTs simultaneously using Qi wireless charging standard. With further testing, the DMS has the potential to become a digital platform to implement and test various cognitive assessment tools integrating Mahjong elements. © 2022, Springer Nature Switzerland AG.

关键词： Internet of things

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Missing Data Recovery for Human Mocap Data Based on A-LSTM and LS Constraint 5

Missing Data Recovery for Human Mocap Data Based on A-LSTM a...

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5th IEEE International Conference on Signal and Image Processing, ICSIP 2020

作者： Ji, Lu Liu, Rui Zhou, Dongsheng Zhang, Qiang Wei, Xiaopeng Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (纸本)9781728168968

Human mocap (motion capture) data has been widely used in many fields. However, influenced by the occlusion and falling, missing of markers is inevitable in optical motion capture system. According to the temporal and spatial characteristics of mocap data, a missing data recovery method based on attention-based LSTM network (A-LSTM) and least-squares (LS) constraint is proposed in this paper. First, the LSTM is chosen as the basic network to estimate the position of the missing markers, in which an attention mechanism is introduced to learn the temporal continuity of human mocap data. Then, a space affine transformation based on least-squares fitting is adopted as the spatial constraint to refine the estimated positions, after which the restored error can be reduced significantly. Experiments show that the proposed A-LSTM+LS method is robust to the length of missing gap and the number of missing markers. Under the same conditions, our method achieves smaller restore error than the state of the art. © 2020 IEEE.

关键词： Least squares approximations

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Software defined edge computing for distributed management and scalable control in IoT multinetworks

arXiv

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arXiv 2021年

作者： Wu, Di Nie, Xiang Deng, Hanhui Qin, Zhijin Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University China School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

Recently, edge computing has emerged as a promising paradigm to support mobile access in IoT multinetworks. However, coexistence of heterogeneous wireless communication schemes brings about new challenges to the mobility management and access control in such networks. To maintain its computing continuum, Software-Defined Networking (SDN) architecture can be applied to the distributed edge networks, which provides consistent frameworks for mobility management and creates secure mechanisms for access control. In this paper, we first review the state-of-the-art SDN technology and its applications in edge computing. In particular, we highlight the prominent issues related to mobility management and access control for SDN in the edge computing environment. We also introduce a number of effective solutions to these issues by presenting real-world testbed experiments, prototypes and numerical analysis of adopting SDN based edge computing paradigms for IoT multinetworks. Copyright © 2021, The Authors. All rights reserved.

关键词： Edge computing

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Three-Variable and Five-Variable Weng-Zhang Algorithms of Different Traversal Types Applied to UKHP Prediction

Three-Variable and Five-Variable Weng-Zhang Algorithms of Di...

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2021 China Automation Congress, CAC 2021

作者： Zhang, Yunong Zhang, Yining Li, Litian School of Compute Science and Engineering Sun Yat-sen University Guangzhou510006 China Research Institute of Sun Yat-sen University in Shenzhen Shenzhen518057 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou510006 China Guangdong Key Laboratory of Modern Control Technology Guangzhou510070 China School of Artificial Intelligence Sun Yat-sen University Zhuhai519082 China

ISBN: (纸本)9781665426473

UK hung parliament (UKHP) prediction is a concern. It may not only cause political issues, but also lead to troubles in other fields such as economics. It is important to make predictions on UKHP. In this paper, we use three-variable and five-variable Weng-Zhang algorithms (also termed addition-subtraction and frequency [ASF] algorithms) of different traversal types to predict the risky time-intervals and years of UKHP to give an early alert for avoidance. Experiment results show that 2021-2023, 2047-2068, and 2098-2104 are more potentially risky, and 2023, 2053, 2055, 2061, 2068, and 2098 have a greater risk for the occurrence of UKHP. © 2021 IEEE

关键词： Forecasting

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UCL-AST: Active Self-Training with Uncertainty-Aware Clouded Logits for Few-Shot Text Classification

UCL-AST: Active Self-Training with Uncertainty-Aware Clouded...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yi Xu Jie Hu Zhiqiao Gao Jinpeng Chen School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China China Telecom Corporation Limited Research Institute Beijing China

Although the recent advances in pre-training language models have achieved great success and migrated annotation bottleneck for many tasks, the task-specific fine-tuning for text classification still requires thousands of labeled samples or even more. When few human annotations are available, active self-training is one of the efficient approaches for reducing human annotation costs which integrates pseudo labels of self-training into the procedure of active learning. The traditional active self-training approaches mix manual labels and pseudo labels indistinguishably without considering the impact of active learning and self-training on each other. There are two problems for active self-training in few-shot text classification: (1) The noise in a large number of pseudo-labels may mislead the model training. (2) The confusing samples in overlap boundaries between classes are especially difficult to identify correctly with a few labels. To solve these problems, we propose UCL-AST, an active self-training framework that boosts the performance of the active teacher model to provide superior pseudo labels for the self-training student model and facilitates the active teacher model to learn more clear class boundaries with uncertainty-aware clouded logits in few-shot. The extensive experiments and visualization analysis have demonstrated the effectiveness of the proposed framework on four datasets compared with state-of-the-art methods.

关键词： Training Visualization Uncertainty Costs Annotations Text categorization Measurement uncertainty

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Wireless Large AI Model: Shaping the AI-Native Future of 6G and Beyond

arXiv

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arXiv 2025年

作者： Zhu, Fenghao Wang, Xinquan Li, Xinyi Zhang, Maojun Chen, Yixuan Huang, Chongwen Yang, Zhaohui Chen, Xiaoming Zhang, Zhaoyang Jin, Richeng Huang, Yongming Feng, Wei Yang, Tingting Bai, Baoming Gao, Feifei Yang, Kun Liu, Yuanwen Muhaidat, Sami Yuen, Chau Huang, Kaibin Wong, Kai-Kit Niyato, Dusit Debbah, Mérouane College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Automation Southeast University Nanjing210096 China Tsinghua University Beijing100084 China Pengcheng Laboratory Shenzhen518066 China State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China State Key Laboratory of Novel Software Technology Nanjing University Nanjing210008 China Suzhou215163 China The University of Hong Kong Hong Kong KU 6G Research Center Computer and Communication Engineering Khalifa University Abu Dhabi127788 United Arab Emirates School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates

The emergence of sixth-generation and beyond communication systems is expected to fundamentally transform digital experiences through introducing unparalleled levels of intelligence, efficiency, and connectivity. A promising technology poised to enable this revolutionary vision is the wireless large AI model (WLAM), characterized by its exceptional capabilities in data processing, inference, and decision-making. In light of these remarkable capabilities, this paper provides a comprehensive survey of WLAM, elucidating its fundamental principles, diverse applications, critical challenges, and future research opportunities. We begin by introducing the background of WLAM and analyzing the key synergies with wireless networks, emphasizing the mutual benefits. Subsequently, we explore the foundational characteristics of WLAM, delving into their unique relevance in wireless environments. Then, the role of WLAM in optimizing wireless communication systems across various use cases and the reciprocal benefits are systematically investigated. Furthermore, we discuss the integration of WLAM with emerging technologies, highlighting their potential to enable transformative capabilities and breakthroughs in wireless communication. Finally, we thoroughly examine the high-level challenges hindering the practical implementation of WLAM and discuss pivotal future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Sorting

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Cross-lingual semantic role labeling with high-quality translated training corpus 58

Cross-lingual semantic role labeling with high-quality trans...

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58th Annual Meeting of the Association for Computational Linguistics, ACL 2020

作者： Fei, Hao Zhang, Meishan Ji, Donghong Department of Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China School of New Media and Communication Tianjin University China

ISBN: (纸本)9781952148255

Many efforts of research are devoted to semantic role labeling (SRL) which is crucial for natural language understanding. Supervised approaches have achieved impressing performances when large-scale corpora are available for resource-rich languages such as English. While for the low-resource languages with no annotated SRL dataset, it is still challenging to obtain competitive performances. Cross-lingual SRL is one promising way to address the problem, which has achieved great advances with the help of model transferring and annotation projection. In this paper, we propose a novel alternative based on corpus translation, constructing high-quality training datasets for the target languages from the source gold-standard SRL annotations. Experimental results on Universal Proposition Bank show that the translation-based method is highly effective, and the automatic pseudo datasets can improve the target-language SRL performances significantly. © 2020 Association for Computational Linguistics

关键词： Semantics

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Data-Driven Structural Design Optimization for Petal-Shaped Auxetics Using Isogeometric Analysis

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Computer Modeling in engineering & sciences 2020年第2期122卷 433-458页

作者： Yingjun Wang Zhongyuan Liao Shengyu Shi Zhenpei Wang Leong Hien Poh National Engineering Research Center of Novel Equipment for Polymer Processing the Key Laboratory of Polymer Processing Engineering of the Ministry of Education(South China University of Technology)Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced ManufacturingSouth China University of TechnologyGuangzhou510641China. Institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)1 Fusionopolis Way138632Singapore. Department of Civil and Environmental Engineering National University of Singapore 1 Engineering Drive 2E1A 07-03117576Singapore

Focusing on the structural optimization of auxetic materials using data-driven methods,a back-propagation neural network(BPNN)based design framework is developed for petal-shaped auxetics using isogeometric *** a NURBSbased parametric modelling scheme with a small number of design variables,the highly nonlinear relation between the input geometry variables and the effective material properties is obtained using BPNN-based fitting method,and demonstrated in this work to give high accuracy and *** BPNN-based fitting functions also enable an easy analytical sensitivity analysis,in contrast to the generally complex procedures of typical shape and size sensitivity approaches.

关键词： Data-driven BP neural network petal-shaped auxetics negative Poisson’s ratio structural design isogeometric analysis.

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Leveraging VOSviewer approach for mapping, visualisation, and interpretation of crisis data for disaster management and decision-making

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International Journal of Information Management Data Insights 2025年第1期5卷

作者： Bukar, Umar Ali Sayeed, Md Shohel Amodu, Oluwatosin Ahmed Razak, Siti Fatimah Abdul Yogarayan, Sumendra Othman, Mohamed Centre for Intelligent Cloud Computing (CICC) Faculty of Information Science and Technology Multimedia University Jalan Ayer Keroh Lama Melaka Bukit Beruang 75450 Malaysia Department of Electronics Electrical and System Engineering Universiti Kebangsaan Malaysia Bangi 43600 Malaysia Department of Communication Technology and Network Universiti Putra Malaysia UPM Serdang Selangor 43400 Malaysia Laboratory of Computational Science and Mathematical Physics Institute for Mathematical Research Universiti Putra Malaysia UPM Serdang Selangor 43400 Malaysia

Analysing social media data is crucial for crisis management organisations to make timely decisions. Researchers in crisis informatics have devised various methods and systems to process and classify large volumes of crisis-related social media data for effective crisis response and recovery. However, the complexity of previous solutions hampers the timely processing of this data, its visualisation, and its interpretation, which is necessary for effective crisis response. Hence, this study addresses this challenge by employing visualisation of similarities to analyse and visualise crisis datasets to aid crisis management and decision-making. The results reveal a "nine-cluster community” of relevant keywords comprising “Green, Brown, Red, Blue, Pink, Purple, Yellow, Orange, and Cyan” colours, in both binary and full count. Specifically, the findings reveal various keywords such as the needs for food, water, shelter, medicine, and electricity. Thereafter, the study discusses the implications of VOSviewer for analysing crisis data theoretically and practically. © 2024

关键词： Co-occurrence networks Data analysis Decision-making Disaster management Social media Visualisation VOSviewer

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