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2021 China Automation Congress, CAC 2021

作者： Zhang, Jianxing Liu, Luyao Xue, Jianqiang Xiao, Wendong School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education University of Science and Technology Beijing Beijing China Shunde Graduate School University of Science and Technology Beijing Guangdong China

ISBN: (纸本)9781665426473

Device-free localization (DFL) based on Channel State Information (CSI) has received widespread attention for its non-invasiveness and easy deployment as a new indoor localization technology. The machine learning is one of the main research methods for DFL, while it constructs a fine-grained reference database laboriously. In this paper, we propose a CSI reference database expansion approach based on Kriging Interpolation to address the problem. Concretely, we collect CSI amplitude of specific locations, then estimate all sub-carriers information for other locations through Kriging Interpolation to expand database, and finally use Deep Belief Networks (DBN) as the localization model. The experiment is carried out in real environment. The experiment result verifies the proposed approach addresses the difficulty of insufficient database and collecting data for a long time efficiently, as well as improves the localization accuracy. © 2021 IEEE

关键词： Channel state information

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QoS Optimization via Computation Offloading in Metaverse Environment

QoS Optimization via Computation Offloading in Metaverse Env...

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IEEE International Conference on Web Services (ICWS)

作者： Zhiyuan Ge Pengcheng Zhang Huiying Jin Hai Dong Shunhui Ji Jiajia Li Qi Wang Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computing Technologies RMIT University Melbourne Australia

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

The emergence of the metaverse signifies a paradigm shift in Internet technology, offering a comprehensive virtual social platform spanning various domains such as social interaction, gaming, healthcare, and tourism. This new era of the metaverse is facilitated by advancements in next-generation digital technologies including edge computing, artificial intelligence, virtual reality, augmented reality, and blockchain. In the metaverse, the quantity and variety of services requested by users may surpass those in other environments, and existing work cannot be applied to metaverse QoS (Quality of Service) optimization. To address this problem, this paper proposes Meta-PPO, an optimization method for enhancing the QoS of metaverse services using reinforcement learning. Firstly, metaverse services are categorized into virtual scene services and meta-services, providing a comprehensive framework for analysis. Secondly, Meta-PPO, based on the proximal policy optimization algorithm, is introduced to optimize the QoS of metaverse services. This method effectively balances the objectives of minimizing average delay and maximizing resource utilization of mobile devices by making informed offloading decisions for the identified service categories. Simulation results demonstrate the superiority of the proposed method over existing techniques, showcasing its suitability and effectiveness for enhancing the QoS of metaverse service.

关键词： Metaverse Web services Simulation Optimization methods Quality of service Reinforcement learning Mobile handsets Delays Resource management Object recognition

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Resource Aware Multi-User Task Offloading In Mobile Edge Computing

Resource Aware Multi-User Task Offloading In Mobile Edge Com...

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IEEE International Conference on Web Services (ICWS)

作者： Shunhui Ji Jiajia Li Huiying Jin Hai Dong Zhiyuan Ge Shuhan Yang Pengcheng Zhang Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computing Technologies RMIT University Melbourne Australia

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

Mobile edge computing (MEC) relies on offloading tasks to edge nodes to avoid delays and failures caused by local computing. However, developing efficient offloading decisions is challenging, as it involves addressing the intricacies of tasks and the instability of edge node resources(e.g. available computer resources, memory, and bandwidth). In this paper, we propose a novel approach to tackle the problem of task offloading. Our approach involves dividing tasks into smaller units and considering the correlations between these sub-tasks. To make optimal offloading decisions, we employ a deep reinforcement learning algorithm that takes into account user movement patterns and the availability of resources at edge nodes. Through simulations, we demonstrate that our proposed algorithm outperforms several existing algorithms in terms of offloading decisions. It effectively reduces task execution delays and energy costs. These findings highlight the potential of our approach in improving the performance of task offloading in MEC systems.

关键词： Multi-access edge computing Costs Correlation Web services Heuristic algorithms Image edge detection Prediction algorithms Dynamic scheduling Deep reinforcement learning Delays

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Review of computational neuroaesthetics: bridging the gap between neuroaesthetics and computer science

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Brain Informatics 2020年第1期7卷 16页

作者： Li, Rui Zhang, Junsong National Engineering Laboratory for Educational Big Data Central China Normal University WuhanHubei China Fujian Key Laboratory of Brain-Inspired Computing Technique and Applications Department of Artificial Intelligence School of Informatics Xiamen University Xiamen China

The mystery of aesthetics attracts scientists from various research fields. The topic of aesthetics, in combination with other disciplines such as neuroscience and computer science, has brought out the burgeoning fields of neuroaesthetics and computational aesthetics within less than two decades. Despite profound findings are carried out by experimental approaches in neuroaesthetics and by machine learning algorithms in computational neuroaesthetics, these two fields cannot be easily combined to benefit from each other and findings from each field are isolated. Computational neuroaesthetics, which inherits computational approaches from computational aesthetics and experimental approaches from neuroaesthetics, seems to be promising to bridge the gap between neuroaesthetics and computational aesthetics. Here, we review theoretical models and neuroimaging findings about brain activity in neuroaesthetics. Then machine learning algorithms and computational models in computational aesthetics are enumerated. Finally, we introduce studies in computational neuroaesthetics which combine computational models with neuroimaging data to analyze brain connectivity during aesthetic appreciation or give a prediction on aesthetic preference. This paper outlines the rich potential for computational neuroaesthetics to take advantages from both neuroaesthetics and computational aesthetics. We conclude by discussing some of the challenges and potential prospects in computational neuroaesthetics, and highlight issues for future consideration. © 2020, The Author(s).

关键词： Brain

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Stacking-Based Attention Temporal Convolutional Network for Action Segmentation

Stacking-Based Attention Temporal Convolutional Network for ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Liu Yang Yu Jiang Junkun Hong Zhenjie Wu Zhan Yang Jun Long School of Computer Science and Engineering Central South University China Big Data Institute Central South University Central South University China Network Resources Management and Trust Evaluation Key Laboratory of Hunan Province Central South University Changsha China

Action segmentation plays an important role in video understanding, which is implemented by frame-wise action classification. Recent works on action segmentation capture long-term dependencies by increasing temporal convolution layers in Temporal Convolution Networks (TCNs). However, high layers in TCNs are more coarse access to video features, resulting in the loss of fine-grained information for frame-wise action classification. To address the above issues, we propose a novel Attention-based Temporal Convolution (ATC) block to capture fine-grained information of temporal dependencies for frame-wise action classification by self-attention mechanism. Via stacking ATC blocks, we design a Stacking-based Attention Temporal Convolutional Network (SATC) to adaptively capture long-term and short-term dependencies, according to the semantic similarity of features on different temporal receptive fields simultaneously. The experimental results demonstrate that our SATC outperforms other baselines on all three challenging datasets: GTEA, 50Salads and Breakfast.

关键词： Convolution Stacking Semantics Acoustics Convolutional neural networks Speech processing

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Fairness-aware Multiobjective Evolutionary Learning

arXiv

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arXiv 2024年

作者： Zhang, Qingquan Liu, Jialin Yao, Xin The Research Institute of Trustworthy Autonomous System Southern University of Science and Technology Shenzhen518055 China The Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The School of Data Science Lingnan University Hong Kong

Multiobjective evolutionary learning (MOEL) has demonstrated its advantages of training fairer machine learning models considering a predefined set of conflicting objectives, including accuracy and different fairness measures. Recent works propose to construct a representative subset of fairness measures as optimisation objectives of MOEL throughout model training. However, the determination of a representative measure set relies on dataset, prior knowledge and requires substantial computational costs. What's more, those representative measures may differ across different model training processes. Instead of using a static predefined set determined before model training, this paper proposes to dynamically and adaptively determine a representative measure set online during model training. The dynamically determined representative set is then used as optimising objectives of the MOEL framework and can vary with time. Extensive experimental results on 12 well-known benchmark datasets demonstrate that our proposed framework achieves outstanding performance compared to state-of-the-art approaches for mitigating unfairness in terms of accuracy as well as 25 fairness measures although only a few of them were dynamically selected and used as optimisation objectives. The results indicate the importance of setting optimisation objectives dynamically during training. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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CoGANPPIS: A Coevolution-enhanced Global Attention Neural Network for Protein-Protein Interaction Site Prediction

arXiv

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arXiv 2023年

作者： Guo, Jiaxing Zhu, Xuening Hu, Zixin Hu, Xiaoxi School of Data Science Fudan University Shanghai China State Key Laboratory of Genetic Engineering Innovation Center of Genetics and Development School of Life Sciences Fudan University Shanghai China Artificial Intelligence Innovation and Incubation Institute Fudan University Shanghai China

Protein-protein interactions are of great importance in biochemical processes. Accurate prediction of protein-protein interaction sites (PPIs) is crucial for our understanding of biological mechanism. Although numerous approaches have been developed recently and achieved gratifying results, there are still two limitations: (1) Most existing models have excavated a number of useful input features, but failed to take coevolutionary features into account, which could provide clues for inter-residue relationships;(2) The attention-based models only allocate attention weights for neighboring residues, instead of doing it globally, which may limit the model’s prediction performance since some residues being far away from the target residues might also matter. We propose a coevolution-enhanced global attention neural network, a sequence-based deep learning model for PPIs prediction, called CoGANPPIS. Specifically, CoGANPPIS utilizes three layers in parallel for feature extraction: (1) Local-level representation aggregation layer, which aggregates the neighboring residues’ features as the local feature representation;(2) Global-level representation learning layer, which employs a novel coevolution-enhanced global attention mechanism to allocate attention weights to all residues on the same protein sequences;(3) Coevolutionary information learning layer, which applies CNN & pooling to coevolutionary information to obtain the coevolutionary profile representation. Then, the three outputs are concatenated and passed into several fully connected layers for the final prediction. Extensive experiments on two benchmark datasets have been conducted, demonstrating that our proposed model achieves the state-of-the-art performance. © 2023, CC BY.

关键词： Forecasting

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GeReA: Question-Aware Prompt Captions for knowledge-based Visual Question Answering

arXiv

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arXiv 2024年

作者： Ma, Ziyu Li, Shutao Sun, Bin Cai, Jianfei Long, Zuxiang Ma, Fuyan College of Electrical and Information Engineering The Key Laboratory of Visual Perception and Artificial Intelligence of Hunan Province Hunan University Changsha410082 China The Data Science & AI Department Faculty of IT Monash University ClaytonVIC3800 Australia Defense Innovation Institute Chinese Academy of Military Science Beijing100029 China

knowledge-based visual question answering (VQA) requires world knowledge beyond the image for accurate answer. Recently, instead of extra knowledge bases, a large language model (LLM) like GPT-3 [1] is activated as an implicit knowledge engine to jointly acquire and reason the necessary knowledge for answering by converting images into textual information (e.g., captions and answer candidates). However, such conversion may introduce irrelevant information, which causes the LLM to misinterpret images and ignore visual details crucial for accurate knowledge. We argue that multimodal large language model (MLLM) is a better implicit knowledge engine than the LLM for its superior capability of visual understanding. Despite this, how to activate the capacity of MLLM as the implicit knowledge engine has not been explored yet. Therefore, we propose GeReA, a generate-reason framework that prompts a MLLM like InstructBLIP [2] with question relevant vision and language information to generate knowledge-relevant descriptions and reasons those descriptions for knowledge-based VQA. Specifically, the question-relevant image regions and question-specific manual prompts are encoded in the MLLM to generate the knowledge relevant descriptions, referred to as question-aware prompt captions. After that, the question-aware prompt captions, image-question pair, and similar samples are sent into the multi-modal reasoning model to learn a joint knowledge-image-question representation for answer prediction. GeReA unlocks the use of MLLM as the implicit knowledge engine, surpassing all previous state-of-the-art methods on OK-VQA and A-OKVQA datasets, with test accuracies of 66.5% and 63.3% respectively. Our code will be released at https://***/Upper9527/GeReA. Copyright © 2024, The Authors. All rights reserved.

关键词： knowledge based systems

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OSN Rumor Control Model Based on Community Immunization

OSN Rumor Control Model Based on Community Immunization

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Ming Dong Zhiqiang Sun Zhenhua Tan Bin Zhang School of Computer Science and Engineering Northeastern University Shenyang China Software College Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

This paper proposes a rumor control model based on community immunization. Based on the community division and the trust network inference algorithm, the model redefines the standard to measure the importance of nodes in the network. First, the model uses the Louvain clustering algorithm based on the Ochiai coefficient to discover the network community and then presents the trust network inference algorithm. By analyzing the key factors that affect trust transfer between nodes, the trust evaluation between unfamiliar nodes is inferred, and important nodes with a high degree of trust in the network community are calculated. Finally, combined with the characteristics of inner degree and outer degree centrality of nodes in the network community, five types of important nodes in the network are screened out. To avoid repeated selection of nodes, this paper identifies a group of key nodes in the network community for local immunization by means of deduplication and taking intersection, so as to realize effective control of rumors in the network.

关键词： Distributed processing Clustering algorithms Inference algorithms Standards Open data

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A Chinese multimodal depression dataset with personality labeling for older adults with underlying medical conditions

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IEEE Transactions on Affective Computing 2025年

作者： Zhao, Yuliang Zhang, Huawei Li, Jian Song, Siyang Lian, Chao Liu, Yinghao Wang, Yulin Fu, Changzeng School of Information Science and Engineering Northeastern University Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China Northeastern University SSTC NEU Hebei Key Laboratory of Marine Perception Network and Data Processing Hebei Qinhuangdao066004 China Osaka University Japan University of Cambridge AFAR Lab Department of Computer Science and Technology CambridgeCB3 0FT United Kingdom First Hospital of Qinhuangdao Qinhuangdao066004 China

Elderly individuals often suffer from underlying medical conditions, resulting in a significant decline in quality of life and a heightened susceptibility to depression. Presently, AI screening tools based on behavioral indicators offer an objective and effective approach to diagnosing depression. However, current AI depression screening tools are primarily tailored to adolescents and adults, exhibiting shortcomings in their applicability and accuracy for elderly individuals with underlying medical conditions. To address the above issues, firstly, this paper constructs a depression dataset for elderly people with underlying diseases by using semi-structured interviews. Secondly, based on cognitive science insights, it is recognized that personality factors significantly influence behavioral expressions and also determine the attitudes of elderly individuals toward current life circumstances/health issues. Therefore, besides annotating depression severity, the Big Five-10 personality scale was utilized to annotate participant personalities. Finally, a late fusion-based multi-task learning framework was proposed, and the effects of introducing gait information and personality annotation on the performance of depression assessment were investigated. The experimental findings affirm the importance of integrating gait information and personality assessment in improving depression detection effectiveness. This study provides valuable foundational resources, as well as beneficial references and insights, for the research on depression in the elderly. © 2010-2012 IEEE.

关键词： Depression Multimodal dataset Older adults Personality Underlying diseases

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