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FIFAWC:a dataset with detailed annotation and rich semantics for group activity recognition

Frontiers of Computer Science 2024年第6期18卷 271-272页

作者： Duoxuan PEI Di HUANG Yunhong WANG State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Intelligent Recognition and Image Processing Lab. School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

1 *** Activity Recognition(GAR),which aims to identify activities performed collectively in videos,has gained significant attention *** conventional action recognition centered on single individuals,GAR explores the c... 详细信息

关键词： has collective gained

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Multi-user reinforcement learning based task migration in mobile edge computing

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Frontiers of Computer Science 2024年第4期18卷 161-173页

作者： Yuya CUI Degan ZHANG Jie ZHANG Ting ZHANG Lixiang CAO Lu CHEN Tianjin Key Lab of Intelligent Computing and Novel Software Technology Tianjin University of TechnologyTianjin 300384China School of Internet of Things Engineering Jiangsu Vocational College of Information TechnologyWuxi 214153China School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China School of Sports Economics and Management Tianjin University of SportTianjin 301617China

Mobile Edge computing(MEC)is a promising *** service migration is a key technology in *** order to maintain the continuity of services in a dynamic environment,mobile users need to migrate tasks between multiple servers in real *** to the uncertainty of movement,frequent migration will increase delays and costs and non-migration will lead to service ***,it is very challenging to design an optimal migration *** this paper,we investigate the multi-user task migration problem in a dynamic environment and minimizes the average service delay while meeting the migration *** order to optimize the service delay and migration cost,we propose an adaptive weight deep deterministic policy gradient(AWDDPG)*** distributed execution and centralized training are adopted to solve the high-dimensional *** show that the proposed algorithm can greatly reduce the migration cost and service delay compared with the other related algorithms.

关键词： mobile edge computing mobility service migration deep reinforcement learning deep deterministic policy gradient

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A Sentiment Classification Model Based on Syntactic Graph Attention Networks 24

A Sentiment Classification Model Based on Syntactic Graph At...

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2024 International Conference on intelligent Education and Computer Technology, IECT 2024

作者： Cheng, Yan Zhou, Ziwei School of Computer Information Engineering Jiangxi Normal University Jiangxi Nanchang330022 China Prov. Key Lab. of Intelligent Information Processing and Affective Computing of Jiangxi Province 330022 China School of Software Jiangxi Normal University 330022 China

ISBN: (纸本)9798400709920

Text Sentiment Classification, a significant task in Natural Language Processing, aims to comprehend user needs and expectations by categorizing the sentiments of texts posted on platforms. Despite their utility, existing models for this task do not fully account for the influence of contextual and syntactic text features on sentiment classification. To address these issues, a text sentiment classification model based on syntactic graph attention network is proposed in this study. The model begins by extracting the contextual features of a sentence using BiGRU. It then generates a syntactic graph through a syntactic analyzer, using the contextual features as the node features of the syntactic graph. A syntactic graph attention network is constructed to extract the syntactic features of the syntactic graph. These feature vectors are then input into a softmax function to achieve text sentiment classification. Experimental results indicate that the proposed model outperforms similar models on two public datasets in terms of classification accuracy, thus demonstrating its effectiveness. © 2024 ACM.

关键词： Syntactics

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Optimal Joint Trajectory and Caching with Graph-based Federated Knowledge Distillation in UAV-assisted Networks

Optimal Joint Trajectory and Caching with Graph-based Federa...

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2023 International Conference on Networks, Communications and intelligent computing, NCIC 2023

作者： Maale, Gerald Tietaa Kuadey, Noble Arden Elorm Sun, Guolin Liu, Guisong School of Computer Science and Engineering UESTC Natl. Key Lab. for Intelligent Collab. Computing Intelligent Term. Key Lab. of Sichuan Prov. Yibin Chengdu China School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu China

ISBN: (纸本)9798350371949

The use of unmanned aerial vehicles (UAVs) as mobile cache nodes can enhance data traffic throughput and reduce user latency in urban areas. However, jointly optimizing UAV trajectories for efficient content delivery and onboard caching strategies presents significant challenges stemming from the tight coupling between these two problems, as well as issues related to the non-independent and non-identically distributed (non-IID) nature of real-world content popularity and wireless channel dynamics. To address these challenges, we propose KDLMGCN, a deep learning framework that employs graph convolutional networks with LSTM to capture intricate relationships between UAV locations and ground user content demand patterns. Through empirical evaluation, we demonstrate that KDLMGCN surpasses benchmark methods in terms of convergence, cache hit rates, and average transmission, with real-world trajectory dataset and Zipf-based content popularity data. © 2023 IEEE.

关键词： Distillation

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Multi-view Hypergraph-based Self-supervised Learning Model for Web API Recommendation 22

Multi-view Hypergraph-based Self-supervised Learning Model f...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Shen, Jiexun Li, Dongfan Chen, Wanjun Xiao, Yong Kang, Guosheng Liu, Jianxun Peng, Zhenlian School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology HNUST Xiangtan China

ISBN: (纸本)9798331509712

With the rapid development of service computing technology, how to recommend the desirable Web APIs to developers from the large number of APIs is a challenge. The traditional methods based on collab.rative filtering are limited by data sparsity. With the support of multi-dimensional relational feature modeling, graph neural network-based methods are proposed to mitigate data sparsity, but their convolution nature may amplify the noise effect. Therefore, how to simultaneously reduce the impact of sparsity and noisy data has been an open question in the field of API recommendation. To address the problem, this paper proposes a self-supervised learning method based on multi-view hypergraph for Web API recommendation. First, the Mashup-API interaction graph is transformed into a hypergraph, and the hyperedges are used as intermediate hubs to transfer messages between nodes, maintaining the global collab.ration effect between Mashup and API nodes. Then, a multi-view strategy is adopted to generate embeddings of Mashups and APIs through information fusion, by which recommendation probability is derived by dot product between the embeddings of Mashups and Web APIs. To train the model parameters effectively, a self-supervised learning method is used to reduce the effect of noisy data to improve the embeddings. Extensive experiments are conducted on a real-world dataset, and the experimental results show that the proposed model outperforms the baselines. © 2024 IEEE.

关键词： Self-supervised learning

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Lip Shape Classification of Sounds for Speech Therapy using SlowFast Networks 13

Lip Shape Classification of Sounds for Speech Therapy using ...

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2023 13th International Workshop on Computer Science and engineering, WCSE 2023

作者： Boonsri, Penpicha Eiamboonsert, Salita Siricharoen, Punnarai Perceptual Intelligent Computing Lab. Department of Computer Engineering Faculty of Engineering Chulalongkorn University Thailand Media Technology King Mongkut's University of Technology Thonburi Thailand

ISBN: (纸本)9789811879500

It is important for patients with Aphasia and Dysarthria to have speech and language therapy to practice breathing exercises, tongue strengthening exercises, and especially speech sounds such as short vowel sounds. To ensure the clarity of the pronunciation sound and the correct position of mouth shape, it is required to be monitored by a therapist. We proposed an automated method using convolutional networks to identify the motion of pronunciation of 9 short vowel sounds which is required for speech exercises in the Thai language. Firstly, videos of vowel sound pronunciation are captured, then preprocessed to crop only the mouth area using Dlib library. The cropped image sequence is then fed into audiovisual SlowFast Networks based on convolutional networks which have Slow and Fast visual pathways to capture spatial and temporal information of a video. We compared our selected model with the transformer-based state-of-the-art model, such as TimeSformer. Our proposed framework using SlowFast networks achieved average accuracy at 97.3% for 9-class video classification of Thai vowel sounds. It shows our proposed framework has a potential for use as a tool for speech sound self-exercises and therapy. © WCSE *** rights reserved.

关键词： Convolution

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TH-SLP: Web Service Link Prediction Based on Topic-aware Heterogeneous Graph Neural Network

TH-SLP: Web Service Link Prediction Based on Topic-aware Het...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Peng, Qian Cao, Buqing Xie, Xiang Liu, Shanpeng Kang, Guosheng Liu, Jianxun Hunan University of Science and Technology Xiangtan School of Computer Science and Engineering Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Hunan China

ISBN: (纸本)9798350304855

With the emergence of more and more Web services, finding suitable services becomes a difficult problem. Service link prediction is employed to disclose relationships among services, which facilitates the further development of service composition, selection, and recommendation. But the existing link prediction approaches simply utilize the structural features of the service network. In reality, the rich text content in service node description documents also carries latent but fine-grained semantics generated by multifaceted topic-aware factors, yet few efforts are committed to mining them. In this paper, we propose a Web service link prediction method based on a topic-aware heterogeneous graph neural network. Specifically, the method consists of two main layers, including the meta-path intra-decomposition and the meta-path inter-mergence. Meta-path intra-decomposition aims to mine the topic distribution of the meta-paths-based context while capturing fine-grained topic-aware semantics. Meta-path inter-mergence uniquely aggregates topic-aware factors according to the mined distribution and adopts a multifaceted attention mechanism to aggregate different meta-paths, enabling service nodes to generate multifaceted topic-aware embeddings that preserve not only the structure and but also the topic-aware semantics. In addition, a topic prior guidance regularization item is set up for quality assurance of multifaceted topic-aware embedding that depends on global knowledge of the unstructured text content in description documents. Experimental results on real datasets show that our proposed model outperforms other existing baselines methods in the link prediction task, successfully validating the effectiveness of our proposed method. © 2023 IEEE.

关键词： Semantics

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Green Federated Learning over Cloud-RAN with Limited Fronthaul and Quantized Neural Networks 3

Green Federated Learning over Cloud-RAN with Limited Frontha...

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3rd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023

作者： Wang, Jiali Mao, Yijie Wang, Ting Shi, Yuanming Shanghai Key Lab. of Trustworthy Computing East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application China China

ISBN: (纸本)9798350333732

In this paper, we investigate a green federated learning (FL) framework over cloud radio access network (Cloud-RAN) system that comprises a server, multiple devices and remote radio heads (RRHs). Each device utilizes quantized neural networks (QNNs) and sends quantized model parameters to the server to save energy consumption via RRHs. The server aggregates all the signals to update the global model parameters and broadcasts the updated parameters to the selected devices. In this context, we propose an energy consumption model for the QNN training and communication model over Cloud-RAN. We develop an energy minimization problem based on the proposed energy model. We jointly design fronthaul rate allocation, device transmit power, and precision level of QNNs while ensuring target accuracy, transmit power budget and limited fronthaul capacity. Guided by the convergence analysis, we adopt alternative optimization method to solve the energy minimization problem. The simulation outcomes demonstrate that the FL framework suggested can considerably diminish energy usage in comparison to other traditional methods. This has immense potential in realizing a sustainable and eco-friendly FL over Cloud-RAN. © 2023 IEEE.

关键词： Radio access networks

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Offloading approach for mobile edge computing based on chaotic quantum particle swarm optimization strategy

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Journal of Ambient Intelligence and Humanized computing 2023年第10期14卷 14333-14347页

作者： Zhang, DeGan Sun, GuiXiang Zhang, Jie Zhang, Ting Yang, Peng Tianjin Key Lab of Intelligent Computing and Novel Software Technology Tianjin University of Technology Tianjin300384 China School of Sports Economics and Management Tianjin University of Sport Tianjin301617 China School of Electronics and Information Engineering Beijing Jiaotong University Beijing100093 China

With the development of 5 g, computing-intensive and complex applications in smart-city is growing rapidly. Due to the limited resources of mobile terminal devices in smart-city, new applications have higher requirements for delay, bandwidth, security, and energy consumption. Computation offloading in mobile edge computing (MEC) is effective to reduce delay and energy consumption of Real-time video analysis. An improved chaos quantum-behaved particle swarm optimization (ICQPSO) algorithm is proposed for multi-user and multi-MEC edge Computation offloading scenarios. Compared with other heuristic algorithms, the improved chaos quantum-behaved particle swarm optimization algorithm can effectively reduce the delay and energy consumption of edge computing offloading. Experimental results show that the improved chaotic quantum-behaved particle swarm optimization (ICQPSO) can effectively avoid premature convergence, has stronger global searchability, and can solve multi-dimensional complex NP-hard problems more efficiently. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Energy utilization

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Hybrid Parallel Inference for Large Model on Heterogeneous Clusters for High Throughput 29

Hybrid Parallel Inference for Large Model on Heterogeneous C...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Xu, Chaonong Kong, Weiming Liu, Min Zhang, Mingming Li, Chao Gong, Luqi Beijing Key Lab of Petroleum Data Mining Beijing China Institute of Intelligent Computing Zhejiang Lab Hangzhou China Bupt School of Computer Science National Pilot Software Engineering School Beijing China

ISBN: (纸本)9798350330717

In high-throughput intelligent computing scenarios, multi-device parallelism strategies based on data parallelism or pipeline parallelism have been extensively utilized to accelerate large deep neural network model inference. Data parallelism offers nearly linear improvement in inference speed, but it is limited by the memory capacity of a single device which constrains the model size. On the other hand, pipeline parallelism can support larger models, but the total communication of the activations among devices is high, which limits the improvement of the inference speed. To address the demand for efficient model inference in high-throughput heterogeneous scenarios, we proposes a hybrid parallelism strategy that combines data parallelism and pipeline parallelism. The strategy involves grouping heterogeneous device clusters and then employing inter-group data parallelism along with intra-group pipeline parallelism. Moreover, we propose an algorithm to find an optimal hybrid parallel inference strategy with maximum throughput. The control variables of the strategy includes the number of groups, group-device assignments and model partition ratios. Our experimental evaluation demonstrates that compared to PipeEdge, a pipeline parallel inference framework for heterogeneous cluster, our strategy can achieve 1.7× -3.4× acceleration in an 8-device heterogeneous cluster without loss of accuracy. © 2023 IEEE.

关键词： data parallelism heterogeneous cluster hybrid parallelism model inference pipeline parallelism

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