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Overlapping Community Detection Based on Influence and Seeds Extension

Tien Tzu Hsueh Pao/Acta Electronica Sinica 2019年第1期47卷 153-160页

作者： Yu, Zhi-Yong Chen, Ji-Jie Guo, Kun Chen, Yu-Zhong Xu, Qian College of Mathematics and Computer Sciences Fuzhou University FuzhouFujian350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing FuzhouFujian350116 China Ministry of Education Key Laboratory of Spatial Data Mining & Information Sharing FuzhouFujian350116 China State Grid Info-Telecom Great Power Science and Technology Co. Ltd. FuzhouFujian350003 China

Community detection is a significant research direction in the research of social networks. To improve the quality of seeds selection and expansion, we propose an influence seeds extension overlapping community detection (i-SEOCD) algorithm for overlapping community detection. First, i-SEOCD uses a node influence strategy to find the seed communities with tight structures. Second, on the basis of the seed communities, we calculate the similarity among communities and their neighbor nodes. The nodes whose similarity is greater than a predefined threshold are selected. Third, the strategy of optimizing a self-adaptive function is adopted to expand the communities. Finally, the free nodes in the network are assigned to their corresponding communities in order to find out all the overlapping community structures. Experiments on the real and artificial networks show that i-SEOCD is capable of discovering overlapping communities in complex social networks efficiently. © 2019, Chinese institute of Electronics. All right reserved.

关键词： Population dynamics

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Snake net with a neural network for detecting multiple phases in the phase diagram

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Physical Review E 2023年第6期107卷 065303-065303页

作者： Xiaodong Sun Huijiong Yang Nan Wu T. C. Scott Jie Zhang Wanzhou Zhang College of Physics and Optoelectronics Taiyuan University of Technology Shanxi 030024 China College of Data Science Taiyuan University of Technology Shanxi 030024 China School for Physical Sciences University of Science and Technology of China Hefei 230026 China Institut für Physikalische Chemie RWTH Aachen University Aachen 52056 Germany Hefei National Laboratory for Physical Sciences at the Microscale and Department of Modern Physics University of Science and Technology of China Hefei 230026 China CAS Key Laboratory for Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing 100190 China

Unsupervised machine learning applied to the study of phase transitions is an ongoing and interesting research direction. The active contour model, also called the snake model, was initially proposed for target contour extraction in two-dimensional images. In order to obtain a physical phase diagram, the snake model with an artificial neural network is applied in an unsupervised learning way by the authors of [Phys. Rev. Lett. 120, 176401 (2018)]. It guesses the phase boundary as an initial snake and then drives the snake to convergence with forces estimated by the artificial neural network. In this work we extend this unsupervised learning method with one contour to a snake net with multiple contours for the purpose of obtaining several phase boundaries in a phase diagram. For the classical Blume-Capel model, the phase diagram containing three and four phases is obtained. Moreover, a balloon force is introduced, which helps the snake to leave a wrong initial position and thus may allow for greater freedom in the initialization of the snake. Our method is helpful in determining the phase diagram with multiple phases using just snapshots of configurations from cold atoms or other experiments without knowledge of the phases.

关键词： Phase diagrams Artificial neural networks

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Demand Response Scheme: Achieving Sustainability in Distributed data Processing

Demand Response Scheme: Achieving Sustainability in Distribu...

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IEEE International Conference on data science in Cyberspace (DSC)

作者： Xueying Zhai Yunfeng Peng Xiuping Guo Liang Zhang Wei Zhang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Economics and Management Beijing University of Posts and Telecommunications Beijing China Guizhou Key Laboratory of Electric Power Big Data Guizhou Institute of Technology Guizhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

The rapid development of Internet of Things (IoT) technology has driven the intelligent transformation of various industries, including manufacturing, agriculture, and healthcare, etc., significantly enhancing their management capabilities and decision-making efficiency. data analysis is crucial, serving as both the core technology of this transformation and a major driving force for advancing industry intelligence. However, the increasing volume of data has led to high energy consumption during data analysis, posing significant challenges to environmental and economic sustainability. Integrating renewable energy sources (RESs) is an effective way to reduce greenhouse gas emissions due to their low-carbon and renewable nature. Additionally, the spatiotemporal variability and complementarity of RES generation and grid electricity prices provide favorable conditions for distributed data processing. Therefore, a two-stage demand response scheme is proposed to reduce carbon emissions and electricity costs by prioritizing data processing in data centers (DCs) where RES is abundant or electricity prices are low, based on RES generation and electricity price signals. Simulation results show that the proposed scheme reduces 36.37% carbon emissions and 20.49% electricity costs compared to the benchmark algorithm, indicating its potential for both environmental and economic optimization.

关键词： Renewable energy sources Energy consumption Costs Electricity Simulation Carbon dioxide Spatiotemporal phenomena Internet of Things Sustainable development Optimization

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Modeling the Relative Visual Tempo for Self-supervised Skeleton-based Action Recognition

Modeling the Relative Visual Tempo for Self-supervised Skele...

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International Conference on Computer Vision (ICCV)

作者： Yisheng Zhu Hu Han Zhengtao Yu Guangcan Liu Nanjing University of Posts and Telecommunications Key Laboratory of Intelligent Information Processing Institute of Computing Technology (ICT) Chinese Academy of Sciences (CAS) University of the Chinese Academy of Sciences Faculty of Information Engineering and Automation Kunming University of Science and Technology School of Automation Southeast University

Visual tempo characterizes the dynamics and the temporal evolution, which helps describe actions. Recent approaches directly perform visual tempo prediction on skeleton sequences, which may suffer from insufficient feature representation issue. In this paper, we observe that relative visual tempo is more in line with human intuition, and thus providing more effective supervision signals. Based on this, we propose a novel Relative Visual Tempo Contrastive Learning framework for skeleton action Representation (RVTCLR). Specifically, we design a Relative Visual Tempo Learning (RVTL) task to explore the motion information in intra-video clips, and an Appearance-Consistency (AC) task to learn appearance information simultaneously, resulting in more representative spatiotemporal features. Furthermore, skeleton sequence data is much sparser than RGB data, making the network learn shortcuts, and overfit to low-level information such as skeleton scales. To learn high-order semantics, we further design a new Distribution-Consistency (DC) branch, containing three components: Skeleton-specific data Augmentation (S-DA), Fine-grained Skeleton Encoding Module (FSEM), and Distribution-aware Diversity (DD) Loss. We term our entire method (RVTCLR with DC) as RVTCLR+. Extensive experiments on NTU RGB+D 60 and NTU RGB+D 120 datasets demonstrate that our RVTCLR+ can achieve competitive results over the state-of-the-art methods. Code is available at https://***/Zhuysheng/RVTCLR.

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An efficient quantum algorithm for independent component analysis

arXiv

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arXiv 2023年

作者： Xu, Xiao-Fan Xue, Cheng Chen, Zhao-Yun Wu, Yu-Chun Guo, Guo-Ping CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei230026 China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei230026 China Hefei National Laboratory University of Science and Technology of China Hefei230088 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230026 China Origin Quantum Computing Company Limited Anhui Hefei230026 China

Independent component analysis (ICA) is a fundamental data processing technique to decompose the captured signals into as independent as possible components. computing the contrast function, which serves as a measure of independence of signals, is vital in the separation process using ICA. This paper presents a quantum ICA algorithm which focuses on computing a specified contrast function on a quantum computer. Using the quantum acceleration in matrix operations, we efficiently deal with Gram matrices and estimate the contrast function with the complexity of O(Ε1−2polylog(N/Ε1)). This estimation subprogram, combined with the classical optimization framework, enables our quantum ICA algorithm, which exponentially reduces the complexity dependence on the data scale compared with classical algorithms. The outperformance is further supported by numerical experiments, while a source separation of a transcriptomic dataset is shown as an example of application. © 2023, CC0.

关键词： data handling

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LSN:Long-Term Spatio-Temporal network for Video Recognition

LSN:Long-Term Spatio-Temporal Network for Video Recognition

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作者： Zhenwei Wang Wei Dong Bingbing Zhang Jianxin Zhang School of Computer Science and Engineering Dalian Minzu University SEAC Key Laboratory of Big Data Applied Technology Dalian Minzu University Institute of Machine Intelligence and Bio-Computing Dalian Minzu University School of Information and Communication Engineering Dalian University of Technology

Although recurrent neural networks(RNNs) are widely leveraged to process temporal or sequential data,they have attracted too little attention in current video action recognition ***,this work attempts to model the long-term spatio-temporal information of the video based on a variant of RNN,i.e.,higher-order ***,we propose a novel long-term spatio-temporal network(LSN) for solving this video task,the core of which integrates the newly constructed high-order ConvLSTM(HO-ConvLSTM) modules with traditional2D convolutional ***,each HO-Conv LSTM module consists of an accumulated temporary state(ATS) module as well as a standard Conv LSTM module,and several previous hidden states in the ATS module are accumulated to one temporary state that will enter the standard Conv LSTM to determine the output together with the current *** HO-Conv LSTM module can be inserted into different stages of the 2D convolutional neural network(CNN) in a plug-andplay manner,thus well characterizing the long-term temporal evolution at various spatial *** results on three commonly used video benchmarks demonstrate that the proposed LSN model can achieve competitive performance with the representative models.

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Low Complexity SLM for OFDMA System with Implicit Side Information

Low Complexity SLM for OFDMA System with Implicit Side Infor...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Shicheng Hu Miao Yang Kai Kang Hua Qian School of Information Science and Technology Shanghai Tech University Shanghai China Shanghai Advanced Research Institute Chinese Academy of Sciences (CAS) Shanghai China University of Chinese Academy of Sciences Beijing China Key Laboratory of Wireless Sensor Network Communication CAS China

Selected mapping (SLM) is an efficient peak-to-average-power-ratio (PAPR) reduction algorithm for orthogonal frequency division multiplexing (OFDM) systems. Conventional SLM requires extra resources for side information transmission. If the side information is transmitted implicitly, significantly high computation overhead is imposed to the receiver at the user equipment (UE) side. In the orthogonal frequency division multiple access (OFDMA) system, the SLM can not be directly applied since the UE does not have access to the signal of other UEs. In this paper, we propose a novel SLM algorithm for the OFDMA system that requires no side information transmission. With the proposed SLM algorithm, each UE can receive its own data without the knowledge of other UEs. The SLM demapping at the UE side is much simplified. Besides, detection of the implicit side information with the proposed algorithm is more robust than existing SLM algorithms. Numerical results validate the theoretical performance of the proposed algorithm.

关键词： Signal processing algorithms Peak to average power ratio Information processing Receivers Signal processing Frequency conversion Complexity theory

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A Method of Gesture Recognition Using CNN-SVM Model with Error Correction Strategy

A Method of Gesture Recognition Using CNN-SVM Model with Err...

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2018 International Conference on Computer,Communication and network technology(CCNT 2018)

作者： Jian LI Zhi-quan FENG We XIE Chang-sheng AI School of information Science and Engineering University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing School of Information and Electrial Engineering Harbin Institute of Technology at Weihai School of Mechanical Engineering University of Jinan

The gesture recognition methods based on artificial feature extraction are time-consuming and low recognition rate. The generalization ability of hand gesture recognition using convolution neural network is not strong. Therefore, this paper combines the advantages of CNN and SVM to propose a hybrid model to automatically extract the features and improve the generalization ability, in addition, we use an error correction strategy to reduce the error recognition rate of confusing gestures. First, the segmentation preprocessing of gesture data collected by Kinect. Then, the hybrid model automatically extracts features from the data and generates the predictions. Finally, using the error correction strategy to adjust the prediction result. We get a recognition rate of 95.81% without error correction strategy on our database, the average recognition rate of 97.32% with error correction strategy.

关键词： Gesture recognition Convolution neural network Support vector machine Probability estimation Error correction strategy

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SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Shuo Zhao Qiliang Gu Jianqiang Zhang Gongpeng Song Qin Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets.

关键词： Deep learning Computational modeling Semantics Neural networks Feature extraction Natural language processing Multilingual Convolutional neural networks Cybernetics Overfitting

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BiKT: Unleashing the potential of GNNs via Bi-directional Knowledge Transfer

arXiv

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arXiv 2023年

作者： Zheng, Shuai Liu, Zhizhe Zhu, Zhenfeng Zhang, Xingxing Li, Jianxin Zhao, Yao The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Qiyuan Lab Beijing China The Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing100083 China

Based on the message-passing paradigm, there has been an amount of research proposing diverse and impressive feature propagation mechanisms to improve the performance of GNNs. However, less focus has been put on feature transformation, another major operation of the message-passing framework. In this paper, we first empirically investigate the performance of the feature transformation operation in several typical GNNs. Unexpectedly, we notice that GNNs do not completely free up the power of the inherent feature transformation operation. By this observation, we propose the Bi-directional Knowledge Transfer (BiKT), a plug-and-play approach to unleash the potential of the feature transformation operations without modifying the original architecture. Taking the feature transformation operation as a derived representation learning model that shares parameters with the original GNN, the direct prediction by this model provides a topological-agnostic knowledge feedback that can further instruct the learning of GNN and the feature transformations therein. On this basis, BiKT not only allows us to acquire knowledge from both the GNN and its derived model but promotes each other by injecting the knowledge into the other. In addition, a theoretical analysis is further provided to demonstrate that BiKT improves the generalization bound of the GNNs from the perspective of domain adaption. An extensive group of experiments on up to 7 datasets with 5 typical GNNs demonstrates that BiKT brings up to 0.5% - 4% performance gain over the original GNN, which means a boosted GNN is obtained. Meanwhile, the derived model also shows a powerful performance to compete with or even surpass the original GNN, enabling us to flexibly apply it independently to some other specific downstream tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Message passing

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