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4th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2013

作者： Gui, Jie Mi, Jian-Xun Lei, Ying-Ke Wang, Hong-Qiang State Key Laboratory of Software Engineering Wuhan University 430072 China Hefei Institute of Intelligent Machines Chinese Academy of Sciences Hefei Anhui China State Key Laboratory for Novel Software Technology Nanjing University China Bio-Computing Research Center Shenzhen Graduate School Harbin Institute of Technology Shenzhen Guangdong Province China Electronic Engineering Institute Hefei Anhui 230037 China

ISBN: (纸本)9783642420566

Face recognition in uncontrolled environments, such as large pose variations, and extreme ambient illumination and expressions is a challenging task for traditional face recognition methods. Some recent works show that context information such as clothes and social relationships is very important for solving the problem. Furthermore, sparse representation-based method is very robust for face occlusion and pixel contamination. In this paper, the authors propose a sparse learning framework for face recognition with social context. First, sparse representation based dimensionality reduction method is used to find the low dimension representation of the face images. Second, sparse representation based classification is utilized to classify test face images as known label and unknown label. Third, for test images classified as unknown label, social context descriptor is constructed according to co-existence information. Finally, based on social context descriptor and the low dimension representation of the test images classified as unknown label, sparse representation based clustering is adopted to perform face recognition. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Face recognition

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Crowd evacuation simulation based on emotion contagion

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International Journal of Simulation and Process Modelling 2018年第1期13卷 43-56页

作者： Du, Xuemei He, Wu Mao, Yan Rao, Yunbo College of Movie and Media Sichuan Normal University Chengdu610068 China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Normal University Chengdu610068 China School of Economics and Management Southwest Jiaotong University Chengdu610031 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China

Simulation study on evacuation scenarios has an important influence on public safety. Unfortunately, the traditional evacuation-drill approach for emergency situations focuses on the physiological interaction between individuals/crowds, whereas the approach ignores psychological interaction. In this paper, we present an emotion contagion model that associates components, such as physiological demand, security requirement and personality traits, with individual agents comprising a crowd, and produces emergent behaviours in the crowd as a whole. The emotion contagion model realises the interaction of physiological and psychological factors. Personality traits are associated with emotions by using the OCEAN, OCC and PAD models. To reflect the complexity of emotion contagion of crowd, we consider three processes: contagion, update, and decay. We also consider the effect of emotions on the movement. Testing results demonstrate that the proposed method is effective and realistic for simulating the movement of heterogeneous crowd in emergency situations. © 2018 Inderscience Enterprises Ltd.

关键词： Physiology

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Deep Learning-Based Joint Channel Estimation and Implicit CSI Feedback 11th

Deep Learning-Based Joint Channel Estimation and Implicit C...

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11th China Conference on Command and Control, C2 2023

作者： Wan, Liangtian He, Jifeng Liu, Kaihui Sun, Lu Wang, Xianpeng Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian116620 China School of Electrical Engineering and Intelligentization Dongguan University of Technology Dongguan523820 China Department of Communication Engineering Institute of Information Science Technology Dalian Maritime University Dalian116026 China State Key Laboratory of Marine Resource Utilization in South China Sea and School of Information and Communication Engineering Hainan University Haikou570228 China

ISBN: (纸本)9789819990207

Previous studies often address channel estimation and channel state information (CSI) implicit feedback issues in 5G new radio (NR) system independently, posing a challenge to achieve effective coordination between the two processes in a shared environment. Consequently, we propose a deep learning-based scheme that jointly addresses channel estimation and implicit CSI feedback. In this scheme, the received pilot information from user equipment (UE) is regarded as a two-dimensional image. Channel estimation is performed employing the ResMLP architecture, whereas the Transformer architecture is utilized for implicit CSI feedback. Furthermore, a novel training approach is introduced to further enhance the performance of the proposed scheme. Simulation results illustrate a substantial improvement compared to the reference scheme employed in the 3rd Wireless communication Artificial Intelligence (AI) Competition (WAIC). © Chinese Institute of Command and Control 2024.

关键词： Channel estimation

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Fast fourier transform based ip traffic classification system for SIPTO at H(e)NB

Fast fourier transform based ip traffic classification syste...

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2012 7th International ICST Conference on communications and Networking in China, CHINACOM 2012

作者： Han, Lin Huang, Liusheng Hu, Qian Han, Xue Shi, Jinglin School of Software and Engineering University of Science and Technology of China Suzhou China Wireless Communication Technology Research Center Institute of Computing Technology Chinese Academy of Sciences China Beijing Key Laboratory of Mobile Computing and Pervasive Device China Beijing Sylincom Technologies Co. Ltd. China

ISBN: (纸本)9781467326995

3GPP has recently introduced LIPA(Local IP Access) and SIPTO(Selected IP Traffic Offload) to offload traffic from the core network, which brings new challenge to on-line traffic classification, because of the large amount of data and the difference of mobile network from wired network, such as high bit error rates(BER) and temporary disconnections. Therefore, other proposed schemes which aim at ether LIPA at H(e)NB or SIPTO at macro network could not get high accuracy and high speed at the same time, and traffic classification methodologies in wired IP network are not applicable. This paper proposes a fast fourier transform(FFT) based IP traffic classification system for SIPTO at H(e)NB, which focuses on classifying each packet at H(e)NB by extracting the application layer payload pattern using FFT. Pattern extraction and classification using machine learning algorithms are simulated, and results show that our system outperforms existing methods by offering about 3%-6% improvement in classification accuracy with about 7% time. Simulation of SIPTO shows good reduction of press to the core network and low false rates. © 2012 IEEE.

关键词： Fast Fourier transforms

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General Sparse Normalization and Proportionate ASE Algorithms 5

General Sparse Normalization and Proportionate ASE Algorithm...

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5th IEEE International Conference on Electronic Information and communication Technology, ICEICT 2022

作者： Hadi, Abdul Peng, Yan Qadir, Abdul Li, Yingsong Khan, Khushal College of Information and Communication Engineering Harbin Engineering University Harbin China Anhui Province Key Laboratory of Target Recognition and Feature Extraction LuAn230088 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China College of Intelligent System and Control Science Engineering Harbin Engineering University China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

Andrew's Sine Estimator (ASE) has recently been used to invent adaptive filtering, which can combat more kind of noises than conventional estimators. Inspired by the LMS and its sparse forms, normalization and proportionate ASE algorithms with Lawson constraints are constructed with the respective given names of Lawson normalization ASE (LNASE) and Lawson proportionate NASE (LPNASE). Simulations are performed to analyze the performance of these algorithms under impulsive noise. © 2022 IEEE.

关键词： Impulse noise

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NDRIDC: NMF-based deep representation algorithm for incomplete data clustering

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Knowledge-Based Systems 2025年 324卷

作者： Dexian Wang Zonglin Li Sha Yang Tianrui Ren Pengfei Zhang Ping Deng Tianrui Li Chengdu University of Traditional Chinese Medicine Chengdu 611137 China Chinese Academy of Medical Sciences & Peking Union Medical College Beijing 100730 China School of Computer and Software Engineering Xihua University Chengdu 610039 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu 611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu 611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Chengdu 611756 China

Incomplete data clustering is a hot research topic in the field of data mining. Many methods based on incomplete clustering have two issues: (1) their models do not have non-negative constraints; (2) their model building strategies do not consider combining traditional machine learning and deep learning. To address these issues, we proposed the NMF-based deep representation algorithm for incomplete data clustering (NDRIDC). First, the element-based objective function is constructed based on the known elements in the incomplete data matrix with matrix factorization principle. Then the NDRIDC deep network is constructed based on the element update gradient and nonlinear function with non-negative value domain. The learning of the NDRIDC network enables the optimization of the objective function and the filling of missing values. Further, based on the same learning framework, the NDRIDC network is employed to perform low-dimensional representation of the filled matrix. Finally, extensive experiments on regular and large-scale datasets show that our proposed algorithm has good performance.

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Resource allocation for scalable video streaming in highway VANET

Resource allocation for scalable video streaming in highway ...

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International Conference on Wireless communications and Signal Processing (WCSP)

作者： Yang Xu Hao Zhou Xiaoyan Wang Baohua Zhao Province Key Laboratory of Software in Computing and Communication Hefei China School of Science and Technology University of Science and Technology of China Hefei China National Institute of Informatics Tokyo Japan

ISBN: (纸本)9781467376884

In vehicular ad-hoc network (VANET), the popular video transmission service can serve people with live traffic information, real-time news or even high-definition movies, which can assist people to drive on the road and enhance their driving experience. In this paper, we propose a dynamic programming based algorithm for the resource allocation problem of scalable video streaming through long term evolution (LTE) networks in highway VANET. We manage vehicular mobility by using small group of pictures (GOP) sized scalable video coding (SVC) encoder and valid signal-to-interference-and-noise ratio (SINR) estimator. We also adopt adaptive receive window of GOP to enhance the resource utilization efficiency as well as video quality. The simulation results demonstrate the efficiency of the proposed algorithm.

关键词： Streaming media Resource management Vehicular ad hoc networks Signal to noise ratio Interference Road transportation Static VAr compensators

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A Lightweight OS-Level Virtualization Architecture Based on Android

A Lightweight OS-Level Virtualization Architecture Based on ...

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2017 2nd International Conference on Computer, Network Security and communication Engineering(CNSCE2017)

作者： Bo-wen LIU Nai-jie GU De-he GU School of Computer Science and Technology University of Science and Technology of China Institute of Advanced Technology University of Science and Technology of China Anhui Province Key Laboratory of Computing and Communication Software

All kinds of intelligent terminal equipment emerge in endlessly and grow explosively. Typical devices, such as smartphones and tablets, are becoming more and more popular, furthermore, intelligent equipment market share has more than that on PCs. And on mobile devices area, the demand for virtualization technology is gradually increasing. As an important branch of computer technology development, operating system level virtualization is becoming a hot spot of current research due to its low overhead and the advantages of lightweight. In this paper, based on the Android system on mobile devices, implement a prototype which can run multiple virtual system instances on only an Android OS by modifying the namespace mechanism in the Linux kernel to and expand into driver namespace mechanism. In addition, put forward a model called the active-inactive model guaranteeing the process if and only if in the active namespace to operate the device drivers, which was designed and implemented a new scheme of operating system level virtualization.

关键词： Virtualization Android Namespace

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Personalized video emotion tagging through a topic model

Personalized video emotion tagging through a topic model

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Shan Wu Shangfei Wang Zhen Gao Key Lab of Computing and Communication Software of Anhui Province School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9781509041183

The inherent dependencies among video content, personal characteristics, and perceptual emotion are crucial for personalized video emotion tagging, but have not been thoroughly exploited. To address this, we propose a novel topic model to capture such inherent dependencies. We assume that there are several potential human factors, or "topics," that affect the personal characteristics and the personalized emotion responses to videos. During training, the proposed topic model exploits the latent space to model the relationships among personal characteristics, video content and video tagging behaviors. After learning, the proposed model can generate meaningful latent topics, which help personalized video emotion tagging. Efficient learning and inference algorithms of the model are proposed. Experimental results on the CP-QAE-I database demonstrate the effectiveness of the proposed approach in modeling complex relationships among video content, personal characteristics, and perceptual emotion, as well as its good performance in personalized video emotion.

关键词： Video emotion tagging personal characteristics topic model

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Evolutionary community discovery in dynamic networks based on leader nodes

Evolutionary community discovery in dynamic networks based o...

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International Conference on Big Data and Smart computing (BIGCOMP)

作者： Wenhao Gao Wenjian Luo Chenyang Bu Anhui Province key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Hefei Anhui China University of Science and Technology of China Hefei Anhui CN

Evolutionary community discovery is a hot research topic which clusters the dynamic or temporal network. The communities detected in dynamic network should get reasonable partition for the current data while simultaneously not deviate drastically from the previous ones. In this paper, the evolutionary community discovery algorithm based on leader nodes (EvoLeaders) is proposed to cluster the dynamic network. Compared with the static community discovery algorithm based on leader nodes (the Top Leaders algorithm), experimental results over two real-world datasets demonstrate that the EvoLeaders is more suitable for dynamic scenarios.

关键词： Heuristic algorithms Clustering algorithms Current measurement Partitioning algorithms Social network services History Merging

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