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arXiv 2024年

作者： Wang, Cheng Yuan, Zenghui Zhou, Pan Xu, Zichuan Li, Ruixuan Wu, Dapeng Oliver Hubei Engineering Research Center on Big Data Security Key Laboratory of Distributed System Security of Hubei Province School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Software The Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Dalian University of Technology Dalian116024 China Intelligent and Distributed Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science City University of Hong Kong Kowloon999077 Hong Kong

Mobile Edge computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network’s edge. By shifting the load of cloud computing to individual local servers, MEC helps meet the requirements of ultralow latency, localized data processing, and extends the potential of Internet of Things (IoT) for end-users. However, the crosscutting nature of MEC and the multidisciplinary components necessary for its deployment have presented additional security and privacy concerns. Fortunately, Artificial Intelligence (AI) algorithms can cope with excessively unpredictable and complex data, which offers a distinct advantage in dealing with sophisticated and developing adversaries in the security industry. Hence, in this paper we comprehensively provide a survey of security and privacy in MEC from the perspective of AI. On the one hand, we use European Telecommunications Standards Institute (ETSI) MEC reference architecture as our based framework while merging the software Defined Network (SDN) and Network Function Virtualization (NFV) to better illustrate a serviceable platform of MEC. On the other hand, we focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI. Finally, we comprehensively discuss the opportunities and challenges associated with applying AI to MEC security and privacy as possible future research directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Network security

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Robust and Unsupervised KPI Anomaly Detection Based on Highly Sensitive Conditional Variational Auto-Encoders

Robust and Unsupervised KPI Anomaly Detection Based on Highl...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Shili Yan Bing Tang Qing Yang Yijia He Xiaoyuan Zhang Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China School of Information and Communication Engineering Guangzhou Maritime University Guangzhou China Beijing Jiaotong University Beijing China

To ensure the stability and reliability of service quality, large Internet companies need to closely monitor various KPIs (key Performance Indicators, such as network throughput, CPU usages) and trigger timely troubleshooting or mitigation when any anomaly occurs. However, the diversity and complexity of anomalies bring great challenges to this work, especially when there is no manual label and low delay is required. In this paper, we propose HS-VAE (Highly Sensitive Variational Auto-Encoders), an unsupervised, robust algorithm based on CVAE (Conditional Variational Auto-Encoders) with high sensitivity to anomalies for KPIs, which contains mainly 3 parts: a simple but important data filter before training, improved conditional VAE with two dropout layers and adjusted anomaly detection method based on reconstruction probability. Our experiments using real-world data show that, HS-VAE's best F1-score ranges from 0.91 to 0.98. In addition, HS- VAE is excellent in sensitivity to anomaly and works well even with low latency requirements.

关键词： Training Sensitivity key performance indicator Training data Manuals Throughput Reliability

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Fer-Chc: Facial Expression Recognition with Cross-Hierarchy Contrast

SSRN

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SSRN 2022年

作者： Wu, Xuemei He, Jie Huang, Changqin Huang, Qionghao Zhu, Jia Huang, Xiaodi Fujita, Hamido Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University Jinhua321004 China School of Computing Mathematics and Engineering Charles Sturt University NSW Australia Software and Information Science Iwate Prefectural University Takizawa020-0693 Japan

Remarkable progress has been made in facial expression recognition (FER) tasks with convolutional neural networks (CNNs). However, these CNN-based approaches do not well capture the detailed and crucial features that can distinguish different facial expressions from a global perspective among multiple facial expressions. The performance of existing CNN-based models for FER is still much room for improvement. As such, we propose a novel cross-hierarchy contrast (CHC) framework named FER-CHC for facial expression recognition tasks. The basic idea of FER-CHC is to employ a CHC to regularize the feature learning of the backbone network and enhance global representations of facial expressions. Specifically, the CHC captures common and differential features from different facial expressions with a cross-hierarchy contrast mechanism. The fusion network globally integrates the features learned from a backbone network and CHC to learn a more robust feature representation. Comprehensive experiments were conducted on six popular datasets: CK+, FER2013, FER+, RAF-DB, AffectNet, and JAFFE. The results show that our proposed FER-CHC achieves state-of-the-art performances on these datasets. In addition, the ablation study also demonstrates the effectiveness of the proposed components in FER-CHC. © 2022, The Authors. All rights reserved.

关键词： Convolutional neural networks

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DOA and polarization estimation for non-circular signals in 3-D millimeter wave polarized massive MIMO systems

arXiv

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arXiv 2017年

作者： Wan, Liangtian Liu, Kaihui Liang, Ying-Chang Zhu, Tong Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian116620 China National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu611731 China National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu611731 China School of Electrical and Information Engineering University of Sydney NSW2006 Australia Tianjin Institute of Computing Technology Tianjin300000 China

In this paper, an algorithm of multiple signal classification (MUSIC) is proposed for two-dimensional (2-D) direction-of-arrival (DOA) and polarization estimation of non-circular signal in three-dimensional (3-D) millimeter wave polarized large-scale/massive multiple-input-multiple-output (MIMO) systems. The traditional MUSIC-based algorithms can estimate either the DOA and polarization for circular signal or the DOA for non-circular signal by using spectrum search. By contrast, in the proposed algorithm only the DOA estimation needs spectrum search, and the polarization estimation has a closed-form expression. First, a novel dimension-reduced MUSIC (DR-MUSIC) is proposed for DOA and polarization estimation of circular signal with low computational complexity. Next, based on the quaternion theory, a novel algorithm named quaternion non-circular MUSIC (QNC-MUSIC) is proposed for parameter estimation of non-circular signal with high estimation accuracy. Then based on the DOA estimation result using QNC-MUSIC, the polarization estimation of non-circular signal is acquired by using the closed-form expression of the polarization estimation in DR-MUSIC. In addition, the computational complexity analysis shows that compared with the conventional DOA and polarization estimation algorithms, our proposed QNC-MUSIC and DR-MUSIC have much lower computational complexity, especially when the source number is large. The stochastic Cramér-Rao Bound (CRB) for the estimation of the 2-D DOA and polarization parameters of the non-circular signals is derived as well. Finally, numerical examples are provided to demonstrate that the proposed algorithms can improve the parameter estimation performance when the large-scale/massive MIMO systems are employed. Copyright © 2017, The Authors. All rights reserved.

关键词： MIMO systems

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Noncontact Doppler Radar-based Heart Rate Detection on the SVD and ANC

Noncontact Doppler Radar-based Heart Rate Detection on the S...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Shi, Qiuxia Hu, Bin Tian, Fuze Zhao, Qinglin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University China School of Computer and Communication Lanzhou University of Technology China Key Laboratory of Special Functional Materials and Structural Design Lanzhou University Ministry of Education Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences China Engineering Research Center of Open Source Software and Real-Time System Lanzhou University Ministry of Education China

ISBN: (纸本)9781665468190

In the Doppler biological radar-based applications of noncontact measurement of vital signs, effectively extracting heartbeat information from weak thoracic mechanical motion is an important problem to be solved. This study is aimed at extracting heartbeat signal via the technology combined with Short Time Fourier Transform (STFT), Singular Value Decomposition (SVD) and Adaptive Noise Canceller (ANC) from radar recording. The simulated data and the data collected by Doppler radar biosensor realized in laboratory are employed to validate the proposed method. The results show that the proposed method has the ability of detection for the heart rate and heart rate variability indexes in rest state, it has certain advantages in time-consuming and detection accuracy. Therefore, the current method provides another way to process vital sign signals recorded by Doppler radar. © 2022 IEEE.

关键词： Singular value decomposition

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Generalized Singleton Type Upper Bounds

arXiv

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arXiv 2022年

作者： Chen, Hao Qu, Longjiang Li, Chengju Lyu, Shanxiang Xu, Liqing The College of Information Science and Technology Cyber Security Jinan University Guangdong Province Guangzhou510632 China The College of Liberal Arts and Sciences National University of Defence Technology Hunan Province Changsha410073 China Shanghai Key Laboratory of Trustworthy Computing School of Computer Science and Software Engineering East China Normal University Shanghai200062 China

In this paper, we give upper bounds on the sizes of (d, L) list-decodable codes in the Hamming metric space from covering codes with the covering radius smaller than or equal to d. When the list size L is 1, this gives many new Singleton type upper bounds on the sizes of codes with a given minimum Hamming distance. These upper bounds are stronger than the Griesmer bound when the lengths of codes are large. Some upper bounds on the lengths of general small Singleton defect codes or list-decodable codes attaining the generalized Singleton bound are given. As an application of our generalized Singleton type upper bounds on Hamming metric error-correcting codes, the generalized Singleton type upper bounds on insertion-deletion codes are given, which are much stronger than the direct Singleton bound for insertion-deletion codes when the lengths are large. We also give © 2022, CC0.

关键词： Optimal systems

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software trustworthiness evaluation using structural equation modeling

引用

International Journal of Performability Engineering 2019年第10期15卷 2628-2635页

作者： Deng, Rumei Chen, Yixiang Wu, Hengyang Tao, Hongwei MoE Engineering Center for Software Hardware Co-Design Technology and Application East China Normal University Shanghai200062 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou450002 China

software trustworthiness evaluation results can provide guidance for existing software development. Therefore, it is of great significance to study software trustworthiness evaluation. Weights and the relationship of trustworthy attributes have a huge effect on the results of software trustworthiness evaluation. However, few studies have considered weights and the quantitative relationship among trustworthy attributes at the same time. Structural equation modeling (SEM) provides a more scientific and reasonable method to obtain weights, where the objectification of subjective weights is guaranteed. In this paper, we propose a SEM-based method to evaluate software trustworthiness. Firstly, we establish the trustworthy evaluation indicator system for software. Based on the survey data, we construct the SEM for software trustworthiness evaluation to obtain the weights of trustworthy attributes and the relationship among them. Lastly, we apply the trustworthy measurement model to calculate the trustworthiness value of software to be surveyed. These trustworthiness values demonstrate the reasonability of our method. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Surveys

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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An Optimized Hierarchical Classifier for Pedestrian Detection

An Optimized Hierarchical Classifier for Pedestrian Detectio...

引用

7th World Congress on Intelligent Control and Automation (WCICA 2008), vol.3

作者： Yanwu Xu Xianbin Cao Hong Qiao Department of Computer Science and Technology University of Science and Technology Hefei China Anhui Province Key Laboratory of Software Computing and Communication Hefei China Institute of Automation Chinese Academy and Sciences Beijing China

Classification is an essential technology in Pedestrian Detection System (PDS). Until now, single-classifier and basic cascaded classifier had been widely used in PDS;however, most of them can hardly satisfy the 3 requirements at the same time: high detection speed, high detection rate and low false positive rate. In this paper, we proposed an optimized hierarchical classifier which can satisfy the 3 requirements. The proposed method adopted Corse-to-fine and Early-rejection principles to achieve global high performance. It consists of two hierarchies, the first one is used to quickly reject non-pedestrian objects and select out only a few candidates;the second one makes further verification to these candidates. Furthermore, each hierarchy was optimized with statistical models basing on experiments;and each hierarchy is a treelike classifier which has specific optimization demands. At last, an overall performance evaluation standard is proposed, and the experimental results showed that the proposed classifier had better overall performance.

关键词： Pedestrian detection Hierarchical classifier AdaBoost

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Cellular signal transduction network simulation based on microscopic agent model

引用

Xitong Fangzhen Xuebao / Journal of System Simulation 2008年第17期20卷 4703-4707页

作者： Shao, Chen-Xi Deng, Hong-Li Yang, Ming Wang, Zi-Cai Department of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Control and Simulation Center Harbin Institute of Technology Harbin 150001 China Anhui Province Key Laboratory of Software in Computing and Communication Hefei 230027 China

A hierarchical MAS (multi-agent system) based on a multi-level discrete space and microscopic agent model for signal transduction network modeling and simulation was proposed. The space can effectively represent the heterogeneous cellular space, and is adaptable for systemic expansion. Moreover, the different deformations of one molecular species are packed in one mesoscopic molecule-agent, so that the global complexity is handled by the local state-space, reducing the external communications and computational complexity. The results from simulating two feedback models of MAPK (Mitogen-Activated Protein Kinase) pathway are similar to those of chemical kinetic model, showing the validity of this method.

关键词： Agent Cellular signal transduction Complex system Discrete space Microscopic level

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