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Design of intelligent community security system based on visual tracking and large data natural language processing technology

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Journal of Intelligent and Fuzzy Systems 2020年第6期38卷 7107-7117页

作者： Li, Daming Deng, Lianbing Cai, Zhiming Cai, Kaicheng Post-Doctoral Research Center of Zhuhai da Hengqin Science and Technology Development Co. Ltd. China City University of Macau China Huazhong University of Science and Technology China Zhuhai da Hengqin Science and Technology Development Co. Ltd. China Institute of Data Science City University of Macao China Automotive Engineering Research Institute of Guangzhou Automobile Group Co. Ltd. China

With the improvement of public security awareness, it is of great value to establish an intelligent crowd screening video early warning system for community security. The method is to use artificial intelligence and big data technology to analyze the facial features of past people through video data in video surveillance. In this paper, the authors analyze the design of intelligent community security system based on visual tracking and large data natural language processing technology. Through JMF technology and video acquisition card, the signal can be transmitted from the matrix machine to the server of the system. In the process of transmission, we also need to use multi-threading technology and double buffer technology to compress video, which can improve the compression efficiency and reduce the CPU usage time. The information module is mainly responsible for receiving and playing video. This module needs JAVA technology which can receive video. Once the client requests monitoring, the client can immediately send relevant operation instructions to the server, and the server will return the corresponding video stream in time. The simulation results show that the system can identify risks efficiently and accurately. © 2020 - IOS Press and the authors. All rights reserved.

关键词： security systems

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Robust capturing chromosome conformation using the DLO Hi-C 2.0 method

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Journal of Genetics and Genomics 2020年第10期47卷 655-658页

作者： Zhihui Zhang Chengchao Wu Khaista Rahman Weize Xu Guoliang Li Da Lin Gang Cao State Key Laboratory of Agricultural Microbiology Huazhong Agricultural UniversityWuhan430070China College of Veterinary Medicine Huazhong Agricultural UniversityWuhan430070China National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural UniversityWuhan430070China Agricultural Bioinformatics Key Laboratory of Hubei Province Hubei Engineering Technology Research Center of Agricultural Big Data3D Genomics Research CenterHuazhong Agricultural UniversityWuhan430070China College of Informatics Huazhong Agricultural UniversityWuhan430070China College of Bio-Medicine and Health Huazhong Agricultural UniversityWuhan430070China

The human genome contains around 20,000 protein-coding genes and a vast of transcriptional regulatory elements(ENCODE Project Consortium,2012;Roadmap Epigenomics Consortium et al.,2015;Stunnenberg and Hirst,2016).It still remains elusive how these regulatory elements regulate their target genes across genomic distances of millions of bases.

关键词： transcriptional ). bases

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SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training data

SynFacePAD 2023: Competition on Face Presentation Attack Det...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Meiling Fang Marco Huber Julian Fierrez Raghavendra Ramachandra Naser Damer Alhasan Alkhaddour Maksim Kasantcev Vasiliy Pryadchenko Ziyuan Yang Huijie Huangfu Yingyu Chen Yi Zhang Yuchen Pan Junjun Jiang Xianming Liu Xianyun Sun Caiyong Wang Xingyu Liu Zhaohua Chang Guangzhe Zhao Juan Tapia Lazaro Gonzalez-Soler Carlos Aravena Daniel Schulz Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Norwegian University of Science and Technology (NTNU) Norway ID R&D Inc New York US School of Cyber Science and Engineering Sichuan University Chengdu China School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture China Biometrics and Security Research Group Hochschule Darmstadt Darmstadt Germany I+D Vision Center Santiago Chile

This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks.

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Real-time chaotic video encryption based on multithreaded parallel confusion and diffusion

arXiv

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arXiv 2023年

作者： Jiang, Dong Yuan, Zhen Li, Wen-Xin Lu, Liang-Liang School of Internet Anhui University Hefei230039 China National Engineering Research Center of Agro-Ecological Big Data Analysis and Application Anhui University Hefei230601 China Key Laboratory of Optoelectronic Technology of Jiangsu Province Nanjing Normal University Nanjing210023 China School of Physical Science and Technology Nanjing Normal University Nanjing210023 China National Laboratory of Solid State Microstructures Nanjing University Nanjing210093 China

MSC Codes 68P25 (Primary) 68W10 (Secondary)Due to the strong correlation between adjacent pixels, most image encryption schemes perform multiple rounds of confusion and diffusion to protect the image against attacks. Such operations, however, are time-consuming, cannot meet the real-time requirements of video encryption. Existing works, therefore, realize video encryption by simplifying the encryption process or encrypting specific parts of video frames, which results in lower security compared to image encryption. To solve the problem, this paper proposes a real-time chaotic video encryption strategy based on multithreaded parallel confusion and diffusion. It takes a video as the input, splits the frame into subframes, creates a set of threads to simultaneously perform five rounds of confusion and diffusion operations on corresponding subframes, and efficiently outputs the encrypted frames. The encryption speed evaluation shows that our method significantly improves the confusion and diffusion speed, realizes real-time 480 × 480, 576 × 576, and 768 × 768 24FPS video encryption using Intel Core i5-1135G7, Intel Core i7-8700, and Intel Xeon Gold 6226R, respectively. The statistical and security analysis prove that the deployed cryptosystems have outstanding statistical properties, can resist attacks, channel noise, and data loss. Compared with previous works, to the best of our knowledge, the proposed strategy achieves the fastest encryption speed, and realizes the first real-time chaotic video encryption based on multi-round confusion-diffusion architecture, thus, providing a more secure and feasible solution for practical applications and related research. © 2023, CC BY.

关键词： Cryptography

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VSRQ: Quantitative Assessment Method for Safety Risk of Vehicle Intelligent Connected System

arXiv

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arXiv 2023年

作者： Zhang, Tian Guan, Wenshan Miao, Hao Huang, Xiujie Liu, Zhiquan Wang, Chaonan Guan, Quanlong Fang, Liangda Duan, Zhifei Department of Cyberspace Security College of Information Science and Technology Jinan University Guangzhou511486 China Department of Computer Science College of Information Science and Technology Jinan University Guangzhou510632 China College of Information Science and Technology Jinan University Guangdong Institution of Smart Education Jinan University Guangzhou510632 China College of Cyber Security Jinan University Guangzhou510632 China Guangdong Gene Data Processing and Analysis Engineering Research Center China Pazhou Laboratory Guangzhou510330 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China Guangzhou XPeng Motors Technology Co. Ltd No.8 Songgang Road Changxing Street Cencun Tianhe District Guangzhou China

The field of intelligent connected in modern vehicles continues to expand, and the functions of vehicles become more and more complex with the development of the times. This has also led to an increasing number of vehicle vulnerabilities and many safety issues. Therefore, it is particularly important to identify high-risk vehicle intelligent connected systems, because it can inform security personnel which systems are most vulnerable to attacks, allowing them to conduct more thorough inspections and tests. In this paper, we develop a new model for vehicle risk assessment by combining I-FAHP with FCA clustering: VSRQ model. We extract important indicators related to vehicle safety, use fuzzy cluster analys (FCA) combined with fuzzy analytic hierarchy process (FAHP) to mine the vulnerable components of the vehicle intelligent connected system, and conduct priority testing on vulnerable components to reduce risks and ensure vehicle safety. We evaluate the model on OpenPilot and experimentally demonstrate the effectiveness of the VSRQ model in identifying the safety of vehicle intelligent connected systems. The experiment fully complies with ISO 26262 and ISO/SAE 21434 standards, and our model has a higher accuracy rate than other models. These results provide a promising new research direction for predicting the security risks of vehicle intelligent connected systems and provide typical application tasks for VSRQ. The experimental results show that the accuracy rate is 94.36%, and the recall rate is 73.43%, which is at least 14.63% higher than all other known indicators. Copyright © 2023, The Authors. All rights reserved.

关键词： Vehicle safety

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Rapid Maize Seedling Detection Based on Receptive-Field and Cross-Dimensional Information Interaction

Rapid Maize Seedling Detection Based on Receptive-Field and ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Fengqi Hao Shulei Zhu Dexin Ma Xiangjun Dong Hoiio Kong Xia Liu Chunhua Mu 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 Faculty of Data Science City University of Macau Macau China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Communication College Qingdao Agricultural University Qingdao China National Engineering Laboratory of Wheat and Maize/Key Laboratory of Biology and Genetic Improvement of Maize in Northern Yellow-huai River Plain Ministry of Agriculture Maize Research Institute Shandong Academy of Agricultural Sciences Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The seedling stage is important in the growth and development of maize, and it is also a critical period that affects maize yield and quality. Accurately recognizing this stage of maize is challenging, as current maize seedling detection methods struggle with small sizes and complex environments. This paper proposes a rapid maize seedling detection method that emphasizes spatial feature extraction in the receptive field and cross-dimensional interaction—receptive field triplet attention YOLO (RFT-YOLO). First, we design a convolutional module for cross-dimensional information interaction within the receptive field. Second, we introduce an advanced selective fusion network, which boosts the multi-scale fusion capability of small object features while reducing the GFLOPs of the model. Additionally, we employ Inner-IoU to diminish sensitivity to positional biases of small objects and expedite the convergence of the loss function. Finally, we create a dataset of maize seedlings to conduct our experiments. Experimental results demonstrate that the RFT-YOLO significantly enhances performance, reducing the number of parameters by 35.3% and GFLOPs by 14.24%, compared to the baseline model. Moreover, the mean average precision (mAP) improves from 89.6% to 91.5%. These improvements confirm the model's effectiveness in detecting maize seedlings.

关键词： YOLO Sensitivity Accuracy Semantics Feature extraction Complexity theory data mining Cybernetics Convergence

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Dual Prototypes Contrastive Learning for Semi-Supervised Medical Image Segmentation

SSRN

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SSRN 2024年

作者： Yue, Tianai Xu, Rongtao Wu, Jingqian Yang, Wenjie Du, Shide Wang, Changwei Johns Hopkins University Baltimore21218 United States State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The University of Hong Kong Hong Kong999077 Hong Kong College of Computer and Big Data Fuzhou University Fuzhou350108 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan250014 China

Semi-supervised techniques for medical image segmentation have demonstrated potential, effectively training models using scarce labeled data alongside a wealth of unlabeled data. Therefore, semi-supervised medical image segmentation is one of the key issues in engineering applications of medical artificial intelligence. Consistency constraints based on prototype alignment provide an intuitively sensible way to discover valuable insights from unlabeled data that can motivate segmentation performance. In this work, we propose a dual prototypes contrastive network (DPC-Net) to motivate semi-supervised medical segmentation accuracy by imposing image-level global prototype constraints and pixel-level local prototype constraints. First, we introduce a Background-Separation Global Prototype Contrastive Learning technique that utilizes the natural mutual exclusivity of foreground and background to separate the inter-class distances and encourage the segmentation network to obtain segmentation results that are more complete and do not contain background regions. Second, we design a Cross-Consistent Local Prototype Contrastive Learning techniques to extend the perturbation consistency of the two networks to the prototype's localized response to the feature map, thereby shaping a more stable intra-class prototype space and producing accurate and robust pixel-level predictions. Finally, we comprehensively evaluate our method on mainstream semi-supervised medical image segmentation benchmarks and settings, and experimental results show that our DPC-Net outperforms current state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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TSU: A Two-stage update approach for persistent skiplist 13th

TSU: A Two-stage update approach for persistent skiplist

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13th Conference on Advanced Computer Architecture, ACA 2020

作者： Wang, Shucheng Cao, Qiang Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology School of Computer Science and Technology Huazhong University of Science and Technology Ministry of Education of China Wuhan China

ISBN: (纸本)9789811581342

Skiplist, a widely used in-memory index structure, could incur crash inconsistency when running on emerging NVRAM (Non-Volatile Random Access Memory). Logging or strict serialization can ensure crash consistency at the cost of severe performance degradation. In this paper, we propose TSU, a Two-stage update approach to improve the performance of persistent skiplist while preserve crash consistency. TSU exploits space locality of skiplist and atomic write of NVRAM, thus effectively reducing expensive cache line flush (clflush) operations. To this end, we category all four crash inconsistent states into two types: recoverable and unrecoverable. TSU could guarantee the crash state is recoverable by constraining the memory access order for insertion and deletion. We further design a persistency algorithm to reduce clflush by preserving the memory persistent order of skiplist update. In addition, we develop a concurrent search for TSU. The evaluation result shows that TSU can reduce cache line flush with up to 47.6%, and decrease the average request latency by up to 36% for insertions compared to the strict serialization. © Springer Nature Singapore Pte Ltd 2020.

关键词： Cache memory

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Tailor: Inter-Feature Distinction Filter Fusion Pruningtailor: Inter-Feature Distinction Filter Fusion Pruning

SSRN

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SSRN 2023年

作者： Han, Xuming Chu, Yali Wang, Ke Wang, Limin Yue, Lin Ding, Weiping Gu, Tianlong Weng, Jian College of Information and Science Jinan University Guangzhou510632 China School of Mathematics and Statistics Changchun University of Technology Jilin130000 China Trusted AI Engineering Research Center Ministry of Education Jinan University Guangzhou510632 China School of Artificial Intelligence and Digital Economy Industry Guangdong University of Finance and Economics Guangzhou510320 China University of Newcastle Australia School of Information and Science Nantong University Nantong China Engineering Research Center of Trustworthy AI Ministry of Education Jinan University China Guangdong Key Laboratory of Data Security and Privacy Preserving Jinan University Guangzhou510632 China

Filter pruning is an effective compression method for slimming deep convolutional neural networks by removing the partial structure of the network without sacrificing performance. The pruning criteria directly affect the performance of the network model after compression. Currently, pruning criteria mainly measure the norm of a filter weight matrix (L1 norm and L2 norm)and the matrix rank of a feature map. These criteria are based on the amount of filtered information. We found that inter-feature distinction among filters is also a significant factor in directing filter pruning, in contrast to previous methods. We propose a pruning method that fuses distinction between the output feature maps of filters, namely inTer-feAture distinctIon fiLter fusiOn pRuning (TAILOR). The TAILOR method was implemented by randomly selecting multiple filter combinations in the convolutional layer through the pruning rate and calculating the output feature maps of the next convolutional layer of these combinations. An intelligent distinction optimization scheme was proposed. The approximate optimal solution yields a filter combination, which then supplants the original convolutional layer to facilitate filter pruning. The results indicate that the inter-feature distinctions among filters significantly affect filter pruning. In addition, TAILOR outperforms state-of-the-art filter-pruning methods in terms of model prediction accuracy, floating-point operations, and parameter scale. For example, with VGG-16, we achieve a 70.93% FLOPs reduction by removing 90.05% of the parameters, with a loss of 0.44% in accuracy on CIFAR-10. © 2023, The Authors. All rights reserved.

关键词： Convolution

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An access control scheme in IoT-enabled Smart-Grid systems using blockchain and PUF

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Internet of Things (Netherlands) 2023年 22卷

作者： Zahoor, Amina Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Ayub, Muhammad Faizan Conti, Mauro Das, Ashok Kumar Department of Computer Science COMSATS University Islamabad Sahiwal Campus 57000 Pakistan Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Software Engineering The University of Lahore Lahore 54590 Pakistan Department of Computer Science University of Sahiwal Sahiwal 57000 Pakistan Department of Mathematics University of Padua Padua 35131 Italy Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad 500 032 India

IoT-enabled Smart Grid (IoT-SG) is an emerging paradigm that enables the bi-direction communication of IoT devices and hardware to efficiently collect and transmit the consumer's information over the Internet. However, the underlying open communication network and resource-constrained capabilities of devices invite manifold challenges and threats in terms of security and privacy. To alleviate such issues, we designed a private blockchain-based access control protocol for IoT-SG using Physically Unclonable Function (PUF). Our protocol allows the Service Provider (SP) and smart meters to transfer the data in an efficient and secure manner. The participating SPs form the Peer-to-Peer (P2P) network, and each peer node is responsible for securely creating the blocks from the gathered data. Thereafter, all the peer nodes employ a voting-based consensus mechanism to verify and add the recently created block into the blockchain network. We have rigorously validated the security of our protocol under the Random or Real (RoR) model. The testbed results and security feature analysis show that our protocol is more efficient than competing ones while it also provides significant security properties. © 2023 Elsevier B.V.

关键词： Authentication Blockchain Internet of Things (IoT) Key agreement security Smart power grids

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