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检索条件"任意字段=Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition"

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Automatic Optimization of Variational Quantum Algorithm-Based Classifiers 22

Automatic Optimization of Variational Quantum Algorithm-Base...

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proceedings of the 2022 5th international conference on Artificial Intelligence and pattern recognition

作者： Ruihong Jia Guang Yang Min Nie Yuanhua Liu Meiling Zhang Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

With the advent of the era of big data, how to efficiently process data has become an urgent problem. Although classical machine learning algorithms are very mature in processing data, they require a large amount of data to complete the training process. Quantum machine learning uses the superposition, entanglement, parallelism and other characteristics of quantum computing to improve the performance of classical machine learning algorithms. At the same time, circuit optimization of the quantum classifier can reduce the computational complexity. this paper proposes an automatic optimization model of the classifier based on the variational quantum algorithm, which reduces the complexity of the parameter update process through the deep optimization of the core circuit. this model greatly speeds up the training process and has lower complexity than other quantum classifiers. In addition, a unique regularization method is used to avoid overfitting. the simulation results show that the classification of some typical data sets can achieve better classification results, and the training parameters and training speed are significantly better than other quantum classifiers. Finally, the robustness of the classifier is further verified in the presence of noise.

关键词： Automatic optimization Machine learning Quantum classifiers Quantum computing

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Design and Implementation of SSSP Accelerator based-on Reconfigurable and Programmable computing Array 23

Design and Implementation of SSSP Accelerator based-on Recon...

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proceedings of the 2023 6th international conference on Artificial Intelligence and pattern recognition

作者： Junyong Deng Jingwen Deng Xiaoyan Xie Pan Zhang Junjie Wang Kai Zhou Xi'an University of Posts & Telecommunications China

ISBN: (纸本)9798400707674

Most of the existing researches only implement one SSSP algorithm, which cannot fit the diversity of applications, since different applications demand implementations with different efficiency and flexibility. the conflict between the requirements of applications and fixed design of accelerators are still one of the main problems currently faced. the reconfigurable array processor can well ease the above problem due to its property of accommodating both flexibility and efficiency. In this paper, we propose an accelerator based on APR-16, a reconfigurable and programmable computing array to realize the Dijkstra algorithm and the Bellman-Ford algorithm for SSSP, which can switch between the two algorithms when facing different graph data. To deal with the problem of graph data's poor locality, preserving node attributes in the frame buffer is proposed to reduce redundant operations in the computing process. To deal with the problem of an unbalanced load, a parallel mapping scheme for the Dijkstra algorithm and Bellman-Ford algorithm is proposed to reduce the impact of an unbalanced load. According to experimental results, we can improve Bellman- Ford's energy efficiency in comparison to the GPU platform by 22 times and the Dijkstra algorithm's energy efficiency by 15 times.

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Combining Attention Mechanism and Local-Global Features Association Network for Vehicle Re-identification 22

Combining Attention Mechanism and Local-Global Features Asso...

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proceedings of the 2022 11th international conference on computing and pattern recognition

作者： Cai-Xia Li Xia Du Yun Wu Da-Han Wang Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology China

ISBN: (纸本)9781450397056

Vehicle re-identification (Re-ID) aims to retrieve the target vehicle from a large dataset composed of vehicle images captured by multiple cameras. Most vehicles are difficult to recognize in the environment of low resolution, occlusion, and viewpoint change, which brings challenges to vehicle Re-ID. Existing work usually uses additional attribute information to distinguish different vehicles, such as color, viewpoint, and model. However, this requires expensive manual annotation. therefore, we propose a three-branch network based on attention mechanism and local-global feature association (AM-LGFA) to improve the accuracy of vehicle Re-ID. In the global branch, the global features of the vehicle are extracted. A multi-scale channel attention module is introduced into the attention branch to suppress irrelevant information and extract important channel features. the features extracted from the backbone are divided into different stripe features in the horizontal direction in the local branch. then connect each stripe feature with the global information to enhance the context between features. Finally, the features extracted from the three branches are concatenated as the feature representation of the test phase. the experimental results show that the features extracted by the AM-LGFA network are complementary. the effectiveness of this method is verified on two challenging public datasets, VehicleID and VeRi-776.

关键词： Attention mechanism Vehicle re-identification Local-global feature association

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Resource Allocation for Blockchain-Enabled Fog computing with Deep Reinforcement Learning 22

Resource Allocation for Blockchain-Enabled Fog Computing wit...

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proceedings of the 2022 12th international conference on Communication and Network Security

作者： Yihe Zhang Shanshan Tu Muhammad Waqas Yongjie Yang Aiming Wu Xuetao Bai Engineering Research Center of Intelligent Perception and Autonomous Control faculty of Information Technology Beijing University of Technology China Computer Engineering Department College of Information Technology University of Bahrain Bahrain and School of Engineering Edith Cowan University Australia

ISBN: (纸本)9781450397520

With the developments of the Internet of things, the demands of low latency, high bandwidth and high-performance computing has increased higher. therefore, the distributed computing named Fog computing has proposed to solve the problem above. Fog computing can provide lower transmission latency, faster response time and less network congestion. However, the fog devices are unable to guarantee the security and privacy of data transmission, due to they are vulnerable to attack. Blockchain technology works as a decentralized public ledger to store and share transactions. Blockchain can improve security and protect data privacy of Fog computing. Moreover, there are still issues in the blockchain-enabled Fog computing, the two main issues are the energy consumption and computing efficiency. thus, in this paper, we propose an optimization framework for blockchain-enabled Fog computing systems to optimize resource allocation. Besides, we adopt the dueling deep reinforcement learning to obtain the optimal resource allocation strategy, with dynamically selecting the fog server, offloading decision, block size. Simulation results show that the proposed framework can reduce the energy consumption and computation overhead of the system, as well as can improve the computing efficiency.

关键词： Fog computing Deep Reinforcement Learning Blockchain Internet of things

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Research on blockchain-based microgrid power trading method 23

Research on blockchain-based microgrid power trading method

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proceedings of the 2023 6th international conference on Artificial Intelligence and pattern recognition

作者： Sheping Zhai Haonan Nie Xianjing Lu Rui Yang Department of computing Xi'an University of Posts&Telecommunications China

ISBN: (纸本)9798400707674

the current microgrid electricity market suffers from inefficient, risky, and time-consuming transaction processes. However, with the introduction of blockchain technology with decentralized and open and transparent features, automated electricity trading with high security, high efficiency, and audit traceability can be achieved, thus providing a new market model with potential for microgrids. In this paper, we propose a blockchain-based approach to microgrid electricity trading, which utilizes blockchain technology, anonymous message flow and other technical means to achieve privacy and security guarantees for transactions. Smart contracts are utilized for the exchange of power ownership and tokens between the power purchaser and the power seller to ensure that the rights and interests of both parties are protected. the design of the auction rules achieves the balance of interests between the power purchaser and the power seller. Experiments show that the blockchain-based microgrid trading method proposed in this paper can conduct multilateral bidding transactions, effectively improve the utilization of electricity within the microgrid, and ensure the economic efficiency and safe operation of the microgrid.

关键词： Blockchain auction algorithm microgrid smart contract

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Detection Scheme of Impersonation Attack Based on Sarsa(Lambda) Algorithm in Fog computing 22

Detection Scheme of Impersonation Attack Based on Sarsa(Lamb...

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proceedings of the 2022 12th international conference on Communication and Network Security

作者： Aiming Wu Shanshan Tu Junwei Liu Yihe Zhang Yongjie Yang Xuetao Bai Engineering Research Center of Intelligent Perception and Autonomous Control faculty of Information Technology Beijing University of Technology China China Mobile (Suzhou) Software Technology Co. Ltd. China Mobile (Suzhou) Software Technology Co. Ltd. China

ISBN: (纸本)9781450397520

Fog computing is a technology that provides distributed computing, storage and other services between cloud servers and Internet of things(IoT) devices. It reduces the pressure on cloud servers and utilizes network edges for computing and storage, thereby improving the performance of the entire IoT system. Since fog computing uses traditional security technologies to realize the communication between fog nodes and end users, it is inevitable for fog computing to face some security threats, such as eavesdropping attacks, camouflage attacks and other security threats. Aiming at this problem, this paper proposes a impersonation attack detection scheme based on Sarsa(lambda) algorithm. the scheme first builds a camouflage attack model and a key generation model based on physical layer security technology, then performs hypothesis testing at the receiving end to verify the legitimacy of the training signal, and then obtains the optimal threshold through the proposed algorithm. the optimal threshold realizes the terminal communication security between user nodes and fog nodes. the experimental results show that the algorithm can effectively detect camouflage attacks in a dynamic fog computing environment and have a low average detection error rate. When , AER decreased to 0.4925.

关键词： Fog computing Sarsa Impersonation Attack Physical Layer Security Hypothesis Testing

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Robust Object Detection and Tracking Model for Visually Impaired People Using Deep Convolution Neural Network Model

Robust Object Detection and Tracking Model for Visually Impa...

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Sustainable Communication Networks and Application (ICSCNA), international conference on

作者： R. Priyanka K. Vivekrabinson A. Kathijanasreen W T Chembian Atharvaa Veerasundar Srimathi S Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Department of CSE Kalasalingam Academy of Research and Education Krishnankoil CSE department R.M.K College of Engineering and Technology Department of CSE Vel Tech High Tech Dr. Rangarajan Dr.Sakuntala Engineering College 12th Greenwood High International High School Bengaluru Department of Bio Technology Saveetha School of Engineering SIMATS Chennai Tamilnadu India

Visual Impairment (VI) has become the most significant challenge over the past few years. Even to complete their day-to-day activities, visually impaired persons seek assistance. A currency recognition system and automated common object could enhance the safe movement and transaction people activity with a disability. A model is generated for an object detector that could identify items for people with a disability and other significant uses by identifying them at a certain distance. the present object detection techniques need a massive quantity of training dataset that take longer time, are difficult, and it is a more complicated process. therefore, in this work, a Robust Object Detection and Tracking for Visually Impaired People using Deep Convolution Neural Network (RODT-DCNN) model is developed. the main purpose of the RODT-DCNN model lies in the accurate identification and classification of objects for VIP. the RODT-DCNN technique comprises two stages of operations such as object detection and object classification. For object detection, YOLO-v5 approach was used. Next, Elman neural network (ENN) approach can be exploited for the effectual identification and classification of objects. At last, sine cosine algorithm (SCA) can be employed for adjusting the parameters of the ENN model. the performance analysis of the RODT-DCNN approach occurs on standard dataset and the outcomes demonstrated the improved detection results of the RODT-DCNN approach in terms of diverse measures.

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Overview of the NLPCC 2023 Shared Task: Chinese Medical Instructional Video Question Answering 12th

Overview of the NLPCC 2023 Shared Task: Chinese Medical In...

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12th National CCF conference on Natural Language Processing and Chinese computing, NLPCC 2023

作者： Li, Bin Weng, Yixuan Guo, Hu Sun, Bin Li, Shutao Luo, Yuhao Qi, Mengyao Liu, Xufei Han, Yuwei Liang, Haiwen Gao, Shuting Chen, Chen College of Electrical and Information Engineering Hunan University Changsha China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy Sciences Beijing China

ISBN: (纸本)9783031446986

In this paper, we present an overview of the NLPCC 2023 shared task, named Chinese Medical Instructional Video (CMIVQA), which includes three sub-tracks: temporal answer grounding in a single video, video corpus retrieval, and temporal answer grounding in video corpus. the CMIVQA datasets containing the videos, audios, and corresponding subtitles are made public, and the corresponding labels are manually annotated by medical experts. Details of the shared task, datasets, evaluation metrics, and final results will be provided in order. We hope this shared task can provide more insights into the first-aid, medical emergency, or medical education. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Medical education

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Embedded Real-time System Trusted Protection Technology for Supply Chain Scenarios 22

Embedded Real-time System Trusted Protection Technology for ...

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proceedings of the 2022 12th international conference on Communication and Network Security

作者： Jin Zhou Zhenhu Ning Qi Wang Yuchen Wang faculty of Information Technology Beijing University Of Technology China Trusted Computing Technology Co.LTD Shenzhen China

ISBN: (纸本)9781450397520

Embedded real-time operating systems are widely used in communication, industrial control and weapon intelligent control and other fields. However, due to the complex personnel and environment involved in the development of embedded systems, the development software is difficult to control independently, so it is extremely vulnerable to malicious code implantation attacks. Based on this, this paper focuses on the UCOS real-time operating system, analyzes the security threats faced by the UCOS development environment GCC in the process of precompiling, compiling, assembling and linking, and designs a plug-in-based GCC attack mode, which can implement malicious code implantation attacks on UCOS. . At the same time, a trusted protection framework for embedded real-time systems is proposed. By implementing active control and integrity measurement of firmware, operating systems and applications in embedded real-time systems, the trusted startup of embedded real-time systems is guaranteed and the supply chain stage is independently resisted. malicious code implantation attack.

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Semantic Maximum Relevance and Modal Alignment for Cross-Modal Retrieval 22

Semantic Maximum Relevance and Modal Alignment for Cross-Mod...

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proceedings of the 2022 11th international conference on computing and pattern recognition

作者： Pingping Sun Baohua Qiang Zhiguang Liu Xianyi Yang Guangyong Xi Weigang Liu Ruidong Chen Shihao Zhang Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology China Hebei Key Laboratory of Intelligent Information Perception and Processing The 54th Research Institute of CETC China

ISBN: (纸本)9781450397056

With the increasing abundance of multimedia data resources, researches on mining the relationship between different modalities to achieve refined cross-modal retrieval are gradually emerging. In this paper, we propose a novel Semantic Maximum Relevance and Modal Alignment (SMR-MA) for Cross-Modal Retrieval, which utilizes the pre-trained model with abundant image text information to extract the features of each image text, and further promotes the modal information interaction between the same semantic categories through the modal alignment module and the multi-layer perceptron with shared weights. In addition, multi-modal embedding is distributed to the normalized hypersphere, and angular edge penalty is applied between feature embedding and weight in angular space to maximize the classification boundary, thus increasing both intra-class similarity and inter-class difference. Comprehensive analysis experiments on three benchmark datasets demonstrate that the proposed method has superior performance in cross-modal retrieval tasks and is significantly superior to the state-of-the-art cross-modal retrieval methods.

关键词： Semantic Relevance CLIP Cross-Modal Retrieval Modal Alignment

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