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2021 3rd International Conference on Robotics, Intelligent Control and Artificial Intelligence, ICRICA 2021

作者： Yang, Ya Zhou, Xinyu Jiang, Kaixun Hou, Xiaonan Wang, Chunlei Zhang, Wenqiang 21st Research Institute of China Electronics Technology Group Corporation Shanghai China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

Visual object tracking is a basic task in computer vision, which can be applied to different applications. Former methods of single object tracking problem demands to perform target state estimation according to object position of previous frame, but these methods are poorly capable of handling the fast movement of the target. In this paper, we introduce a Siamese network for single object tracking task. The network consists of two branches, one of which is the classification branch used to predict positive or negative samples, and the other is the regression branch used to predict the specific location of the object in sequence. In addition, we propose a local to global algorithm for long-term tracking to expand the search region when the target is out-of-view or full occlusion. Experiments show that our tracker achieves very promising results on VOT2018LT datasets while running at over 89 FPS. © Published under licence by IOP Publishing Ltd.

关键词： Target tracking

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Object Detection Algorithm Based on Bimodal Feature Alignment 7th

Object Detection Algorithm Based on Bimodal Feature Alignme...

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7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Sun, Ying Hou, Zhiqiang Yang, Chen Ma, Sugang Fan, Jiulun School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Key Laboratory of Network Data Analysis and Intelligent Processing of Shaanxi Province Xi’an710121 China School of Communications and Information Engineering Xi’an University of Posts and Telecommunications Xi’an710121 China

ISBN: (纸本)9783031476334

A dual-modal feature alignment based object detection algorithm is proposed for the full fusion of visible and infrared image features. First, we propose a two stream detection model. The algorithm supports simultaneous input of visible and infrared image pairs. Secondly, a gated fusion network is designed, consisting of a dual-modal feature alignment module and a feature fusion module. Medium-term fusion is used, which will be used as the middle layer of the dual-stream backbone network. In particular, the dual-mode feature alignment module extracts detailed information of the dual-mode aligned features by computing a multi-scale dual-mode aligned feature vector. The feature fusion module recalibrates the bimodal fused features and then multiplies them with the bimodal aligned features to achieve cross-modal fusion with joint enhancement of the lower and higher level features. We validate the performance of the proposed algorithm using both the publicly available KAIST pedestrian dataset and a self-built GIR dataset. On the KAIST dataset, the algorithm achieves an accuracy of 77.1%, which is 17.3% and 5.6% better than the accuracy of the benchmark algorithm YOLOv5-s for detecting visible and infrared images alone;on the self-built GIR dataset, the detection accuracy is 91%, which is 1.2% and 14.2% better than the benchmark algorithm for detecting visible and infrared images alone respectively. And the speed meets the real time requirements. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Research on Wireless Channel Modeling for Complex Water Surface Environments

Research on Wireless Channel Modeling for Complex Water Surf...

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2023 International Conference on Intelligent Communication and Networking, ICN 2023

作者： Li, Xujie Ge, Tianzhang Wei, Baochao Li, Haotian Xu, Ning Jiangsu Health Development Research Center Nanjing210036 China Nhc Contraceptives Adverse Reaction Surveillance Center Nanjing210036 China Jiangsu Provincial Medical Key Laboratory of Fertility Protection and Health Technology Assessment Nanjing210036 China Hohai University College of Computer and Information Nanjing210098 China

ISBN: (纸本)9798350357677

Accurate and efficient channel modeling has always been a critical foundation for the deployment and application of 5G communication technology. To meet the requirements of continuous and extensive coverage in 5G, higher demands have been placed on wireless channel characteristics across various scenarios. However, the propagation of wireless signals over water may involve more complex reflection and scattering phenomena. Additionally, the presence of vegetation on water surface further complicates the channel modeling. Addressing these challenges, we assess relevant theories and representative signal attenuation models, and propose a channel model for complex water-based scenarios based on the open water channel model. To ascertain the precision of the proposed model, we establish an experimental platform and conduct measurements on the attenuation of signals across a water pool populated with various obstacles. The results demonstrate that the suggested model more accurately corresponds to the distinctive patterns of wireless signal attenuation encountered within complex water surface environments. © 2023 IEEE.

关键词： 5G mobile communication systems

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Blockchain-Based Multi-Cloud Data Storage System Disaster Recovery

Blockchain-Based Multi-Cloud Data Storage System Disaster Re...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wang, Feiyu Zhou, Jian-Tao College of Computer Science Inner Mongolia University Inner Mongolia Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

ISBN: (纸本)9798350337020

Cloud storage services have been used by most businesses and individual users. However, data loss, service interruptions and cyber attacks often lead to cloud storage services not being provided properly, and these incidents have caused financial losses to users. Second, traditional and single-cloud model disaster recovery services are no longer suitable for the current complex cloud storage systems. Therefore, a scheme to provide disaster recovery for cloud storage services in a multi-cloud storage environment is needed in real production. In this paper, we propose a disaster recovery scheme based on blockchain technology. The proposed scheme outlined in this study aims to address the issue of data availability within the cloud storage landscape. The proposed scheme achieves this goal by dividing data into hot and cold categories, verifying the integrity of copy data via blockchain technology, and utilizing blockchain networks to manage multi-cloud storage systems. Experimental findings demonstrate that the proposed scheme yields superior results in terms of computation and time overheads. © 2023 IEEE.

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Code Generation Based on Quality-Aware Reordering

Code Generation Based on Quality-Aware Reordering

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6th International Conference on Intelligent Computing and Signal processing (ICSP)

作者： Yuan Song Luo Yu College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Provincial Key Laboratory of Intelligent Information Processing and Real-time Industrial Systems Wuhan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Translation efforts from natural language to code generation have achieved great success with large pre-trained language models. However, selecting the correct code output for the problem posed by the generated set of candidate codes is still challenging. In this paper, we address this problem by proposing an outcome-based quality-aware reordering method for code selection. This method scores each code by whether the set of candidate codes passes the test cases or not as a reference to select the top k best code outputs. In addition, this method depends on the quality and number of test cases. However, it is impractical to manually create a large number of test cases for each problem, so this paper adopts an automated approach that utilizes the current model to generate test cases for each problem. The experimental results show that the approach proposed in this paper obtains a significant improvement in the pass@k benchmark for each model, and the correctness and quality of test cases generated automatically using the model are discussed experimentally. Summarizing the experimental results and discussions, the result-based quality-aware reordering method proposed in this paper achieves a significant improvement in solving the problem of selecting the correct output from natural language to code generation, effectively improves the accuracy of evaluation by automating the generation of test cases, and provides an effective solution to improving the correctness of code generation.

关键词： Codes Accuracy Computational modeling Estimation Signal processing Benchmark testing

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Analysis of the Research Status of information technology Education Literature in China and Abroad 24

Analysis of the Research Status of Information Technology Ed...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhao, Yulin Liu, Kai Li, Junke School of Information Engineering Suqian University Jiangsu Suqian223800 China School of Computer and Information Qiannan Normal University for Nationalities Duyun558000 China Jiangsu Province Engineering Research Center of Smart Poultry Farming and Intelligent Equipment Jiangsu 223800 China Key Laboratory of Machine Learning and Unstructured Data Processing of Qiannan Duyun558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun558000 China

ISBN: (纸本)9798350319934

information technology education contributes to the development of national education. Understanding the research status of information technology education at home and abroad is helpful to the implementation of education informatization. This paper takes the information technology education research literature collected by CNKI and the Web of Science database as the object and uses the bibliometric method to analyze the literature characteristics of information technology education at home and abroad. Specifically, the paper mainly carries out statistical analysis from the time distribution of literature, journal distribution, and hot keywords, and visually describes the literature features of the information technology education discipline, providing references for the research of information technology education discipline at home and abroad. © 2022 IEEE.

关键词： Informatization

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DHCT-DT: Dual-branch Hybrid CNN-Transformer Combined with Dual-Teacher Semi-Supervised Network for Kidney Ultrasound Image Segmentation

DHCT-DT: Dual-branch Hybrid CNN-Transformer Combined with Du...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yang Luo Jun Liu Mengqian Ding School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Accurate segmentation of kidney ultrasound (KUS) images is of great significance for the diagnosis of kidney diseases. However, KUS images have challenges such as low contrast, large size variations, and acoustic shadow interference. In addition, obtaining labeled data is a major challenge in the field of medical image segmentation. This paper proposes a dual-branch hybrid CNN Transformer combined with a dual teacher semi-supervised segmentation network (DHCT-DT) based on the MT model. The reliability of guidance can be improved between two teachers by constructing consistency regularization constraints. The U-Net architecture is employed between the main teacher and student model. The auxiliary teacher model adopts a dual-branch hybrid CNN Transformer architecture. The multi-scale channel fusion attention mechanism (MSCF) is introduced to capture the feature correlation information of KUS images at different scales and channels. Two different training methods can combine their advantages to fully extract local and global information from images. To demonstrate the applicability of the proposed method, experiments were conducted on two datasets: a KUS dataset and a breast ultrasound (BUS) dataset. Both datasets demonstrated the most competitive segmentation performance. For example, in a semi-supervised experiment using only 10% labeled data in the BUS dataset, the Dice coefficient, Jaccard coefficient, HD, and ASSD reached 82.28%, 76,36%, 13.36mm, and 3.71mm.

关键词： Training Image segmentation Ultrasonic imaging Attention mechanisms Accuracy Predictive models Network architecture Transformers Reliability Kidney

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A Lightweight and key Escrow-Free Access Control Scheme on Encrypted Data in Edge Computing

A Lightweight and Key Escrow-Free Access Control Scheme on E...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Heng He Yuejia Chen Jianfeng Lu Jiacheng Zheng School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (数字)9798331511074

ISBN: (纸本)9798331511081

Attribute-based encryption provides fine-grained access control, ensuring data security in edge computing. However, existing access control schemes face issues such as high computational complexity due to bilinear pairing and key escrow with a single authority, which limit their effectiveness for lightweight terminal devices. To address these challenges, we propose a lightweight and key escrow-free access control scheme on encrypted data. This scheme replaces bilinear pairing with fast elliptic curve scalar multiplication, transferring most computational tasks to edge servers while preserving privacy, thus alleviating the computational burden on terminal devices. Additionally, we design a blockchain-based distributed key management method that allows multiple edge servers to distribute private keys collaboratively, resolving the key-escrow issue. Security analysis and performance evaluation demonstrate that our scheme effectively ensures data confidentiality, resists collusion attacks, and achieves high computational efficiency on the user side with low storage overheads, making it highly suitable for lightweight devices in edge computing.

关键词： Access control Performance evaluation Privacy Elliptic curves Process control Transforms Resists Encryption Servers Edge computing

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Improving generalization in offline reinforcement learning via adversarial data splitting 24

Improving generalization in offline reinforcement learning v...

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Proceedings of the 41st International Conference on Machine Learning

作者： Da Wang Lin Li Wei Wei Qixian Yu Jianye Hao Jiye Liang Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Taiyuan China College of Intelligence and Computing Tianjin University Tianjin China

Offline Reinforcement Learning (RL) commonly suffers from the out-of-distribution (OOD) overestimation issue due to the distribution shift. Prior work gradually shifts their focus from suppressing OOD overestimation to avoiding overly conservative learning from suboptimal behavior policies to improve generalization. However, most approaches explicitly delimit boundaries for OOD actions based on the support in the dataset, which can potentially impede the data near these boundaries from acquiring realistic estimates. This paper investigates how to loosen the rigid demarcation of OOD boundaries, adaptively extracting knowledge from empirical data to implicitly improve the model's generalization to nearby unseen data. We introduce an adversarial data splitting (ADS) framework that enforces the model to generalize the distribution shifts simulated from the train=validation subsets splitting of the dataset. Specifically, ADS is modeled as a min-max optimization problem inspired by metalearning and solved by iterating over the following two steps. First, we train the model on the train-subset to minimize its loss on the validation-subset. Then, we adversarially generate the "hardest" train=validation subsets with the maximum distribution shift, making the model incapable of generalization at that splitting. We derive a generalization error bound for theoretically understanding ADS and verify the effectiveness with extensive experiments. Code is available at https://***/DkING-lv6/ADS.

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Delay-Aware Digital Twin Synchronization in Mobile Edge Networks With Semantic Communications

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IEEE Transactions on Vehicular technology 2025年

作者： Li, Bin Cai, Haichen Liu, Lei Fei, Zesong Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Ministry of Education Guilin University of Electronic Technology Key Laboratory of Cognitive Radio and Information Processing Guilin541004 China Xidian University Guangzhou Institute of Technology Guangzhou510555 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China

The synchronization of digital twins (DT) serves as the cornerstone for effective operation of the DT framework. However, the limitations of channel capacity can greatly affect the data transmission efficiency of wireless communication. Unlike traditional communication methods, semantic communication transmits the intended meanings of physical objects instead of raw data, effectively saving bandwidth resource and reducing DT synchronization latency. Hence, we are committed to integrating semantic communication into the DT synchronization framework within the mobile edge computing system, aiming to enhance the DT synchronization efficiency of user devices (UDs). Our goal is to minimize the average DT synchronization latency of all UDs by jointly optimizing the synchronization strategy, transmission power of UDs, and computational resource allocation for both UDs and base station. The formulated problem involves sequential decision-making across multiple coherent time slots. Furthermore, the mobility of UDs introduces uncertainties into the decision-making process. To solve this challenging optimization problem efficiently, we propose a soft actor-critic-based deep reinforcement learning algorithm to optimize synchronization strategy and resource allocation. Numerical results demonstrate that our proposed algorithm can reduce synchronization latency by up to 13.2% and improve synchronization efficiency compared to other benchmark schemes. © 1967-2012 IEEE.

关键词： Resource allocation

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