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Real-Time Systems Symposium (RTSS)

作者： Jianfeng Shi Heng Wu Wang Liu Heran Gao Wenbo Zhang University of Chinese Academy of Sciences Beijing China Software Engineering Technology Research and Development Center Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Nanjing China Nanjing institute of software technology Nanjing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

Blockchain, as an emerging transaction processing system, suffers from low throughput and high latency. Sharded blockchains can significantly increase throughput by dividing nodes into groups (i.e., shards) to process disjoint transactions in parallel. However, the diverse latency requirements of transactions in current sharded blockchains are not well met. Three challenges prevent the sharded blockchain from becoming a real-time transaction processing system, namely, the static block size, the first-come-first-served transaction packing strategy, and the load imbalance. Therefore, this paper proposes 3 methods to help turn the sharded blockchain into a real-time transaction processing system. First, we propose an inter-shard dynamic block size negotiation method that enables shards to adaptively determine the globally optimal block size based on the deadlines of pending transactions. Then, we propose a DAG-based transaction packing method for reducing the number of deadline violations and improving parallelism. Finally, we propose a minimum-cost-flow-based shard reconfiguration method to address load imbalance. Under real datasets on Ethereum, experimental results show that AgileShard using the above three methods can effectively improve the transaction deadline satisfaction rate.

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An Enhanced Authentication Protocol Suitable for Constrained RFID Systems

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IEEE Access 2024年 12卷 61610-61628页

作者： Hosseinzadeh, Mehdi Servati, Mohammad Reza Rahmani, Amir Masoud Safkhani, Masoumeh Lansky, Jan Janoscova, Renata Ahmed, Omed Hassan Tanveer, Jawad Lee, Sang-Woong Duy Tan University Institute of Research and Development Da Nang550000 Viet Nam Duy Tan University School of Medicine and Pharmacy Da Nang550000 Viet Nam Shahid Rajaee Teacher Training University Department of Computer Engineering Tehran16788-15811 Iran National Yunlin University of Science and Technology Future Technology Research Center Douliou Yunlin64002 Taiwan School of Computer Science Tehran19395-5746 Iran University of Finance and Administration Faculty of Economic Studies Department of Computer Science and Mathematics Prague101 00 Czech Republic University of Human Development Department of Information Technology Sulaymaniyah0778-6 Iraq Sejong University Department of Computer Science and Engineering Seoul05006 Korea Republic of Gachon University Pattern Recognition and Machine Learning Laboratory Department of Ai Software Seongnam-si13557 Korea Republic of

RFID technology offers an affordable and user-friendly solution for contactless identification of objects and individuals. However, the widespread adoption of RFID systems raises concerns regarding security and privacy. Vulnerabilities such as message tampering, interception, and eavesdropping pose significant risks to the integrity of the system. This study examines the effectiveness of two recently proposed ultra-lightweight RFID authentication protocols, URASP and KUAJB, in addressing these security challenges. Surprisingly, our findings reveal that both URASP and KUAJB protocols are susceptible to secret disclosure attacks, despite their claims of providing robust security. Consequently, the development of a truly ultra-lightweight protocol that ensures adequate security becomes a formidable task. As a potential solution, the enhancement of the URASP protocol through the integration of a simple cryptographic primitive is suggested to bolster its security measures. © 2013 IEEE.

关键词： Hash functions

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MTT-Road: Dynamic Updating of Road Diseases Classification Model Based on Datasets Distillation

MTT-Road: Dynamic Updating of Road Diseases Classification M...

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Electronic technology, Communication and Information (ICETCI), IEEE International Conference on

作者： Haihang Han Yongjun Ge Xinxiao Wang Zhi Yu Chenchen Xi Yue Wang Zhejiang Scientific Research Institute of Transport Research and Development Center of Transport Industry of New Generation of Artificial Intelligence Technology Hangzhou China School of Software Technology Zhejiang University Hangzhou China Polytechnic Institute Zhejiang University Hangzhou China

The apparent diseases of road will cause great losses to driving safety and economic benefits, so when the apparent diseases of road appear, we need to detect them quickly and accurately. Engineers need to use the classified error data in the inspection process to continually and dynamically update the model to avoid similar errors in the subsequent inspection journey. The existing methods do not balance the resource consumption and model effect in the iterative update of the model. In order to solve the problem of continual dynamic update of road disease classification model, the MTT-Road algorithm is proposed in this *** experimental results show that the MTT-Road algorithm achieves the best results among the mainstream transfer learning algorithms considering both the time consumption and model effect.

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Cross-scene Transfer of Road Disease Classification Model Based on Dynamic Knowledge Distillation Network

Cross-scene Transfer of Road Disease Classification Model Ba...

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Control, Electronics and Computer technology (ICCECT), IEEE International Conference on

作者： Haihang Han Xinxiao Wang Yangyang Wang Zhi Yu Shoujing Yan Yue Wang Research and Development Center of Transport Industry of New Generation of Artificial Intelligence Technology Zhejiang Scientific Research Institute of Transport Hangzhou China School of Software Technology Zhejiang University Hangzhou China Polytechnic Institute Zhejiang University Hangzhou China

There will be various apparent diseases on the road, which will affect the driving safety and cause economic losses, so it is very important to maintain the road. researchers need to transfer the classification model of road diseases across scenes, but the data of road diseases in the target scene are few and only partially marked, so the existing transfer learning method has poor effect. Aiming at the problem of cross-scene transfer of road disease classification model, the DNN-Road algorithm is proposed in this paper. The experimental results show that the DNN-Road algorithm achieves the best results among the mainstream transfer learning algorithms.

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Single Image 3D Reconstruction Based on Attention Mechanism and Graph Convolution Network 22

Single Image 3D Reconstruction Based on Attention Mechanism ...

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8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Gao, Wei Yu, Liyang Du, Yuanyuan Lu, Songfeng Software Development Center of Industrial and Commercial Bank of China Limited Zhuhai519000 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430072 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057 China

ISBN: (纸本)9781450396110

This paper innovatively proposes a channel attention mechanism and graph convolutional network model adapted to 3D reconstruction, and combines the target detection model to construct a neural network that generates a target 3D model from a single RGB image. The model first generates the target 3D Voxels, and further generates a more refined 3D Meshes model through the graph convolutional network. Compared with the control group algorithm, the Pix3D datasetAPmesh index has been improved by 2.8%, which fully proves the effectiveness of the model in single-image 3D reconstruction. The experimental results show that the algorithm has good usability in the 3D reconstruction of actual mesh points. © 2022 Association for Computing Machinery. All rights reserved.

关键词： Convolution

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Consistency Check for Multimodal Transportation Data: A Case Study of Vision and LiDARA

Consistency Check for Multimodal Transportation Data: A Case...

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International Conference on Information Management and technology (ICIMTech)

作者： Zhengyu Liu Zhenwu Chen Xiangmin Yang Lei Peng Software Engineering Institute University of Science and Technology of China HeFei China Research Center for Automotive Electronics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences ShenZhen China Innovation Center for ITS Shenzhen Urban Transport Planning Center Co.Ltd. ShenZhen China Dept. Research and Development Shenzhen Shenzhentong Co.Ltd. ShenZhen China

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

This study proposes a novel consistency verification method for multimodal transportation data, targeting spatial feature issues caused by multisource heterogeneous data in the transportation field. We unify different sources of data and point cloud data into a single 3D space through image data dimensionality increase and point cloud registration, addressing data inconsistencies caused by perspective differences. Through the application of data dimensionality augmentation and point cloud registration, our method effectively resolves spatial feature discrepancies between various data sources, significantly improving the accuracy of consistency verification. Experimental results indicate that the model trained with our method exhibits superior precision in consistency verification of multisource heterogeneous data compared to traditional approaches.

关键词： Point cloud compression Training Accuracy Three-dimensional displays Soft sensors System performance Transportation Data processing Spatial databases Spatial resolution

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RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling Under Continuous Representation 15th

RobNODDI: Robust NODDI Parameter Estimation with Adaptive S...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024

作者： Xiao, Taohui Cheng, Jian Fan, Wenxin Yang, Jing Li, Cheng Dong, Enqing Wang, Shanshan School of Mechanical Electrical and Information Engineering Shandong University Weihai264209 China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Peng Cheng Laboratory Guangdong Shenzhen China

ISBN: (纸本)9783031869198

Neurite Orientation Dispersion and Density Imaging (NODDI) is an important imaging technology used to evaluate the microstructure of brain tissue, which is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods perform parameter estimation through diffusion magnetic resonance imaging (dMRI) with a small number of diffusion gradients. These methods speed up parameter estimation and improve accuracy. However, the diffusion directions used by most existing deep learning models during testing needs to be strictly consistent with the diffusion directions during training. This results in poor generalization and robustness of deep learning models in dMRI parameter estimation. In this work, we first verify that the parameter estimation performance of current mainstream methods will significantly decrease when the testing diffusion directions and the training diffusion directions are inconsistent. A robust NODDI parameter estimation method with adaptive sampling under continuous representation (RobNODDI) is proposed. Furthermore, long short-term memory (LSTM) units and fully connected layers are selected to learn continuous representation signals. To this end, we use a total of 100 subjects to conduct experiments based on the Human Connectome Project (HCP) dataset, of which 60 are used for training, 20 are used for validation, and 20 are used for testing. The test results indicate that RobNODDI improves the generalization performance and robustness of the deep learning model, enhancing the stability and flexibility of deep learning NODDI parameter estimatimation applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Vacancy-Type Defects in HfO2 Layers and Their Role in Amorphous-to-Crystalline Transition Studied by Monoenergetic Positron Beams

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IEEE Transactions on Semiconductor Manufacturing 2025年

作者： Uedono, Akira Kawakami, Kazuya Kuribara, Tatsunori Hasunuma, Ryu Harashima, Yosuke Shigeta, Yasuteru Ni, Zeyuan Yan, Ruolin Nakamura, Hideo Notake, Akira Moriya, Tsuyoshi Michishio, Koji Ishibashi, Shoji University of Tsukuba Faculty of Pure and Applied Science Tsukuba Ibaraki305-8573 Japan Nara Institute of Science and Technology Graduate School of Science and Technology Ikoma Nara630-0192 Japan Tokyo Electron Technology Solutions Ltd S-Technology Development Center Nirasaki Yamanashi650 Japan Tokyo Electron Technology Solutions Ltd Development Department1 Nirasaki Yamanashi407-0192 Japan Tokyo Electron Ltd Digital Design Center Sapporo Hokkaido060-0003 Japan Tokyo Electron Technology Solutions Ltd Software and Data Engineering Division Nirasaki Yamanashi407-0192 Japan National Institute of Advanced Industrial Science and Technology Research Institute for Measurement and Analytical Instrumentation Tsukuba Ibaraki305-8568 Japan National Institute of Advanced Industrial Science and Technology Research Center for Computational Design of Advanced Functional Materials Tsukuba Ibaraki305-8568 Japan

The annealing properties of vacancies in HfO2 layers deposited on Si substrates and their role in amorphous-to-crystalline transition were studied with monoenergetic positron beams. HfO2 layers with a thickness of 4.30 nm were fabricated by the atomic layer deposition technique. The major vacancytype defects in these layers were identified as a Hf vacancy (VHf) coupled with multiple oxygen vacancies (VOs) and larger vacancy clusters. After annealing at 500°C, the concentration of vacancy clusters started to increase, which was attributed to the agglomeration of intrinsic open spaces in the amorphous phase upon the phase transition from amorphous to monoclinic crystalline phases. The amorphous-crystalline transition started near the interface between the HfO2 layer and bottom electrodes (TiN). After the crystallization, the concentration of vacancy clusters decreased as the annealing temperature increased. © 2025 IEEE.

关键词： Hafnium oxides

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A 3-D whole-body human thermoregulatory model to simulate cold-induced vasodilation in the hands and feet

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Computers in Biology and Medicine 2024年 180卷 108935页

作者： Singh, Manpreet Reifman, Jaques Rubio, Jose E. Department of Defense Biotechnology High Performance Computing Software Applications Institute Telemedicine and Advanced Technology Research Center United States Army Medical Research and Development Command Fort Detrick MD United States The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc. Bethesda MD United States

The cold-induced vasodilation (CIVD) response of the human body to Arctic-like environments helps delay or prevent cold injuries to peripheral regions, such as the hands and feet. To more comprehensively predict the thermal responses of these body regions to cold stress, here we extended our previously developed and validated anatomically accurate three-dimensional whole-body thermoregulatory human model by incorporating a new phenomenological formulation of the CIVD mechanism. In this formulation, we modulated the cyclic vasodilation and vasoconstriction flow of warm blood from the body core to the peripheral regions solely by determining the heat-transfer exchange between the skin and the surrounding environment, and deactivated it when the core body temperature decreased to 36.5 °C. In total, we calibrated and validated the model using eight distinct studies involving 153 unique male subjects exposed to 10 diverse experimental conditions, including cold-air exposure of the whole body as well as air exposure and cold-water immersion of the hand or the foot. With CIVD incorporated, the model predictions generally yielded root mean square errors (RMSEs) of © 2024

关键词： Heat transfer

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CLGT: A Graph Transformer for Student Performance Prediction in Collaborative Learning

arXiv

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arXiv 2023年

作者： Peng, Tianhao Liang, Yu Wu, Wenjun Ren, Jian Pengrui, Zhao Pu, Yanjun State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Engineering Beihang University China Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology China Institute of Artificial Intelligence Beihang University China

Modeling and predicting the performance of students in collaborative learning paradigms is an important task. Most of the research presented in literature regarding collaborative learning focuses on the discussion forums and social learning networks. There are only a few works that investigate how students interact with each other in team projects and how such interactions affect their academic performance. In order to bridge this gap, we choose a software engineering course as the study subject. The students who participate in a software engineering course are required to team up and complete a software project together. In this work, we construct an interaction graph based on the activities of students grouped in various teams. Based on this student interaction graph, we present an extended graph transformer framework for collaborative learning (CLGT) for evaluating and predicting the performance of students. Moreover, the proposed CLGT contains an interpretation module that explains the prediction results and visualizes the student interaction patterns. The experimental results confirm that the proposed CLGT outperforms the baseline models in terms of performing predictions based on the real-world datasets. Moreover, the proposed CLGT differentiates the students with poor performance in the collaborative learning paradigm and gives teachers early warnings, so that appropriate assistance can be provided. Copyright © 2023, The Authors. All rights reserved.

关键词： Students

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