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IEEE International Conference on Big Data Security on Cloud (BigDataSecurity), High Performance and Smart Computing (HPSC) and Intelligent Data and Security (IDS)

作者： Huixuan Xiong Kai Jin Jingnian Liu Jiahong Cai Lijun Xiao School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China the College of Computer Science and Electronics Engineering Hunan University Changsha China Big Data Development and Research Center Guangzhou College of Technology and Business Guangzhou China

Deep learning is a powerful multi-layer architecture that has important applications in image processing and text classification. This paper first introduces the development of deep learning and two important algorithms of deep learning: convolutional neural networks and recurrent neural networks. The paper then introduces three applications of deep learning for image recognition, image detection, and image forensics, as well as three text classification methods based on convolutional neural networks, recurrent neural networks, and other text classification methods. Finally, the development trend of deep learning in the field of text and image processing and the difficulties to be further researched are summarised and prospected.

关键词： Deep learning Image forensics Recurrent neural networks Computational modeling Text categorization Feature extraction Data models

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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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Highly Sensitive Photonic Bionic Sensor Based on Strongly Coupled Fiber with Flexible Carbomer Polymer Coating

SSRN

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SSRN 2024年

作者： Lu, Qin Li, Shuang Shi, Yueyan Xu, Jinchao Zhang, Xiajing Wu, Tao Zhou, Liang Guo, Jiachen Network and Information Center Shanghai Jiao Tong University School of Medicine Shanghai200025 China China Hospital Development Institute Shanghai Jiao Tong University Shanghai200025 China Collaborative Innovation Center Shanghai University of Medicine and Health Sciences Shanghai200025 China School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Optical‐Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Jiading District Central Hospital Shanghai University of Medicine and Health Sciences Shanghai201899 China Biomedical Engineering Fusion Laboratory Jiangning Hospital Nanjing Medical University Jiangsu211166 China

Future wearable devices for health monitoring, robotics, and ultra-precise industrial positioning will require flexible optical systems. Flexible fiber optic sensors are important components of wearable smart devices that sense and respond to changes in physical quantities of the external environment. However, the sensitivity and response time of most sensors need to be further improved to detect humidity and temperature in relation to the human body. In this paper, a photonic bionic sensor based on strongly coupled fiber coated with carbomer polymer has been reported for the first time. The sensor itself adopts 2 × 2 coupler structure with waist diameter of 24 μm. The carbomer polymer film is coated in the strongly coupling area to increase the sensitivity and response time of the sensor. The thickness of the coating layer is about 1.5 μm. The sensitivity of the sensor is up to 0.017/%RH in the relative humidity range of 64 %RH - 84 %RH, and the temperature sensitivity is up to 0.016/°C in the range of 30°C - 45°C. Moreover, the photonic bionic sensor has a fast response time with a dynamic response of 1.2 s and a recovery of 1.8 s. Considering the excellent performance of the sensor, it can be utilized as the prototype of an ultra-sensitive flexible optical sensor, making it a suitable candidate for wearable medical devices aimed at both human and animal health monitoring. © 2024, The Authors. All rights reserved.

关键词： Bionics

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Bandwidth Allocation for Low-Latency Wireless Federated Learning: An Evolutionary Game Approach

Bandwidth Allocation for Low-Latency Wireless Federated Lear...

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IEEE International Conference on Communications (ICC)

作者： Shanshan Zhang Yang Xu Jia Liu Hiroki Takakura Lyu Chen Norio Shiratori School of Computer Science and Technology Xidian University Xi'an China Center for Strategic Cyber Resilience Research and Development National Institute of Informatics Tokyo Japan School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai China Research and Development Initiative Chuo University Tokyo Japan

As a new distributed data training framework, federated learning (FL) has attracted increasing attention owing to its advantages of preserving data privacy and low communication cost. However, in a wireless FL network, due to the limited bandwidth resources, when a large number of clients participate in FL, the communication burden is too heavy. Therefore, efficient bandwidth allocation is critical to facilitate the application of wireless FL. In this paper, we investigate bandwidth allocation in wireless FL networks with the objective of minimizing the latency of FL services. We first analyze and formulate the latency minimization problem. Then, considering that the computing and transmitting capabilities of each client cannot be completely and truly acquired, we develop an evolutionary game (EG) framework to model the dynamic process of bandwidth allocation in wireless FL. We further show the optimal bandwidth allocation solution is equivalent to the evolutionary equilibrium (EE) obtained by the replicator dynamics in the EG model, and prove the EE is asymptotically stable. With the help of these results, we propose the EG-based bandwidth allocation algorithm, which enables the latency of FL services to be reduced by performing the replicator dynamics iteratively. Numerical simulations are provided to demonstrate the evolutionary behaviors in the EG-based bandwidth allocation Algorithm.

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Test Generation for Mutation Testing by Symbolic Execution

Test Generation for Mutation Testing by Symbolic Execution

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IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： Rui Su Zhiyi Zhang Yuqian Zhou Yongming Yao Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Nanjing China Army Engineering of PLA Nanjing China

Mutation testing is a crucial technique to assess the effectiveness of test suites, but it can be computationally expensive. Symbolic execution is a formal technique that generates high-coverage test cases, but its scalability is limited. In this paper, we present a novel approach to mutation testing that utilizes symbolic execution, which explores only program regions impacted by mutations, prioritizes paths containing mutant branches, and generates test cases aimed at mutations. We implemented our approach in MutSym and evaluated it on real-world programs, demonstrating its effectiveness and efficiency. Our approach enhances the quality and reliability of software systems by detecting and eliminating faults early in the development process.

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PPBFL: A Privacy Protected Blockchain-based Federated Learning Model

arXiv

引用

arXiv 2024年

作者： Li, Yang Xia, Chunhe Lin, Wanshuang Wang, Tianbo The Key Laboratory of Beijing Network Technology Beihang University Beijing100191 China The Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin541004 China The School of Cyber Science and Technology Beihang University Beijing100191 China The Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China

With the rapid development of machine learning and a growing concern for data privacy, federated learning has become a focal point of attention. However, attacks on model parameters and a lack of incentive mechanisms hinder the effectiveness of federated learning. Therefore, we propose A Privacy Protected Blockchain-based Federated Learning Model (PPBFL) to enhance the security of federated learning and encourage active participation of nodes in model training. Blockchain technology ensures the integrity of model parameters stored in the InterPlanetary File System (IPFS), providing protection against tampering. Within the blockchain, we introduce a Proof of Training Work (PoTW) consensus algorithm tailored for federated learning, aiming to incentive training nodes. This algorithm rewards nodes with greater computational power, promoting increased participation and effort in the federated learning process. A novel adaptive differential privacy algorithm is simultaneously applied to local and global models. This safeguards the privacy of local data at training clients, preventing malicious nodes from launching inference attacks. Additionally, it enhances the security of the global model, preventing potential security degradation resulting from the combination of numerous local models. The possibility of security degradation is derived from the composition theorem. By introducing reverse noise in the global model, a zero-bias estimate of differential privacy noise between local and global models is achieved. Furthermore, we propose a new mix transactions mechanism utilizing ring signature technology to better protect the identity privacy of local training clients. Security analysis and experimental results demonstrate that PPBFL, compared to baseline methods, not only exhibits superior model performance but also achieves higher security. Copyright © 2024, The Authors. All rights reserved.

关键词： Blockchain

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MGC: Multiple-Gray-Code for 3D NAND Flash based High-Density SSDs

MGC: Multiple-Gray-Code for 3D NAND Flash based High-Density...

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IEEE Symposium on High-Performance computer Architecture

作者： Yina Lv Liang Shi Qiao Li Congming Gao Yunpeng Song Longfei Luo Youtao Zhang School of Computer Science and Technology East China Normal University Shanghai China Ministry of Education Software/Hardware Co-Design Engineering Research Center School of Informatics Xiamen University Xiamen China Computer Science Department University of Pittsburgh Pittsburgh USA

QLC (4-bit-per-cell) and more-bit-per-cell 3D NAND flash memories are increasingly adopted in large storage systems. While achieving significant cost reduction, these memories face degraded performance and reliability issues. The industry has adopted two-step programming (TSP), rather than one-step programming, to perform fine-granularity program control and choose gray-code encoding, as well as LDPC (Low-Density Parity-Check Code) for error correction. Different flash manufacturers often integrate different gray-codes in their products, which exhibit different performance and reliability characteristics. Unfortunately, a fixed gray-code encoding design lacks the ability to meet the dynamic read and program performance requirements at both application and device *** this paper, we propose MGC, a multiple-gray-code encoding strategy, that adaptively chooses the best gray-code to meet the optimization goals at runtime. In particular, MGC first extracts the performance and reliability requirements based on application-level access patterns and detects the reliability degree of SSD. It then determines the appropriate gray-code to encode the data, either from host/user application or due to garbage collection, before writing the pages to the flash memory. MGC is integrated in FTL (flash translation layer) and enhances the flash controller to enable runtime gray-code arbitration. We evaluate the proposed MGC scheme. The results show that MGC achieves better performance and lifetime guarantee compared with state-of-the-arts and introduces little overhead.

关键词： Performance evaluation Three-dimensional displays Runtime Programming Writing Encoding Parity check codes

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FedDRC: A Robust Federated Learning-based Android Malware Classifier under Heterogeneous Distribution

FedDRC: A Robust Federated Learning-based Android Malware Cl...

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International Conference on computer Supported Cooperative Work in Design

作者： Changnan Jiang Chunhe Xia Mengyao Liu Chen Chen Huacheng Li Tianbo Wang Pengfei Li Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China SNationa Innovation Center of Intelligent and Connected Vehicles Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In the traditional centralized Android malware classification framework, privacy concerns exist due to collected users’ apps containing sensitive information. A new classification framework based on Federated Learning (FL) has emerged to protect privacy. However, significant spatiotemporal heterogeneity exists in the distribution of Android malware samples in different clients. It presents a huge challenge to existing FL schemes, as trained local models differ significantly, resulting in slower model convergence and lower classification accuracy. To bridge this gap, we propose FedDRC, a robust FL-based Android malware classifier. First, we design a functional semantic embedding mechanism of API features, FSEM, using word embedding to improve the robustness of the model to the time heterogeneity of the client’s samples. Secondly, we use the idea of Information Bottleneck (IB) and transfer learning to design a robust local model, PAMIB, to deal with the model degradation caused by the space heterogeneity of the distribution of client samples. Extensive experiments on the Androzoo dataset show that FedDRC has the best robustness for Android malware classification tasks in various heterogeneity distribution settings: fastest convergence and best classification accuracy.

关键词： Degradation Privacy Accuracy Federated learning Transfer learning Semantics Malware

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Efficient adaptive test case selection for DNNs robustness enhancement

引用

Journal of Systems and software 2025年 229卷

作者： Zhang, Zhiyi Meng, Huanze Ding, Yuchen Chen, Shuxian Yao, Yongming Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Jiangsu Nanjing China School of Mathematical Science Yangzhou University Jiangsu Yangzhou China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing China Army Engineering of PLA Jiangsu Nanjing China

Deep neural networks (DNNs) have been widely used in various fields, and testing for DNN-based software has become increasingly important. To discover potential faults in DNNs, a large number of test cases and their corresponding labels are required. However, labeling so many test cases consumes enormous costs. Although there have been many test case selection techniques for DNN models, these techniques still have problems such as high overhead, low efficiency, and poor diversity. To address this problem, this paper proposes an efficient adaptive test case selection method based on the principle of uniform distribution of test cases called EATS. Based on the idea of adaptive testing, EATS combines the uncertainty of the model and the diversity of faults to calculate the distance of test cases and sort them, then gives priority to test cases with a higher probability of causing faults. We conduct experiments on four popular datasets and four representative DNN models. Experiment results show that, compared with the existing eight methods, EATS performs better in uniformity of test case distribution, diversity of errors found, model optimization, and optimization efficiency. © 2025 Elsevier Inc.

关键词： software testing

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DUALWISE-MAR: Dual-domain Multi-view Weakly supervised Segmentation Network for CBCT Metal Artifact Reduction

DUALWISE-MAR: Dual-domain Multi-view Weakly supervised Segme...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xinyun Zhong Zhan Wu Yang Yang Tianling Lyu Wenxue Yu Yan Xi Yang Chen School of Software Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China Research Center for Augmented Intelligence Zhejiang Lab Hangzhou China First-Imaging Technology Shanghai China

Intraoperative Cone-Beam Computed Tomography (CBCT) provides fast multiplanar cross-sectional imaging and three-dimensional reconstructions in clinical scene. However, high-attenuation metallic implants result in the obstruction of low-energy X-rays and further lead to metal artifacts in the reconstructed CT images. Existing metal artifact reduction approaches do not consider automated fine-grained metallic implants segmentation. Imprecise metallic implants segmentation severely limits the clinical practical applicability of MAR approaches. To address the above challenge, in this study, we present a novel DUAL-domain multi-view Weak supervIsed SEgmentation network for Metal Artifact Reduction (DUALWISE-MAR) to coarse-to-fine segment metallic implants without segmentation ground-truth. First, an Iterative Segmentation Refinement Mechanism is constructed to progressively enhance initial coarse label and guide segmentation network close to the ideal metal mask. Second, a Multi-view Consistent Segmentation Network is constructed for data-consistent segmentation to capture the metal shape relationship between adjacent views in projection-domain. Third, a Segmentation Recalibration Module is proposed to calibrate the inaccurate prediction regions in the imagedomain. Extensive experiments have been conducted on the real clinical CBCT dataset and demonstrate that the proposed DUALWISE-MAR framework achieves excellent performance in metal segmentation compared to the state-of-the-art methods.

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