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Frontiers technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Mengshi Qi Xin Wang Yunpeng He Ming Yang Xiaoming Wu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Federated learning (FL) is a decentralized machine learning technique that protects data privacy by trainng models locally on devices and sharing only model updates instead of raw data. However, FL also faces multiple security challenges. Clientside poisoning attacks can compromise global model training by injecting malicious updates. Additionally, the server may infer private information from client model updates. Most existing studies address these two risks separately, lacking a systematic analysis and response to their combined threats. This paper proposes a method called Sliced-Clustering Guard (SCGuard), which slices gradients and performs clustering analysis on each segment, enabling the server to more accurately detect malicious updates and effectively differentiate malicious client updates with differential privacy (DP) protection. Experimental results demonstrate that SCGuard can mitigate poisoning attacks while effectively safeguarding clients' sensitive information.

关键词： Training Privacy Differential privacy systematics Federated learning Scalability Data models Servers Protection Faces

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Robin: A Novel Method to Produce Robust Interpreters for Deep Learning-Based Code Classifiers

arXiv

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arXiv 2023年

作者： Li, Zhen Zhang, Ruqian Zou, Deqing Wang, Ning Li, Yating Xu, Shouhuai Chen, Chen Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science University of Colorado Colorado Springs United States Center for Research in Computer Vision University of Central Florida United States School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Deep learning has been widely used in source code classification tasks, such as code classification according to their functionalities, code authorship attribution, and vulnerability detection. Unfortunately, the black-box nature of deep learning makes it hard to interpret and understand why a classifier (i.e., classification model) makes a particular prediction on a given example. This lack of interpretability (or explainability) might have hindered their adoption by practitioners because it is not clear when they should or should not trust a classifier's prediction. The lack of interpretability has motivated a number of studies in recent years. However, existing methods are neither robust nor able to cope with out-of-distribution examples. In this paper, we propose a novel method to produce Robust interpreters for a given deep learning-based code classifier;the method is dubbed Robin. The key idea behind Robin is a novel hybrid structure combining an interpreter and two approximators, while leveraging the ideas of adversarial training and data augmentation. Experimental results show that on average the interpreter produced by Robin achieves a 6.11% higher fidelity (evaluated on the classifier), 67.22% higher fidelity (evaluated on the approximator), and 15.87x higher robustness than that of the three existing interpreters we evaluated. Moreover, the interpreter is 47.31% less affected by out-of-distribution examples than that of LEMNA. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Ant: An Efficient Lossless Compression Algorithm for IoT Time Series Data

Ant: An Efficient Lossless Compression Algorithm for IoT Tim...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Junhui Li Guangping Xu Hongzhang Yang Yulei Wu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China Faculty of Science and Engineering University of Bristol Bristol U.K

In various applications in Internet of Things like industrial monitoring, large amounts of floating-point time series data are generated at an unprecedented rate. Efficient compression algorithms can effectively reduce the size of data, enhance transmission performance and storage efficiency, and simultaneously lower storage costs. Therefore, there is a need for lightweight and efficient stream compression algorithms. In this paper, we propose a novel lossless floating-point data compression algorithm called Ant. The main idea is to encode double-precision floating-point numbers into integer form, calculate the delta between adjacent values, and then convert the delta into unsigned integers. This encoding method effectively reduces storage costs and improves data compression efficiency. Extensive experiments on real-world datasets demonstrate that our algorithm achieves compression speeds at least as fast as state-of-the-art streaming methods, and a 63% relative improvement in average compression rate.

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Fence Theorem: Towards Dual-Objective Semantic-Structure Isolation in Preprocessing Phase for 3D Anomaly Detection

arXiv

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arXiv 2025年

作者： Liang, Hanzhe Zhou, Jie Chen, Xuanxin Dai, Tao Wang, Jinbao Gao, Can College of Computer Science and Software Engineering Shenzhen University China Shenzhen Audencia Financial Technology Institute Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Faculty of Education Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

3D anomaly detection (AD) is prominent but difficult due to lacking a unified theoretical foundation for preprocessing design. We establish the Fence Theorem, formalizing preprocessing as a dual-objective semantic isolator: (1) mitigating cross-semantic interference to the greatest extent feasible and (2) confining anomaly judgements to aligned semantic spaces wherever viable, thereby establishing intra-semantic comparability. Any preprocessing approach achieves this goal through a two-stage process of Semantic-Division and Spatial-Constraints stage. Through systematic deconstruction, we theoretically and experimentally subsume existing preprocessing methods under this theorem via tripartite evidence: qualitative analyses, quantitative studies, and mathematical proofs. Guided by the Fence Theorem, we implement Patch3D, consisting of Patch-Cutting and Patch-Matching modules, to segment semantic spaces and consolidate similar ones while independently modeling normal features within each space. Experiments on Anomaly-ShapeNet and Real3D-AD with different settings demonstrate that progressively finer-grained semantic alignment in preprocessing directly enhances point-level AD accuracy, providing inverse validation of the theorem’s causal logic. Copyright © 2025, The Authors. All rights reserved.

关键词： Fences

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IBFNet: A Dual Auxiliary Branch Network for Multimodal Hidden Emotion Recognition

IBFNet: A Dual Auxiliary Branch Network for Multimodal Hidde...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jianmeng Zhou Xinyu Liu Shiwei He Mengyan Li Huijie Gu Tong Chen College of Electronic and Information Engineering Southwest University Chongqing China Chongqing Key Laboratory of Generic Technology and System of Service Robots Southwest University Chongqing China Institute of Legal Psychology and Intelligent Computing Southwest University Chongqing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Micro-expression (ME) is a crucial cue to reveal hidden emotions and helps to diagnose mental illnesses such as depression. However, their rapid and subtle characteristics make them difficult to recognize, and relying on a single ME feature is insufficient to capture comprehensive information about hidden emotions. This paper proposes an inverted bottleneck fusion network (IBFNet), which combines ME and facial tissue oxygen saturation (StO2) for multimodal feature fusion to improve the recognition of hidden emotions. Specifically, IBFNet learns different representations of each modal feature, namely common and individual features. The modalities are then mapped to different subspaces for feature refinement. We design the recurrent cross-modal attention (RCA) module to explore commonalities across modalities, retaining modality-specific information through auxiliary branches to enhance diversity. Finally, the inverted bottleneck structure is used to fuse the common and individual feature of ME and StO2. Experimental results demonstrate a recognition accuracy of 90.47%, which surpasses the limitations of single-modality recognition.

关键词： Emotion recognition Accuracy Fuses Face recognition Redundancy Feature extraction Depression Character recognition Bioinformatics

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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize Computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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Securing Sdn/Nfv-Enabled Campus Networks with Software-Defined Perimeter-Based Zero-Trust Architecture

SSRN

引用

SSRN 2023年

作者： Ruambo, Francis A. Zou, Deqing Lopes, Ivandro O. Yuan, Bin School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Distributed System Security Wuhan China Hubei Engineering Research Center on Big Data Security Wuhan China National Engineering Research Center for Big Data Technology and System Wuhan China Services Computing Technology and System Lab Wuhan China Cluster and Grid Computing Lab Wuhan China Mbeya university of Science and Technology Mbeya131 Tanzania United Republic of Nucleo Operacional para a Sociedade de Informacao Cape Verde Songshan Laboratory Zhengzhou China

Network softwarization is a breakthrough in designing modern networks and providing numerous new network operations and services. This change is exemplified by Software Defined Networks (SDN) and Network Function Virtualization (NFV). As a result, the combination of SDN and NFV (SDN/NFV) frameworks and individual SDN and NFV frameworks have gained substantial traction in network security over the past decade, significantly supporting on-demand protection measures. However, the inherent security shortcomings of SDN, NFV, and SDN/NFV frameworks provide the fundamental issue in implementing security procedures via network softwarization. To solve this issue, we present a Software-Defined Perimeter (SDP)-powered Zero-Trust Architecture (ZTA) to improve security in the SDN/NFV-enabled campus network holistically. In addition to this, we also propose a threat model and quantitatively assess the suggested approach's performance and security. Furthermore, we highlight potential zero-trust network security function implementations in SDN/NFV-enabled infrastructure to increase protection for endpoint devices, network resources, and flows for detecting threats and responding effectively with minimal latency and overhead. © 2023, The Authors. All rights reserved.

关键词： Access control

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A Secure keyword Spotting Scheme Based on Complex Residual Network

A Secure Keyword Spotting Scheme Based on Complex Residual N...

引用

Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Jingyi Chen Xiangyu Gao Peijia Zheng Huiyu Zhou Jian Li Dong Wei Jianbin Zou School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangxi Key Laboratory of Digital Infrastructure Guangxi Zhuang Autonomous Region Information Center Nanning China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) Jinan China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Amazon Web Services Beijing China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

To address the privacy risks associated with intelligent voice assistants, this paper proposes a non-interactive, end-to-end keyword spotting scheme in the encrypted domain. The scheme introduces an innovative complex-valued residual neural network (CR-NN) and leverages low-degree polynomial approximations to enable encrypted computation of complex activation functions. By supporting single instruction multiple data (SIMD) operations, the scheme significantly enhances computational efficiency while reducing ciphertext size. Experimental results demonstrate that the proposed method outperforms existing approaches across multiple performance metrics, balancing efficient keyword spotting with robust privacy protection and offering new insights for the application of deep learning in the encrypted domain.

关键词： Deep learning Measurement Privacy Single instruction multiple data Image processing Personal voice assistants Polynomials Cryptography Protection Residual neural networks

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Modeling and Analysis of Integrated Air Transportation Cooperative Information SoS Based on Complex Network Theory

Modeling and Analysis of Integrated Air Transportation Coope...

引用

Chinese Automation Congress (CAC)

作者： Song Li Zhenfeng Wu Hans de Jong State Key Laboratory of Air Traffic Management System and Technology Nanjing China DFS Aviation Services GmbH Beijing China

In this paper, a system of systems (SoS) modeling and evaluation method based on complex network theory is proposed for the Integrated Air Transportation Cooperative Information SoS (IATCI-SoS). In the proposed method, the target SoS is abstracted into a network model composed of multiple types of nodes and their interconnections, so as to facilitate its standardized description and quantitative evaluation. On this basis, SoS evaluation indicators in the aspects of flexibility, robustness, and efficiency are designed and applied to the case simulation of the ‘Belt and Road Initiative’ air transportation collaborative information environment. By simulating the network model of the current SoS and the target IATCI-SoS, both of them are evaluated and analyzed. Simulation results show the advantages and disadvantages of the target IATCI-SoS, and validate that the proposed method can provide theoretical support for the top-level architecture construction of the future IATCI-SoS.

关键词： Analytical models Automation Atmospheric modeling Simulation Roads Collaboration Complex networks

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VCFGAN: A Two-stage Collaborative Filtering Framework based on Variational Autoencoders and Generative Adversarial Networks

VCFGAN: A Two-stage Collaborative Filtering Framework based ...

引用

Information systems and computing technology (ISCTech), International Conference on

作者： Zhiqiang Huang Jinzhong Li Xueyu Huang Junqi Liu School of Software Engineering Jiangxi University of Science and Technology Nanchang China College of Electronics and Information Engineering Jinggangshan University Ji’an China Ministry of Education Key Laboratory of Embedded System and Service Computing (Tongji University) Shanghai China

ISBN: (数字)9798350379860

ISBN: (纸本)9798350379877

Deep generative models, such as Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE), are widely used in collaborative filtering. They usually learn users’ preferences for items directly from a highly sparse user rating matrix (URM), and then recommend the top-N items to users. Due to the high sparsity of URM, GAN has limited ability to handle sparse data, while VAE’s encoder can extract features. Therefore, we propose a two-stage collaborative filtering framework based on variational autoencoders and generative adversarial networks, named VCFGAN. It first uses two VAEs to extract features from URM and side information (SI), and then uses the extracted latent vectors to train the GAN network. To evaluate the performance of our proposed VCFGAN model, some experiments are conducted on two real datasets, and the experimental results show that our model outperforms other representative models.

关键词： Training Collaborative filtering Autoencoders Generative adversarial networks Feature extraction Vectors Data mining Sparse matrices Recommender systems Information systems

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