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35th IEEE International Symposium on Software Reliability engineering Workshops, ISSREW 2024

作者： Jiang, Wei Dong, Sijin Zhang, Jiaming Tang, Jin Zhang, Zichao Sun, Chang-Ai Zhang, Xiaoyi China Electronics Technology Group Corporation Information Science Academy Beijing China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798350367041

Embedded software commonly executes safety-critical tasks and thus is expected to be highly reliable, which calls for stronger quality assurance techniques. As a fault-based testing technique, mutation testing is widely recognized to have stronger fault detection capability than those coverage or function-based testing techniques. However, some unique features of embedded software, such as limited hardware resources, real-time requirements, and differences between development and execution environments, pose challenging issues for the application of mutation testing. In this study, we explore the application of mutation testing to embedded software. As a result, we propose a mutation testing framework for embedded software, and evaluate the simulation capability of mutation operators for real-life faults in embedded software. A case study was conducted on a realistic open-source embedded software to evaluate the effectiveness and efficiency of the proposed technique. The experimental results demonstrate that mutation testing is able to detect various faults of embedded software, thus delivering a promising supplement for embedded software testing. © 2024 IEEE.

关键词： Case Study Embedded software Mutation Operators Mutation testing Software testing

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Effective short text classification via the fusion of hybrid features for IoT social data

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Digital communications and Networks 2022年第6期8卷 942-954页

作者： Xiong Luo Zhijian Yu Zhigang Zhao Wenbing Zhao Jenq-Haur Wang School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Shunde Innovation School University of Science and Technology BeijingFoshan528399China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China Department of Electrical Engineering and Computer Science Cleveland State UniversityClevelandOH44115USA Department of Computer Science and Information Engineering National Taipei University of TechnologyTaipei10608China

Nowadays short texts can be widely found in various social data in relation to the 5G-enabled Internet of Things (IoT). Short text classification is a challenging task due to its sparsity and the lack of context. Previous studies mainly tackle these problems by enhancing the semantic information or the statistical information individually. However, the improvement achieved by a single type of information is limited, while fusing various information may help to improve the classification accuracy more effectively. To fuse various information for short text classification, this article proposes a feature fusion method that integrates the statistical feature and the comprehensive semantic feature together by using the weighting mechanism and deep learning models. In the proposed method, we apply Bidirectional Encoder Representations from Transformers (BERT) to generate word vectors on the sentence level automatically, and then obtain the statistical feature, the local semantic feature and the overall semantic feature using Term Frequency-Inverse Document Frequency (TF-IDF) weighting approach, Convolutional Neural Network (CNN) and Bidirectional Gate Recurrent Unit (BiGRU). Then, the fusion feature is accordingly obtained for classification. Experiments are conducted on five popular short text classification datasets and a 5G-enabled IoT social dataset and the results show that our proposed method effectively improves the classification performance.

关键词： Information fusion Short text classi fication BERT Bidirectional encoder representations fr 0om transformers Deep learning Social data

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A DDOS attack detection method based on recurrent neural network

A DDOS attack detection method based on recurrent neural net...

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International Conference on Internet of Things and Machine Learning, IoTML 2023

作者： Wu, Yue School of Computer and Communication Engineering University of Science and Technology Beijing100083 China

ISBN: (纸本)9781510671805

With the rapid development of cloud computing and mobile Internet, there have been a variety of network attacks, among which distributed denial of service (DDoS) is one of the most fatal attacks. Traditional machine learning detection methods face serious challenges. This paper proposes a method for identifying DDoS attacks based on RNNs. Taking Recurrent Neural Network (RNN) as the research and improvement object, long short-term memory, bidirectional recurrent neural network and other technologies are used. Experiments on the public dataset indicate that the accuracy of the model can reach 99.97%, which is better than the traditional machine learning model and achieves a good recognition effect.93 © 2023 SPIE.

关键词： Denial-of-service attack

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HIET:Hybrid Information Enhancement Transformer Network for Single-Photon Image Reconstruction

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Journal of beijing Institute of technology 2025年第1期34卷 1-17页

作者： Yiming Liu Xuri Yao Tao Zhang Yifei Sun Ying Fu School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China Center for Quantum Technology Research and Key Laboratory of Advanced Optoelec-tronic Quantum Architecture and Measurements(MOE) School of PhysicsBeijing Institute of TechnologyBeijing 100081China School of Communication Engineering Hangzhou Dianzi UniversityHangzhou 310018China Uniview Technology Hangzhou 310056China

Single-photon sensors are novel devices with extremely high single-photon sensitivity and temporal ***,these advantages also make them highly susceptible to ***,single-photon cameras face severe quantization as low as 1 bit/*** factors make it a daunting task to recover high-quality scene information from noisy single-photon *** current image reconstruction methods for single-photon data are mathematical approaches,which limits information utilization and algorithm *** this work,we propose a hybrid information enhancement model which can significantly enhance the efficiency of information utilization by leveraging attention mechanisms from both spatial and channel ***,we introduce a structural feature enhance module for the FFN of the transformer,which explicitly improves the model's ability to extract and enhance high-frequency structural information through two symmetric convolution ***,we propose a single-photon data simulation pipeline based on RAW images to address the challenge of the lack of single-photon *** results show that the proposed method outperforms state-of-the-art methods in various noise levels and exhibits a more efficient capability for recovering high-frequency structures and extracting information.

关键词： single-photon images hybrid information enhancement structual feature enhancement data simulation pipeline

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Breaking demodulation limitations:AWG and Deep Learning in Dynamic Spectral Reconstruction for Differential FBG Accelerometers

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IEEE Sensors Journal 2025年第11期25卷 19210-19222页

作者： Yang, Qian Liu, Lei Niu, Ning Quan, ChenYi Ren, SuFen Chen, ShengChao Zhang, JiaLin Yan, YuMeng Xiang, LongTeng Wang, YiPing Liao, ChangRui He, Jun Wang, GuanJun School of Information and Communication Engineering Hainan University College of Electronic Science and Technology China Henan Mechanical and Electrical Vocational College School of Electrical Engineering Henan451100 China Hainan University College of Electronic Science and Technology School of Information and Communication Engineering Haikou570228 China University of Technology Sydney School of Computer Science Faculty of Engineering and Information Technology SydneyNSW Australia Hainan University College of Electronic Science and Technology Haikou570228 China Shenzhen University School of Physics and Optoelectronic Engineering Shenzhen518060 China

Conventional fiber Bragg grating (FBG) accelerometer demodulation often suffers from high environmental sensitivity, complexity, and cost. To address these issues, this paper presents two arrayed waveguide grating (AWG)-based dynamic spectral reconstruction models. First, a novel differential FBG accelerometer with an equal-intensity triangular cantilever beam and a symmetric string-like design enhances sensitivity and mitigates temperature interference. A power ratio-nonlinear logarithmic regression (PR-NLR) model derived from static calibration then enables spectral reconstruction and wavelength demodulation across varying accelerations, achieving 4-82pm accuracy, slightly below that of high-speed demodulators (HSD). Next, by integrating convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), attention mechanisms, and a generative adversarial network (GAN) for data augmentation, prediction errors drop to 1-26pm at 20Hz and 0.2-21pm at 40Hz-a reduction of at least 60% over PR-NLR. This outcome underscores the advantages of deep learning (DL) in managing complex spectral shifts. By overcoming frequency-only demodulation limits, the proposed AWG approach achieves precise wavelength demodulation through dynamic spectral reconstruction. Its compact design, low manufacturing cost, and robust performance facilitate scalable deployment in structural health monitoring, particularly for bridge vibration analysis and industrial machinery diagnostics. © 2001-2012 IEEE.

关键词： Arrayed waveguide gratings

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RecStitchNet:Learning to stitch images with rectangular boundaries

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Computational Visual Media 2024年第4期10卷 687-703页

作者： Yun Zhang Yu-Kun Lai Lang Nie Fang-Lue Zhang Lin Xu Key Lab of Film and TV Media Technology of Zhejiang Province College of Media EngineeringCommunication University of ZhejiangHangzhou 310018China School of Computer Science and Informatics Cardiff UniversityCardiff CF244AGUK Institute of Information Science Beijing Jiaotong UniversityBeijing 100091China School of Engineering and Computer Science Victoria University of WellingtonWellington 6012New Zealand STEM University of South AustraliaAdelaide 5095Australia

Irregular boundaries in image stitching naturally occur due to freely moving *** deal with this problem,existing methods focus on optimizing mesh warping to make boundaries regular using the traditional explicit ***,previous methods always depend on hand-crafted features(e.g.,keypoints and line segments).Thus,failures often happen in overlapping regions without distinctive *** this paper,we address this problem by proposing RecStitchNet,a reasonable and effective network for image stitching with rectangular *** that both stitching and imposing rectangularity are non-trivial tasks in the learning-based framework,we propose a three-step progressive learning based strategy,which not only simplifies this task,but gradually achieves a good balance between stitching and imposing *** the first step,we perform initial stitching by a pre-trained state-of-the-art image stitching model,to produce initially warped stitching results without considering the boundary ***,we use a regression network with a comprehensive objective regarding mesh,perception,and shape to further encourage the stitched meshes to have rectangular boundaries with high content ***,we propose an unsupervised instance-wise optimization strategy to refine the stitched meshes iteratively,which can effectively improve the stitching results in terms of feature alignment,as well as boundary and structure *** to the lack of stitching datasets and the difficulty of label generation,we propose to generate a stitching dataset with rectangular stitched images as pseudo-ground-truth labels,and the performance upper bound induced from the it can be broken by our unsupervised *** and quantitative results and evaluations demonstrate the advantages of our method over the state-of-the-art.

关键词： image stitching boundaries convolutional neural network

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FusFormer: global and detail feature fusion transformer for semantic segmentation of small objects

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Multimedia Tools and Applications 2024年第41期83卷 88717-88744页

作者： Li, Zheng Chen, Houjin Li, Jupeng Peng, Song Zhang, Zhenhao Wang, Baozheng Wang, Changyong School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China School of Computer Science and Technology University of Science and Technology of China Hefei230026 China Beijing Institute of Basic Medical Sciences Beijing100850 China

Improving the segmentation accuracy of small objects is essential for tasks such as autono-mous driving and remote sensing. However, the current main semantic segmentation methods are inadequate for small objects. To improve the segmentation accuracy of small objects, long-range global information and fine local details are needed, and neither pure Convolutional Neural Networks (CNNs) nor Vision Transformers (ViTs) can effectively provide these two different types of information simultaneously. In this paper, we introduce a novel model FusFormer for finding a solution, which contains a global branch and a detailed branch to fully capture the long-range features and spatial detail features from the input image. The global branch is based on MiT-B2 to efficiently acquire global context, while the detailed branch acquires rich local detail information by Spatial Prior Module (SPM) and Multi-scale Module (MSM). Feature Interaction Module (FIM) is proposed to perform information fusion across features at a dual scale. In addition, Multi-scale Edge Extraction Module (MSEEM) is utilized to supplement the missing edge information during model training, helping the model to better enhance the intra-class consistency of small objects. Extensive experiments on Cityscapes, ADE20K and PASCAL VOC 2012 show that our model achieves competitive overall segmentation accuracy, especially on small objects. FusFormer achieves 82.6%, 47.3% and 82.4% mIoU on the Cityscapes, ADE20K and PASCAL VOC 2012 validation sets, compared with other state-of-the-art methods, the proposed model significantly improves the IoU on small objects by 2%-4%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantic Segmentation

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A blockchain-based access control protocol for secure resource sharing with mobile edge-cloud collaboration

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13661-13672页

作者： Sun, Haipeng Tan, Yu-an Zhu, Liang Zhang, Qikun Ai, Shan Zheng, Jun School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou450002 China Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou510006 China Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China

The application scenarios of edge-cloud collaboration are very wide. In order to ensure the operational security of resource sharing among intelligent terminals in edge-cloud collaboration scenarios, and prevent unauthorized entities from accessing sensitive data, a blockchain-based access control protocol for secure resource sharing is proposed. For the characteristics of edge-cloud collaborative application scenarios, the attribute authentication, secure storage, intra-domain access control, inter-domain access control and dynamic update of access permissions are studied in this paper. The proposed protocol has the following advantages. (1) Privacy protection: in the edge-cloud collaborative application scenario, the privacy of mobile terminals is easily leaked. The access control technology with hidden attributes is adopted, which can not only achieve the purpose of access control, but also ensure that the identity and attribute information of terminals are not leaked;(2) cross-domain access control: edge-cloud collaborative application scenarios, resource sharing among terminals may span multiple different security domains, and the proposed protocol supports cross-domain access control;(3) dynamic access control: some mobile terminals may frequently join or exit some application scenarios, the access permissions of these terminals can be dynamically updated using blockchain in this protocol;(4) fine-grained access control: the permissions for access resources are set by the combination of attribute permissions of the terminals. The terminal can access a variety of resources by setting different combinations of its attribute permissions. The performance analysis shows that compared with the cited literatures, the proposed protocol has advantages in terms of computational time, computational complexity and communication overhead. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Authentication

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VTensor:Using Virtual Tensors to Build a Layout-Oblivious AI Programming Framework

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Journal of computer science & technology 2023年第5期38卷 1074-1097页

作者：俞峰赵家程崔慧敏冯晓兵薛京灵 Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100080China School of Computer Science and Engineering University of New South WalesSydney 1466Australia

Tensors are a popular programming interface for developing artificial intelligence(AI)*** refers to the order of placing tensor data in the memory and will affect performance by affecting data locality;therefore the deep neural network library has a convention on the *** AI applications can use arbitrary layouts,and existing AI systems do not provide programming abstractions to shield the layout conventions of libraries,operator developers need to write a lot of layout-related code,which reduces the efficiency of integrating new libraries or developing new ***,the developer assigns the layout conversion operation to the internal operator to deal with the uncertainty of the input layout,thus losing the opportunity for layout *** on the idea of polymorphism,we propose a layout-agnostic virtual tensor programming interface,namely the VTensor framework,which enables developers to write new operators without caring about the underlying physical layout of *** addition,the VTensor framework performs global layout inference at runtime to transparently resolve the required layout of virtual tensors,and runtime layout-oriented optimizations to globally minimize the number of layout transformation *** results demonstrate that with VTensor,developers can avoid writing layout-dependent *** with TensorFlow,for the 16 operations used in 12 popular networks,VTensor can reduce the lines of code(LOC)of writing a new operation by 47.82%on average,and improve the overall performance by 18.65%on average.

关键词： artificial intelligence(AI)programming layout-oblivious tensor processing

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Unveiling factuality and injecting knowledge for LLMs via reinforcement learning and data proportion

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science China(Information sciences) 2024年第10期67卷 389-390页

作者： Wenjun KE Ziyu SHANG Zhizhao LUO Peng WANG Yikai GUO Qi LIU Yuxuan CHEN School of Computer Science and Engineering Southeast University Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications(Southeast University) Beijing Institute of Technology Zhuhai Beijing Institute of Computer Technology and Application

Large language models(LLMs) have demonstrated remarkable effectiveness across various natural language processing(NLP) tasks, as evidenced by recent studies [1, 2]. However, these models often produce responses that conflict with reality due to the unreliable distribution of facts within their training data, which is particularly critical for applications requiring high credibility and accuracy [3].

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