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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Yu Zhang, Jianqiang Song, Gongpeng Lu, Qin Zhao, Shuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9798331515966

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method. © 2024 IEEE.

关键词： cross-modal retrieval feature enhancement image-text matching relationship interaction

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SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Shuo Gu, Qiliang Zhang, Jianqiang Song, Gongpeng Lu, Qin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9781665410205

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets. © 2024 IEEE.

关键词： Semantics

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AdvTG: An Adversarial Traffic Generation Framework to Deceive DL-Based Malicious Traffic Detection Models 25

AdvTG: An Adversarial Traffic Generation Framework to Deceiv...

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34th ACM Web Conference, WWW 2025

作者： Sun, Peishuai Yun, Xiaochun Li, Shuhao Yin, Tao Si, Chengxiang Xie, Jiang Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Beijing China Zhongguancun Laboratory Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China

ISBN: (纸本)9798400712746

Deep learning-based (DL-based) malicious traffic detection models are effective but vulnerable to adversarial attacks. Existing adversarial attacks have shown promising results when targeting traffic detection models based on statistics and sequence features. However, these attacks are less effective against models that rely on payload analysis. The main reason is the difficulty in generating semantic, compliant, and functional payloads, which limits their practical application. In this paper, we propose AdvTG, an adversarial traffic generation framework to deceive DL-based malicious traffic based on the large language model (LLM) and reinforcement learning (RL). Specifically, AdvTG is designed to attack various DL-based detection models across diverse payload features and architectures, thereby enhancing the generalization capabilities of the generated adversarial traffic. Moreover, we design a specialized prompt for traffic generation tasks, where functional fields and target types are supplied as input, while non-functional fields are generated to produce the mutated traffic. This fine-tuning endows the LLM with task comprehension and traffic pattern reasoning abilities, allowing it to generate traffic that remains compliant and functional. Furthermore, leveraging RL, AdvTG automatically selects traffic fields that exhibit more robust adversarial properties. Experimental results show that AdvTG achieves over 40% attack success rate (ASR) across six detection models on four base datasets and two extended datasets, significantly outperforming other adversarial attack methods. © 2025 Copyright held by the owner/author(s).

关键词： Reinforcement learning

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Before Toasters Rise Up: A View into the Emerging DoH Resolver's Deployment Risk 28

Before Toasters Rise Up: A View into the Emerging DoH Resolv...

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Qiu, Yuqi Li, Baiyang Li, Zhiqian Jiao, Liang Zhu, Yujia Liu, Qingyun Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China Shandong Branch of National Computer Network Emergency Response Technical Team Coordination Center of China China

ISBN: (纸本)9798350300482

As an encryption protocol for DNS queries, DNS-over-HTTPS (DoH) is becoming increasingly popular, and it mainly addresses the last-mile privacy protection problem. However, the security of DoH is in urgent need of measurement and analysis due to its reliance on certificates and upstream servers. In this paper, we focus on the DoH ecosystem and conduct a one-month measurement to analyze the current deployment of DoH resolvers. Our findings indicate that some of these resolvers use invalid certificates, which can compromise the security and privacy advantages of the protocol. Furthermore, we found that many providers are at risk of certificate outages, which could cause significant disruptions to the DoH ecosystem. Additionally, we observed that the centralization of DoH resolvers and upstream DNS servers is a potential issue that needs addressing to ensure the stability of the ecosystem. © 2023 IEEE.

关键词： Ecosystems

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Clone-Resistant Secured Booting Based on Unknown Hashing Created in Self-Reconfigurable Platform 17th

Clone-Resistant Secured Booting Based on Unknown Hashing Cre...

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17th International Symposium on Applied Reconfigurable Computing, ARC 2021

作者： Zarrouk, Randa Mulhem, Saleh Adi, Weal Berekovic, Mladen Institute of Computer and Network Engineering Technical University of Braunschweig Braunschweig38106 Germany Institute of Computer Engineering University of Lübeck Lübeck23562 Germany

ISBN: (纸本)9783030790240

Deploying a physically unclonable trusted anchor is required for securing software running on embedded systems. Common mechanisms combine secure boot with either stored secret keys or keys extracted from a Physical Unclonable Function (PUF). We propose a new secure boot mechanism that is hardware-based, individual to each device, and keyless to prohibit any unauthorized alteration of the software running on a particular device. Our solution is based on the so-called Secret Unknown Hash (SUH), a self-created random secret unknown hardwired hash function residing as a permanent digital hardware-module in the device’s physical layout. It is initiated in the device in a post-manufacturing, unpredictable single event process in self-reconfigurable non-volatile SoC FPGAs. In this work, we explain the SUH creation process and its integration for a device-specific secure boot. The SUH is shown to be lightweight when implemented in a sample scenario as a DM-PRESENT-based hash function. A security analysis is also presented, highlighting the different proposed sample SUH-class entropies. © 2021, Springer Nature Switzerland AG.

关键词： Field programmable gate arrays (FPGA)

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Federated Learning for 6G:Applications,Challenges,and Opportunities

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engineering 2022年第1期8卷 33-41页

作者： Zhaohui Yang Mingzhe Chen Kai-Kit Wong H.Vincent Poor Shuguang Cui Department of Electronic and Electrical Engineering University College LondonLondon WC1E 6BTUK Department of Electrical and Computer Engineering Princeton UniversityPrincetonNJ 08544USA Shenzhen Research Institute of Big Data The Chinese University of Hong KongShenzhen 518172China School of Science and Engineering and Future Network of Intelligence Institute The Chinese University of Hong KongShenzhen 518172China

Standard machine-learning approaches involve the centralization of training data in a data center,where centralized machine-learning algorithms can be applied for data analysis and ***,due to privacy restrictions and limited communication resources in wireless networks,it is often undesirable or impractical for the devices to transmit data to parameter *** approach to mitigate these problems is federated learning(FL),which enables the devices to train a common machine learning model without data sharing and *** paper provides a comprehensive overview of FL applications for envisioned sixth generation(6G)wireless *** particular,the essential requirements for applying FL to wireless communications are first *** potential FL applications in wireless communications are *** main problems and challenges associated with such applications are ***,a comprehensive FL implementation for wireless communications is described.

关键词： Federated learning 6G Reconfigurable intelligent surface Semantic communication Sensing Communication and computing

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Learning Rich Information for Quad Bayer Remosaicing and Denoising 17th

Learning Rich Information for Quad Bayer Remosaicing and D...

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17th European Conference on computer Vision, ECCV 2022

作者： Jia, Jun Sun, Hanchi Liu, Xiaohong Xiao, Longan Xu, Qihang Zhai, Guangtao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China John Hopcroft Center for Computer Science Shanghai Jiao Tong University Shanghai China Shanghai Transsion Information Technology Shanghai China

ISBN: (纸本)9783031250712

In this paper, we propose a DNNs-based solution to jointly remosaic and denoise the camera raw data in Quad Bayer pattern. The traditional remosaic problem can be viewed as an interpolation process that converts the Quad Bayer pattern to a normal CFA pattern, such as the RGGB one. However, this process becomes more challenging when the input Quad Bayer data is noisy. In addition, the limited amount of data available for this task is not sufficient to train neural networks. To address these issues, we view the remosaic problem as a bayer reconstruction problem and use an image restoration model to remove noises while remosaicing the Quad Bayer data implicitly. To make full use of the color information, we propose a two-stage training strategy. The first stage uses the ground-truth RGGB Bayer map to supervise the reconstruction process, and the second stage leverages the provided Image Signal Processor (ISP) to generate the RGB images from our reconstructed bayers. With the use of color information in the second stage, the quality of reconstructed bayers is further improved. Moreover, we propose a data pre-processing method including data augmentation and bayer rearrangement. The experimental results show it can significantly benefit the network training. Our solution achieves the best KLD score with one order of magnitude lead, and overall ranks the second in Quad Joint Remosaic and Denoise @ MIPI-challenge. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image reconstruction

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BECIA: a behaviour engineering-based approach for change impact analysis

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International Journal of Information Technology (Singapore) 2024年第1期16卷 159-168页

作者： Anwer, Sajid Wen, Lian Zhang, Shaoyang Wang, Zhe Sun, Yong School of Information and Communication Technology Griffith University Brisbane Australia Institute for Integrated and Intelligent Systems (IIIS) Griffith University Brisbane Australia National University of Computer and Emerging Sciences Islamabad Pakistan School of Information Engineering Chang’an University Xi’an China Information and Network Management Chang’an University Xi’an China

This paper introduces Behaviour engineering-based Change Impact Analysis (BECIA), a novel approach to Change Impact Analysis (CIA). BECIA enables visualization of change impacts from modified requirements on architecture design. It also introduces the Change Impact Indicator (CII) metric, quantifying impact by calculating the Kolmogorov Complexity (KC) of design models. Our contributions include: an algorithm to convert behavior trees to composition trees, a requirements components dependency network, and a metric for quantifying change impacts. © 2023, The Author(s).

关键词： Behaviour trees Change impact analysis Changeability Composition trees Kolmogorov complexity Requirements change

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A Rolling Bearing Fault Diagnosis Method Using Multi-Sensor Data and Periodic Sampling

A Rolling Bearing Fault Diagnosis Method Using Multi-Sensor ...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Zheng, Jianbo Yang, Chao Zheng, Fangrong Jiang, Bin College of Computer Science and Electronic Engineering Hunan University China Key Laboratory for Embedded and Network Computing of Hunan Province China School of Computational Science and Electronics Hunan Institute of Engineering China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

In recent years, bearing fault diagnosis based on deep learning has gradually become the mainstream. However, the existing studies still have some defects, such as unreasonable sampling and incomplete utilization of bearing data, limiting the further improvement of the performance of the fault diagnosis model. This paper proposes a fault diagnosis method using multi-sensor data and periodic sampling to solve the problems above. First, the vibration data of different bearing positions are fused into multi-channel fusion data to improve the defect of insufficient data utilization. Second, based on the sampling length and sampling stride, periodic sampling is carried out for the fusion data to solve the problem of unreasonable sampling. Third, the traditional convolutional neural network is adjusted to extract more detailed fault features and obtain the best recognition effect. Finally, the experimental results verify the effectiveness of the proposed method. © 2022 IEEE.

关键词： Failure analysis

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Efficient Data Completion and Augmentation

Efficient Data Completion and Augmentation

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International Conference on Data Science and Advanced Analytics (DSAA)

作者： Antonina Krajewska Ewa Niewiadomska-Szynkiewicz Research and Academic Computer Network (NASK) Warsaw Poland Institute of Control and Computation Engineering Warsaw University of Technology Warsaw Poland

ISBN: (数字)9798350364941

ISBN: (纸本)9798350364958

Fast data completion and augmentation methods with provable theoretical guarantees enable efficient and reliable data analysis. Many datasets can be represented as real-valued matrices and filled using matrix completion (MC) methods. This paper proposes a novel two-step approach for matrix recovery involving the sequential solution of convex optimization tasks. Firstly, a subset of columns is randomly chosen, and the matrix is completed using a nuclear norm minimization algorithm. Secondly, we solve the least squares problem by incorporating the known elements and the completed columns. We introduce three algorithms for implementing our Columns Selected Matrix Completion (CSMC) method, tailored to different problem sizes. Provided necessary assumptions and the probability of achieving accurate solutions are validated through extensive numerical experiments. Synthetic data experiments assess the method's performance regarding matrix size, rank, and missing element proportion. Evaluation in image completion and graph link prediction demonstrates that CSMC provides comparable solution quality to state-of-the-art convex optimization-based MC algortthms while significantly reduciny computation time.

关键词： Accuracy Reliability theory Data science Minimization Convex functions Optimization Synthetic data

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