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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Wang, Xiaohua Fei, Wenlong Hu, Min Zhang, Qingyu Zhu, Aoqiang School of Computer Science and Information Engineering HeFei University of Technology China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine China

ISBN: (纸本)9782493814104

The goal of multilingual modelling is to generate multilingual text representations for various downstream tasks in different languages. However, some state-of-the-art pre-trained multilingual models perform poorly on many low-resource languages due to the lack of representation space and model capacity. To alleviate this issue, we propose a Multilingual model Enhanced with Visual Text Representations (MEVTR), which complements textual representations and extends the multilingual representation space with visual text representations. First, the visual encoder focuses on the glyphs and structure of the text to obtain visual text representations, and the textual encoder obtains textual representations. Then, multilingual representations are enhanced by aligning and fusing visual text representations and textual representations. Moreover, we propose similarity constraint, a self-supervised task to prompt the visual encoder to focus on more additional information. Prefix alignment and multi-head bilinear module are designed to acquire an improved integration effect of visual text representations and textual representations. Experimental results indicate that MEVTR benefits from visual text representations and achieves significant performance gains in downstream tasks. In particular, in the zero-shot cross-lingual transfer task, MEVTR achieves results that outperform the state-of-the-art adapter-based framework without the target language adapter. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Signal encoding

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SCFNet: A Spatial-Channel Features Network Based on Heterocentric Sample Loss for Visible-Infrared Person Re-identification 16th

SCFNet: A Spatial-Channel Features Network Based on Heteroc...

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16th Asian Conference on Computer Vision, ACCV 2022

作者： Su, Peng Liu, Rui Dong, Jing Yi, Pengfei Zhou, Dongsheng Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China

ISBN: (纸本)9783031262838

Cross-modality person re-identification between visible and infrared images has become a research hotspot in the image retrieval field due to its potential application scenarios. Existing research usually designs loss functions around samples or sample centers, mainly focusing on reducing cross-modality discrepancy and intra-modality variations. However, the sample-based loss function is susceptible to outliers, and the center-based loss function is not compact enough between features. To address the above issues, we propose a novel loss function called Heterocentric Sample Loss. It optimizes both the sample features and the center of the sample features in the batch. In addition, we also propose a network structure combining spatial and channel features and a random channel enhancement method, which improves feature discrimination and robustness to color changes. Finally, we conduct extensive experiments on the SYSU-MM01 and RegDB datasets to demonstrate the superiority of the proposed method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Infrared imaging

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Semantic Understanding of Source and Binary Code based on Natural Language Processing 4

Semantic Understanding of Source and Binary Code based on Na...

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4th IEEE advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2021

作者： Zhang, Zhongtang Liu, Shengli Yang, Qichao Guo, Shichen State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781728185347

With the development of open source projects, a large number of open source codes will be reused in binary software, and bugs in source codes will also be introduced into binary codes. In order to detect the reused open source codes in binary codes, it is sometimes necessary to compare and analyze the similarity between source codes and binary codes. One of the main challenge is that the compilation process can generate different binary code representations for the same source code, such as different compiler versions, compilation optimization options and target architectures, which greatly increases the difficulty of semantic similarity detection between source code and binary code. In order to solve the influence of the compilation process on the comparison of semantic similarity of codes, this paper transforms the source code and binary code into LLVM intermediate representation (LLVM IR), which is a universal intermediate representation independent of source code and binary code. We carry out semantic feature extraction and embedding training on LLVM IR based on natural language processing model. Experimental results show that LLVM IR eliminates the influence of compilation on the syntax differences between source code and binary code, and the semantic features of code are well represented and preserved. © 2021 IEEE.

关键词： Open source software

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Inter-domain Link Inference with Confidence Using Naïve Bayes Classifier 2021

Inter-domain Link Inference with Confidence Using Naïve Bay...

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2021 13th International Conference on Machine Learning and computing, ICMLC 2021

作者： Zhao, Yi Liu, Yan Guo, XiaoYu Sui, ZhongHang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781450389310

Inter-domain link inference is not only important for network security and fault diagnosis, but also helps to conduct research on inter-domain congestion detection and network resilience assessment. Current researches on this issue lack confidence analysis of the inferred results. In this paper, the IP link types (i.e., intra-domain link and inter-domain link) are considered as the latent variable in probability model, while the parameters are probabilities of different link types with particular features. The expectation maximization algorithm is applied to estimate parameters of the model. In each iteration of EM algorithm, Naïve Bayes is used for classification. The final result is determined according to the probability, and the probability is the confidence of the result. The experimental results show that our method can achieve better precision and recall on the validation set than two existing general methods. © 2021 ACM.

关键词： Network security

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Deep neural network-enabled battery open-circuit voltage estimation based on partial charging data

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Journal of Energy Chemistry 2024年第3期90卷 120-132,I0005页

作者： Ziyou Zhou Yonggang Liu Chengming Zhang Weixiang Shen Rui Xiong State Key Laboratory of Mechanical Transmission for Advanced Equipment&College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing 400000China Department of Vehicle Engineering School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China Department of Electrical Engineering Harbin Institute of TechnologyHarbin 150000HeilongjiangChina School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthornVictoria 3122Australia

Battery management systems(BMSs) play a vital role in ensuring efficient and reliable operations of lithium-ion *** main function of the BMSs is to estimate battery states and diagnose battery health using battery open-circuit voltage(OCV).However,acquiring the complete OCV data online can be a challenging endeavor due to the time-consuming measurement process or the need for specific operating conditions required by OCV estimation *** addressing these concerns,this study introduces a deep neural network-combined framework for accurate and robust OCV estimation,utilizing partial daily charging *** incorporate a generative deep learning model to extract aging-related features from data and generate high-fidelity OCV *** analysis is employed to identify the optimal partial charging data,optimizing the OCV estimation precision while preserving exceptional *** validation results,using data from nickel-cobalt-magnesium(NCM) batteries,illustrate the accurate estimation of the complete OCV-capacity curve,with an average root mean square errors(RMSE) of less than 3 *** this level of precision for OCV estimation requires only around 50 s collection of partial charging *** validations on diverse battery types operating under various conditions confirm the effectiveness of our proposed *** cases of precise health diagnosis based on OCV highlight the significance of conducting online OCV *** method provides a flexible approach to achieve complete OCV estimation and holds promise for generalization to other tasks in BMSs.

关键词： Lithium-ion battery Open-circuit voltage Health diagnosis Deep learning

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Dynamic Instrumentation Framework Based on Entity Equipment 6

Dynamic Instrumentation Framework Based on Entity Equipment

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6th IEEE International Conference on Computer and Communication Systems, ICCCS 2021

作者： Jiangtao, Zhao Dong, Weiyu Zhang, Haotian Lin, Jian State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9780738126043

Dynamic binary analysis is an important way of program analysis. Most of the existing dynamic analysis tools are based on dynamic binary instrumentation platforms. The existing gdb tool, which is often used for debugging of embedded devices, has low performance on dynamic instrumentation. This paper implements basic block-level instrumentation based on dynamic binary rewriting technology and provides a simple and flexible application programming interface. Through the evaluation, the local instrumentation efficiency of this system is mostly better than the Pin platform, and the remote instrumentation efficiency is about 28.7 times that of the gdb+gdbserver platform. The instrumentation performance has been greatly improved. © 2021 IEEE.

关键词： Efficiency

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Advancing Quantization Steps Estimation: A Two-Stream Network Approach for Enhancing Robustness 24

Advancing Quantization Steps Estimation: A Two-Stream Networ...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Cheng, Xin Wang, Hao Wang, Jinwei Luo, Xiangyang Ma, Bin Nanjing University of Information Science and Technology Nanjing China Huaiyin Institute of Technology Huai'an China Nankai University Tianjin China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Qilu University of Technology Jinan China

ISBN: (纸本)9798400706868

In Joint Photographic Experts Group (JPEG) image steganalysis and forensics, the quantization step can reveal the history of image operations. Several methods for estimating the quantization step have been proposed by researchers. However, existing algorithms fail to account for robustness, which limits the application of these algorithms. To solve the above problems, we propose a two-stream network structure based on Swin Transformer. The spatial domain features of JPEG images exhibit strong robustness but low accuracy. Conversely, frequency domain features demonstrate high accuracy but weak robustness. Therefore, we design a two-stream network with the multi-scale feature of Swin Transformer to extract spatial domain features with high robustness and frequency domain features with high accuracy, respectively. Furthermore, to adaptively fuse features in both the frequency domain and spatial domain,we design a Spatial-frequency Information Dynamic Fusion (SIDF) module to dynamically allocate weights. Finally, we modify the network from a regression model to a classification model to speed up convergence and improve the accuracy of the algorithm. The experiment results show that the accuracy of the proposed method is higher than 98% on clean images. Meanwhile, in robust environments, the algorithm proposed maintains an average accuracy of over 81%. © 2024 ACM.

关键词： Frequency domain analysis

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Research on security assessment based on big data and multi-entity profile 5

Research on security assessment based on big data and multi-...

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5th IEEE advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2021

作者： Liu, Wenfu Pang, Jianmin Yang, Shudan Li, Nan Du, Qiming Sun, Daozhu Liu, Fudong State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Security assessment is one of the important problems, especially in the fields of military training, transportation and network management. The security "related parties"(entities in this paper) have internal relations in the three fields. It is of great significance of constructing the attributes of these entities or even designing a unified security assessment method. Security assessment in these areas has been studied and explored for a long time. However, the research is relatively scattered and unilateral. It is no unified and reusable method from data collection to security assessment. Based on the advantages of big data technology in the storage and analysis of massive data, the paper proposed a security risk element acquisition method from data collection and preprocessing, storage and mining to attribute extraction and aggregation. In addition, drawing on the idea of user portraits in other fields, constructed personnel entities, equipment entities and "other"entity labels related to security risks. Finally, a security risk assessment process based on these entities is designed. © 2021 IEEE.

关键词： Big data

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SROBR: Semantic Representation of Obfuscation-Resilient Binary Code

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Wireless Communications and Mobile computing 2022年第0期2022卷

作者： Tang, Ke Shan, Zheng Liu, Fudong Huang, Yizhao Sun, Rongbo Qiao, Meng Zhang, Chunyan Wang, Jue Gui, Hairen State Key Laboratory of Mathematical Engineering and Advanced Computing China

With the rapid development of information technology, the scale of software has increased exponentially. Binary code similarity detection technology plays an important role in many fields, such as detecting software plagiarism, vulnerabilities discovery, and copyright solution issues. Nevertheless, what cannot be ignored is that a variety of approaches to binary code semantic representation have been introduced recently, but few can catch up with existing code obfuscation techniques due to their maturing and extensive development. In order to solve this problem, we propose a new neural network model, named SROBR, which is a deep integration of natural language processing model and graph neural network. In SROBR, BERT is applied to capture sequence information of the binary code at the first place, and then GAT is utilized to capture the structural information. It combines natural language processing and graph neural network, which can capture the semantic information of binary programs while resisting obfuscation options in a more efficient way. Through binary code similarity detection task and obfuscated option classification task, the experimental results demonstrate that SROBR outperforms existing binary similarity detection methods in resisting obfuscation techniques. © 2022 Ke Tang et al.

关键词： Semantics

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An anonymous key agreement protocol with robust authentication for smart grid infrastructure

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Science China(Information Sciences) 2022年第09期65卷 291-293页

作者： Ting CHEN Qingfeng CHENG Xinghua LI School of Cyber Engineering Xidian University State Key Laboratory of Mathematical Engineering and Advanced Computing

Dear editor,The issue on how to ensure the smart grid environment’s security and reliability has always been a focus in current research. The rapid expansion of the Internet of Things enables billions of smart devices to be involved in the smart grid, such as smart meters, who play a part in monitoring and recording consumers’ power usage. The smart meter first amasses the information of energy consumption and then sends it to utility control.

关键词： smart grid infrastructure impersonation attack perfect forward security ephemeral key compromise attack key agreement

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