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FedTop:a constraint-loosed federated learning aggregation method against poisoning attack

Frontiers of Computer Science 2024年第5期18卷 233-235页

作者： Che WANG Zhenhao WU Jianbo GAO Jiashuo ZHANG Junjie XIA Feng GAO Zhi GUAN Zhong CHEN School of Computer Science Peking UniversityBeijing 100871China Peking University Chongqing Research Institute of Big Data Chongqing 401329China China Unicom Beijing 100033China National Engineering Research Center for Software Engineering Peking UniversityBeijing 100871China

Federated learning(FL)is a decentralized machine learning paradigm,which has significant advantages in protecting data privacy[1].However,FL is vulnerable to poisoning attacks that malicious participants perform attacks by injecting dirty data or abnormal model parameters during the local model training and aim to manipulate the performance of the global model[2].

关键词： constraint loose aggregation

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Secret Sharing Based Key Agreement Protocol for Body Area Networks 18th

Secret Sharing Based Key Agreement Protocol for Body Area N...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Sheng, Weihong Cai, Bin Hu, Chunqiang Li, Ruinian School of Big Data and Software Engineering Chongqing University Chongqing China Computer Science Department Bowling Green State University Bowling Green United States

ISBN: (纸本)9783031714634

In order to provide more comprehensive medical services and personalized health monitoring according to individual needs, Body Area Networks (BANs) have been extensively studied by many researchers. As BANs involve the transmission of personal private data, the security of the communication is of utmost importance. Unfortunately, existing encryption techniques cannot be directly applied with the limited power or computation ability of the sensors in BANs. An alternative direction is to use physiological signals for key agreement. However, many of the current physiological signal-based key agreement schemes either have high overheads or are vulnerable to certain security issues. In this paper, we proposed a novel secret sharing and Bloom filter based key agreement scheme that balances overhead and security for BANs. Also, we use an approximate hash table to free ourselves from the problem of dependence on the ordering of features, which has been neglected by previous researchers. We validate the security of our scheme with based on real datasets and compare overheads with other solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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A Zero-Shot Relation Extraction Approach Based on Contrast Learning 34

A Zero-Shot Relation Extraction Approach Based on Contrast L...

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34th International Conference on software engineering and Knowledge engineering, SEKE 2022

作者： Zhu, Hongyu Zeng, Jun Yang, Yu Wu, Yingbo School of Big Data & Software Engineering Chongqing University Chongqing China

ISBN: (纸本)1891706543

The most significant advantage of the unseen relation extraction is that it can recognize unlabeled relations. While Zero-Shot Learning can meet the requirements of the identification of unseen relation through relation description information without labeled datasets. However, unseen relation extraction requires an effective method in representation and generalization, which become a challenge for zero-shot learning approach. In this paper, we propose a Zero-Shot learning Relation Extraction based on Contrastive learning Model (ZRCM) to capture deep interrelation text information. We design a comparison sample generation method which can produce several instances for one input sentence and compare the distance between positive instance and negative ones, so as to improve the hidden text information mining ability. Experiments conducted on relation extraction common datasets confirmed the promotion of ZRCM compared with the existing methods. Especially, our model can improve the F1 value by up to 7% at best. When there are fewer unseen relations to predict, our model can achieve better performance. © 2022 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Extraction

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CVAS: An Interactive Visual Analytics System for Exploring COVID-19 Information on the Web 2022

CVAS: An Interactive Visual Analytics System for Exploring C...

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6th International Conference on Compute and data Analysis, ICCDA 2022

作者： Yang, Tao Zhou, Yixuan Feng, Dan Hu, Haibo School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9781450395472

The outbreak of COVID-19 has been a critical social event in the past two years. The pandemic has seriously affected the world. Meanwhile, various forms of data about COVD-19 emerge on the Web endlessly, such as SNS discussions, Press releases, WHO statistics, etc. It is valuable work for government departments, news media, and health organizations to integrate and analyze these pandemics-related multi-source data on the web. In this work, we propose an interactive visual analytics system as CVAS that aims at mining and analyzing multi-source data concerned with COVD-19. Having been inspired by the Sankey diagram, we developed a view elaborately. Through appropriate interactions, massive patients' mobility data can be visualized, thus showing the spread features of the pandemic in time and space more specifically. In addition, we collected more than 10,000 trending topics and nearly 10 million related comments on the SNS as Sina Weibo. We performed NLP to analyze their sentiment, identifying key events since the outbreak and the impact of the pandemic on public sentiment. Part of our work was awarded at the China visualization and visual analysis conference (ChinaVis2020) and recognized by peers. © 2022 ACM.

关键词： Visualization

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Efficient Relaxed Functional Dependency Discovery with Minimal Set Cover 40

Efficient Relaxed Functional Dependency Discovery with Minim...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Ding, Xiaoou Liu, Yida Wang, Hongzhi Wang, Chen Song, Yichen Yang, Donghua Wang, Jianmin School of Computer Science and Technology Harbin Institute of Technology China National Engineering Research Center for Big Data Software EIRI Tsinghua University China

ISBN: (纸本)9798350317152

Assessing data quality through Functional Depen-dencies (FDs) is a crucial aspect of data governance. However, with the diverse range of data sources and the exponential growth in data volume, exact FDs can sometimes be impractical for real-world applications. In contrast, relaxed functional dependencies (RFDs), which allows for some flexibility in attribute value comparisons, demonstrates greater adaptability and flexibility for big data scenarios. To address the efficient discovery of RFDs, this paper proposes a novel mining method to supplement the current research gaps. By establishing a difference table for tuples, we transform the problem into a specialized minimal set covering problem. Additionally, we introduce two optimization strategies: reducing the time complexity of enumerating the left-hand side of the base RFDs to 0 (1) and decreasing the search complexity for feasible LHS attributes and threshold candidates from O(2m-l) to O(1.5m-1). We rigorously proof that our mining approach guarantees the identification of validity and minimal RFDs. Experiments on nine real-world datasets reveal that our method significantly improves efficiency compared to existing techniques. Furthermore, it uncovers more concise and higher-quality RFDs. Importantly, the RFDs extracted through our methodology exhibit better performance in downstream cleaning tasks. © 2024 IEEE.

关键词： Quality management

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Key Factor Analysis of Computer Applications and Technologies Curriculum Based on Professional Certificate Program in Higher Vocational Education 12

Key Factor Analysis of Computer Applications and Technologie...

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12th International Conference on Orange Technology, ICOT 2024

作者： Yang, Kai Cai, Wenbo Nie, Zhanglong Tao, Dawei Tseng, Shih-Pang School of Electronic Engineering Changzhou College of Information Technology Changzhou China School of Software and Big Data Changzhou College of Information Technology Changzhou China

ISBN: (纸本)9798331523602

With the advancement of professional certification in higher vocational education, the formulation of talent training programs has become particularly important. Taking the Computer Application Technology major at Changzhou College of Information Technology as a case study, this paper explores several key elements in writing talent training programs under the background of professional certification: career orientation, training objectives, and graduation requirements. Firstly, it analyzes the sources of content in the career orientation table, especially the focus on main positions. Secondly, it clarifies the method for setting training objectives, providing a general template and an example for the Computer Application Technology major. Finally, it elaborates on the formulation of graduation requirements, particularly the decomposition of indicators to ensure they sufficiently support the graduation requirements. The research aims to provide references and insights for higher vocational colleges to optimize their talent training programs in the context of professional certification. © 2024 IEEE.

关键词： Apprentices

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DAAL: Domain Adversarial Active Learning Based on Dual Features for Rumor Detection 12th

DAAL: Domain Adversarial Active Learning Based on Dual Feat...

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12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Zhang, Chaoran Gao, Min Huang, Yinqiu Jiang, Feng Wang, Jia Wen, Junhao School of Big Data and Software Engineering Chongqing University Chongqing China Chongqing Business Vocational College Chongqing China

ISBN: (纸本)9783031446955

The widespread of rumors has a negative impact on society, and rumor detection has attracted significant attention. When a new event appears, the scarcity of corresponding labeled data causes a severe challenge to rumor detection. It is necessary to query high-quality unlabeled data and annotate it for detection. Previous studies on active learning for rumor detection can query the unlabeled samples on the decision boundary based on the textual features of posts. These studies, when selecting the optimal samples, have not sufficiently considered that new event posts are usually far from old events and differ in terms of sentiment, which often plays a key role in rumor detection. Therefore, overlooking these characteristics could potentially lead to sub-optimal performance in rumor detection based on these active learning methods. To this end, we propose domain adversarial active learning based on dual features (DAAL) for rumor detection, considering these characteristics. Specifically, we first extract dual features, including affective and textual features, to obtain representations of the posts. We then propose a new active learning method that selects the samples furthest from all labeled samples based on their dual features. This method helps our rumor detection model gain more labels from new, distant event posts for training. Finally, we introduce adversarial domain training, a method designed to extract transferable features across different events (or domains), to enhance the adaptability of our rumor detection model to new events. Experimental results demonstrate DAAL can select high-quality candidates and achieve superior performance compared to existing methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Feature extraction

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TSDDISCOVER: Discovering data Dependency for Time Series data 40

TSDDISCOVER: Discovering Data Dependency for Time Series Dat...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Ding, Xiaoou Li, Yingze Wang, Hongzhi Wang, Chen Liu, Yida Wang, Jianmin School of Computer Science and Technology Harbin Institute of Technology China Tsinghua University National Engineering Research Center for Big Data Software EIRI China

ISBN: (纸本)9798350317152

Intelligent devices often produce time series data that suffer from significant data quality issues. While the utilization of data dependency in error detection and data repair has been somewhat beneficial, it remains inadequate in accurately representing the data quality of time series datasets. In recognition of the obvious characteristics inherent in time series data, we introduce a novel data dependency, termed TSDD. It effectively captures the contextual relationships embedded within multivariate time series, thereby enhancing the semantic richness of data quality representations. We analyze the complexity of both implication and consistency problems for TSDD reasoning, and develop TSDD discovery algorithm TSDDISCOVER, which consists of functional structure discovery, allowable error bound determination, and validation of TSDD patterns. Experimental results on real-life datasets verify TSDDISCOVER efficiently discovers high-quality TSDD patterns. In comparing the performance of TSDD-based error detection with several leading data quality constraints, our findings reveal that the former achieves an average improvement of 12% in accuracy and 30% in the F1 score over other dependency-based detection methods. © 2024 IEEE.

关键词： Semantics

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Context Aware Deep Learning-Based Fault Localization for Hardware Design Code

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Hu, Jian Liu, Zhenlei Chongqing University School of Big Data and Software Engineering Chongqing400044 China

Fault localization is a technique that plays a valuable role in reducing the burdensome task of hardware design code, such as Verilog or VHDL. The recent advancements in deep learning have demonstrated significant progress, showcasing the promising potential of various neural network architectures in comprehending and extracting meaningful insights from data. Importantly, this potential offers a new perspective that could potentially benefit fault localization techniques. In this paper, we proposed Carpel: a context aware deep learning-based fault localization method for hardware design code. First, a dynamic execution coverage matrix and an error vector are generated by simulating the hardware code with various stimuli. Subsequently, a failing context is established to narrow the search space for fault localization. Next, three customized deep neural network models are developed and trained using the context-aware coverage matrix and error vector. Finally, a virtual test set is employed to these models to assess the suspiciousness of each statement. Experimental results indicate that our method outperforms state-of-the-art dynamic fault localization techniques, namely Detraque, Tarsel, and Cirfix. Specifically, our approach identifies 37.03%, 22.22%, and 33.33% more faults than Detraque, Tarsel, and Cirfix, respectively, using the Top-1 metric. © 1982-2012 IEEE.

关键词： Hardware-software codesign

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Research on Integrated Circuit Packaging Testing Technology Based on Large Language Model 3

Research on Integrated Circuit Packaging Testing Technology ...

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3rd International Symposium on Semiconductor and Electronic Technology, ISSET 2024

作者： Li, Qiang Ma, Xinqiang Yan, Meng School of Big Data and Software Engineering Chongqing University Chongqing China School of Mathematics and Artificial Intelligence Chongqing University of Arts and Sciences Chongqing China

ISBN: (纸本)9798350390285

The integrated circuit packaging testing technology is highly demanding, and the task is arduous. The existing sorting efficiency of integrated circuit packaging testing is low, and it is difficult to detect unqualified products for rapid centralized processing. There is an urgent need for upgrading and improvement. This paper proposes to fine-tune the large language model and establish a large language model monitoring system for integrated circuit packaging and inspection, embedding it in a CNC system, thereby providing support for the intelligent transformation and upgrading of integrated circuit production and processing. © 2024 IEEE.

关键词： Integrated circuit testing

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