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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Li, Hao Wu, Yirui Hu, Hexuan Lu, Hu Lai, Yong Wan, Shaohua Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources China College of Computer and Information Hohai University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China School of Computer Science and Communication Engineering Jiangsu University China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China

ISBN: (纸本)9781665468190

Deep learning methods have shown significant performance in medical image analysis tasks. However, they generally act like 'black box' without explanations in both feature extraction and decision processes, leading to lack of clinical insights and high risk assessments. To aid deep learning in envisioning diseases with visual clues, we propose Representation Group-Disentangling Network (RGD-Net), which can completely disentangle feature space of input X-ray images into several independent feature groups, each corresponding to a specific disease. Taking several semantically related and labeled X-ray images as input, RGD-Net firstly extracts completely group-disentangled representations of diseases through Group-Disentangle Module, which applies group-swap and linking operations to construct latent space by enforcing semantic consistency of attributes. To prevent learning degenerate representations defined as shortcut problem, we further introduce adversarial constricts on mapping from features to diseases, thus avoiding model collapse with former free-form disentanglement. Experiments on chestxray-14 and ChestXpert datasets demonstrate that RGD-Net are effective in predicting diseases with remarkable advantages, which leverage potential factors contributing to different diseases, thus enhancing interpretability in working patterns of deep learning methods. © 2022 IEEE.

关键词： Forecasting

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An Exact Potential Game-Based End-Edge-Cloud Collaborative Task Offloading Approach 19

An Exact Potential Game-Based End-Edge-Cloud Collaborative T...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Shen, Yue Liu, Bowen Dai, Fei Dou, Wanchun Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Shenzhen China Southwest Forestry University College of Big Data and Intelligent Engineering Kunming China

ISBN: (纸本)9798350346558

With the development of Industrial Internet of Things(IIoT), massive industrial devices produce a large number of delay-sensitive and computing-intensive tasks. Offloading tasks under the end-edge-cloud collaboration mode can improve the efficiency of completing tasks. However, on the one hand, tasks need to be finished before corresponding deadlines and pay for consuming resources, so delay and cost constitute the main factors affecting task offloading decisions. On the other hand, limited real-time resources in the system may not meet the needs of all devices, so we should ensure that as many devices can complete tasks on time as possible while optimizing task delay and cost of each device. To tackle the challenges, we propose an end-edge-cloud collaborative task offloading approach based on exact potential game. Aiming at optimizing delay-cost of each device and task completion rate of the whole system, the end-edge-cloud collaborative task offloading problem is modeled as an exact potential game and solved. Extensive experiments show that the approach can improve the task completion rate of the system while optimizing the delay-cost of each device. © 2022 IEEE.

关键词： Edge computing

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FIRE: combining multi-stage filtering with taint analysis for scalable recurring vulnerability detection 24

FIRE: combining multi-stage filtering with taint analysis fo...

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Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Siyue Feng Yueming Wu Wenjie Xue Sikui Pan Deqing Zou Yang Liu Hai Jin 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 and School of Cyber Science and Engineering Huazhong University of Science and Technology China Nanyang Technological University Singapore 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 and School of Cyber Science and Engineering Huazhong University of Science and Technology China and Jinyinhu Laboratory China 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 and School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781939133441

With the continuous development of software open-sourcing, the reuse of open-source software has led to a significant increase in the occurrence of recurring vulnerabilities. These vulnerabilities often arise through the practice of copying and pasting existing vulnerabilities. Many methods have been proposed for detecting recurring vulnerabilities, but they often struggle to ensure both high efficiency and consideration of semantic information about vulnerabilities and patches. In this paper, we introduce FIRE, a scalable method for large-scale recurring vulnerability detection. It utilizes multi-stage filtering and differential taint paths to achieve precise clone vulnerability scanning at an extensive scale. In our evaluation across ten open-source software projects, FIRE demonstrates a precision of 90.0% in detecting 298 recurring vulnerabilities out of 385 ground truth instance. This surpasses the performance of existing advanced recurring vulnerability detection tools, detecting 31.4% more vulnerabilities than VUDDY and 47.0% more than MOVERY. When detecting vulnerabilities in large-scale software, FIRE outperforms MOVERY by saving about twice the time, enabling the scanning of recurring vulnerabilities on an ultra-large scale.

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Reversible data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年第13期12卷 23933-23945页

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： Cryptography Encryption Security data mining Adaptive coding Correlation Cloud computing Polynomials Image coding Streaming media

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SGSH: Stimulate Large Language Models with Skeleton Heuristics for knowledge Base Question Generation

arXiv

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arXiv 2024年

作者： Guo, Shasha Liao, Lizi Zhang, Jing Wang, Yanling Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education China Singapore Management University Singapore Zhongguancun Laboratory China

knowledge base question generation (KBQG) aims to generate natural language questions from a set of triplet facts extracted from KB. Existing methods have significantly boosted the performance of KBQG via pre-trained language models (PLMs) thanks to the richly endowed semantic knowledge. With the advance of pre-training techniques, large language models (LLMs) (e.g., GPT-3.5) undoubtedly possess much more semantic knowledge. Therefore, how to effectively organize and exploit the abundant knowledge for KBQG becomes the focus of our study. In this work, we propose SGSH — a simple and effective framework to Stimulate GPT-3.5 with Skeleton Heuristics to enhance KBQG. The framework incorporates "skeleton heuristics", which provides more fine-grained guidance associated with each input to stimulate LLMs to generate optimal questions, encompassing essential elements like the question phrase and the auxiliary verb. More specifically, we devise an automatic data construction strategy leveraging ChatGPT to construct a skeleton training dataset, based on which we employ a soft prompting approach to train a BART model dedicated to generating the skeleton associated with each input. Subsequently, skeleton heuristics are encoded into the prompt to incentivize GPT-3.5 to generate desired questions. Extensive experiments demonstrate that SGSH derives the new state-of-the-art performance on the KBQG tasks. The code is now available on Github1 Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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A Query Optimization Method Utilizing Large Language Models

arXiv

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arXiv 2025年

作者： Yao, Zhiming Li, Haoyang Zhang, Jing Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Engineering Research Center of Database and Business Intelligence MOE China

Query optimization is a critical task in database systems, focused on determining the most efficient way to execute a query from an enormous set of possible strategies. Traditional approaches rely on heuristic search methods and cost predictions, but these often struggle with the complexity of the search space and inaccuracies in performance estimation, leading to suboptimal plan choices. This paper presents LLMOpt, a novel framework that leverages Large Language Models (LLMs) to address these challenges through two innovative components: (1) LLM for Plan Candidate Generation (LLMOpt(G)), which eliminates heuristic search by utilizing the reasoning abilities of LLMs to directly generate high-quality query plans, and (2) LLM for Plan Candidate Selection (LLMOpt(S)), a list-wise cost model that compares candidates globally to enhance selection accuracy. To adapt LLMs for query optimization, we propose fine-tuning pre-trained models using optimization data collected offline. Experimental results on the JOB, JOB-EXT, and Stack benchmarks show that LLMOpt(G) and LLMOpt(S) outperform state-of-the-art methods, including PostgreSQL, BAO, and HybridQO. Notably, LLMOpt(S) achieves the best practical performance, striking a balance between plan quality and inference efficiency. Copyright © 2025, The Authors. All rights reserved.

关键词： Structured Query Language

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Research on Production Condition Identification Method of Pumping Well Based on an Improved Pattern Recognition Algorithm 9

Research on Production Condition Identification Method of Pu...

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9th Annual International Conference on Network and Information Systems for Computers, ICNISC 2023

作者： Wu, Haili Zhang, Xiangyang Wu, Dan Li, Qun Yao, Gang Research Institute of Petroleum Exploration and Development-Northwest PetroChina Key Laboratory of Internet of Things Cnpc Petroleum and Petrochemical Research Center of National Engineering Laboratory of Big Data Collaborative Security Cnpc Lanzhou China Research Institute of Petroleum Exploration and Development-Northwest PetroChina Lanzhou China

ISBN: (纸本)9798350306682

In the process of oil and gas production, indicator diagram is an important basis for production management. Diagnosing the working condition of pumping unit based on indicator diagram can quickly grasp the working state of oil well, analyze and judge whether the production parameters of oil well are reasonable. The pattern recognition technology of indicator diagram generally includes two stages: feature extraction and classification. Classification methods mainly use a few common machine learning methods such as artificial neural network and SVM. In recent years, feature extraction methods mainly include geometric feature method, grid feature and moment invariant feature methods. Due to the limitations of these extraction methods, most of them can only extract some primary feature quantities. However, using these primary feature quantities to recognize the indicator diagram often has the technical problems of low recognition efficiency and low recognition accuracy. In this paper, an improved indicator diagram pattern recognition method based on binary gradient direction histogram feature is proposed. Compared with the previous feature extraction methods, this method has good robustness, fast calculation speed and high recognition accuracy. © 2023 IEEE.

关键词： Extraction

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Analysis on the causes of massive stranding of Yellow Sea green tide on Lianyungang and Rizhao coasts in 2022

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Journal of Oceanology and Limnology 2024年第3期42卷 816-830页

作者： Lingjuan WU Juan HUANG Yi DING Guiyan LIU Shuai HUANG Song GAO Chao YUAN Jiangling XU Ping WU Rui HUANG Ruobing WEN Jie MEI North China Sea Marine Forecasting and Hazard Mitigation Center Ministry of Natural ResourcesQingdao 266061China Shandong Key Laboratory of Marine Ecological Environment and Disaster Prevention and Mitigation Qingdao 266061China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an 710129China East China Sea Marine Forecasting and Hazard Mitigation Center Ministry of Natural ResourcesShanghai 200136China North China Sea Ecological Center Ministry of Natural ResourcesQingdao 266033China

In 2022,Yellow Sea green tide caused by Ulva prolifera reached a historic minimum and the coastal areas of Shandong Peninsula were less ***,the largest amount of seaweed biomass has been washed ashore on Lianyungang and Rizhao coasts since *** studied the development pattern of Yellow Sea green tide in 2022,and analyzed the key environmental factors on the growth and drifting,then discussed the possible reasons that resulted in the massive stranding of green tide biomass in Lianyungang and *** show under the combined influence of the east to southeast winds and currents with shoreward anomalies,green tide drifted to the coastal waters between Shandong and Jiangsu provinces and the distribution areas located westward compared with previous years(2008–2021).Floating *** rafts from the coastal waters of Binhai and Sheyang drifted continuously into the coastal waters of Lianyungang and Rizhao,providing important supplements for Yellow Sea green *** green tide in 2022 distributed close to the coastal waters,the abundant nutrients might support their continuous high growth *** addition,the amount of rainfall around Shandong Peninsula from late June to early July were significantly higher than in previous years,which might promote the development of green tide to some extent.

关键词： Yellow Sea green tide stranding biomass development pattern environmental factor

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Preprocessing technology of consuming big data based on user interest with internet of location mining

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International Journal of Computers and Applications 2021年第2期43卷 147-152页

作者： Dagao, Duan Yang, Gao Department of Computer Science and Information Engineering Beijing Technology and Business University Beijing China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing China

The current group discovery research mainly uses the communication relationship to mine user groups and fails to make full use of the user social relationships implied in the network, so that the mining groups can not truly reflect the user’s social relations in the social life. In this paper, the consumer big data preprocessing technology based on user interest location mining is proposed. The sequence pattern mining algorithm is used to mine user’s positional regularity sequence, and the position sequence correlation metrics are established. The positional regularity correlation is used to reveal the social relationship of the user’s social activities. Second, this paper combines the local similarity measure method to calculate the user’s communication distance index, which reflects the degree of understanding between users. Finally, using the communication distance index to weight the position correlation to calculate the user group correlation and then use the split clustering algorithm to mine user groups with communication relationship and social relationship. The experimental results showed that this method can effectively mine the communication relevance and position correlation in the user social activities and reflect the user’s group relations in real social activities. © 2018 Informa UK Limited, trading as Taylor & Francis Group.

关键词： data mining

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Explicit and Implicit Consumer Electronics Information Integration via Automatic Large Language Model Construction

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IEEE Transactions on Consumer Electronics 2025年

作者： Xue, Xingsi Li, Xingwang the Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou China the School of Physics and Electronic Information Engineering Henan Polytechnic University China

—Integrating diverse Consumer Electronics (CE) information is essential to enhance and optimize user experiences, but CE Information Integration (CEII) faces challenges arising from differences in entity descriptions. In recent years, Large Language Models (LLMs) have emerged as valuable tools for analyzing heterogeneous CE data due to their deep contextual embeddings. However, different LLMs capture various nuances and complexity levels within the CE data, and none of them can ensure their effectiveness in all heterogeneous scenarios. To address this issue, this paper proposes an automatic LLM Construction for CEII, which can determine both explicit and implicit CE entity mappings. First, a hybrid Genetic Programming is developed to determine explicit entity mappings using a new individual representation, an innovative fitness function, and a Probability-based Incremental Learning algorithm. Then, a novel Rough Set based matching method is presented to efficiently induce implicit CE entity mappings, which uses the entity feature relevance metric and the dominance-based matching approach. The experiment tests the performance of our approach with the OAEI’s KG and common KG datasets, and the practical CE datasets in the smart home context. The experimental results show the effectiveness of the developed approach, which significantly outperforms the state-of-the-art entity matching methods. © 2025 IEEE.

关键词： Genetic programming

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