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International Conference on Data engineering

作者： Haodi Zhang Wenxi Huang Zhenhan Su Junyang Chen Di Jiang Lixin Fan Chen Zhang Defu Lian Kaishun Wu College of Computer Science and Software Engineering Shenzhen University Department of Computing Hong Kong Polytechnic University WeBank Institute of Financial Technology Shenzhen University Webank Co. Ltd. School of Data Science University of Science and Technology of China

With the rapid and continuous development of data-driven technologies such as supervised learning, high-quality labeled data sets are commonly required by many applications. Due to the easiness of crowdsourcing small tasks with low cost, a straightforward solution for label quality improvement is to collect multiple labels from a crowd, and then aggregate the answers. The aggregation strategies include majority voting and its many variants, EM-based approaches, Graph Neural Nets and so on. However, due to the uncertainty information loss and commonly existing task correlations, the aggregated labels usually contain errors and may damnify the downstream model *** address the above problem, we propose a hierarchical crowdsourcing framework 1 for data labeling with noisy answers about correlated data. We make use of the heterogeneity of the labeling crowd and form an initialization-checking-update loop to improve the quality of labeled data. We formalize and successfully solve the core optimization problem, namely, selecting a proper set of checking tasks for each round. We prove that maximizing the expected quality improvement is equivalent to minimizing the conditional entropy of the observations given the crowdsourced answer families for the selected task set, which is NP-hard to solve. Therefore, we design an efficient approximation algorithm and conduct a series of experiments on real data. The experimental results show that the proposed method effectively improves the quality of the labeled data sets as well as the SOTA performance, yet without extra human labor costs.

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CNS-Net: Conservative Novelty Synthesizing Network for Malware Recognition in an Open-set Scenario

arXiv

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arXiv 2023年

作者： Guo, Jingcai Guo, Song Ma, Shiheng Sun, Yuxia Xu, Yuanyuan The Department of Computing The Hong Kong Polytechnic University Hong Kong The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200030 China The Department of Computer Science Jinan University Guangzhou510632 China The School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China

We study the challenging task of malware recognition on both known and novel unknown malware families, called malware open-set recognition (MOSR). Previous works usually assume the malware families are known to the classifier in a close-set scenario, i.e., testing families are the subset or at most identical to training families. However, novel unknown malware families frequently emerge in real-world applications, and as such, require to recognize malware instances in an open-set scenario, i.e., some unknown families are also included in the test-set, which has been rarely and non-thoroughly investigated in the cyber-security domain. One practical solution for MOSR may consider jointly classifying known and detecting unknown malware families by a single classifier (e.g., neural network) from the variance of the predicted probability distribution on known families. However, conventional well-trained classifiers usually tend to obtain overly high recognition probabilities in the outputs, especially when the instance feature distributions are similar to each other, e.g., unknown v.s. known malware families, and thus dramatically degrades the recognition on novel unknown malware families. To address the problem and construct an applicable MOSR system, we propose a novel model that can conservatively synthesize malware instances to mimic unknown malware families and support a more robust training of the classifier. More specifically, we build upon the generative adversarial networks (GANs) to explore and obtain marginal malware instances that are close to known families while falling into mimical unknown ones to guide the classifier to lower and flatten the recognition probabilities of unknown families and relatively raise that of known ones to rectify the performance of classification and detection. A cooperative training scheme involving the classification, synthesizing and rectification are further constructed to facilitate the training and jointly improve the model p

关键词： Malware

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DISTDET: a cost-effective distributed cyber threat detection system 23

DISTDET: a cost-effective distributed cyber threat detection...

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Proceedings of the 32nd USENIX Conference on Security Symposium

作者： Feng Dong Liu Wang Xu Nie Fei Shao Haoyu Wang Ding Li Xiapu Luo Xusheng Xiao School of Cyber Science and Engineering Huazhong University of Science and Technology and Sangfor Technologies Inc Beijing University of Posts and Telecommunications Case Western Reserve University School of Cyber Science and Engineering Huazhong University of Science and Technology Key Laboratory of High-Confidence Software Technologies (MOE) School of Computer Science Peking University The Hong Kong Polytechnic University Arizona State University

ISBN: (纸本)9781939133373

Building provenance graph that considers causal relationships among software behaviors can better provide contextual information of cyber attacks, especially for advanced attacks such as Advanced Persistent Threat (APT) attacks. Despite its promises in assisting attack investigation, existing approaches that use provenance graphs to perform attack detection suffer from two fundamental limitations. First, existing approaches adopt a centralized detection architecture that sends all system auditing logs to the server for processing, incurring intolerable costs of data transmission, data storage, and computation. Second, they adopt either rule-based techniques that cannot detect unknown threats, or anomaly-detection techniques that produce numerous false alarms, failing to achieve a balance of precision and recall in APT detection. To address these fundamental challenges, we propose DISTDET, a distributed detection system that detects APT attacks by (1) performing light weight detection based on the host model built in the client side, (2) filtering false alarms based on the semantics of the alarm proprieties, and (3) deriving global models to complement the local bias of the host models. Our experiments on a large-scale industrial environment (1,130 hosts, 14 days, ∼1.6 billion events) and the DARPA TC dataset show that DISTDET is as effective as sate-of-the-art techniques in detecting attacks, while dramatically reducing network bandwidth from 11.28Mb/s to 17.08Kb/S (676.5× reduction), memory usages from 364MB to 5.523MB (66× reduction), and storage from 1.47GB to 130.34MB (11.6× reduction). By the time of this writing, DISTDET has been deployed to 50+ industry customers with 22,000+ hosts for more than 6 months, and identified over 900 real-world attacks.

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Deep Learning-Based Approach for Identification of Potato Leaf Diseases Using Wrapper Feature Selection and Feature Concatenation

arXiv

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arXiv 2025年

作者： Naeem, Muhammad Ahtsam Saleem, Muhammad Asim Sharif, Muhammad Imran Akber, Shahzad Saleem, Sajjad Akhtar, Zahid Siddique, Kamran School of Information and Software Engineering University of Electronic Science and Technology of China China Faculty of Computing Riphah International University Faisalabad Campus Pakistan Department of Computer Science COMSATS University Islamabad Wah Campus Pakistan Department of Information and Technology Washington University of Science and Technology AlexandriaVA United States Department of Network and Computer Security State University of New York Polytechnic Institute United States Department of Computer Science and Engineering University of Alaska Anchorage Anchorage United States

The potato is a widely grown crop in many regions of the world. In recent decades, potato farming has gained incredible traction in the world. Potatoes are susceptible to several illnesses that stunt their development. This plant seems to have significant leaf disease. Early Blight and Late Blight are two prevalent leaf diseases that affect potato plants. The early detection of these diseases would be beneficial for enhancing the yield of this crop. The ideal solution is to use image processing to identify and analyze these disorders. Here, we present an autonomous method based on image processing and machine learning to detect late blight disease affecting potato leaves. The proposed method comprises four different phases: (1) Histogram Equalization is used to improve the quality of the input image;(2) Feature extraction is performed using a Deep CNN model, then these extracted features are concatenated;(3) Feature selection is performed using wrapper-based feature selection;(4) Classification is performed using an SVM classifier and its variants. This proposed method achieves the highest accuracy of 99% using SVM by selecting 550 features. © 2025, CC BY.

关键词： Deep learning

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Microstructure and wear resistance of CoCrNbNiW high-entropy alloy coating prepared by laser melting deposition

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Rare Metals 2019年第12期38卷 1153-1159页

作者： Yan-Jun Jia Han-Ning Chen Xiao-Dan Liang School of Mechanical Engineering Tianjin Polytechnic UniversityTianjin 300387China School of Computer Science and Technology Tianjin Polytechnic UniversityTianjin 300387China

The defect-free CoCrNbNiW high-entropy alloy coating was successfully prepared by laser melting deposition,and its microstructure and wear resistance were *** results showed that the microstructure of CoCrNbNiW high-entropy alloy coating consisted of fee phase rich in Nb and fee phase including un-melted W particles and rich in ***,an amount of fee phase was formed at the middle and top of coating,while the bcc phase rich in Cr was formed at the ***,the un-melted W particles were diffusely distributed in the ***,the microhardness of CoCrNbNiW high-entropy alloy coating was improved and was 2.78 times as high as that of *** wear loss and wear rate of coating were 0.26 and 0.23 times higher than those of substrate,*** wear resistance of substrate was obviously improved due to the preparation of CoCrNbNiW high-entropy alloy coating.

关键词： Microstructure High-entropy alloy Microhardness Wear resistance

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Llm-Based Automated Modeling in Symbolic Execution for Securing Medical software

SSRN

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SSRN 2024年

作者： Chen, Jingxue Deng, Liangjun Qiu, Yao Zhao, Pengbiao Lei, Hang Song, Jingcheng Wang, Xiaopei School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu610054 China Institute of Building Information Modeling Ningbo Polytechnic College Zhejiang Ningbo315800 China College of Information Science and Engineering Linyi University Shandong Linyi276000 China Department of Computer Science and Engineering University of California RiversideCA92521 United States

There are lots of sensitive information in medical consumer devices. Since Consumer electronics products in the healthcare mainly focus on health monitoring and personal health management, such as wearable ECG monitoring devices, smart weight scales and so on. It is paramount to ensure the security and reliability of software in healthcare delivery, especially for sensitive medical data of integration with artificial intelligence (AI). Therefore, it is necessitating strict security verification and continuous vigilance against potential software vulnerabilities. WebAssembly, known for its security, portability, and speed, has emerged as a crucial technology for medical device software, significantly impacting the healthcare electronics sector, particularly in AI-generated content. There is a novel machine learning-based approach to address the challenges of symbolic execution in WebAssembly by automatically modeling unknown functions using a fine-tuned large language model (LLM) with 20 billion parameters, enhancing the execution's integrity and efficiency. The method, applicable across multiple programming languages and extendable to WebAssembly-compiled programs, shows promising experimental results in model performance and efficiency. This ensures enhanced security and reliability of software for CE, setting the stage for more flexible WebAssembly standards in medical CE and AI-driven healthcare devices. © 2024, The Authors. All rights reserved.

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An End-to-End Conformer-Based Speech Recognition Model for Mandarin Radiotelephony Communications in Civil Aviation 16th

An End-to-End Conformer-Based Speech Recognition Model for M...

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16th Chinese Conference on Biometric Recognition, CCBR 2022

作者： Shi, Yihua Ma, Guanglin Ren, Jin Zhang, Haigang Yang, Jinfeng Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic Shenzhen518055 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9783031202322

In civil aviation radiotelephony communications, misunderstandings between air traffic controllers and flight crews can result in serious aviation accidents. Automatic semantic verification is a promising assistant solution to decrease miscommunication, thanks to advancements in speech and language processing. Unfortunately, existing general speech recognition models are ineffective when it comes to capturing contextual long-distance dependent local similarity features in radiotelephony communications. To address these problems, this paper proposes an end-to-end Conformer-based multi-task learning speech recognition model for Mandarin radiotelephony communications in civil aviation. The Conformer model improves local information capture while retaining the global information modeling capabilities of contextual long-distance dependencies, owing to the introduction of the convolution module to the Transformer model. Meanwhile, multi-task learning is used to further improve performance by combining connectionist temporal classification (CTC) and attention-based encoder-decoder (AED) models. The experimental results show that the proposed model can perform global and local acoustic modeling effectively, making it particularly suitable for extracting acoustic features of Mandarin civil aviation radiotelephony communications. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Radio telephone

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Co-occurrence order-preserving pattern mining

arXiv

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arXiv 2024年

作者： Wu, Youxi Wang, Zhen Li, Yan Guo, Yingchun Jiang, He Zhu, Xingquan Wu, Xindong School of Artificial Intelligence Hebei University of Technology Tianjin300401 China ShiJiaZhuang Posts and Telecommunications Technical College Shijiazhuang050021 China School of Economics and Management Hebei University of Technology Tianjin300401 China School of Software Dalian University of Technology Dalian116023 China Department of Computer & Electrical Engineering and Computer Science Florida Atlantic University FL33431 United States Hefei University of Technology Hefei230009 China

Recently, order-preserving pattern (OPP) mining has been proposed to discover some patterns, which can be seen as trend changes in time series. Although existing OPP mining algorithms have achieved satisfactory performance, they discover all frequent patterns. However, in some cases, users focus on a particular trend and its associated trends. To efficiently discover trend information related to a specific prefix pattern, this paper addresses the issue of co-occurrence OPP mining (COP) and proposes an algorithm named COP-Miner to discover COPs from historical time series. COP-Miner consists of three parts: extracting keypoints, preparation stage, and iteratively calculating supports and mining frequent COPs. Extracting keypoints is used to obtain local extreme points of patterns and time series. The preparation stage is designed to prepare for the first round of mining, which contains four steps: obtaining the suffix OPP of the keypoint sub-time series, calculating the occurrences of the suffix OPP, verifying the occurrences of the keypoint sub-time series, and calculating the occurrences of all fusion patterns of the keypoint sub-time series. To further improve the efficiency of support calculation, we propose a support calculation method with an ending strategy that uses the occurrences of prefix and suffix patterns to calculate the occurrences of superpatterns. Experimental results indicate that COP-Miner outperforms the other competing algorithms in running time and scalability. Moreover, COPs with keypoint alignment yield better prediction performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Time series

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An Efficient Blockchain-based Privacy-Preserving Authentication Scheme in VANET

An Efficient Blockchain-based Privacy-Preserving Authenticat...

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IEEE Conference on Vehicular Technology (VTC)

作者： Shiyuan Xu Xue Chen Weimin Kong Yibo Cao Yunhua He Ke Xiao School of Information Engineering North China University of Technology Beijing China Department of Computer Science The University of Hong Kong Pok Fu Lam Hong Kong Department of Computing The Hong Kong Polytechnic University Hung Hom Hong Kong College of Software Tianjin Normal University Tianjin China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

With emerging technology, the blockchain-based Vehicle Ad-hoc Network (VANET) can alleviate traffic congestion and optimize resource management to improve the transportation system's efficiency significantly. However, since the information transmitted in VANET is distributed in an open environment, security, and privacy are now critical issues. Recently, many authentication protocols based on Tamper-Proof Devices (TPD) have been proposed to solve the above-mentioned hindrances, and most of them rely on the ideal TPD with extreme security assumptions. Moreover, when the aggregate signature verification fails, these schemes can only completely discard the aggregate signature, which is impractical for VANET. In order to solve the above problems, we propose a more realistic TPD-based identity verification scheme with a privacy protection function for the blockchain-based VANET. Specifically, it uses an offline self-update method to periodically update the data in the TPD to resist side-channel attacks. Our performance analysis shows that the proposed scheme is superior to the existing schemes regarding security and average delay, so it is more suitable for the actual blockchain-based VANET environment.

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FGRL-Net: Fine-Grained Personalized Patient Representation Learning for Clinical Risk Prediction Based on EHRs

FGRL-Net: Fine-Grained Personalized Patient Representation L...

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IEEE International Conference on Systems, Man and Cybernetics

作者： KaKit Chio Wenhao Zhu Lihua He Dian Zhang Xu Yang Wuman Luo Faculty of Applied Sciences Macao Polytechnic University Macao S.A.R China School of Information and Communication Engineering University of Electronic Science and Technology of China Sichuan Province China Department of Computer Science and Software Engineering Shenzhen University Shenzhen China

Personalized patient representation learning (PPRL) is a critical element in clinical risk prediction. It aims to obtain a complete portrait of each patient based on Electronic Health Records (EHR). Although existing works have achieved remarkable progress in healthcare prediction, there are still three major issues. First, feature correlation is crucial for risk prediction, but it has not yet been fully exploited by existing works. Second, variation pattern of dynamic feature contains useful information about patient's physical status, but adaptive pattern recognition is still a challenge. Third, existing works usually adopt a two-stage embedding process to process each dimension of the EHR data. However, some useful low-level information for PPRL will be lost. To address these issues, in this paper, we propose a fine-grained PPRL architecture named FG RL- N et for clinical risk prediction based on EHR. Specifically, we propose a Medical Feature Correlation Detection Module (FCM) to effectively learn the feature correlations for each patient and a Temporal Variation Pattern Recognition Module (TVM) to effectively detect the variation patterns of each dynamic feature. Moreover, we design a Fine-Grained Representation Mechanism (FGRM) to preserve the low-level information (from both feature and visit dimensions) useful for risk prediction. In addition, in the stage of data preprocessing, We utilize generic medical classification knowledge to classify numerical dynamic data. We conduct the in-hospital mortality experiment and the decompensation experiment on a real-world dataset. The experiment results show that the FGRL-Net outperforms state-of-the-art approaches. The source code is provided in github https://***/JackyChio/FGRL-Net.

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