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arXiv 2024年

作者： Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Department of Computer Science University of Colorado Colorado Springs Colorado SpringsCO United States Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 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 China JinYinHu Laboratory Wuhan China

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter-Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers;and (ii) function-level vulnerability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities. Copyright © 2024, The Authors. All rights reserved.

关键词： Open source software

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MGSTCN: A multi-graph spatio-temporal convolutional network for metro passenger flow prediction 7

MGSTCN: A multi-graph spatio-temporal convolutional network ...

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7th International Conference on Big Data computing and Communications, BigCom 2021

作者： Yang, Jiahao Liu, Tong Li, Chengfan Tong, Weiqin Zhu, Yanmin Ai, Wenwei Shanghai University School of Computer Engineering and Science China Shanghai Engineering Research Center of Intelligent Computing System China Shanghai Jiao Tong University Department of Computer Science and Engineering China Shanghai Shentong Metro Co. Ltd. China

ISBN: (纸本)9781665442527

Short-term passenger flow forecasting is significantly important in the urban rail transit system. However, there are many factors that affect passenger flows, and traditional statistical prediction methods are unable to model these complex factors comprehensively. Emerging deep learning models have become effective methods to overcome this problem. In this study, we propose a new deep learning model MGSTCN (Multi-Graph Spatio-Temporal Convolutional Network) to predict short-term passenger flows for all stations in an urban subway network. Specifically, MGSTCN is composed of three modules with the same structure, which models the temporal dependencies of passenger flows in three different time scales, i.e., recent, daily and weekly periods respectively. Each module contains three main components: a multi-graph convolutional layer, a feature fusion layer, and a recurrent neural network. In addition, we construct three topological graphs based on the subway network, station attributes, and passenger flow trends, to extract spatial features from different perspectives through graph convolutional networks. Besides, we also employ some external factors such as weather conditions and holidays to enhance the forecasting accuracy of our proposed model. We conduct experiments based on two real-world passenger flow datasets in Shanghai and Hangzhou, respectively. The results show that our proposed model has superior performance compared to the baselines. © 2021 IEEE.

关键词： Forecasting

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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation

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Procedia computer science 2023年 220卷 339-347页

作者： Leila Ismail Muhammad Danish Waseem Clouds and Distributed Computing and Systems (CLOUDS) Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia. Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE. National Water and Energy Center United Arab Emirates University UAE.

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately.

关键词： computer Vision Deep Learning Image Processing Machine Learning Pain Detection Patient-Centric Smart healthcare eHealth

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HPC-Coder: Modeling Parallel Programs using Large Language Models

arXiv

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arXiv 2023年

作者： Nichols, Daniel Marathe, Aniruddha Menon, Harshitha Gamblin, Todd Bhatele, Abhinav Department of Computer Science University of Maryland College ParkMD United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA United States Livermore Computing Lawrence Livermore National Laboratory LivermoreCA United States

Parallel programs in high performance computing (HPC) continue to grow in complexity and scale in the exascale era. The diversity in hardware and parallel programming models make developing, optimizing, and maintaining parallel software even more burdensome for developers. One way to alleviate some of these burdens is with automated development and analysis tools. Such tools can perform complex and/or remedial tasks for developers that increase their productivity and decrease the chance for error. Until recently, such tools for code development and performance analysis have been limited in the complexity of tasks they can perform, especially for parallel programs. However, with recent advancements in language modeling, and the availability of large amounts of open-source code related data, these tools have started to utilize predictive language models to automate more complex tasks. In this paper, we show how large language models (LLMs) can be applied to tasks specific to high performance and scientific codes. We introduce a new dataset of HPC and scientific codes and use it to fine-tune several pre-trained models. We compare several pre-trained LLMs on HPC-related tasks and introduce a new model, HPC-Coder, fine-tuned on parallel codes. In our experiments, we show that this model can auto-complete HPC functions where generic models cannot, decorate for loops with OpenMP pragmas, and model performance changes in scientific application repositories as well as programming competition solutions. Copyright © 2023, The Authors. All rights reserved.

关键词： Modeling languages

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A Systematic Literature Review on Blockchain Consensus Mechanisms’ Security: Applications and Open Challenges

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computer Systems science & Engineering 2024年第6期48卷 1437-1481页

作者： Muhammad Muntasir Yakubu Mohd Fadzil B Hassan Kamaluddeen Usman Danyaro Aisha Zahid Junejo Muhammed Siraj Saidu Yahaya Shamsuddeen Adamu Kamal Abdulsalam Department of Computer and Information Science Universiti Teknologi PETRONASPerak32610Malaysia Department of Information Technology Faculty of ComputingFederal University Dutsin-MaKatsina5001Nigeria Institute of Autonomous Systems Universiti Teknologi PETRONASPerak32610Malaysia Institut für Luftfahrtsysteme Universität StuttgartStuttgart70569Germany Department of Computer Science Academic City University CollegeAgbogbaAccra421Ghana Department of Mathematical Sciences National University of the Center of the Province of Buenos AiresPintoTandil399Argentina

This study conducts a systematic literature review(SLR)of blockchain consensus mechanisms,an essential protocols that maintain the integrity,reliability,and decentralization of distributed ledger *** aim is to comprehensively investigate prominent mechanisms’security features and vulnerabilities,emphasizing their security considerations,applications,challenges,and future *** existing literature offers valuable insights into various consensus mechanisms’strengths,limitations,and security vulnerabilities and their real-world ***,there remains a gap in synthesizing and analyzing this knowledge *** this gap would facilitate a structured approach to understanding consensus mechanisms’security and vulnerabilities *** study adheres to Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines and computer science standards and reviewed 3749 research papers from 2016 to 2024,excluding grey literature,resulting in 290 articles for descriptive *** research highlights an increased focus on blockchain consensus security,energy efficiency,and hybrid mechanisms within 60%of research papers post-2019,identifying gaps in scalability,privacy,and interoperability for future *** synthesizing the existing research and identifying the key trends,this SLR contributes to advancing the understanding of blockchain consensus mechanisms’security and guiding future research and structured innovation in blockchain systems and applications.

关键词： Blockchain consensus mechanisms supply chain management proof of work(PoW) proof of stake(PoS) practical byzantine fault tolerance(PBFT)

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A Systematic Review of Machine Learning Approaches for Detecting Deceptive Activities on Social Media: Methods, Challenges, and Biases

arXiv

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arXiv 2024年

作者： Liu, Yunchong Shen, Xiaorui Zhang, Yeyubei Wang, Zhongyan Tian, Yexin Dai, Jianglai Cao, Yuchen School of Engineering and Applied Science University of Pennsylvania United States Khoury college of computer science Northeastern University United States Center for Data Science New York University United States Georgia Institute of Technology College of Computing United States Department of EECS University of California Berkeley United States

Social media platforms like Twitter, Facebook, and Instagram have facilitated the spread of misinformation, necessitating automated detection systems. This systematic review evaluates 36 studies that apply machine learning (ML) and deep learning (DL) models to detect fake news, spam, and fake accounts on social media. Using the Prediction model Risk Of Bias ASsessment Tool (PROBAST), the review identified key biases across the ML lifecycle: selection bias due to non-representative sampling, inadequate handling of class imbalance, insufficient linguistic preprocessing (e.g., negations), and inconsistent hyperparameter tuning. Although models such as Support Vector Machines (SVM), Random Forests, and Long Short-Term Memory (LSTM) networks showed strong potential, over-reliance on accuracy as an evaluation metric in imbalanced data settings was a common flaw. The review highlights the need for improved data preprocessing (e.g., resampling techniques), consistent hyperparameter tuning, and the use of appropriate metrics like precision, recall, F1 score, and AUROC. Addressing these limitations can lead to more reliable and generalizable ML/DL models for detecting deceptive content, ultimately contributing to the reduction of misinformation on social media. © 2024, CC BY.

关键词： Adversarial machine learning

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Multirate Time-Integration based on Dynamic ODE Partitioning through Adaptively Refined Meshes for Compressible Fluid Dynamics

arXiv

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arXiv 2024年

作者： Doehring, Daniel Schlottke-Lakemper, Michael Gassner, Gregor J. Torrilhon, Manuel Applied and Computational Mathematics RWTH Aachen University Schinkelstrasse 2 North Rhine-Westphalia Aachen52062 Germany University of Stuttgart Germany High-Performance Scientific Computing Center for Advanced Analytics and Predictive Sciences University of Augsburg Germany Department of Mathematics and Computer Science Center for Data and Simulation Science University of Cologne Germany

In this paper, we apply the Paired-Explicit Runge-Kutta (P-ERK) schemes by Vermeire et. al. [1, 2] to dynamically partitioned systems arising from adaptive mesh refinement. The PERK schemes enable multirate time-integration with no changes in the spatial discretization methodology, making them readily implementable in existing codes that employ a method-of-lines approach. We show that speedup compared to a range of state of the art Runge-Kutta methods can be realized, despite additional overhead due to the dynamic re-assignment of flagging variables and restricting nonlinear stability properties. The effectiveness of the approach is demonstrated for a range of simulation setups for viscous and inviscid convection-dominated compressible flows for which we provide a reproducibility repository. In addition, we perform a thorough investigation of the nonlinear stability properties of the Paired-Explicit Runge-Kutta schemes regarding limitations due to the violation of monotonicity properties of the underlying spatial discretization. Furthermore, we present a novel approach for estimating the relevant eigenvalues of large Jacobians required for the optimization of stability *** Codes 65L06, 65M20, 76Mxx, 76Nxx © 2024, CC BY.

关键词： Runge Kutta methods

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities

On the Effectiveness of Function-Level Vulnerability Detecto...

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International Conference on Software Engineering (ICSE)

作者： Zhen Li Ning Wang Deqing Zou Yating Li Ruqian Zhang Shouhuai Xu Chao Zhang 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 School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jin YinHu Laboratory Wuhan China Department of Computer Science University of Colorado Colorado Springs Colorado Springs Colorado USA Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers; and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities.

关键词： Deep learning Detectors Open source software Software engineering

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QFDNN: A Resource-Efficient Variational Quantum Feature Deep Neural Networks for Fraud Detection and Loan Prediction

arXiv

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arXiv 2025年

作者： Das, Subham Meghanath, Ashtakala Behera, Bikash K. Mumtaz, Shahid Al-Kuwari, Saif Farouk, Ahmed Department of Physics Indian Institute of Science Education and Research Thiruvananthapuram India Pvt. Ltd. WB Mohanpur741246 India Nottingham Trent University Engineering Department United Kingdom Qatar Center for Quantum Computing College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Department of Computer Science Faculty of Computers and Artificial Intelligence Hurghada University Hurghada Egypt

Social financial technology focuses on trust, sustainability, and social responsibility, which require advanced technologies to address complex financial tasks in the digital era. With the rapid growth in online transactions, automating credit card fraud detection and loan eligibility prediction has become increasingly challenging. Classical machine learning (ML) models have been used to solve these challenges;however, these approaches often encounter scalability, overfitting, and high computational costs due to complexity and high-dimensional financial data. Quantum computing (QC) and quantum machine learning (QML) provide a promising solution to efficiently processing high-dimensional datasets and enabling real-time identification of subtle fraud patterns. However, existing quantum algorithms lack robustness in noisy environments and fail to optimize performance with reduced feature sets. To address these limitations, we propose a quantum feature deep neural network (QFDNN), a novel, resource efficient, and noise-resilient quantum model that optimizes feature representation while requiring fewer qubits and simpler variational circuits. The model is evaluated using credit card fraud detection and loan eligibility prediction datasets, achieving competitive accuracies of 82.2% and 74.4%, respectively, with reduced computational overhead. Furthermore, we test QFDNN against six noise models, demonstrating its robustness across various error conditions. Our findings highlight QFDNN’s potential to enhance trust and security in social financial technology by accurately detecting fraudulent transactions while supporting sustainability through its resource-efficient design and minimal computational overhead. © 2025, CC BY.

关键词： Qubits

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Internal shortest absent word queries 32

Internal shortest absent word queries

引用

32nd Annual Symposium on Combinatorial Pattern Matching, CPM 2021

作者： Badkobeh, Golnaz Charalampopoulos, Panagiotis Pissis, Solon P. Department of Computing Goldsmiths University of London United Kingdom Efi Arazi School of Computer Science The Interdisciplinary Center Herzliya Israel CWI Amsterdam Netherlands Vrije Universiteit Amsterdam Netherlands

ISBN: (纸本)9783959771863

Given a string T of length n over an alphabet Σ ⊂ {1, 2, . . ., nO(1)} of size s, we are to preprocess T so that given a range [i, j], we can return a representation of a shortest string over Σ that is absent in the fragment T[i] · · · T[j] of T. For any positive integer k ϵ [1, log logσ n], we present an O((n/k) · log logσ n)-size data structure, which can be constructed in O(n logs n) time, and answers queries in time O(log logσ k). © Golnaz Badkobeh, Panagiotis Charalampopoulos, and Solon P. Pissis.

关键词： Query processing

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