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Probabilistic Graph Modeling based Safety Classifier Algorithm for Smart Transportation

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Procedia computer science 2024年 236卷 502-507页

作者： Najib El Karkouri Smail Tigani Rachid Saadane Abdelah Chehri Samuel Pierre Noureddine Neya Mathematics and Informatics Lab Faculty of Sciences University Ibno Toufial Kenitra Morocco Research and Development Unit Accsellium LLC Fez Morocco SIRC-LaGeS Hassania School of Public Labors 20250 Casablanca Morocco Department of Mathematics and Computer Science Royal Military College of Canada Kingston Canada Department of Computer and Software Engineering Polytechnique Montreal Canada

This research introduces a novel Probabilistic Graph Modeling-based Safety Classifier Algorithm designed for the purpose of classifying road safety in smart transportation systems. Leveraging a combination of numerical integration methods and Gaussian kernel density estimation models, the proposed algorithm offers an effective approach to assess and categorizing the safety levels of roads. The integration of these techniques enables a comprehensive analysis, allowing for a more nuanced understanding of the complex interactions within transportation networks. The algorithm's efficacy in road safety classification holds promising implications for enhancing traffic management and promoting safer urban environments.

关键词： Probabilistic Graph Numerical Computation Smart Transportation Classification Road Safety

AutoSAT: Automatically Optimize SAT Solvers via Large Language Models

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arXiv 2024年

作者： Sun, Yiwen Ye, Furong Zhang, Xianyin Huang, Shiyu Zhang, Bingzhen Wei, Ke Cai, Shaowei School of Data Science Fudan University Shanghai China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China 4paradigm Inc Beijing China Department of Industrial Engineering Tsinghua University Beijing China

Conflict-Driven Clause Learning (CDCL) is the mainstream framework for solving the Satisfiability problem (SAT), and CDCL solvers typically rely on various heuristics, which have a significant impact on their performance. Modern CDCL solvers, such as MiniSat and Kissat, commonly incorporate several heuristics and select one to use according to simple rules, requiring significant time and expert effort to fine-tune in practice. The pervasion of Large Language Models (LLMs) provides a potential solution to address this issue. However, generating a CDCL solver from scratch is not effective due to the complexity and context volume of SAT solvers. Instead, we propose AutoSAT, a framework that automatically optimizes heuristics in a pre-defined modular search space based on existing CDCL solvers. Unlike existing automated algorithm design approaches focusing on hyperparameter tuning and operator selection, AutoSAT can generate new efficient heuristics. In this first attempt at optimizing SAT solvers using LLMs, several strategies including the greedy hill climber and (1 + 1) Evolutionary Algorithm are employed to guide LLMs to search for better heuristics. Experimental results demonstrate that LLMs can generally enhance the performance of CDCL solvers. A realization of AutoSAT outperforms MiniSat on 9 out of 12 datasets and even surpasses the state-of-the-art hybrid solver Kissat on 4 datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Fault tolerance

RaftFed: A Lightweight Federated Learning Framework for Vehicular Crowd Intelligence

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arXiv 2023年

作者： Yang, Changan Chen, Yaxing Zhang, Yao Cui, Helei Yu, Zhiwen Guo, Bin Yan, Zheng Yang, Zijiang School of Software Northwestern Polytechnical University Xi’an China School of Computer Science Northwestern Polytechnical University Xi’an China School of Cyber Engineering Xidian University Xi’an China Turing Research Center for Interdisciplinary Information Science Xi'an Jiaotong University Xi’an China

Vehicular crowd intelligence (VCI) is an emerging research field. Facilitated by state-of-the-art vehicular ad-hoc networks and artificial intelligence, various VCI applications come to place, e.g., collaborative sensing, positioning, and mapping. The collaborative property of VCI applications generally requires data to be shared among participants, thus forming network-wide intelligence. How to fulfill this process without compromising data privacy remains a challenging issue. Although federated learning (FL) is a promising tool to solve the problem, adapting conventional FL frameworks to VCI is nontrivial. First, the centralized model aggregation is unreliable in VCI because of the existence of stragglers with unfavorable channel conditions. Second, existing FL schemes are vulnerable to Non-IID data, which is intensified by the data heterogeneity in VCI. This paper proposes a novel federated learning framework called RaftFed to facilitate privacy-preserving VCI. The experimental results show that RaftFed performs better than baselines regarding communication overhead, model accuracy, and model convergence. Copyright © 2023, The Authors. All rights reserved.

关键词： Vehicular ad hoc networks

Assessing the Robustness of LLM-based NLP software via Automated Testing

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arXiv 2024年

作者： Xiao, Mingxuan Xiao, Yan Ji, Shunhui Cai, Hanbo Xue, Lei Zhang, Pengcheng Key Laboratory of Water Big Data Technology Ministry of Water Resources College of Computer Science and Software Engineering Hohai University Nanjing211100 China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University Shenzhen518107 China

Benefiting from the advancements in LLMs, NLP software has undergone rapid development. Such software is widely employed in various safety-critical tasks, such as financial sentiment analysis, toxic content moderation, and log generation. Unlike traditional software, LLM-based NLP software relies on prompts and examples as inputs. Given the complexity of LLMs and the unpredictability of real-world inputs, quantitatively assessing the robustness of such software is crucial. However, to the best of our knowledge, no automated robustness testing methods have been specifically designed to evaluate the overall inputs of LLM-based NLP software. To this end, this paper introduces the first AutOmated Robustness Testing frAmework, AORTA, which reconceptualizes the testing process into a combinatorial optimization problem. Existing testing methods designed for DNN-based software can be applied to LLM-based software by AORTA, but their effectiveness is limited. To address this, we propose a novel testing method for LLM-based software within AORTA called Adaptive Beam Search. ABS is tailored for the expansive feature space of LLMs and improves testing effectiveness through an adaptive beam width and the capability for backtracking. We successfully embed 18 test methods in the designed framework AORTA and compared the test validity of ABS with three datasets and five threat models. ABS facilitates a more comprehensive and accurate robustness assessment before software deployment, with an average test success rate of 86.138%. Compared to the currently best-performing baseline PWWS, ABS significantly reduces the computational overhead by up to 3441.895 seconds per successful test case and decreases the number of queries by 218.762 times on average. Furthermore, test cases generated by ABS exhibit greater naturalness and transferability. © 2024, CC BY-NC-ND.

关键词： software testing

WMAJL: Watcher-Mediated Attention Joint Learning Model for Multimodal Relation Extraction

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WMAJL: Watcher-Mediated Attention Joint Learning Model for M...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yunrui Dong Guiduo Duan Tianxi Huang Yunhao Li School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China Chengdu China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu China Chengdu Textile College College of Humanities and General Education Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the domain of Multimodal Relation Extraction (MRE), we present the $\color{Red}{\text{W}}$atcher-$\color{Red}{\text{M}}$ediated $\color{Red}{\text{A}}$ttention $\color{Red}{\text{J}}$oint $\color{Red}{\text{L}}$earning Model ($\color{Red}{\text{WMAJL}}$), a novel approach addressing the challenges of modality alignment noise, cross-modal fusion disparity, preservation of textual relative position information, and the distinctiveness of classification labels. WMAJL employs an integrative framework leveraging contrastive learning and variational autoencoder constraints to mitigate modality alignment noise by prioritizing relevant semantic data and effectively reducing extraneous noise that does not contribute to the task. The model’s innovative architecture includes a mediator watcher, which facilitates enhanced cross-modal fusion by enabling nuanced information exchange between textual and visual modalities while preserving the unique characteristics of each modality. Additionally, the design of auxiliary tasks, such as Named Entity Recognition (NER), and output supervision constructs loss functions that preserve relative position information, ensuring a precise depiction of entity relationships throughout the multilayer encoding processes. A key differentiator of WMAJL is its label-centric self-information loss technique, inspired by InfoNCE, which trains the model to cluster similar relation labels in semantically coherent areas, thereby optimizing classification label uniqueness by discerning subtle differences among relation types. The synergistic application of these strategies has led to a significant enhancement of WMAJL’s performance, as evidenced by its state-of-the-art F1 score of $\color{Red}{84.93\%}$ on the MNRE dataset. This achievement surpasses existing benchmarks and sets a new standard for multimodal knowledge extraction, underscoring WMAJL’s potential to revolutionize the MRE landscape.

关键词： Resistance Visualization Technological innovation Noise Autoencoders Semantics Contrastive learning Nonhomogeneous media Speech processing Standards

Split and Merge: Component Based Segmentation Network for Text Detection 1

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2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020

作者： Gao, Pan Wan, Qi Shen, Linlin Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9783030598303

ISBN: (纸本)9783030598297

This paper presents a novel component-based detector to locate scene texts with arbitrary orientations, shapes and lengths. Our approach detects text by predicting four components like text region (TR), text skeleton (TS), text sub-region (TSR) and text connector (TC). TR and TS can well separate adjacent text instance. TSR are merged by TC to form a complete text instance. Experimental results show that the proposed approach outperforms state-of-the-art methods on two curved text datasets, i.e. 82.42% and 82.63% F-measures were achieved for the Total-Text and CTW1500, respectively. Our approach also achieves competitive performance on multi-oriented dataset, i.e. 85.86% f-measure for the ICDAR2015 was achieved. © 2020, Springer Nature Switzerland AG.

关键词： Image segmentation

TAD-Bench: A Comprehensive Benchmark for Embedding-Based Text Anomaly Detection

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arXiv 2025年

作者： Cao, Yang Yang, Sikun Li, Chen Xiang, Haolong Qi, Lianyong Liu, Bo Li, Rongsheng Liu, Ming School of Computing and Information Technology Great Bay University China Great Bay Institute for Advanced Study Great Bay University China Graduate School of Informatics Nagoya University Japan School of Software Nanjing University of Information Science and Technology China College of Computer Science and Technology China University of Petroleum [East China China College of Cyberspace Security Zhengzhou University China School of Computer Harbin Engineering University China School of IT Deakin University Australia

Text anomaly detection is crucial for identifying spam, misinformation, and offensive language in natural language processing tasks. Despite the growing adoption of embedding-based methods, their effectiveness and generalizability across diverse application scenarios remain under-explored. To address this, we present TAD-Bench, a comprehensive benchmark designed to systematically evaluate embedding-based approaches for text anomaly detection. TAD-Bench integrates multiple datasets spanning different domains, combining state-of-the-art embeddings from large language models with a variety of anomaly detection algorithms. Through extensive experiments, we analyze the interplay between embeddings and detection methods, uncovering their strengths, weaknesses, and applicability to different tasks. These findings offer new perspectives on building more robust, efficient, and generalizable anomaly detection systems for real-world applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Embeddings

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

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

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.

关键词：

Detecting Memory-Related Bugs by Tracking Heap Memory Management of C++ Smart Pointers 36

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Detecting Memory-Related Bugs by Tracking Heap Memory Manage...

36th IEEE/ACM International Conference on Automated software engineering, ASE 2021

作者： Ma, Xutong Yan, Jiwei Wang, Wei Yan, Jun Zhang, Jian Qiu, Zongyan Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software China Technology Center of Software Engineering Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China Peking University School of Mathematical Sciences China

ISBN: (纸本)9781665403375

The smart pointer mechanism, which is improved in the continuous versions of the C++ standards over the last decade, is designed to prevent memory-leak bugs by automatically deallocating the managed memory blocks. However, not all kinds of memory errors can be immunized by adopting this mechanism. For example, dereferencing a null smart pointer will lead to a software failure. Due to the lack of specialized support for smart pointers, the off-the-shelf C++ static analyzers cannot effectively reveal these *** this paper, we propose a static approach to detecting memory-related bugs by tracking the heap memory management of smart pointers. The behaviors of smart pointers are modeled during their lifetime to trace the state transitions of managed memory blocks. and the specially designed checkers are used to check the state changes according to five collected bug patterns. To evaluate the effectiveness of our approach, we implement it on the top of the Clang Static Analyzer. A set of handmade code snippets, as well as nine popular open-source C++ projects, are used to compare our tool against four other analyzers. The results show that our approach can successfully discover nearly all the built-in bugs. and 442 out of 648 reports generated from the open-source projects are true positives after manual reviewing, where the bugs of dereferencing null smart pointers are most frequently reported. To further confirm our reports, we design patches for Aria2, Restbed, MySQL and LLVM, in which seven pull requests covering 76 bug reports have been merged by the developers up to now. The results indicate that pointers should always be carefully used even after migrated to smart pointers and static analysis upon specialized models can effectively detect such bugs. © 2021 IEEE.

关键词： Static analysis