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arXiv 2023年

作者： Jia, Zian Xiong, Yun Nan, Yuhong Zhang, Yao Zhao, Jinjing Wen, Mi Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China School of Software Engineering Sun Yat-sen University China National Key Laboratory of Science and Technology on Information System Security China Shanghai University of Electric Power China

Advance Persistent Threats (APTs), adopted by most delicate attackers, are becoming increasing common and pose great threat to various enterprises and institutions. Data provenance analysis on provenance graphs has emerged as a common approach in APT detection. However, previous works have exhibited several shortcomings: (1) requiring attack-containing data and a priori knowledge of APTs, (2) failing in extracting the rich contextual information buried within provenance graphs and (3) becoming impracticable due to their prohibitive computation overhead and memory consumption. In this paper, we introduce MAGIC, a novel and flexible self-supervised APT detection approach capable of performing multi-granularity detection under different level of supervision. MAGIC leverages masked graph representation learning to model benign system entities and behaviors, performing efficient deep feature extraction and structure abstraction on provenance graphs. By ferreting out anomalous system behaviors via outlier detection methods, MAGIC is able to perform both system entity level and batched log level APT detection. MAGIC is specially designed to handle concept drift with a model adaption mechanism and successfully applies to universal conditions and detection scenarios. We evaluate MAGIC on three widely-used datasets, including both real-world and simulated attacks. Evaluation results indicate that MAGIC achieves promising detection results in all scenarios and shows enormous advantage over state-of-the-art APT detection approaches in performance overhead. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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Dual-View Interaction-Aware Lane Change Prediction for Autonomous Driving 39

Dual-View Interaction-Aware Lane Change Prediction for Auton...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lu, Yuhuan Zhang, Zhen Bai, Rufan Liu, Han Wang, Wei Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University China Department of Computer and Information Science University of Macau China Department of Computer Science and Engineering Southeast University China School of Software Dalian University of Technology China School of Medical Technology Beijing Institute of Technology China

ISBN: (纸本)157735897X

As artificial intelligence techniques evolve, we are approaching a critical moment for the widespread deployment of autonomous vehicles. Subsequently, the emergence of mixed-autonomy traffic environments presents formidable challenges to autonomous vehicles, especially for the accurate prediction of lane change intentions of their surrounding human-driven vehicles, which is crucial for ensuring the safety of autonomous vehicles. Existing lane change prediction models mainly focus on capturing the temporal variations in the movement dynamics of individual vehicles. However, the neglect to consider inter-vehicle interactions hinders their capability in complex lane change scenarios, resulting in suboptimal prediction performance. Moreover, current interaction-aware approaches for autonomous driving fail to explicitly model future interactions between vehicles, leading to unreasonable prediction results that can cause collisions between vehicles. To address the above issues, we propose to incorporate the concept of perceived safety into future interaction modeling and design a dual-view interaction-aware lane change prediction model. We evaluate the proposed model on two real-world datasets and experimental results show that the proposed model achieves average improvements of 11.7-12.4% in classification ability and 75.6-95.7% in forecast ability over the best-performing baselines across the two datasets. The ablation study and investigation into future interaction modeling demonstrate that our model has advantages in interpreting lane change scenarios from a driving safety perspective. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Online Dynamic Acknowledgement with Learned Predictions

Online Dynamic Acknowledgement with Learned Predictions

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Sungjin Im Benjamin Moseley Chenyang Xu Ruilong Zhang Electrical Engineering and Computer Science University of California at Merced Merced US Tepper School of Business Carnegie Mellon University Pittsburgh US Software Engineering Institute East China Normal University Shanghai China Department of Computer Science City University of Hong Kong Hong Kong China

We revisit the online dynamic acknowledgment problem. In the problem, a sequence of requests arrive over time to be acknowledged, and all outstanding requests can be satisfied simultaneously by one acknowledgement. The goal of the problem is to minimize the total request delay plus acknowledgement cost. This elegant model studies the tradeoff between acknowledgement cost and waiting experienced by requests. The problem has been well studied and the tight competitive ratios have been determined. For this well-studied problem, we focus on how to effectively use machine-learned predictions to have better *** develop algorithms that perform arbitrarily close to the optimum with accurate predictions while concurrently having the guarantees arbitrarily close to what the best online algorithms can offer without access to predictions, thereby achieving simultaneous optimum consistency and robustness. This new result is enabled by our novel prediction error measure. No error measure was defined for the problem prior to our work, and natural measures failed due to the challenge that requests with different arrival times have different effects on the objective. We hope our ideas can be used for other online problems with temporal aspects that have been resisting proper error measures.

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Prototype Learning Based Realistic 3D Terrain Generation from User Semantics 22nd

Prototype Learning Based Realistic 3D Terrain Generation fro...

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22nd Asia Simulation Conference, AsiaSim 2023

作者： Gao, Yan Li, Jimeng Xu, Jianzhong Song, Xiao Quan, Hongyan School of Computer Science and Technology East China Normal University Shanghai200062 China School of Software Engineering East China Normal University Shanghai200062 China Training and Simulation Center Army Infantry Academy Jiangxi Nanchang330103 China School of Cyber Science and Technology Beihang University Beijing100191 China

ISBN: (纸本)9789819972395

Customizing 3D terrain based on user semantics plays an important role in military simulation, but it is difficult to realize realistic results because of the limited ability of some simple Convolutional neural network (CNN) models. In order to meet the personalized needs of users, this article proposes a prototype learning based terrain generation network (ProTG Net). Concretely, it extracts terrain semantics based prototype features from a small number of terrain surface samples, and then transfers the pre-learned features to user customization. Specifically, a prototype learning based framework is designed, including a terrain texture generation module (TGM), prototype feature generation module (PGM), and multiple prototype features matching module (FMM). TGM is designed as the CGAN based Pix2pix (Pixel to Pixel) structure, which can generate realistic terrain textures based on user semantics, providing a reliable terrain texture data source for prototype learning. Based on the semantic terrain texture generated by TGM, multi-features are extracted in PGM including the adaptive super-pixel guided features and the terrain spatial feature. In addition, multiple feature matching strategy is proposed for achieving the better matching between prototype matching and user semantic features. Taking a public dataset of real terrain as an example, it was verified that the prototype based method can generate realistic 3D terrain and achieve user customization to obtain realistic results. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Ant: An Efficient Lossless Compression Algorithm for IoT Time Series Data

Ant: An Efficient Lossless Compression Algorithm for IoT Tim...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Junhui Li Guangping Xu Hongzhang Yang Yulei Wu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China Faculty of Science and Engineering University of Bristol Bristol U.K

In various applications in Internet of Things like industrial monitoring, large amounts of floating-point time series data are generated at an unprecedented rate. Efficient compression algorithms can effectively reduce the size of data, enhance transmission performance and storage efficiency, and simultaneously lower storage costs. Therefore, there is a need for lightweight and efficient stream compression algorithms. In this paper, we propose a novel lossless floating-point data compression algorithm called Ant. The main idea is to encode double-precision floating-point numbers into integer form, calculate the delta between adjacent values, and then convert the delta into unsigned integers. This encoding method effectively reduces storage costs and improves data compression efficiency. Extensive experiments on real-world datasets demonstrate that our algorithm achieves compression speeds at least as fast as state-of-the-art streaming methods, and a 63% relative improvement in average compression rate.

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Cognitive Disentanglement for Referring Multi-Object Tracking

arXiv

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arXiv 2025年

作者： Liang, Shaofeng Guan, Runwei Lian, Wangwang Liu, Daizong Sun, Xiaolou Wu, Dongming Yue, Yutao Ding, Weiping Xiong, Hui Qingdao China Guangzhou China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao China Wangxuan Institute of Computer Technology Peking University Beijing China School of Automation Southeast University Nanjing China School of Computer Science Beijing Institute of Technology Beijing China School of Artificial Intelligence and Computer Science Nantong University Nantong China Guangzhou China Department of Electrical Engineering and Electronics University of Liverpool Liverpool United Kingdom

As a significant application of multi-source information fusion in intelligent transportation perception systems, Referring Multi-Object Tracking (RMOT) involves localizing and tracking specific objects in video sequences based on language references. However, existing RMOT approaches often treat language descriptions as holistic embeddings and struggle to effectively integrate the rich semantic information contained in language expressions with visual features. This limitation is especially apparent in complex scenes requiring comprehensive understanding of both static object attributes and spatial motion information. In this paper, we propose a Cognitive Disentanglement for Referring Multi-Object Tracking (CDRMT) framework that addresses these challenges. It adapts the "what" and "where" pathways from the human visual processing system to RMOT tasks. Specifically, our framework first establishes cross-modal connections while preserving modality-specific characteristics. It then disentangles language descriptions and hierarchically injects them into object queries, refining object understanding from coarse to fine-grained semantic levels. Finally, we reconstruct language representations based on visual features, ensuring that tracked objects faithfully reflect the referring expression. Extensive experiments on different benchmark datasets demonstrate that CDRMT achieves substantial improvements over state-of-the-art methods, with average gains of 6.0% in HOTA score on Refer-KITTI and 3.2% on Refer-KITTI-V2. Our approach advances the state-of-the-art in RMOT while simultaneously providing new insights into multi-source information fusion. Copyright © 2025, The Authors. All rights reserved.

关键词： Object detection

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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

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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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Styles Energy Consumption Analysis of Lane-Changing Maneuvers in Autonomous Vehicles: The Role of Driving Styles

SSRN

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SSRN 2024年

作者： Guo, Yu Nie, Guigen Zou, Xiaowei Zhu, Dongliang Gao, Wenliang Liao, Mi GNSS Research Center Wuhan University Wuhan China Hubei Luojia Laboratory Wuhan China National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan University Wuhan China

HIGHLIGHTS·Optimized lane-change trajectories considering safety, comfort, and time efficiency.·Developed a model for identifying different lane changing styles to meet personalized driving preferences.·Comprehensive analysis uncovers significant energy consumption patterns in electric autonomous vehicles during lane-change maneuvers.·Conducted rigorous examination of energy consumption during lane changes, identifying consumption trends in aggressive, conservative, and normal driving styles.·Correlation analysis of key factors affecting energy consumption paves the way for customizing driving styles and powertrain design to reduce energy usage. © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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AutoSAT: Automatically Optimize SAT Solvers via Large Language Models

arXiv

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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

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