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A comparative analysis of risk factors in taxi-related crashes using XGBoost and SHAP

International Journal for Consumer and Product Safety 2024年第3期31卷

作者： Zhipeng Peng Jingping Zuo Hao Ji Yuan RengTeng Yonggang Wang a School of Economics and Management Xi’an Technological University Xi’an China b Jiangsu Key Laboratory of Urban ITS School of Transportation Southeast University Nanjing China c College of Transportation Engineering Chang’an University Xi’an Chinad Key Laboratory of Transport Industry of Management Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier Area Xi’an China

Taxis play a crucial role in urban public transportation, but the traffic safety situation of taxi drivers is far from optimistic, especially considering the introduction of ride-hailing services into the taxi industry. This study conducted a comparative analysis of risk factors in crashes between traditional taxi drivers and ride-hailing taxi drivers in China, including their demographic characteristics, working conditions, and risky driving behaviors. The data was collected from 2,039 traditional taxi drivers and 2,182 ride-hailing taxi driversviaself-reported questionnaires. Four XGBoost models were established, taking into account different types of taxi drivers and crash types. All models showed acceptable performance, and SHAP explainer was used to analyze the model results. The results showed that for both taxi drivers, risk factors related to risky driving behaviors are more important in predicting property damage (PD) crashes, while risk factors related to working conditions are more important in predicting person injury (PI) crashes. However, the relative importance of each risk factor varied depending on the type of crashes and the type of taxi drivers involved. Furthermore, the results also validated certain interactions among the risk factors, indicating that the combination of certain factors generated a greater impact on crashes compared to individual factors alone. These findings can provide valuable insights for formulating appropriate measures to enhance road safety for taxi driver.

关键词： Traditional taxi drivers ride-hailing taxi drivers taxi-related crashes comparative analysis safety implications

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Learning discrete representations via constrained clustering for effective and efficient dense retrieval

arXiv

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arXiv 2021年

作者： Zhan, Jingtao Mao, Jiaxin Liu, Yiqun Guo, Jiafeng Zhang, Min Ma, Shaoping Department of Computer Science and Technology Institute for Artificial Intelligence Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

Dense Retrieval (DR) has achieved state-of-the-art first-stage ranking effectiveness. However, the efficiency of most existing DR models is limited by the large memory cost of storing dense vectors and the time-consuming nearest neighbor search (NNS) in vector space. Therefore, we present RepCONC, a novel retrieval model that learns discrete Representations via CONstrained Clustering. RepCONC jointly trains dual-encoders and the Product Quantization (PQ) method to learn discrete document representations and enables fast approximate NNS with compact indexes. It models quantization as a constrained clustering process, which requires the document embeddings to be uniformly clustered around the quantization centroids and supports end-to-end optimization of the quantization method and dual-encoders. We theoretically demonstrate the importance of the uniform clustering constraint in RepCONC and derive an efficient approximate solution for constrained clustering by reducing it to an instance of the optimal transport problem. Besides constrained clustering, RepCONC further adopts a vector-based inverted file system (IVF) to support highly efficient vector search on CPUs. Extensive experiments on two popular ad-hoc retrieval benchmarks show that RepCONC achieves better ranking effectiveness than competitive vector quantization baselines under different compression ratio settings. It also substantially outperforms a wide range of existing retrieval models in terms of retrieval effectiveness, memory efficiency, and time efficiency. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding

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An efficient access control scheme based on CP-ABE with supporting attribute change in cloud storage systems

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International Journal of Wireless and Mobile computing 2019年第1期16卷 41-49页

作者： Ye, Tao Cai, Yongquan Zhao, Xu Yang, Yongli Wang, Wei Zhu, Yi Faculty of Information Technology Beijing University of Technology Beijing China College of Computer Qinghai Nationalities University Xining China Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Trusted Computing Natl. Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing100124 China College of Applied Sciences Beijing University of Technology Beijing100124 China

The CP-ABE-based access control scheme, which can better realise the access control of many-to-multi-ciphertext shared in the cloud storage architecture, is still facing the problems that the system cost is too large, and the policy attribute revocation or restore is not flexible. This paper proposes an efficient access control scheme based on CP-ABE with supporting attribute change in cloud storage system. The fine-grained access control can be achieved by re-encryption mechanism which takes the minimum shared re-encryption key for policy attribute set. And then the access structure tree is expanded by creating a corresponding virtual attribute for each leaf node attribute. The analysis results of the scheme indicate that the efficient and flexibility of the attribute change is not only improved, but also the system cost is reduced. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Access control

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Model-Based Reinforcement Learning for Quantized Federated Learning Performance Optimization

Model-Based Reinforcement Learning for Quantized Federated L...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Christopher G. Brinton Changchuan Yin Walid Saad Shuguang Cui Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory Of Networking And Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Bradley Department of Electrical and Computer Engineering Virginia Tech Arlington VA USA Shenzhen Research Institute of Big Data (SRIBD) and the Future Network of Intelligence Institute (FNii) Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781665435413

This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which, in turn, aggregates them into a quantized global model and synchronizes the devices. With the goal of jointly determining the set of participating devices in each training iteration and the bitwidths employed at the devices, we pose an optimization problem for minimizing the training loss of quantized FL under a device sampling budget and delay requirement. Our analytical results show that the improvement of FL training loss between two consecutive iterations depends on not only the device selection and quantization scheme, but also on several parameters inherent to the model being learned. As a result, we propose, a model-based reinforcement learning (RL) method to optimize action selection over iterations. Compared to model-free RL, the proposed approach leverages the derived mathematical characterization of the FL training process to discover an effective device selection and quantization scheme without imposing additional device communication overhead. Numerical evaluations show that the proposed FL framework can achieve the same classification performance while reducing the number of training iterations needed for convergence by 20% compared to model-free RL-based FL.

关键词： Training Performance evaluation Wireless communication Quantization (signal) Computational modeling Reinforcement learning Mathematical models

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6G Enabled Advanced Transportation Systems

arXiv

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arXiv 2023年

作者： Liu, Ruiqi Hua, Meng Guan, Ke Wang, Xiping Zhang, Leyi Mao, Tianqi Zhang, Di Wu, Qingqing Jamalipour, Abbas The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China The Wireless and Computing Research Institute ZTE Corporation Beijing100029 China The Department of Electrical Engineering City University of Hong Kong 999077 Hong Kong The State Key Laboratory of Advanced Rail Autonomous Operation The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Beijing100081 China The School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China The School of Electrical Engineering Korea University Seoul02841 Korea Republic of Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Electrical and Information Engineering University of Sydney CamperdownNSW2006 Australia

With the emergence of communication services with stringent requirements such as autonomous driving or on-flight Internet, the sixth-generation (6G) wireless network is envisaged to become an enabling technology for future transportation systems. In this paper, two ways of interactions between 6G networks and transportation are extensively investigated. On one hand, the new usage scenarios and capabilities of 6G over existing cellular networks are firstly highlighted. Then, its potential in seamless and ubiquitous connectivity across the heterogeneous space-air-ground transportation systems is demonstrated, where railways, airplanes, high-altitude platforms and satellites are investigated. On the other hand, we reveal that the introduction of 6G guarantees a more intelligent, efficient and secure transportation system. Specifically, technical analysis on how 6G can empower future transportation is provided, based on the latest research and standardization progresses in localization, integrated sensing and communications, and security. The technical challenges and insights for a road ahead are also summarized for possible inspirations on 6G enabled advanced transportation. © 2023, CC BY-NC-ND.

关键词： Wireless networks

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Interpretation-enabled software reuse detection based on a multi-level birthmark model

arXiv

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arXiv 2021年

作者： Xu, Xi Zheng, Qinghua Yan, Zheng Fan, Ming Jia, Ang Liu, Ting Key Laboratory of Intelligent Networks and Network Security Ministry of Education China School of Computer Science and Technology Xi'an Jiaotong University China School of Cyber Science and Engineering Xi'an Jiaotong University China State Key Lab on Integrated Services Networks School of Cyber Engineering Xidian University China Department of Communications and Networking Aalto University Finland

Software reuse, especially partial reuse, poses legal and security threats to software development. Since its source codes are usually unavailable, software reuse is hard to be detected with interpretation. On the other hand, current approaches suffer from poor detection accuracy and efficiency, far from satisfying practical demands. To tackle these problems, in this paper, we propose ISRD, an interpretation-enabled software reuse detection approach based on a multi-level birthmark model that contains function level, basic block level, and instruction level. To overcome obfuscation caused by cross-compilation, we represent function semantics with Minimum Branch Path (MBP) and perform normalization to extract core semantics of instructions. For efficiently detecting reused functions, a process for "intent search based on anchor recognition" is designed to speed up reuse detection. It uses strict instruction match and identical library call invocation check to find anchor functions (in short anchors) and then traverses neighbors of the anchors to explore potentially matched function pairs. Extensive experiments based on two real-world binary datasets reveal that ISRD is interpretable, effective, and efficient, which achieves 97.2% precision and 94.8% recall. Moreover, it is resilient to cross-compilation, outperforming state-of-the-art approaches. © 2021, CC BY-NC-SA.

关键词： Computer software reusability

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Interpretation-Enabled Software Reuse Detection Based on a Multi-level Birthmark Model

Interpretation-Enabled Software Reuse Detection Based on a M...

引用

International Conference on Software Engineering (ICSE)

作者： Xi Xu Qinghua Zheng Zheng Yan Ming Fan Ang Jia Ting Liu Key Laboratory of Intelligent Networks and Network Security Ministry of Education China School of Computer Science and Technology Xi’an Jiaotong University China State Key Lab on Integrated Services Networks School of Cyber Engineering Xidian University China Department of Communications and Networking Aalto University Finland School of Cyber Science and Engineering Xi’an Jiaotong University China

ISBN: (纸本)9781665402965;9781665448314

关键词： Law Semantics Libraries security Software reusability Software engineering Resilience

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Upper security Bounds for Coherent-One-Way Quantum key Distribution

引用

Physical Review Letters 2020年第26期125卷 260510-260510页

作者： Javier González-Payo Róbert Trényi Weilong Wang Marcos Curty Escuela de Ingeniería de Telecomunicación Department of Signal Theory and Communications University of Vigo Vigo E-36310 Spain State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan 450001 China Henan Key Laboratory of Network Cryptography Technology Zhengzhou Henan 450001 China

The performance of quantum key distribution (QKD) is severely limited by multiphoton pulses emitted by laser sources due to the photon-number splitting attack. Coherent-one-way (COW) QKD has been introduced as a promising solution to overcome this limitation, and thus extend the achievable distance of practical QKD. Indeed, thanks to its experimental simplicity, the COW protocol is already used in commercial applications. Here, we derive simple upper security bounds on its secret key rate, which demonstrate that it scales at most quadratically with the system’s transmittance, thus solving a long-standing problem. That is, in contrast to what has been claimed, this approach is inappropriate for long-distance QKD transmission. Remarkably, our findings imply that all implementations of the COW protocol performed so far are insecure.

关键词： Quantum communication Quantum cryptography

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New constructions of MDS self-dual and self-orthogonal codes via GRS codes

arXiv

引用

arXiv 2021年

作者： Huang, Ziteng Fang, Weijun Fu, Fang-Wei Chern Institute of Mathematics and LPMC Nankai University Tianjin300071 China Shenzhen International Graduate School Tsinghua University PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518055 China Chern Institute of Mathematics and LPMC Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin300071 China

Recently, the construction of new MDS Euclidean self-dual codes has been widely investigated. In this paper, for square q, we utilize generalized Reed-Solomon (GRS) codes and their extended codes to provide four generic families of q-ary MDS Euclidean self-dual codes of lengths in the form s q-1/a + t q-1/b , where s and t range in some interval and a, b | (q-1). In particular, for large square q, our constructions take up a proportion of generally more than 34% in all the possible lengths of q-ary MDS Euclidean self-dual codes, which is larger than the previous results. Moreover, two new families of MDS Euclidean self-orthogonal codes and two new families of MDS Euclidean almost self-dual codes are given similarly. © 2021, CC BY.

关键词： Reed-Solomon codes

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Contrastive Learning for Robust Android Malware Familial Classification

arXiv

引用

arXiv 2021年

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China University of Texas at Dallas Dallas United States National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. © 2021, CC0.

关键词： Android (operating system)

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