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2nd IEEE International Conference on Advanced Learning Technologies on Education and Research, ICALTER 2022

作者： Shi, Yong Shahriar, Hossain Lo, Dan Chi, Hongmei College of Computing and Software Engineering Kennesaw State University Marietta United States Florida A&m University Department of Computer and Information Sciences TallahasseeFL United States

ISBN: (数字)9781665456968

ISBN: (纸本)9781665456968

As an emerging technology that has already impacted various sectors including finance, energy, education, and more, blockchain provides a decentralized ledger system that ensures the integrity of the recorded transactions. With its wide application comes the huge demand for blockchain researchers and engineers, however, most schools have not offered courses related to blockchain research and development. To fill the gap between the increasing needs for blockchain professionals and shortage of blockchain curriculum, we propose a portable labware that covers the complete cycle of blockchain development. In this labware we apply active learning strategies to engage students in learning the blockchain knowledge more efficiently and effectively. © 2022 IEEE.

关键词： Blockchain

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Dynamic Bi-Elman Attention Networks (DBEAN): Dual-Directional Context-Aware Representation Learning for Enhanced Text Classification

arXiv

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arXiv 2025年

作者： Liao, Mengyao Lai, Zhenglin Xu, Dong College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China

Text classification, a fundamental task in natural language processing (NLP), aims to categorize textual data into predefined labels. Traditional methods struggled with complex linguistic structures and semantic dependencies. The advent of deep learning, particularly recurrent neural networks (RNNs) and Transformer-based models, has significantly advanced the field by enabling nuanced feature extraction and context-aware predictions. Despite improvements, existing models exhibit limitations in balancing interpretability, computational efficiency, and long-range contextual understanding. This paper proposes the Dynamic Bidirectional Elman with Attention Network (DBEAN), which integrates bidirectional temporal modelling with self-attention mechanisms. DBEAN dynamically assigns weights to critical segments of input, improving contextual representation while maintaining computational efficiency. Copyright © 2025, The Authors. All rights reserved.

关键词： Elman neural networks

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Statically-Directed Dynamic Taint Analysis

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Chinese Journal of Electronics 2025年第1期23卷 18-24页

作者： Kai Chen Yingjun Zhang State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Trusted Computing and Information Assurance Laboratory Institute of Software Chinese Academy of Sciences Beijing China

Taint analysis is a popular method in software analysis field including vulnerability/malware analysis. By identifying taint source and making suitable taint propagation rules, we could directly know whether variables in software have any relationship with input data. Static taint analysis method is efficient, but it is imprecise since runtime information is lacked. Dynamic taint analysis method usually instruments every instruction in software to catch the taint propagation process. However, this is inefficient since it usually takes lots of time for context switches between original code and instrumenting code. In this paper, we propose a statically-directed dynamic taint analysis method to increase the efficiency of taint analysis process without any loss of accuracy. In this way, there is no need to instrument every instruction. Several experiments are made on our prototype SDTaint and the results show that our method is several times more efficient than traditional dynamic taint analysis method.

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Towards Planning Package Delivery Paths by Exploiting the Crowd of Private Cars 24

Towards Planning Package Delivery Paths by Exploiting the Cr...

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24th IEEE International Conference on High Performance computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Nie, Hejing Wen, Yiping Li, Tiancai Ouyang, Sipeng Tian, Muyang School of Computer Science and Engineering Hunan University of Science and Technology China Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology China

ISBN: (纸本)9798350319934

Crowdsourced delivery can provide cost-effective deliveries and it is becoming increasingly important in practice. This paper considers a package delivery problem which exploits relays of private cars to transport packages via their commuter routes. Specifically, we propose a method of planning package delivery paths based on the commuter routes of private cars. Using the commuter routes and time constraints as the reference, it can find optimal delivery paths and assign drivers of private cars to transport packages. The simulation experiment results show the validity and effectiveness of such method. © 2022 IEEE.

关键词： Cost effectiveness

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Complex-Valued Gabor-Attention Residual Fusion Network for Iris Recognition 27

Complex-Valued Gabor-Attention Residual Fusion Network for I...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Li, Zhuoru Xiao, Jian Bai, Xiaowei Wang, Xiaodong Li, Yingxi Fang, Zhenyu Xie, Liang Yan, Ye Yin, Erwei College of Intelligence and Computing Tianjin University Tianjin300450 China Beijing100071 China Intelligent Game and Decision Laboratory Beijing100071 China Tianjin300450 China College of Engineering Peking University Beijing100071 China School of Software Northwestern Polytechnical University Xian710000 China

ISBN: (纸本)9781643685489

Iris recognition has gained significant attention in identity verification due to the unique, stable texture patterns in iris. Successfully extracting these patterns is essential for quick and precise identification. Although deep learning methods have automated the iris recognition, they predominantly rely on real-valued networks that overlook the complex-valued representation of iris texture. This means they cannot effectively process phase and amplitude information, and fail to integrate domain-specific knowledge of iris, thereby not fully capturing the intricate details of the iris texture. Inspired by classical manual methods that efficiently harness the complex-valued representation of the iris to extract both amplitude and phase information. We integrate Gabor filters with complex-valued neural networks, propose a Complex-Valued Gabor-Attention Residual Fusion Network (GRFN) tailored for iris recognition, aiming to comprehensively capture the iris texture's multi-scale and multi-orientation phase and amplitude features. The GRFN incorporates adaptive Gabor Complex-Valued Convolution Kernels (GCVK) to introduce a Gabor attention mechanism focused on iris biometric characteristics. Furthermore, we propose a novel residual feature fusion approach that selects and merges local and global features across multiple directions and scales, mitigating model degradation and enhancing the network's ability to extract iris texture features effectively. Extensive experiments show that the proposed network outperforms the state-of-the-art performance on two benchmark datasets. © 2024 The Authors.

关键词： Gabor filters

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Building a Reusable and Extensible Automatic Compiler Infrastructure for Reconfigurable Devices

Building a Reusable and Extensible Automatic Compiler Infras...

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International Conference on Field Programmable Logic and Applications

作者： Zhenya Zang Uwe Dolinsky Pietro Ghiglio Stefano Cherubin Mehdi Goli Shufan Yang Codeplay Software Ltd. *School of Computing Engineering and Build Environment Edinburgh Napier University Edinburgh United Kindom Codeplay Software Ltd Edinburgh United Kindom School of Computing Engineering and Build Environment Edinburgh Napier University Edinburgh United Kindom

Multi-Level Intermediate Representation (MLIR) is gaining increasing attention in reconfigurable hardware communities due to its capability to represent various abstract levels for software compilers. This project aims to be the first to provide an end-to-end framework that leverages open-source, cross-platform compilation technology to generate MLIR from SYCL. Additionally, it aims to explore a lowering pipeline that converts MLIR to RTL using open-source hardware intermediate representation (IR) and compilers. Furthermore, it aims to couple the generated hardware module with the host CPU using vendor-specific crossbars. Our preliminary results demonstrated the feasibility of lowering customized MLIR to RTL, thus paving the way for an end-to-end compilation.

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Improving Network Threat Detection by Knowledge Graph, Large Language Model, and Imbalanced Learning

arXiv

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arXiv 2025年

作者： Zhang, Lili Zhu, Quanyan Ray, Herman Xie, Ying Hewlett Packard Enterprise United States Department of Electrical and Computer Engineering New York University United States College of Computing and Software Engineering Kennesaw State University United States

Network threat detection has been challenging due to the complexities of attack activities and the limitation of historical threat data to learn from. To help enhance the existing practices of using analytics, machine learning, and artificial intelligence methods to detect the network threats, we propose an integrated modelling framework, where Knowledge Graph is used to analyze the users’ activity patterns, Imbalanced Learning techniques are used to prune and weigh Knowledge Graph, and LLM is used to retrieve and interpret the users’ activities from Knowledge Graph. The proposed framework is applied to Agile Threat Detection through Online Sequential Learning. The preliminary results show the improved threat capture rate by 3%-4% and the increased interpretabilities of risk predictions based on the users’ activities. Copyright © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Effective Entry-Wise Flow for Molecule Generation 40

Effective Entry-Wise Flow for Molecule Generation

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Zhang, Qifan Yao, Junjie Yang, Yuquan Shi, Yizhou Gao, Wei Wang, Xiaoling School of Computer Science and Technology East China Normal University China Software Engineering Institute East China Normal University China Shanghai Key Laboratory of Trustworthy Computing China University of Massachusetts Department of Chemistry Boston United States University of Michigan Ann Arbor United States

ISBN: (纸本)9798350317152

Molecule generation is a critical process in the fields of drug discovery and materials science. Recently, generative models based on normalizing flows have demonstrated significant potential in this domain. These models are particularly suited for handling the symmetrical and complex chemical structures often encountered in molecular datasets. Despite their promising nature, normalizing flow-based models for molecule generation face considerable challenges. The complexity of molecule representation, the rigorous demands of optimization, and the scarcity of training labels in molecular datasets contribute to these difficulties. Additionally, adequately and comprehensively learning the distribution of molecular datasets remains a formidable task. In this paper, we delve into the intricate entry-wise modules in vanilla flows, introducing an effective variation of flow-based models. Our proposed approach innovatively encapsulates affine coupling transformations within normalizing flows. Furthermore, we deconstruct existing invertible flow models, integrating them with newly developed entry-wise transformations. Our experimental studies demonstrate that these proposed entry-wise modules, when incorporated into standard flow-based models, surpass other generative models in performance on various representative datasets and generation tasks. Notably, in the context of low-resourced molecular graph generation, our model achieves remarkable performance compared to its counterparts. © 2024 IEEE.

关键词： Complex networks

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A Novel and Pragmatic Scenario Modeling Framework with Verification-in-the-loop for Autonomous Driving Systems

A Novel and Pragmatic Scenario Modeling Framework with Verif...

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IEEE/ACM International Conference on software engineering: New Ideas and Emerging Technologies Results Track (ICSE-NIER)

作者： Dehui Du Bo Li Chenghang Zheng Xinyuan Zhang Shanghai Key Laboratory of Trustworthy Computing Software Engineering Institute East China Normal University Shanghai China

Scenario modeling for Autonomous Driving Systems (ADS) enables scenario-based simulation and verification which are critical for the development of safe ADS. However, with the increasing complexity and uncertainty of ADS, it becomes increasingly challenging to manually model driving scenarios and conduct verification analysis. To tackle these challenges, we propose a novel and pragmatic framework for scenario modeling, simulation and verification. The novelty is that it’s a verification-in-the-loop scenario modeling framework. The scenario modeling language with formal semantics is proposed based on the domain knowledge of ADS. It facilitates scenario verification to analyze the safety of scenario models. Moreover, the scenario simulation is implemented based on the scenario executor. Compared with existing works, our framework can simplify the description of scenarios in a non-programming, user-friendly manner, model stochastic behavior of vehicles, support safe verification of scenario models with UPPAAL-SMC and generate executable scenario in some open-source simulators such as CARLA. To preliminarily demonstrate the effectiveness and feasibility of our approach, we build a prototype tool and apply our approach in several typical scenarios for ADS.

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Real-Time CNN-Based Driver Distraction&Drowsiness Detection System

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Intelligent Automation & Soft computing 2023年第8期37卷 2153-2174页

作者： Abdulwahab Ali Almazroi Mohammed A.Alqarni Nida Aslam Rizwan Ali Shah University of Jeddah College of Computing and Information Technology at KhulaisDepartment of Information TechnologyJeddahSaudi Arabia University of Jeddah College of Computer Science and EngineeringDepartment of Software EngineeringJeddahSaudi Arabia Department of Computer Science National College of Business Administration&EconomicsBahawalpur Campus63100Pakistan Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan CampusPunjab64200Pakistan

Nowadays days,the chief grounds of automobile accidents are driver fatigue and *** the development of computer vision technology,a cutting-edge system has the potential to spot driver distractions or sleepiness and alert them,reducing *** paper presents a novel approach to detecting driver tiredness based on eye and mouth movements and object identification that causes a distraction while operating a motor *** the facial landmarks that the camera picks up and sends to classify using a Convolutional Neural Network(CNN)any changes by focusing on the eyes and mouth zone,precision is *** of the tasks that must be performed in the transit system is seat belt detection to lessen accidents caused by sudden stops or high-speed collisions with other cars.A method is put forth to use convolution neural networks to determine whether the motorist is wearing a seat belt when a driver is sleepy,preoccupied,or not wearing their seat belt,this system alerts them with an alarm,and if they don’t wake up by a predetermined time of 3 s threshold,an automatic message is sent to law enforcement *** suggested CNN-based model exhibits greater accuracy with 97%.It can be utilized to develop a system that detects driver attention or sleeps in real-time.

关键词： Deep learning convolutional neural network Tensorflow drowsiness and yawn detection seat belt detection object detection

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