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49th Euromicro Conference on software engineering and Advanced Applications, SEAA 2023

作者： Soud, Majd Qasse, Ilham Liebel, Grischa Hamdaqa, Mohammad Reykjavik University Department of Computer Science Reykjavik Iceland Polytechnique Montreal Department of Computer and Software Engineering Montreal Canada

ISBN: (纸本)9798350342352

Due to the risks associated with vulnerabilities in smart contracts, their security has gained significant attention in recent years. However, there is a lack of open datasets on smart contract vulnerabilities and their fixes that allows for data-driven research. Towards this end, we propose an automated framework for mining and classifying Ethereum's smart contract vulnerabilities and their corresponding fixes from GitHub and from the Common Vulnerabilities and Exposures (CVE) records in the National Vulnerability Database. We implemented the proposed method in a fully automated framework, which we call AutoMESC. AutoMESC uses seven of the most well-known smart contract security tools to classify and label the collected vulnerabilities based on vulnerability types. Furthermore, it collects metadata that can be used in data-intensive smart contract security research (e.g., vulnerability detection, vulnerability classification, severity prediction, and automated repair). We used AutoMESC to construct a sample dataset and made it publicly available. Currently, the dataset contains 6.7K smart contract vulnerability-fix pairs written in Solidity. We assess the quality of the constructed dataset in terms of accuracy, provenance, and relevance, and compare it with existing datasets. AutoMESC is designed to collect data continuously and keep the corresponding dataset up-to-date with newly discovered smart contract vulnerabilities and their fixes from GitHub and CVE records. © 2023 IEEE.

关键词： Smart contract

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Jamming-aided Maritime Physical Layer Encrypted Dual-UAVs Communications Exploiting Collaborative Beamforming 26

Jamming-aided Maritime Physical Layer Encrypted Dual-UAVs Co...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Huang, Jiawei Wang, Aimin Sun, Geng Li, Jiahui Jilin University College of Software Engineering College of Computer Science and Technology Changchun130012 China

ISBN: (纸本)9798350331684

Unmanned aerial vehicles (UAVs) are widely used as relay platforms to assist in maritime wireless communications. However, due to the open channel of UAV communications, security issue needs to be paid extra attention. This paper proposes a dual-UAVs jamming-aided system to implement physical layer encryption in maritime wireless communication. Specifically, one UAV acts as a relay to transmit data to the legitimate vessel and a set of UAVs can form a maritime UAV-enabled virtual antenna array (MUVAA) as a jammer to send jamming signals to the eavesdropper. In this system, an encrypted and energy-efficient maritime communication multi-objective optimization problem (EEMCMOP) is formulated to maximize the signal-to-interference-plus-noise ratio (SINR) of the legitimate vessel, minimize the SINR of the eavesdropping vessel and minimize the overall flight energy consumption of UAVs. Since the EEMCMOP is proven to be NP-hard, we propose an enhanced evolutionary computation algorithm with some upgraded characteristics to solve the problem. Simulation results indicate that the performance of the proposed algorithm is superior to other benchmark algorithms. © 2023 IEEE.

关键词： Multiobjective optimization

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A Deep Understanding Video Q&A System for Film Education in Acting Department

A Deep Understanding Video Q&A System for Film Education in ...

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2023 International Conference on Intelligent Education and Intelligent Research, IEIR 2023

作者： Wu, Zhengqian Li, Ruizhe Guo, Jiahao Wang, Zhongyuan Liang, Chao Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science China National Engineering Research Center for Multimedia Software School of Computer Science China Wuhan University School of Computer Science China

ISBN: (纸本)9798350342895

Recently, advancements in artificial intelligence technology have greatly influenced the field of education, particularly in the area of intelligent homework assistance. However, current approaches are primarily designed for procedural and logical tasks and often lack comprehension abilities. This limitation is particularly evident when it comes to multi-hop and continuous tasks. To address this challenge, the integration of Large Language Model (LLM) has significantly enhanced the capability of AI systems to handle multi-hop and highly interconnected inputs. In this study, we focus on the learning needs of students in Acting Department, specifically their study of movies and the significance of classic movie videos in their learning process. However, assessing deep comprehension of classic movies poses its own challenges. To overcome these challenges, we develop a quiz system utilizing Knowledge Graphs (KG) and LLM to facilitate a deeper understanding of classic films. The generation of video quiz pairs is achieved through the use of Automatic Speech Recognition (ASR) technology, which leverages movie subtitles for question generation. For answering these questions, we employ techniques KG and LLM to process questions and retrieve corresponding answers. The proposed method achieves good performance in Deep Video Understanding (DVU) task of NIST TRECVID, demonstrating its effectiveness. © 2023 IEEE.

关键词： Motion pictures

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An elastic framework construction method based on task migration in edge computing

An elastic framework construction method based on task migra...

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作者： Pu, Yonglin Li, Ziyang Yu, Jiong Lu, Liang Guo, Binglei School of Software Nanjing University of Information Science and Technology Nanjing China School of Software Xinjiang University Urumqi China College of Computer Science and Technology Civil Aviation University of China Tianjin China School of Computer Engineering Hubei University of Arts and Science Xiangyang China

Edge computing (EC) serves as an effective technology, empowering end-users to attain high bandwidth and low latency by offloading tasks with high computational demands from mobile devices to edge servers. However, a major challenge arises when the processing load fluctuates continuously, leading to a performance bottleneck due to the inability to rescale edge node (EN) resources. To address this problem, the approach of task migration is introduced, and EN load prediction model, the resource constrained model, optimal communication overhead model, optimal task migration model, and energy consumption model are built to form a theoretical foundation from which to propose a task migration based resilient framework construction method in EC. With the aid of the domino effect and the combined effect of task migration, a dynamic node-growing algorithm (DNGA) and a dynamic node-shrinking algorithm (DNSA), both based on the task migration strategy, are proposed. Specifically, the DNGA smoothly expands the EN scale when the processing load increases, while the DNSA shrinks the EN scale when the processing load decreases. The experimental results show that for standard benchmarks deployed on an elastic framework, the proposed method realizes a smooth scaling mechanism in the EC, which reduces the latency and improves the reliability of data processing. © 2023 John Wiley & Sons, Ltd.

关键词： Energy utilization

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A Cloud-Based 3D Digital Twin for Arabic Sign Language Alphabet Using Machine Learning Object Detection Model 15

A Cloud-Based 3D Digital Twin for Arabic Sign Language Alpha...

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15th International Conference on Innovations in Information Technology, IIT 2023

作者： Abduljabbar, Mohammed Gochoo, Munkhjargal Sultan, Mohamad T. Batnasan, Ganzorig Otgonbold, Munkh-Erdene Berengueres, Jose Alnajjar, Fady Rasheed, Ashika Abdul Alshamsi, Ahmed Alsaedi, Naser United Arab Emirates University Department of Computer Science and Software Engineering United Arab Emirates

ISBN: (纸本)9798350382396

People with hearing loss or hard hearing struggle with daily life activities as sign language is not widely known by the public. There are many attempts to use technology to help assist hearing loss individuals. However, most proposed solutions are standalone applications or require special hardware like a wearable glove. Our goal is to leverage cloud computing and artificial intelligence (AI) to provide a solution that is portable and does not require any special hardware. We created a lightweight 3D model and rendered it on the browser along with another lightweight object detection model for Arabic Sign Language (ArSL) for real-Time detection. Our contribution is primarily based on integrating our novel functional lightweight 3D avatar model and a lightweight ArSL alphabet detection model, which is trained on public ArSL21L dataset, that are suitable to be given as a cloud service. Prototypes of the 3D digital twin avatar model and AI model are publicly offered for the research community on GitHub. We will be working on a full-scale real-Time cloud-based communication system in ArSL. © 2023 IEEE.

关键词： Object detection

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SparseCoder: Identifier-Aware Sparse Transformer for File-Level Code Summarization 31

SparseCoder: Identifier-Aware Sparse Transformer for File-Le...

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31st IEEE International Conference on software Analysis, Evolution and Reengineering, SANER 2024

作者： Wang, Yanlin Huang, Yanxian Guo, Daya Zhang, Hongyu Zheng, Zibin School of Software Engineering Sun Yat-sen University China School of Computer Science and Technology Sun Yat-sen University China School of Big Data and Software Engineering Chongqing University China

ISBN: (纸本)9798350330663

Code summarization aims to generate natural language descriptions of source code, facilitating programmers to understand and maintain it rapidly. While previous code summarization efforts have predominantly focused on method-level, this paper studies file-level code summarization, which can assist programmers in understanding and maintaining large source code projects. Unlike method-level code summarization, file-level code summarization typically involves long source code within a single file, which makes it challenging for Transformer-based models to understand the code semantics for the maximum input length of these models is difficult to set to a large number that can handle long code input well, due to the quadratic scaling of computational complexity with the input sequence length. To address this challenge, we propose SparseCoder, an identifier-aware sparse transformer for effectively handling long code sequences. Specifically, the SparseCoder employs a sliding window mechanism for self-attention to model short-term dependencies and leverages the structure message of code to capture long-term dependencies among source code identifiers by introducing two types of sparse attention patterns named global and identifier attention. To evaluate the performance of SparseCoder, we construct a new dataset FILE-CS for file-level code summarization in Python. Experimental results show that our SparseCoder model achieves state-of-the-art performance compared with other pre-trained models, including full self-attention and sparse models. Additionally, our model has low memory overhead and achieves comparable performance with models using full self-attention mechanism. Furthermore, we verify the generality of SparseCoder on other code understanding tasks, i.e., code clone detection and code search, and results show that our model outperforms baseline models in both tasks, demonstrating that our model can generate better code representations for various downstream tasks. Our

关键词： Semantics

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Sri Potha: A Voice-Based Sinhala Document Maker

Sri Potha: A Voice-Based Sinhala Document Maker

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： De Silva, D.I. Piyadali, D.S.P. Dilrukshi, M.G.S. Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Sri Lanka

ISBN: (纸本)9798350319200

This study discusses the development of a web application aimed at facilitating Sinhala document creation, with a specific emphasis on Sinhala voice-to-text conversion and the handling of Sinhala commands through vocal input. Leveraging machine learning techniques, including convolutional neural networks and Natural Language Processing, the application's core features were established. Extensive research was conducted to tailor the application's content to the needs of its primary users, ensuring maximum effectiveness. The user-friendly interfaces of the web application are designed for clarity, simplicity, and consistency. The primary objective of this research is to comprehensively analyze the implementation of Sinhala voice-to-text conversion and Sinhala command handling systems. These systems are primarily designed to benefit diverse users, including journalists, content writers, and differently abled individuals with verbal abilities, by enhancing the efficiency of creating Sinhala documents. Through a detailed exploration of the research methodology, this study offers insight into the development process of the web-based system. The outcomes of the linguistic model training, presented within the study, reveal achievements and advancements that address the limitations inherent in existing solutions. Key findings from this research demonstrate the successful functionality of the Sinhala voice-to-text converter and the efficacy of the Sinhala command handler. The voice-to-text conversion system achieved an impressive accuracy rate of over 80%, while the Sinhala command handler exhibited an accuracy of approximately 80%. Moreover, this research envisions potential applications that extend beyond document creation. The technology showcased in the web application holds promise for broader language-based applications, impacting education, accessibility, and communication across the native Sinhala-speaking community in Sri Lanka. In summary, this research showcases th

关键词： convolutional neural network machine learning natural language processing

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On Bounding the Behavior of a Neuron 36th

On Bounding the Behavior of a Neuron

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36th International Florida Artificial Intelligence Research Society Conference, FLAIRS-36 2023

作者： Borowski, Richard Choi, Arthur Computer Science Department College of Computing and Software Engineering Kennesaw State University United States

A neuron with binary inputs and a binary output represents a Boolean function. Our goal is to extract this Boolean function into a tractable representation that will facilitate the explanation and formal verification of a neuron's behavior. Unfortunately, extracting a neuron's Boolean function is in general an NP-hard problem. However, it was recently shown that prime im-plicants of this Boolean function can be enumerated efficiently, with only polynomial time delay. Building on this result, we propose a best-first search algorithm that is able to incrementally tighten inner and outer bounds of a neuron's Boolean function. These bounds correspond to truncated prime-implicant covers of the Boolean function. We provide two case studies that highlight our ability to bound the behavior of a neuron. © 2023 by the authors. All rights reserved.

关键词： Boolean functions

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Proactive Caching at the Wireless Edge:A Novel Predictive User Popularity-Aware Approach

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computer Modeling in engineering & sciences 2024年第8期140卷 1997-2017页

作者： Yunye Wan Peng Chen Yunni Xia Yong Ma Dongge Zhu Xu Wang Hui Liu Weiling Li Xianhua Niu Lei Xu Yumin Dong College of Computer Science Chongqing UniversityChongqing400044China School of Computer and Software Engineering Xihua UniversityChengdu610039China School of Computer and Information Engineering Jiangxi Normal UniversityNanchang330022China Electric Power Research Institute of State Grid Ningxia Electric Power Co. Ltd.Yinchuan750002China College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing400030China School of Computer Science and Technology Beijing Institute of TechnologyBeijing100083China School of Computer Science and Technology Dongguan University of TechnologyDongguan523808China School of Emergency Management Xihua UniversityChengdu610039China College of Computer and Information Science Chongqing Normal UniversityChongqing401331China

Mobile Edge Computing(MEC)is a promising technology that provides on-demand computing and efficient storage services as close to end users as *** an MEC environment,servers are deployed closer to mobile terminals to exploit storage infrastructure,improve content delivery efficiency,and enhance user ***,due to the limited capacity of edge servers,it remains a significant challenge to meet the changing,time-varying,and customized needs for highly diversified content of ***,techniques for caching content at the edge are becoming popular for addressing the above *** is capable of filling the communication gap between the users and content providers while relieving pressure on remote cloud ***,existing static caching strategies are still inefficient in handling the dynamics of the time-varying popularity of content and meeting users’demands for highly diversified entity *** address this challenge,we introduce a novel method for content caching over MEC,i.e.,*** synthesizes a content popularity prediction model,which takes users’stay time and their request traces as inputs,and a deep reinforcement learning model for yielding dynamic caching *** results demonstrate that PRIME,when tested upon the MovieLens 1M dataset for user request patterns and the Shanghai Telecom dataset for user mobility,outperforms its peers in terms of cache hit rates,transmission latency,and system cost.

关键词： Mobile edge computing content caching system average cost deep reinforcement learning collaborative mechanism

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MmRotation: Unlocking Versatility of a Single mmWave Radar via Horizontal Mobility and Azimuthal Rotation

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6045-6061页

作者： Zhang, Duo Zhang, Xusheng Yin, Zhehui Xie, Yaxiong Wei, Hewen Chang, Zhaoxin Li, Wenwei Zhang, Daqing Peking University School of Electronics Engineering and Computer Science Beijing100871 China University at Buffalo BuffaloNY United States Peking University School of Software and Microelectronics Beijing100871 China Peking University Key Lab of High Confidence Software Technologies Ministry of Education School of Computer Science Beijing100871 China Institut Polytechnique de Paris Telecom SudParis Cedex Evry91011 France

Indoor mmWave-based sensing technologies have garnered substantial interest from both the industrial and academic. Yet, the intrinsic challenge posed by the limited Field-of-View (FOV) of mmWave radars significantly restricts their coverage. This limitation necessitates careful selection of installation positions and orientations to optimize performance, thereby severely curtailing the versatility and widespread adoption of these systems. Traditionally, expanding coverage involved increasing the number of radar units. This paper introduces a novel approach to enhance the FOV by incorporating mobility, achieved by affixing the radar onto a pan-tilt unit capable of rotating along both the horizontal and azimuthal. Nevertheless, the disparity between the pan-tilt and the radar presents significant challenges for accurately rotating the radar's orientation. To mitigate this, we propose an automated calibration algorithm for radar and pan-tilt, ensuring precise calibration. Additionally, we have devised a radar orientation adjustment algorithm intended to automatically align the radar's FOV with the positions of detected objects to facilitate various applications. Through three case studies, we have demonstrated that mmRotation can greatly expand the sensing range, enabling support for multiple applications on a single radar, such as vital signs monitoring and fall detection. Comprehensive experimental results underscore that our system surpasses the current state-of-the-art (SOTA). © 2002-2012 IEEE.

关键词： Calibration

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