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Design and Development of IoT Smart Driving Protocol for Unmanned Ground Vehicles 6

Design and Development of IoT Smart Driving Protocol for Unm...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Seelam, Santhosh Kumar Nagakishore Bhavanam, S. Midasala, Vasujadevi Reddy, Edara Sreenivasa Department of Computer Science and Engineering University College of Engineering and Technology Acharya Nagarjuna University Andhra Pradesh Guntur India Jabalpur Madhya Pradesh Barela India Department of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

ISBN: (纸本)9798331522667

UGVs have enormous potential in industries such as defense, agriculture, and autonomous transportation. Efficiency and effectiveness can be achieved by UGVs through the sophistication of their control systems, mainly with regard to navigation and decision-making. In this study, a new IoT-based smart direction control system is proposed for UGVs. The system presented herein integrates state-of-the-art sensor fusion techniques against the backdrop of a robust and adaptive communication framework for enhanced navigation accuracy and reduced latency, with the overall goal of improving system efficiency. Equipped with GPS, IMU, and LiDAR sensors, our system provides accurate environmental perception and real-time adaptability to various terrains. The heart of this device is a high-performance, military-grade microcontroller, which ensures the reliability and handling of numerous data streams simultaneously with minimal latency. On the software side, modular design makes use of edge computing and machine learning algorithms to facilitate adaptive control and real-time decision-making. The performance was verified by rigorous testing in simulated environments and real-world field trials. The results show an improvement in the navigation accuracy and system responsiveness compared to previous solutions. A comparative deep analysis shows the superiority of our approach to hardware design, software integration, and prototype testing. © 2025 IEEE.

关键词： Adaptive control systems

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Applications of Causality and Causal Inference in software engineering 21

Applications of Causality and Causal Inference in Software E...

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21st IEEE/ACIS international conference on software engineering Research, Management and Applications, SERA 2023

作者： Chadbourne, Patrick Eisty, Nasir U. Boise State University Department of Computer Science BoiseID United States

ISBN: (纸本)9798350345889

Causal inference is a study of causal relationships between events and the statistical study of inferring these relationships through interventions and other statistical techniques. Causal reasoning is any line of work toward determining causal relationships, including causal inference. This paper explores the relationship between causal reasoning and various fields of software engineering. This paper aims to uncover which software engineering fields are currently benefiting from the study of causal inference and causal reasoning, as well as which aspects of various problems are best addressed using this methodology. With this information, this paper also aims to find future subjects and fields that would benefit from this form of reasoning and to provide that information to future researchers. This paper follows a systematic literature review, including;the formulation of a search query, inclusion and exclusion criteria of the search results, clarifying questions answered by the found literature, and synthesizing the results from the literature review. Through close examination of the 45 found papers relevant to the research questions, it was revealed that the majority of causal reasoning as related to software engineering is related to testing through root cause localization. Furthermore, most causal reasoning is done informally through an exploratory process of forming a Causality Graph as opposed to strict statistical analysis or introduction of interventions. Finally, causal reasoning is also used as a justification for many tools intended to make the software more human-readable by providing additional causal information to logging processes or modeling languages. © 2023 IEEE.

关键词： Professional aspects

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Innovations in Mechanical Drawing and computer Graphics: Enhancing Precision and Visualization in engineering Design 4

Innovations in Mechanical Drawing and Computer Graphics: Enh...

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4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Shrivastava, Anurag Goyal, Himanshu Rai Hussein, Layth Bansal, Saloni Sharma, Vishal Harikrishna, M. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gla University Department of Computer Engineering and Applications Mathura India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331518981

This study examines the significant advancements in computer graphics and mechanical drawing, with an emphasis on how these developments have revolutionised engineering design precision and visualisation. For engineers and designers, integrating cutting-edge computer-aided design (CAD) tools and visualisation technologies has become crucial as sectors continue to adapt to issues that are ever more complex. The study focusses on significant developments that improve design accuracy and enable detailed visual representations of engineering concepts, including augmented reality (AR), simulation methods, and 3D modelling. Professionals can streamline the engineering workflow by using CAD software to produce complex drawings that are both exact and easily customisable. Before physical prototyping takes place, decision-making based on examination of the product's performance under different conditions is made possible by the integration of simulation modelling. Furthermore, by offering immersive experiences that let stakeholders see and interact with 3D models in actual settings, augmented reality applications transform design assessments. The research emphasises the importance of these advances in enhancing engineering teams' cooperation, efficiency, and communication through a thorough examination of them. The ultimate goal of this research is to pinpoint future paths for the integration of computer graphics and mechanical drawing, opening the door for improved engineering design and development processes. © 2024 IEEE.

关键词： Precision engineering

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A One-Time Encryption Consensus Algorithm Based on (t, n) Threshold Secret Sharing

A One-Time Encryption Consensus Algorithm Based on (t, n) Th...

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2024 international conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Fanchang Yadav, Amit Khan, Asif Chengdu Neusoft University Department Of Software Engineering Chengdu China College Of Engineering It And Environment Charles Darwin University NT Australia Integral University Department Of Computer Application Lucknow India

ISBN: (纸本)9798350371567

A one-time encryption consensus algorithm based on (t, n) threshold secret sharing is proposed to address the performance and security issues of consensus mechanisms in blockchain applications. The theoretical basis of this algorithm is the chaos algorithm, finite state automata, secure multi-party computation, and lagrange interpolating formula in cryptography. After analyzing the security of the threshold secret sharing scheme, it can resist various attacks and achieve a one-time encryption security level, It also has the characteristics of key dynamism and nodes management flexibility, ensuring the strong security of the proposed consensus algorithm. © 2024 IEEE.

关键词： Cryptography

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MalwareTotal: Multi-Faceted and Sequence-Aware Bypass Tactics Against Static Malware Detection 24

MalwareTotal: Multi-Faceted and Sequence-Aware Bypass Tactic...

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44th ACM/IEEE international conference on software engineering, ICSE 2024

作者： He, Shuai Fu, Cai Hu, Hong Chen, Jiahe Lv, Jianqiang Jiang, Shuai School of Cyber Science and Engineering Huazhong Science and Technology University China Huazhong Science and Technology University Hubei Key Laboratory of Distributed System Security China Pennsylvania State University United States

ISBN: (纸本)9798400702174

Recent methods have demonstrated that machine learning (ML) based static malware detection models are vulnerable to adversarial attacks. However, the generated malware often fails to generalize to production-level anti-malware software (AMS), as they usually involve multiple detection methods. This calls for universal solutions to the problem of malware variants generation. In this work, we demonstrate how the proposed method, MalwareTotal, has allowed malware variants to continue to abound in ML-based, signature-based, and hybrid anti-malware software. Given a malicious binary, we develop sequential bypass tactics that enable malicious behavior to be concealed within multi-faceted manipulations. Through 12 experiments on real-world malware, we demonstrate that an attacker can consistently bypass detection (98.67%, and 100% attack success rate against ML-based methods EMBER and MalConv, respectively;95.33%, 92.63%, and 98.52% attack success rate against production-level anti-malware software ClamAV, AMS A, and AMS B, respectively) without modifying the malware functionality. We further demonstrate that our approach outperforms state-of-the-art adversarial malware generation techniques both in attack success rate and query consumption (the number of queries to the target model). Moreover, the samples generated by our method have demonstrated transferability in the real-world integrated malware detector, VirusTotal. In addition, we show that common mitigation such as adversarial training on known attacks cannot effectively defend against the proposed attack. Finally, we investigate the value of the generated adversarial examples as a means of hardening victim models through an adversarial training procedure, and demonstrate that the accuracy of the retrained model against generated adversarial examples increases by 88.51 percentage points. © 2024 ACM.

关键词： computer viruses

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software Testing Effort Estimation Based on Machine Learning Techniques: Single and Ensemble Methods 16

Software Testing Effort Estimation Based on Machine Learning...

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16th international conference on Knowledge Discovery and Information Retrieval, KDIR 2024 as part of 16th international Joint conference on Knowledge Discovery, Knowledge engineering and Knowledge Management, IC3K 2024

作者： Hosni, Mohamed Medarhri, Ibtissam Carrillo de Gea, Juan Manuel MOSI Research Team LM2S3 ENSAM Moulay Ismail University of Meknes Morocco MMCS Research Team LMAID ENSMR Rabat Morocco Department of Informatics and Systems Faculty of Computer Science University of Murcia Spain

ISBN: (纸本)9789897587160

Delivering an accurate estimation of the effort required for software system development is crucial for the success of any software project. However, the software development lifecycle (SDLC) involves multiple activities, such as software design, software build, and software testing, among others. software testing (ST) holds significant importance in the SDLC as it directly impacts software quality. Typically, the effort required for the testing phase is estimated as a percentage of the overall predicted SDLC effort, typically ranging between 10% and 60%. However, this approach poses risks as it hinders proper resource allocation by managers. Despite the importance of this issue, there is limited research available on estimating ST effort. This paper aims to address this concern by proposing four machine learning (ML) techniques and a heterogeneous ensemble to predict the effort required for ST activities. The ML techniques employed include K-nearest neighbor (KNN), Support Vector Regression, Multilayer Perceptron Neural Networks, and decision trees. The dataset used in this study was obtained from a well-known repository. Various unbiased performance indicators were utilized to evaluate the predictive capabilities of the proposed techniques. The overall results indicate that the KNN technique outperforms the other ML techniques, and the proposed ensemble showed superior performance accuracy compared to the remaining ML techniques. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Contrastive Learning

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Being Agile in the software engineering Classroom: Using Agile Approaches Instead of Plan-Driven Approaches 2023

Being Agile in the Software Engineering Classroom: Using Agi...

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28th Annual conference on Innovation and Technology in computer science Education (ITiCSE)

作者： Scharlau, Bruce Univ Aberdeen Comp Sci Aberdeen Scotland

ISBN: (纸本)9798400701399

We had an outdated, unsuitable pair of courses covering software engineering over an academic year, which were rewritten last summer. Out went the plan-driven project approach of GANNT charts, and a belief that 'better estimates' would save the day. In came a lightweight focus on a mix of extreme programming and scrum to incrementally, and iteratively build products. The classroom changed too. Out went lecture slides in the classroom, plus self-directed pick and choose practical sessions. In came video-led lectures, experiential learning, and practical sessions to guide students in what they need know to build their product prototypes. The initial results suggest we still need more work, but shows students are developing products more confidently as teams.

关键词： Agile development agile games classroom exercises

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Governance-Focused Classification of Security and Privacy Requirements from Obligations in software engineering Contracts 30th

Governance-Focused Classification of Security and Privacy Re...

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30th international Working conference on Requirements engineering - Foundation for software Quality (REFSQ)

作者： Anish, Preethu Rose Verma, Aparna Venkatesan, Sivanthy Logamurugan, V Ghaisas, Smita TCS Res Pune Maharashtra India TATA Consultancy Serv Mumbai Maharashtra India

ISBN: (纸本)9783031573262;9783031573279

[Context and Motivation] Security and Privacy (SP) compliance is an important aspect of running businesses successfully. Compliance with SP requirements by software engineering (SE) vendors, both in terms of the systems they implement and the practices they follow while implementing, gives customers an assurance that their data is accessed, stored, and processed securely. Failure to comply on the other hand, can entail heavy fines, lawsuits, and may even lead to loss of business through prohibition of those software in corresponding jurisdictions. SE contracts are known to be a useful source for deriving software requirements. [Question/problem] Mining any kind of information from contracts is a dauting task given that contracts are large and complex documents employing Legalese. [Principal ideas/results] We employ an exploratory study to come up with a model for a governance-focused classification of the SP requirements present in SE contracts for governance. Next, we report experiments conducted with Recurrent Neural Networks and Transformer-based models to automate this classification. Experiments conducted on 960 SE contracts received from a large vendor organization indicate that T5 performs best for both SP identification and classification tasks. With T5, we obtained an average F1 score of 0.90 each for identification of SP requirements. For the governance-focused classification, we obtained an average F1 score of 0.81 for the Security class and 0.80 for the Privacy class. [Contribution] Through an exploratory study, we present a model for a governance-focused classification of the SP requirements present in SE contracts. We further automate the extraction and the governance-focused classification of SP requirements by conducting experiments using 960 real-life SE contracts received from a large vendor organization.

关键词： security privacy requirements contracts classification

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Pothole Detection in Asphalt Roads: A Comprehensive Approach for Enhanced Road Maintenance and Safety with AlexNet Model

Pothole Detection in Asphalt Roads: A Comprehensive Approach...

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2024 international Telecommunications conference, ITC-Egypt 2024

作者： Abdelmalak, Mark Emad Sobhi Khodadadi, Nima Zaki, Ahmed Mohamed Eid, Marwa M. Rizk, Faris H. Ibrahim, Abdelhameed Abdelhamid, Abdelaziz A. Abualigah, Laith El-Kenawy, El-Sayed M. Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura35111 Egypt University of Miami Department of Civil and Architectural Engineering Coral GablesFL United States Computer Science and Intelligent Systems Research Center BlacksburgVA24060 United States Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt Bahrain Polytechnic Faculty of Engineering Isa Town Bahrain Ain Shams University Faculty of Computer and Information Sciences Department of Computer Science Cairo11566 Egypt Al al-Bayt University Computer Science Department Mafraq25113 Jordan Middle East University MEU Research Unit Amman11831 Jordan Jadara University Jadara Research Center Irbid Jordan

ISBN: (纸本)9798350351408

The research article described in this paper puts forward a novel method of using an integrated software approach and high-end hardware devices for adaptive and intelligent detection of potholes on asphalt roads. The Pothole Detection Dataset is used for the dataset analysis, and we put VGG19Net, ResNet-50, GoogLeNet, and AlexNet among the computer vision models to analyze the applicability of these models. Different types of networks were compared, and AlexNet showed the best results as it achieved 92.15% accuracy, 91.38 % sensitivity (TPR), and a surprisingly high F-score, which reached 96.52%. Furthermore, by using its time of 279.35 seconds, which might be considered very fast, AlexNet shows many strengths in helping to do this, as well as identifying road anomalies, making it a perfect candidate for real-world utilization. This research demonstrates the emergence of sophisticated integrated pothole repair solutions, emphasizing the importance of both software and hardware in developing sophisticated pothole detection. Practices and this research could be an example for further surveying road inspection technologies. © 2024 IEEE.

关键词： Digital storage

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Can a Simple Approach Perform Better for Cross-Project Defect Prediction? 19

Can a Simple Approach Perform Better for Cross-Project Defec...

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19th international conference on Evaluation of Novel Approaches to software engineering, ENASE 2024

作者： Hossain, Md. Arman Akhter, Suravi Islam, Md. Shariful Alam, Muhammad Mahbub Shoyaib, Mohammad Institute of Information Technology University of Dhaka Dhaka Bangladesh Department of Computer Science and Engineering University of Liberal Arts Bangladesh Dhaka Bangladesh Department of Mathematics University of Dhaka Dhaka Bangladesh Department of Computer Science and Engineering Islamic University of Technology Dhaka Gazipur Bangladesh

ISBN: (纸本)9789897586965

Cross-Project Defect Prediction (CPDP) has gained considerable research interest due to the scarcity of historical labeled defective modules in a project. Although there are several approaches for CPDP, most of them contains several parameters that need to be tuned optimally to get the desired performance. Often, higher computational complexities of these methods make it difficult to tune these parameters. Moreover, existing methods might fail to align the shape and structure of the source and target data which in turn deteriorates the prediction performance. Addressing these issues, we investigate correlation alignment for CPDP (CCPDP) and compare it with state-of-the-art transfer learning methods. Rigorous experimentation over three benchmark datasets AEEEM, RELINK and SOFTLAB that include 46 different project-pairs, demonstrate its effectiveness in terms of F1-score, Balance and AUC compared to six other methods TCA, TCA+, JDA, BDA, CTKCCA and DMDA JFR. In terms of AUC, CCPDP wins at least 32 and at most 42 out of 46 project pairs compared to all transfer learning based method. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Forecasting

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