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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Tomar, Ashish Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350370249

Security and privacy are always major issues in Vehicular ad hoc networks. For ensuring privacy and avoiding tracking of the vehicle, recently some researchers proposed the idea of providing multiple pseudo identities to the vehicles. Although multiple pseudo identity approach provides anonymity, it could lead to a harmful type of attack called Sybil attack, in which a vehicle poses as multiple vehicles using the multiple identities at the same time. In this paper, a blockchain based decentralized approach has been proposed to mitigate the Sybil attack. In this approach, the blockchain utilization reduces the dependency on certificate authority which results in less computation and communication delay for verification of identities. Further, with the security analysis of this scheme, different security features of the scheme have been proved. Finally, for evaluating the performance and efficiency of the proposed scheme, simulation has been done using NS3 and SUMO environments. © 2024 IEEE.

关键词： anonymity blockchain security Sybil attack VANET

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Exploring The Trade-off between Efficient Service Placement Time and Optimized Fog Colonies Utilizing Advanced Genetic Algorithm with Diverse Network Topologies 24

Exploring The Trade-off between Efficient Service Placement ...

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13th International Conference on Software and computer Applications, ICSCA 2024

作者： Verma, Nilesh Kumar Khetavath, Jairam Naik Department Of Computer Science And Engineering National Institute Of Technology Raipur Raipur India

ISBN: (纸本)9798400708329

Optimized Fog colony is one of the aid of fog computing, consisting of fog devices, enable efficient management of large fog domains. Well-designed fog colonies can operate independently, resulting in effectively resource utilization and improved system performance. However, there is a lack of optimization approaches for organizing fog devices into colonies and determining which fog colony should be used to stipulated and execute the services required by an IoT application is its primary challenge known as the "efficient service placement time"(ESPT). Contrary to the prevalent practice of treating ESPT as a single objective optimization problem, which often proves insufficient for accommodating the escalating complexities of engineering practice, our study takes a different approach. We present a modeling framework that considers the ESPT in fog computing as a constrained multi-objective optimization problem. Our secondary objective is to minimize the response time of services within the fog colonies. We employ the advanced elitist non-dominated sorting genetic algorithm (MS-NSGA) to optimize the fog colonies for constrained multi-objective service placement problem. We compare our experiment for three different network topologies with various configuration. The experimental result demonstrate the best trade-off between service placement time and proposed scheme for Barabasi-Albert network topology. Additionally, the results also indicate a clear trend towards reduced response time. © 2024 ACM.

关键词： Fog

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Extend Wave Function Collapse Algorithm to Large-Scale Content Generation 5

Extend Wave Function Collapse Algorithm to Large-Scale Conte...

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5th Annual IEEE Conference on Games, CoG 2023

作者： Nie, Yuhe Zheng, Shaoming Zhuang, Zhan Song, Xuan Southern University of Science and Technology Computer Science and Engineering Guangdong China

ISBN: (纸本)9798350322774

Wave Function Collapse (WFC) is a widely used tile-based algorithm in procedural content generation, including textures, objects, and scenes. However, the current WFC algorithm and related research lack the ability to generate commercialized large-scale or infinite content due to constraint conflict and time complexity costs. This paper proposes a Nested WFC (N-WFC) algorithm framework to reduce time complexity. To avoid conflict and backtracking problems, we offer a complete and sub-complete tileset preparation strategy, which requires only a small number of tiles to generate aperiodic and deterministic infinite content. We also introduce the weight-brush system that combines N-WFC and sub-complete tileset, proving its suitability for game design. Our contribution addresses WFC's challenge in massive content generation and provides a theoretical basis for implementing concrete games. © 2023 IEEE.

关键词： Textures

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Banking in the Digital Age: Predicting Eligible Customers through Machine Learning and AWS 1

Banking in the Digital Age: Predicting Eligible Customers th...

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1st IEEE Karachi Section Humanitarian technology Conference, Khi-HTC 2024

作者： Ali, Asfand Irfan, Asim Raza, Ali Memon, Zainab Mehran University of Engineering and Technology Department of Telecommunication Engineering Jamshoro Pakistan Mehran University of Engineering and Technology Computer Systems Engineering Jamshoro Pakistan Quaid-e-Awam University of Engineering Science and Technology Computer Systems Engineering Nawabshah Pakistan

ISBN: (纸本)9798350373042

This research project focuses on utilizing machine learning techniques to predict loan default among applicants in the context of financial organizations. Loan approval decisions carry substantial risks, and not all applicants can be deemed trustworthy. The primary goal of this research is to determine the feasibility of accurately presenting loan defaults by analyzing a dataset sourced from *** project involves data analysis and the application of machine learning algorithms, including logistic regression, K Nearest Neighbor, and Decision tree. Implementation will be carried out using Python programming language within Visual Studio Code, with version control through GitHub, and eventual deployment on the AWS Cloud platform. The anticipated outcomes aim for high prediction accuracy and moderate training and validation data loss, contributing to more informed decision-making in the lending process. © 2024 IEEE.

关键词： Data handling

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Optimized Model Identification for Malware Detection in a File Using Machine Learning 3

Optimized Model Identification for Malware Detection in a Fi...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Laya, N. Shruthi Shetty, J. Sarojadevi, H. Sowmya, P. Nitte Meenakshi Institute Of Technology Department Of Computer Science And Engineering Bengaluru India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318609

Malware remains one of the most critical digital threats today due to the rapidly evolving landscape of online activities. Detecting malware has become a pivotal aspect of safeguarding computer system, especially as polymeric malware arises by utilizing dynamic modifications to confound traditional signature-based detection methods. To combat the surging volume of malware, Machine Learning (ML) has emerged as a key strategy. Hence, ML-based detection mechanisms are imperative. This overview underscores the diverse array of machine learning-driven detection techniques. The primary objective is to construct an optimal machine learning representation for malware detection within files. Three distinct machine learning algorithms are employed: random forest, logistic regression, and a basic neural network. From these, the superior model is determined based on rigorous evaluation metrics-accuracy, f1 score, and both normalized and non-normalized confusion matrices on both training and testing data. The selection process hinges on achieving the highest accuracy and f1 score. The random forest algorithm was depicted to be having highest train accuracy with 98.28% and test accuracy with 98.38% and f1 score was found to be 97.3% which was chosen as a final model for malware detection in a file. The chosen model subsequently serves as the foundation for malware detection. © 2024 IEEE.

关键词： Logistic regression

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Enhancing User Experience and Design in Agricultural Information Portals: A Study of Websites for Indian Farmers

Enhancing User Experience and Design in Agricultural Informa...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Sharma, Mradula Garg, Sahil Srivastava, Ashutosh Srivastava, Divya Verma, Madhushi Bhardwaj, Arpit School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350379716

Agriculture plays a significant role in India's economy, not only in terms of its GDP share but also in employment, rural development, and food security. As per the 2022-2023 economic survey, the gross value added (GVA) of agriculture in Indian economy is 18.3%. The economic survey also shows that there has been a decline of this percentage from 35% to 18% in the last 20 years. This is an alarming situation! India has got a very good agricultural land but due to lack of awareness in this field its contribution in GVA is decreasing. The present article took a small initiative to present a study of various agriculture information portals that are although designed to help farmers, but are they actually helpful? This question is taken over in this article as it discusses the various issues like, technological literacy of Indian Farmers, user friendly website design, accessibility in rural areas, language differences, and etc. Total ten (10) government agricultural websites are analyzed and we suggest some changes/improvements that these websites can implement for a better user experience to reach out to more farmers and deliver better information regarding agricultural issues. © 2024 IEEE.

关键词： Agricultural economics

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Choosing the Right Cloud Provider for Limitless Growth and Maximize ROI

Choosing the Right Cloud Provider for Limitless Growth and M...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Kumar, Naween Saini, Vaibhav School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350379716

The present article explores the most critical criteria organizations must consider in choosing a proper Cloud Service Provider (CSP) to enable growth strategies and optimize Return on Investment (ROI). It assists organizations in decision-making by comparing the critical aspects of cloud computing, including the major services offered, cost structures, and ROI measurements among the top platforms. The first category of comparative analysis is conducted based on cloud service categories and different Service Providers, economic and technical factors to enable businesses to optimize cost efficiency and performance. The second comparative category is evaluated based on scalability, performance, security, compatibility, monthly estimated costs, Potential savings vs. On-premises., and ROI factors. Such analysis allows for an informed decision that maximizes operational efficiency and cost-effectiveness. By combining and evaluating this analysis, organizations can decide which cloud platform to adopt for the best performance and future growth. The final objective is to provide people with the tools needed to harness the power of cloud computing for better efficiency and effectiveness of business technologies to gain a better competitive advantage. © 2024 IEEE.

关键词： Cloud platforms

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Arbitrary Time Information Modeling via Polynomial Approximation for Temporal Knowledge Graph Embedding 30

Arbitrary Time Information Modeling via Polynomial Approxima...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Fang, Zhiyu Qin, Jingyan Zhu, Xiaobin Yang, Chun Yin, Xu-Cheng School of Computer & Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9782493814104

Distinguished from traditional knowledge graphs (KGs), temporal knowledge graphs (TKGs) must explore and reason over temporally evolving facts adequately. However, existing TKG approaches still face two main challenges, i.e., the limited capability to model arbitrary timestamps continuously and the lack of rich inference patterns under temporal constraints. In this paper, we propose an innovative TKGE method (PTBox) via polynomial decomposition-based temporal representation and box embedding-based entity representation to tackle the above-mentioned problems. Specifically, we decompose time information by polynomials and then enhance the model's capability to represent arbitrary timestamps flexibly by incorporating the learnable temporal basis tensor. In addition, we model every entity as a hyperrectangle box and define each relation as a transformation on the head and tail entity boxes. The entity boxes can capture complex geometric structures and learn robust representations, improving the model's inductive capability for rich inference patterns. Theoretically, our PTBox can encode arbitrary time information or even unseen timestamps while capturing rich inference patterns and higher-arity relations of the knowledge base. Extensive experiments on real-world datasets demonstrate the effectiveness of our method. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Polynomial approximation

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Towards characterizing bug fixes through dependency-level changes in Apache Java open source projects

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science China(Information sciences) 2022年第7期65卷 102-120页

作者： Di CUI Lingling FAN Sen CHEN Yuanfang CAI Qinghua ZHENG Yang LIU Ting LIU School of Computer Science and Technology Xi'an Jiaotong University College of Cyber Science Nankai University College of Intelligence and Computing Tianjin University Department of Computer Science Drexel University School of Computer Science and Engineering Nanyang Technological University School of Cyber Science and Engineering Xi'an Jiaotong University

The complexity and diversity of bug fixes require developers to understand bug fixes from multiple perspectives in addition to fine-grained code changes. The dependencies among files in a software system are an important dimension to inform software quality. Recent studies have revealed that most bug-prone files are always architecturally connected with dependencies, and as one of the best practices in the industry,changes in dependencies should be avoided or carefully made during bug fixing. Hence, in this paper, we take the first attempt to understand bug fixes from the dependencies perspective, which can complement existing code change perspectives. Based on this new perspective, we conducted a systematic and comprehensive study on bug fixes collected from 157 Apache open source projects, involving 140456 bug reports and182621 bug fixes in total. Our study results show that a relatively high proportion of bug fixes(30%) introduce dependency-level changes when fixing the corresponding 33% bugs. The bugs, whose fixes introduce dependency-level changes, have a strong correlation with high priority, large fixing churn, long fixing time,frequent bug reopening, and bug inducing. More importantly, patched files with dependency-level changes in their fixes, consume much more maintenance costs compared with those without these changes. We further summarized three representative patch patterns to explain the reasons for the increasing costs. Our study unveils useful findings based on qualitative and quantitative analysis and also provides new insights that might benefit existing bug prediction techniques. We release a large set of benchmarks and also implement a prototype tool to automatically detect dependency-level changes from bug fixes, which can warn developers and remind them to design a better fix.

关键词： empirical software engineering software maintenance software evolution software architecture software design

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Suricata-Based Intrusion Detection and Isolation System for Local Area Networks

Suricata-Based Intrusion Detection and Isolation System for ...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Mohamed Shalman Kursheeth, K. Sree, Thinnavalli Sendil Vadivu, D. Harsha, Y Sai Sri Rajagopalan, Narendran National Institute of Technology Puducherry Department of Computer Science and Engineering Karaikal India

ISBN: (纸本)9798331540685

Malicious activity includes intentionally disrupting or damaging computer systems, networks, or data, including distributing malware, phishing attempts, denial of service, and unauthorized access. Criminals exploit vulnerabilities for financial gain or espionage. Effective prevention involves strong cybersecurity measures such as firewalls, anti-virus software, and user training. Failure to address vulnerabilities can result in data security breaches and financial losses. An Intrusion Detection System (IDS) is essential to cybersecurity measures. It scrutinizes network data or system operations for any indications of malicious actions. It plays a vital role in identifying potential threats and taking appropriate action to ensure the security of the network or system. An IDS simplifies identifying and reacting to accurate data, safeguarding critical data and systems from potential risks. Utilizing Suricata IDS, we put forth an Intrusion Detection and Isolation System (IDIS). This system is designed to identify any harmful nodes present in the Local Area Network (LAN) and isolate them. This is achieved by utilizing the configuration of an Access Control List (ACL) in the routers. This approach enhances the security measures, ensuring a safer network environment. © 2024 IEEE.

关键词： Network intrusion

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