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2024 MIT Art, Design and Technology school of computing International Conference, MITADTSoCiCon 2024

作者： Mukherjee Mathur, Shubhra Jadhav, Nagesh Gupta, Ravindra Design and Technology University Pune Sarvepalli Radakrishnan University School of Computing MIT Art Department of Computer Science and Engineering Bhopal India Design and Technology University School of Computing MIT Art Department of Computer Science and Engineering Pune India Sarvepalli Radakrishnan University School of Computing Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350362879

A developing technology that can be highly helpful for both intelligent transportation services and safety and security is the Automobile ad hoc network, or VANET. Nevertheless because of the great mobility and wireless connection of cars, there are numerous security risks that jeopardize people's safety and security when driving. The Sybil assault, in which the perpetrator makes up a false identity to obtain significant influence within the network and delay certain services and manipulate others by fictitious voting, is one of the main security issues. People can avoid being duped by an attacker or falling into traps when attacks are detected early. This study describes two methods for detecting Sybil attacks: the first uses the Poisson Distribution Method in order to forecast traffic on the street, whereas the latter examines network efficiency for packet delivery ratio (PDR) in both benign additionally malicious environments. According to the simulation's findings, PDR lowers the number of fictitious cars in the network. Our method is straightforward and efficient because it doesn't involve a lot of computational overhead and doesn't breach users' privacy concerns. © 2024 IEEE.

关键词： Poisson distribution

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Out-of-Distribution Data: An Acquaintance of Adversarial Examples - A Survey

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ACM computing Surveys 2025年第8期57卷 1-40页

作者： Karunanayake, Naveen Gunawardena, Ravin Seneviratne, Suranga Chawla, Sanjay School of Computer Science The University of Sydney Sydney Australia Computer Science and Engineering University of New South Wales Sydney Australia Qatar Computing Research Institute Doha Qatar and Hamad Bin Khalifa University Doha Qatar

Deep neural networks (DNNs) deployed in real-world applications can encounter out-of-distribution (OOD) data and adversarial examples. These represent distinct forms of distributional shifts that can significantly impact DNNs’ reliability and robustness. Traditionally, research has addressed OOD detection and adversarial robustness as separate challenges. This survey focuses on the intersection of these two areas, examining how the research community has investigated them together. Consequently, we identify two key research directions: robust OOD detection and unified robustness. Robust OOD detection aims to differentiate between in-distribution (ID) data and OOD data, even when they are adversarially manipulated to deceive the OOD detector. Unified robustness seeks a single approach to make DNNs robust against both adversarial attacks and OOD inputs. Accordingly, first, we establish a taxonomy based on the concept of distributional shifts. This framework clarifies how robust OOD detection and unified robustness relate to other research areas addressing distributional shifts, such as OOD detection, open set recognition, and anomaly detection. Subsequently, we review existing work on robust OOD detection and unified robustness. Finally, we highlight the limitations of the existing work and propose promising research directions that explore adversarial and OOD inputs within a unified framework. © 2025 Copyright held by the owner/author(s)

关键词： Generative adversarial networks

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Towards Developing Privacy-Preserved Data Security Approach(PP-DSA)in Cloud computing Environment

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computer Systems science & engineering 2023年第3期44卷 1881-1895页

作者： S.Stewart Kirubakaran V.P.Arunachalam S.Karthik S.K annan Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia School of Computing Science and Engineering Vellore Institute of TechnologyBhopalMadhya PradheshIndia

In the present scenario of rapid growth in cloud computing models,several companies and users started to share their data on cloud ***,when the model is not completely trusted,the data owners face several security-related problems,such as user privacy breaches,data disclosure,data corruption,and so on,during the process of data *** addressing and handling the security-related issues on Cloud,several models were *** that concern,this paper develops a Privacy-Preserved Data Security Approach(PP-DSA)to provide the data security and data integrity for the out-sourcing data in Cloud *** preservation is ensured in this work with the Efficient Authentication Technique(EAT)using the Group Signature method that is applied with Third-Party Auditor(TPA).The role of the auditor is to secure the data and guarantee shared data ***,the Cloud Service Provider(CSP)and Data User(DU)can also be the attackers that are to be handled with the ***,the major objective of the work is to enhance cloud security and thereby,increase Quality of Service(QoS).The results are evaluated based on the model effectiveness,security,and reliability and show that the proposed model provides better results than existing works.

关键词： Third-party auditor(TPA) efficient auditing technique(EAT) cloud service provider(CSP) data user(DU) data security privacy-preserving cloud computing cloud security

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Cyber-Syndrome:Concept,Theoretical Characterization,and Control Mechanism

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Tsinghua science and Technology 2024年第3期29卷 721-735页

作者： Feifei Shi Huansheng Ning Liming Chen Sahraoui Dhelim School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Faculty of Computing Ulster UniversityNewtownabbeyBT370QBUK School of Computer Science University College DublinDublinD04 V1W8Ireland

The prevalence of social media and mobile computing has led to intensive user engagement in the emergent Cyber-Physical-Social-Thinking(CPST)***,the easy access,the lack of governance,and excessive use has generated a raft of new behaviors within CPST,which affects users’physical,social,and mental *** this paper,we conceive the Cyber-Syndrome concept to denote the collection of cyber disorders due to excessive or problematic Cyberspace interactions based on CPST *** we characterize the Cyber-Syndrome concept in terms of Maslow’s theory of Needs,from which we establish an in-depth theoretical understanding of Cyber-Syndrome from its etiology,formation,symptoms,and ***,we propose an entropy-based Cyber-Syndrome control mechanism for its computation and *** goal of this study is to give new insights into this rising phenomenon and offer guidance for further research and development.

关键词： Cyber-Syndrome cyber-physical systems Cyber-Physical-Social-Thinking(CPST)space Maslow’sHierarchy of Needs entropy

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SynchroLearn: Master Deadlock and Synchronization Through Interactive Learning 15

SynchroLearn: Master Deadlock and Synchronization Through In...

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

作者： Bhavya, G. Bhanot, Shreya Srinidhi, M. Sidhu, Samreen Krishnan, Sajitha Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350370249

Today, educational tools have become necessary for learners to learn well and improve their knowledge. These tools complement knowledge as they can make it more vivid and add some simulation or game into the learning process. It is self-learning since the students can learn from any place and it also enables the students to go further on content that may interest them, and which may help them in the understanding of the concept. This paper takes the idea of developing SynchroLearn, which is an educational visualization tool developed with the help of HTML, CSS, and JavaScript that enables the teaching of deadlock handling techniques effectively for both the developers and the students. To expound on techniques such as the Producer-Consumer problem, Peterson's method, Test-and-Set Lock, and the Dining Philosophers problem, an online tool known as SynchroLearn helps in providing the learner an actual time visualization. These strategies present several ways of handling resources, and with that, the management of their utilization process, so that the processes could be controlled in terms of what resources they require, which in turn, helps to solve and prevent such cases of deadlocks. This approach helps to learn complex relationships as well as to understand the interactions between the different processes, identify the deadlock situations, and use the essential methods for their resolution. They bring out the idea linking theoretical proficiencies and practical application by demonstrating them as simple-to-follow graphics which are therefore easier to implement. © 2024 IEEE.

关键词： Students

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Monocular 3D object detection with Pseudo-LiDAR confidence sampling and hierarchical geometric feature extraction in 6G network

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Digital Communications and Networks 2023年第4期9卷 827-835页

作者： Jianlong Zhang Guangzu Fang Bin Wang Xiaobo Zhou Qingqi Pei Chen Chen School of Electronic Engineering Xidian UniversityXi'an710071China School of Computer Science and Technology College of Intelligence and ComputingTianjin UniversityTianjin300000China School of Telecommunications Engineering Xidian UniversityXi'an710071China

The high bandwidth and low latency of 6G network technology enable the successful application of monocular 3D object detection on vehicle *** 3D-object-detection-based Pseudo-LiDAR is a low-cost,lowpower solution compared to LiDAR solutions in the field of autonomous ***,this technique has some problems,i.e.,(1)the poor quality of generated Pseudo-LiDAR point clouds resulting from the nonlinear error distribution of monocular depth estimation and(2)the weak representation capability of point cloud features due to the neglected global geometric structure features of point clouds existing in LiDAR-based 3D detection ***,we proposed a Pseudo-LiDAR confidence sampling strategy and a hierarchical geometric feature extraction module for monocular 3D object *** first designed a point cloud confidence sampling strategy based on a 3D Gaussian distribution to assign small confidence to the points with great error in depth estimation and filter them out according to the ***,we present a hierarchical geometric feature extraction module by aggregating the local neighborhood features and a dual transformer to capture the global geometric features in the point ***,our detection framework is based on Point-Voxel-RCNN(PV-RCNN)with high-quality Pseudo-LiDAR and enriched geometric features as *** the experimental results,our method achieves satisfactory results in monocular 3D object detection.

关键词： Monocular 3D object detection Pseudo-LiDAR Confidence sampling Hierarchical geometric feature extraction

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Comprehensive Framework for Analyzing Grammar Error Correction with GPT -3.5

Comprehensive Framework for Analyzing Grammar Error Correcti...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Munda, Rajni Kant Kumar, Abhishek Burman, Ravi Kumar Kumar, Biresh Alam, Md. Shoaib Sinha, Anurag Jharkhand University of Technology Computer Science and Engineering Ranchi India Amity University Computer Science and Engineering Jharkhand Ranchi India Ignou School of Computing and Information Science New Delhi India

ISBN: (纸本)9798331508685

This investigate assesses the viability and exactness of an error-correction approach utilizing GPT-3.5 compared to routine instruments. Letter set rectification plays a vital part in making strides the quality of and improving clarity in different scholastic and proficient situations. The consider utilizes appraisal criteria such as precision, exactness, review, and F1 score to how well GPT-3.5 performs relative to other language structure redress devices. Also, the information incorporates client input and daze testing to gage the viable convenience of the adjustments. Discoveries demonstrate that GPT-3.5 outperforms conventional instruments in taking care of relevant blunders and client fulfilment, illustrating a momentous capacity to redress syntactic botches, indeed in complex sentences with inconspicuous subtleties. Be that as it may, it does confront a few challenges, such as affectability to input varieties and intermittent overcorrection. The think about recommends entering on moving forward relevant understanding and diminishing overcorrection to development these calculations assist. It too investigates potential applications of GPT-3.5 for upgrading composing back and languagelearning assets, highlighting conceivable future investigate ranges to optimize AI-driven language structure rectification models. © 2025 IEEE.

关键词： Instrument errors

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Optimizing Energy Usage and Data Storage in Industrial IoT Settings Through Heuristic-Based Data-Rate Processors 2

Optimizing Energy Usage and Data Storage in Industrial IoT S...

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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Bharathi Kannan, B. Sriramulu, Srinivasan Gopal, Vidhya Karthick, M. Reddy, Yanna Nithin Natarajan, B. Galgotias University School of Computing Science and Engineering India Sona College of Technology Computer Science and Engineering Salem India K Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Tiruchirappalli India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India VIT University School of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350383287

The Internet of Things (IoT) is a fast-expanding field, with applications in areas as diverse as transportation, aviation, automation, electricity, and healthcare. Sensors, protocols, actuators, cloud services, and layers are just few of the components that make up an IoT architecture. The architecture of the Internet of Things is crucial in delivering the goods. The research community now provides dozens of IoT architectures. interoperability, security, privacy, dependability, energy limits, scalability, and a lack of shared standards are only some of the problems that have been recognized by the research community. The proposed IoT architecture should also consider issues of interoperability, scalability, dependability, and trust. By fixing these issues, this research work builds an IoT ecosystem that can accommodate a wide range of devices and services, guarantee smooth communication and scalability, and lay the groundwork for secure, dependable IoT offerings. In effective offloading and resulting interruptions in task execution might result from high latency or network outages. However, because of the importance of a select few delay-sensitive applications, it is important to guarantee their safety. Therefore, this research work aims to unload data at edge and cloud servers, Heuristic based Energy Efficient Data Rate Controller for Offloading has been developed for sensor networks. The bio-inspired NSGA-2 method is used to assess the performance of the suggested algorithm within the context of the problem's multi-objective function formulation. When compared to the current energy aware scheduling approach, the results show that the offloading method can decrease the amount of time and energy needed to run edge servers, maintain load balancing, and maximize resource utilization while still allowing applications that require delays to meet their deadlines. Finally, simulation results demonstrate the proposed approach outperforms 20% high efficiency compared to the

关键词： Internet of things

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A Comparative Analysis of Breast Cancer Classification using Ensemble and Multi Classifier Algorithms

A Comparative Analysis of Breast Cancer Classification using...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Jain, Garima Jain, Sanat Phulre, Ajay Kumar Sharma, Rahul Joshi, Suneet School of Computing Science Engineering and Artificial Intelligence Vit Bhopal University Sehore India School of Computer Science and Engineering Vit Bhopal University Sehore India School of Computer Science and Engineering Parul University Gujarat Vadodara India

ISBN: (纸本)9798350375442

To help and monitor the patients, the initial diagnosis and type prediction of cancer should become mandatory in cancer research. Machine learning (ML) approaches are being studied and evaluated by several research teams from the biomedical sector due to the relevance of classifying cancer patients into two risk groups. It has been suggested that base classifiers be used in conjunction with ensemble methods to predict breast cancer. This work, provide detailed forecasts using data on breast cancer in a novel setting. In order to create precise predictions, this research investigates the many classification-based data mining techniques. Additionally, this study evaluates the dataset using a variety of classifiers to forecast which model will perform the best. The cancer dataset worked upon is the Wisconsin Dataset with 569 instances. This Dataset is given to classifiers like SVM, J48, REP tree and Random Forest after pre-processing and cross validation is applied later, new models are tested subsequently. The results were assessed across multiple parameters, and the analysis indicates that among all the classifiers, the REP tree focusing on node caps delivers the most in-depth predictions and achieves the highest accuracy. This model outperforms other approaches, including Boosting with Random Forest (node caps), Boosting with Random Forest (INV nodes), and Boosting with J48, in terms of yielding superior and precise results. © 2024 IEEE.

关键词： Lung cancer

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Language Models over Large-Scale Knowledge Base: on Capacity, Flexibility and Reasoning for New Facts 31

Language Models over Large-Scale Knowledge Base: on Capacity...

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31st International Conference on Computational Linguistics, COLING 2025

作者： He, Qiyuan Wang, Yizhong Yu, Jianfei Wang, Wenya College of Computing and Data Science Nanyang Technological University Singapore Paul G. Allen School of Computer Science & Engineering University of Washington United States School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9798891761964

Advancements in language models (LMs) have sparked interest in exploring their potential as knowledge bases (KBs) due to their high capability for storing huge amounts of factual knowledge and semantic understanding. However, existing studies face challenges in quantifying the extent of large-scale knowledge packed into LMs and lack systematic studies on LMs' structured reasoning capabilities over the infused knowledge. Addressing these gaps, our research investigates whether LMs can effectively act as large-scale KBs after training over an expansive set of world knowledge triplets via addressing the following three crucial questions: (1) How do LMs of different sizes perform at storing world knowledge of different frequencies in a large-scale KB? (2) How flexible are these LMs in recalling the stored knowledge when prompted with natural language queries? (3) After training on the abundant world knowledge, can LMs additionally gain the ability to reason over such information to infer new facts? Our findings indicate that while medium-scaled LMs hold promise as world knowledge bases capable of storing and responding with flexibility, enhancements in their reasoning capabilities are necessary to fully realize their potential. © 2025 Association for Computational Linguistics.

关键词： Structured Query Language

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