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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Amasala, Likhitha Ponnuru, Mahesh Srideviponmalar, P. School of Computer Science and Engineering Vellore Institute of Technology Amaravathi522237 India School of Computing SRM Institute of Science and Technology Potheri603203 India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Potheri603203 India

At present, technological systems lack a secure and transparent method for tracking goods and preventing theft in e-commerce, leading to trust issues and data vulnerabilities. There is a pressing need for a comprehensive solution that integrates Ethereum blockchain, IPFS, and advanced cryptographic techniques to address these challenges and enhance the security and transparency of transactions. This research paper presents a robust system that harnesses the Ethereum blockchain, IPFS (Interplanetary File System), and advanced cryptographic algorithms to create a secure, decentralized approach for tracking goods and preventing theft incidents. Unique identifiers and related information will be sent to the mail of the customer and same should entered by the customer for successful transaction. By assigning unique identifiers to purchased products and employing cryptographic techniques to encrypt sensitive data, our system ensures both user privacy and the creation of an immutable transaction ledger. Users can efficiently manage their purchased goods, block stolen items, and communicate with sellers through an intuitive interface. Additionally, the system provides sellers with a comprehensive transaction history, enhancing accountability and transparency within the supply chain. Through this research, we demonstrate the effectiveness of our blockchain based anti-theft measures, underpinned by Ethereum, IPFS, and cutting-edge cryptographic algorithms, in fostering secure, trustless transactions. This work highlights the transformative potential of blockchain technology and decentralized protocols in revolutionizing security and transparency across diverse sectors. © 2024 The Authors. Published by Elsevier B.V.

关键词： Smart contract

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Cyber-Attack Defense System Enhanced by Artificial Intelligence

Cyber-Attack Defense System Enhanced by Artificial Intellige...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Rangrez, Usman Showkat Qadri, Syed Absar Ashok Kumar, C. Jothi Kumar, C. SRM Institute of Science and Technology Kattankulathur Computer Science and Engineering Tamil Nadu Chennai India SRM Institute of Science and Technology Kattankulathur Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

In today's rapidly evolving digital landscape, ensuring the security of computer systems and networks is of utmost importance. With the widespread use of internet access, the escalation of cyberattacks and infiltration attempts poses significant threats to both data integrity and user privacy. Addressing these challenges, the integration of AI-Powered Cyber Security Defender Systems has become an essential component within contemporary cybersecurity frameworks. This research delves into the realm of these advanced systems, exploring their development, classification, methodologies, challenges, and future prospects. The study commences by examining key milestones that have shaped the modern architectures of AI-Powered Cyber Security Defender Systems throughout their historical evolution. It provides a comprehensive classification of these systems, elucidating their operations and advantages. Leveraging artificial intelligence and machine learning, these systems have the capability to analyze large datasets, identify patterns, and respond to malicious activities within networks, significantly enhancing overall security. The research paper underscores the pivotal role played by AI-Powered Cyber Security Defender Systems in safeguarding digital assets and user information. In conclusion, the study offers valuable insights for cybersecurity experts, researchers, and policymakers, thoroughly addressing the development, methodologies, challenges, and future trends of these systems, advocating for a proactive approach to securing the digital sphere. © 2024 IEEE.

关键词： Cybersecurity

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Transformer-Enhanced U-Net with Transfer Learning for Accurate Detection of Eye Coloboma 4

Transformer-Enhanced U-Net with Transfer Learning for Accura...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Dhayanithi, A. Bala, K. Bharath Institute of Higher Education and Research School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai600073 India Bharath Institute of Higher Education and Research School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai600073 India

ISBN: (纸本)9798331508845

Eye coloboma is a type of congenital eye abnormality that causes developmental abnormalities of the eye which may culminate in eye disorders that fundamentally derange eyesight. Fractional and timely diagnosis remains highly important to its proper managing and treating. This work proposes a model with a Transformer-Enhanced U-Net design implemented with Transfer Learning to enhance the ability of the model to detect and segment eye coloboma from medical imaging data. This work builds upon the benefits of U-Net which has shown promising results especially in the biomedical image segmentation problem through addition of transformer modules that introduce long range dependencies and contextual information. This enhancement makes helps the model to pay more attention to such minor aspects as coloboma and general diagnostic accuracy consequently. Thanks to Transfer Learning with a pretrained backbone, for example Efficient Net the system is provided with Feature Representations recovered from large datasets that are already available and training time is drastically cut short while the performance of the system is not compromised at *** approach includes fine-tuning of Transformer-Enhanced U-Net model using a selected set of eye images, labelled for coloboma existence. By including the self-attention mechanism, the model is capable of focusing on the critical regions in the image, increasing its responsiveness to any form of coloboma. Preliminary tests show that this scheme performs better than the standard convolutional networks for segmenting complex regions by minimizing false negatives. Further, the model's flexibility to different imaging conditions demonstrates its suitability in realistic clinical applications. The objective of this research is to come up with a dependable, Auto-Generated diagnostic tool that will help the ophthalmologists in the early diagnosis of the eye related illnesses hence making increased positive outcomes for eye patients. The Transf

关键词： Image segmentation

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Regular languages that can be approximated by testing subword occurrences

Computer Software

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computer Software 2023年第2期40卷 49-60页

作者： Sin'ya, Ryoma Yamaguchi, Yutaro Nakamura, Yoshiki Mathematical Science Course Akita University Japan Graduate School of Information Science and Technology Osaka University Japan Department of Computer Science School of Computing Tokyo Institute of Technology Japan

A language L is said to be regular-measurable if there exists an infinite sequence of pairs of regular languages that "converges" to L. Instead of regular languages, this paper examines measuring power of several fragments of regular languages: piecewise testable (PT) and alphabet testable (AT) languages. In particular, we showed that, while AT-measurability for regular languages is co-NP complete, PT-measurability is decidable in linear time. © 2023 Japan Society for Software science and Technology. All rights reserved.

关键词： Software engineering

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Piloting a Revised Diagnostic Tool for CSTA Standards for CS Teachers 2024

Piloting a Revised Diagnostic Tool for CSTA Standards for CS...

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1st ACM Virtual Global computing Education Conference V. 1, SIGCSE Virtual 2024

作者： Thigpen, Laycee McGill, Monica M. Twarek, Bryan Bell, Amanda Institute for Advancing Computing Education LakelandFL United States Institute for Advancing Computing Education PeoriaIL United States Computer Science Teachers Association ChicagoIL United States Computer Science Teachers Association MethuenMA United States

ISBN: (纸本)9798400705984

Problem. Given the newness of the field, those who are assigned to teach computer science (CS) in K-12 schools need targeted professional development (PD) that supports their content and pedagogical content knowledge. Currently, a diagnostic tool does not exist to identify teacher needs, leaving teachers to self-select their PD or accept recommendations from a colleague or supervisor. Research Question. Our research question for this project was: How effective is a diagnostic tool for recommending areas of growth for teachers that are aligned with the CS Standards 2-5 for Teachers? Methodology. Our mixed-methods study included a diagnostic tool to analyze artifacts and justifications submitted by teachers and a self-assessment instrument measuring the frequency of specific practices connected to the CSTA Standards for Teachers. In this second year of testing the revised diagnostic tool, 66 teachers participated. After we trained 23 external reviewers to rate the submissions, we provided PD recommendations to teachers. Findings. Based on feedback from teachers, the diagnostic tool proved to be helpful in providing recommended PD for two-thirds of the teachers. The self-assessment instrument may have suffered from the Dunning-Kruger effect since many teachers rated themselves higher in all areas than in their submitted materials indicated. Implications. This paper provides the components of the diagnostic tool and examples of the self-assessment instrument. We detail the successes, the lessons learned, and future considerations that are needed to strengthen this diagnostic tool and self-assessment instrument in order to be more beneficial for recommending PD for teachers that are aligned with CSTA Standards for Teachers (2-5). © 2024 ACM.

关键词： Teaching

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Machine Learning Based Earlier Identification of Liver Disease Using Ultrasound Images 1st

Machine Learning Based Earlier Identification of Liver Disea...

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1st International Conference on Intelligent computing, Smart Communication, and Network Technologies, ICICSCNT 2023

作者： Saravanakumar, C. Prakash, M. Deepak Kumar, A. Ashokkumar, C. Department of Computer Engineering Government Polytechnic College Purasawalkam Chennai600012 India School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Chennai India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9783031759567

Liver disease begins in the ovaries and is especially hazardous for women. As a result, abnormal cells can potentially spread to other organs. Liver disease is a type of dangerous development that affects the ovaries in females, and it is difficult to detect at an early stage, which is why it remains one of the leading causes of death from disease. Unquestionable confirmation of intrinsic and natural components is crucial for the development of novel systems to detect and eliminate hazard. We propose the detection and categorization of liver illness in ultrasound pictures using hybrid machine learning techniques. Using Machine Learning Algorithms and a vote-based classifier to classify liver tissues as fatty or normal based on ultrasound picture attributes and voting. Our created method provides four major contributions: first, the classification of liver pictures as normal or fatty is performed without a segmentation phase. Secondly, compared to our suggested effort, the datasets in earlier works were inadequate. The third contribution is a collection of 26 characteristics. On the basis of the presented methods, sigmoid radial basis function neural network (SRBFNN) and Gray-Level Co-Occurrence Matrix are the recovered features (GLCM). The fourth contribution is the voting classifier that is utilised to determine the type of liver tissue. Fruit Fly Optimization concludes the categorization process (FFO). We have achieved a classification accuracy of 98.24% using our proposed approach. Convolutional Neural Network (CNN), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest (RF), and Naive Bayes are compared with our proposed system (NB). The experimental results demonstrate that the classification accuracy of our suggested system is superior to that of the leading classification strategies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Radial basis function networks

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Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic Butterfly Optimization with Deep Learning Model

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computers, Materials & Continua 2023年第8期76卷 1995-2013页

作者： Tamilvizhi Thanarajan Youseef Alotaibi Surendran Rajendran Krishnaraj Nagappan Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India School of Computing SRM Institute of Science and TechnologyKattankulathur603203India

Autism spectrum disorder(ASD)can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics,like changes in behavior,social disabilities,and difficulty communicating with *** tracking(ET)has become a useful method to detect *** vital aspect of moral erudition is the aptitude to have common visual *** eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early ***-tracking data can offer insightful information about the behavior and thought processes of people with ASD,but it is important to be aware of its limitations and to combine it with other types of data and assessment techniques to increase the precision of ASD *** operates by scanning the paths of eyes for extracting a series of eye projection points on images for examining the behavior of children with *** purpose of this research is to use deep learning to identify autistic disorders based on eye *** Chaotic Butterfly Optimization technique is used to identify this specific ***,this study develops an ET-based Autism Spectrum Disorder Diagnosis using Chaotic Butterfly Optimization with Deep Learning(ETASD-CBODL)*** presented ETASDCBODL technique mainly focuses on the recognition of ASD via the ET and DL *** accomplish this,the ETASD-CBODL technique exploits the U-Net segmentation technique to recognize interested *** addition,the ETASD-CBODL technique employs Inception v3 feature extraction with CBO algorithm-based hyperparameter ***,the long-shorttermmemory(LSTM)model is exploited for the recognition and classification of *** assess the performance of the ETASD-CBODL technique,a series of simulations were performed on datasets from the figure-shared data *** experimental values of accuracy(99.29%),precision(98.78%),sensitivity(99.29%)and specificity(99.29%)showed a better perfo

关键词： Eye tracking ASD diagnosis deep learning butterfly optimization algorithm behavior analysis

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Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECSCORE 31

Who Wrote This? The Key to Zero-Shot LLM-Generated Text Dete...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wu, Junchao Zhan, Runzhe Wong, Derek F. Yang, B. Shu Liu, Xuebo Chao, Lidia S. Zhang, Min NLP2CT Lab Department of Computer and Information Science University of Macau China Institute of Computing and Intelligence Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9798891761964

The efficacy of detectors for texts generated by large language models (LLMs) substantially depends on the availability of large-scale training data. However, white-box zero-shot detectors, which require no such data, are limited by the accessibility of the source model of the LLM-generated text. In this paper, we propose a simple yet effective black-box zero-shot detection approach based on the observation that, from the perspective of LLMs, human-written texts typically contain more grammatical errors than LLM-generated texts. This approach involves calculating the Grammar Error Correction Score (GECSCORE) for the given text to differentiate between human-written and LLM-generated text. Experimental results show that our method outperforms current state-of-the-art (SOTA) zero-shot and supervised methods, achieving an average AUROC of 98.62% across XSum and Writing Prompts dataset. Additionally, our approach demonstrates strong reliability in the wild, exhibiting robust generalization and resistance to paraphrasing attacks. Data and code are available at: https://***/NLP2CT/GECScore. © 2025 Association for Computational Linguistics.

关键词： Zero-shot learning

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Insights into research on blockchain for smart contracts: a bibliometric analysis

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Multimedia Tools and Applications 2024年 1-25页

作者： Singh, Renu Gupta, Ashlesha Mittal, Poonam Faculty of Informatics and Computing J.C. Bose University of Science and Technology YMCA Faridabad India Department of Computer Science & amp Engineering Indraprastha Institute of Information Technology IIIT New Delhi India

Over the past few years, blockchain technology has gained significant attention. This surge in popularity can be attributed to the emergence of cryptocurrencies and the development of smart contracts. Cryptocurrency is a digital currency that eliminates the problem of double spending. Cryptocurrencies like Bitcoin, Ethereum, Litecoin, Stellar, Zcash, Maker, Aave, etc. become popular and are preferred for money transfers. Smart contracts are the next popular technology on the blockchain after cryptocurrency. It can be considered a piece of code that can execute automatically when the predefined conditions are fulfilled. Researchers believe that the potential of blockchain with smart contracts is only in its initial stages and that its true potential has yet to be fully discovered. Hence, an extensive bibliometric analysis is conducted to understand blockchain trends for smart contracts and to give future directions in this field. For this analysis, various steps are followed, starting with formulating the research question, defining the scope of our research, extracting and analyzing data, answering the research question, and finally, drawing a conclusion. This research paper will be fruitful for scholars and researchers, providing an extensive statistical and network analysis of extracted smart contracts publications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Bibliometric analysis Blockchain Crypto contract Scientometrics Scopus Smart contracts VOSViewer

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Promoting rigor in blockchain energy and environmental footprint research:A systematic literature review

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Blockchain(Research and Applications) 2024年第1期5卷 133-151页

作者： Ashish Rajendra Sai Harald Vranken Department of Computer Science Open UniversiteitHeerlen6419 ATthe Netherlands Department of Advanced Computing Sciences Maastricht UniversityMaastricht6211 LKthe Netherlands Institute for Computing&Information Sciences Radboud UniversityNijmegen6525 XZthe Netherlands

There is a growing interest in understanding the energy and environmental footprint of digital currencies,specifically in cryptocurrencies such as Bitcoin and *** cryptocurrencies are operated by a geographically distributed network of computing nodes,making it hard to estimate their energy consumption *** studies,both in academia and industry,attempt to model cryptocurrency energy consumption often based on a number of assumptions,for instance,about the hardware in use or the geographic distribution of the computing nodes.A number of these studies have already been widely criticized for their design choices and subsequent over-or under-estimation of energy *** this study,we evaluate the reliability of prior models and estimates by leveraging existing scientific literature from fields cognizant of blockchain,such as social energy sciences and information *** first design a quality assessment framework based on existing research,and we then conduct a systematic literature review examining scientific and non-academic literature demonstrating common issues and potential avenues of addressing these *** goal with this article is to advance the field by promoting scientific rigor in studies focusing on blockchain energy *** that end,we provide a novel set of codes of conduct for the five most widely used research methodologies:Quantitative energy modeling,literature reviews,data analysis and statistics,case studies,and *** envision that this code of conduct would assist in standardizing the design and assessment of studies focusing on blockchain-based systems’energy and environmental footprint.

关键词： Blockchain Energy consumption Environmental footprint Scientific rigor Reliability

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