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TECMH:Transformer-Based Cross-Modal Hashing For Fine-Grained Image-Text Retrieval

computers, Materials & Continua 2023年第5期75卷 3713-3728页

作者： Qiqi Li Longfei Ma Zheng Jiang Mingyong Li Bo Jin College of Computer and Information Science Chongqing Normal UniversityChongqing401331China Institute of Systems and Robotics(ISR) Department of Electrical and Computer Engineering(DEEC)University of CoimbraCoimbraPortugal

In recent years,cross-modal hash retrieval has become a popular research field because of its advantages of high efficiency and low ***-modal retrieval technology can be applied to search engines,crossmodalmedical processing,*** existing main method is to use amulti-label matching paradigm to finish the retrieval ***,such methods do not use fine-grained information in the multi-modal data,which may lead to suboptimal *** avoid cross-modal matching turning into label matching,this paper proposes an end-to-end fine-grained cross-modal hash retrieval method,which can focus more on the fine-grained semantic information of multi-modal ***,the method refines the image features and no longer uses multiple labels to represent text features but uses BERT for ***,this method uses the inference capabilities of the transformer encoder to generate global fine-grained ***,in order to better judge the effect of the fine-grained model,this paper uses the datasets in the image text matching field instead of the traditional label-matching *** article experiment on Microsoft COCO(MS-COCO)and Flickr30K datasets and compare it with the previous *** experimental results show that this method can obtain more advanced results in the cross-modal hash retrieval field.

关键词： Deep learning cross-modal retrieval hash learning transformer

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An Overview chatGPT in Higher Education in Indonesia and Malaysia

An Overview chatGPT in Higher Education in Indonesia and Mal...

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2023 International Conference on Informatics, Multimedia, Cyber and information systems, ICIMCIS 2023

作者： Arista, Artika Shuib, Liyana Ismail, Maizatul Akmar Universitas Pembangunan Nasional Veteran Jakarta Information Systems Faculty of Computer Science Jakarta Indonesia Faculty of Computer Science and Information Technology Universiti Malaya Department of Information Systems Kuala Lumpur Malaysia

ISBN: (纸本)9798350307306

ChatGPT is a useful tool for teaching and learning in the context of education. Despite ChatGPT's growing popularity across several fields, little is known about how it is accepted and used, particularly in the context of higher education. ChatGPT holds a lot of potential to be a helpful tool for both students and lecturers. However, in higher education, the introduction of ChatGPT has raised serious questions about academic integrity. Thus, this research focuses on the overview of chatGPT in Higher Education in Indonesia and Malaysia. This study is conducted using the literature review method. According to this research result, this tool can help students and instructors with academic writing, language instruction, and learning in class in Higher Education in Indonesia. Higher Education in Malaysia is also raising optimism that this technology will speed up the transformation of higher education. Even though there are positive and negative effects of ChatGPT. Future research will need to conduct more case studies and interviews to obtain an in-depth comprehension of the application of ChatGPT in learning especially in higher education. © 2023 IEEE.

关键词： Students

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Enhancement of UAV Data Security and Privacy via Ethereum Blockchain Technology

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computers, Materials & Continua 2023年第8期76卷 1797-1815页

作者： Sur Singh Rawat Youseef Alotaibi Nitima Malsa Vimal Gupta Department of Computer Science and Engineering JSS Academy of Technical EducationNoida201301India Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia

Unmanned aerial vehicles(UAVs),or drones,have revolutionized a wide range of industries,including monitoring,agriculture,surveillance,and supply ***,their widespread use also poses significant challenges,such as public safety,privacy,and ***,targetingUAVs have become more frequent,which highlights the need for robust security *** technology,the foundation of cryptocurrencies has the potential to address these *** study suggests a platform that utilizes blockchain technology tomanage drone operations securely and *** incorporating blockchain technology,the proposed method aims to increase the security and privacy of drone *** suggested platform stores information on a public blockchain located on Ethereum and leverages the Ganache platform to ensure secure and private blockchain *** wallet for Ethbalance is necessary for BCT *** present research finding shows that the proposed approach’s efficiency and security features are superior to existing *** study contributes to the development of a secure and efficient system for managing drone operations that could have significant applications in various *** proposed platform’s security measures could mitigate privacy concerns,minimize cyber security risk,and enhance public safety,ultimately promoting the widespread adoption of *** results of the study demonstrate that the blockchain can ensure the fulfillment of core security needs such as authentication,privacy preservation,confidentiality,integrity,and access control.

关键词： Unmanned aerial vehicles(UAVs) blockchain data privacy network security smart contract Ethereum

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A Glimpse of chatGPT: An Introduction of Features, Challenges, and Threads in Higher Education

A Glimpse of chatGPT: An Introduction of Features, Challenge...

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2023 International Conference on Informatics, Multimedia, Cyber and information systems, ICIMCIS 2023

作者： Arista, Artika Shuib, Liyana Ismail, Maizatul Akmar Universitas Pembangunan Nasional Veteran Jakarta Information Systems Faculty of Computer Science Jakarta Indonesia Universiti Malaya Faculty of Computer Science and Information Technology Department of Information Systems Kuala Lumpur Malaysia

ISBN: (纸本)9798350307306

When used in conjunction with education, ChatGPT is a helpful tool for teaching and understanding fundamental concepts. Students who struggle to find study partners or lack the time to attend tutoring sessions may discover that reviewing and studying with the chatGPT are helpful. Students' access to material and completion of assignments can be accelerated using this technology in research and education. More than one-third of university students who participated in a poll said that they used the ChatGPT for writing assessments. Professors have referred to these technologies as a "threat"and a "plague on education,"and ChatGPT has been prohibited from several educational institutions. Thus, this research focuses on the introduction of Features, Challenges, and Threads chatGPT in Higher Education. This study is conducted using the literature review method. According to this research result, this tool can boost student engagement by providing immersive, dynamic, and customized learning environments. However, there are some related issues that need to be considered such as integrity, accuracy and reliability, information bias, and privacy issues. We can therefore infer that there may be benefits as well as drawbacks to employing AI in education that need to be taken into account. © 2023 IEEE.

关键词： Students

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An Innovative Approach Using TKN-Cryptology for Identifying the Replay Assault

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computers, Materials & Continua 2024年第1期78卷 589-616页

作者： Syeda Wajiha Zahra Muhammad Nadeem Ali Arshad Saman Riaz Muhammad Abu Bakr Ashit Kumar Dutta Zaid Alzaid Badr Almutairi Sultan Almotairi Department of Computer Science Alhamd Islamic UniversityIslamabad44000Pakistan Department of Computer Science and Technology University of Science and Technology BeijingBeijing100083China Department of Computer Science National University of TechnologyIslamabad44000Pakistan Department of Electrical Engineering National University of TechnologyIslamabad44000Pakistan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Science Faculty of Computer and Information SystemsIslamic University of MadinahMedinah42351Saudi Arabia Department of Information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia

Various organizations store data online rather than on physical *** the number of user’s data stored in cloud servers increases,the attack rate to access data from cloud servers also *** researchers worked on different algorithms to protect cloud data from replay *** of the papers used a technique that simultaneously detects a full-message and partial-message replay *** study presents the development of a TKN(Text,Key and Name)cryptographic algorithm aimed at protecting data from replay *** program employs distinct ways to encrypt plain text[P],a user-defined Key[K],and a Secret Code[N].The novelty of the TKN cryptographic algorithm is that the bit value of each text is linked to another value with the help of the proposed algorithm,and the length of the cipher text obtained is twice the length of the original *** the scenario that an attacker executes a replay attack on the cloud server,engages in cryptanalysis,or manipulates any data,it will result in automated modification of all associated values inside the *** mechanism has the benefit of enhancing the detectability of replay ***,the attacker cannot access data not included in any of the papers,regardless of how effective the attack strategy *** the end of paper,the proposed algorithm’s novelty will be compared with different algorithms,and it will be discussed how far the proposed algorithm is better than all other algorithms.

关键词： Replay attack malware message attack file encryption cryptology data security

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DeepSurNet-NSGA II:Deep Surrogate Model-Assisted Multi-Objective Evolutionary Algorithm for Enhancing Leg Linkage in Walking Robots

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computers, Materials & Continua 2024年第10期81卷 229-249页

作者： Sayat Ibrayev Batyrkhan Omarov Arman Ibrayeva Zeinel Momynkulov Joldasbekov Institute of Mechanics and Engineering Almaty050000Kazakhstan Department of Mathematical and Computer Modeling International Information Technology UniversityAlmaty050000Kazakhstan Department of Information Systems Al-Farabi Kazakh National UniversityAlmaty050000Kazakhstan

This research paper presents a comprehensive investigation into the effectiveness of the DeepSurNet-NSGA II(Deep Surrogate Model-Assisted Non-dominated Sorting Genetic Algorithm II)for solving complex multiobjective optimization problems,with a particular focus on robotic leg-linkage *** study introduces an innovative approach that integrates deep learning-based surrogate models with the robust Non-dominated Sorting Genetic Algorithm II,aiming to enhance the efficiency and precision of the optimization *** a series of empirical experiments and algorithmic analyses,the paper demonstrates a high degree of correlation between solutions generated by the DeepSurNet-NSGA II and those obtained from direct experimental methods,underscoring the algorithm’s capability to accurately approximate the Pareto-optimal frontier while significantly reducing computational *** methodology encompasses a detailed exploration of the algorithm’s configuration,the experimental setup,and the criteria for performance evaluation,ensuring the reproducibility of results and facilitating future advancements in the *** findings of this study not only confirm the practical applicability and theoretical soundness of the DeepSurNet-NSGA II in navigating the intricacies of multi-objective optimization but also highlight its potential as a transformative tool in engineering and design *** bridging the gap between complex optimization challenges and achievable solutions,this research contributes valuable insights into the optimization domain,offering a promising direction for future inquiries and technological innovations.

关键词： Multi-objective optimization genetic algorithm surrogate model deep learning walking robots

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Software Defect Prediction Using Ensemble Technique 9

Software Defect Prediction Using Ensemble Technique

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9th International Conference on information Management and Technology, ICIMTech 2024

作者： Wicaksono, Pandu Arisaputra, Panji Yunanda, Rezky Kristian, Willy Mujhid, Almuzhidul School of Computer Science Bina Nusantara University Software Engineering Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Software defect prediction is the methodical process of identifying code segments that are likely to have problems. This is done by analyzing software metrics and using categorization algorithms. This work introduces an alternate approach that utilizes ensemble learning techniques to improve the effectiveness of fault detection models. The models utilized in this study are CodeGPT and CodeBERT, both of which are transformer-based deep learning models that can extract code features from source code. The models are trained using the PROMISE dataset, which consists of Java projects that have been labeled with defects. The test results show that using ensemble learning methods can improve the accuracy, precision, recall, and F1 score of a single model by around 1-3%. This study contributes to the progress of software dependability and quality by utilizing advanced software fault prediction algorithms. © 2024 IEEE.

关键词： Prediction models

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Validity Claims in Children-AI Discourse: Experiment with ChatGPT 16

Validity Claims in Children-AI Discourse: Experiment with Ch...

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16th International Conference on computer Supported Education, CSEDU 2024

作者： Schneider, Johannes Kruse, Leona Chandra Seeber, Isabella Department of Computer Science and Information Systems University of Liechtenstein Vaduz Liechtenstein Department of Information Systems University of Agder Norway Department of Management Technology and Strategy Grenoble Ecole de Management France

ISBN: (纸本)9789897586972

Large language models like ChatGPT are increasingly used by people from all age groups. They have already started to transform education and research. However, these models are also known to have a number of shortcomings, i.e., they can hallucinate or provide biased responses. While adults might be able to assess such shortcomings, the most vulnerable group of our society – children – might not be able to do so. Thus, in this paper, we analyze responses to commonly asked questions tailored to different age groups by OpenAI’s ChatGPT. Our assessment uses Habermas’ validity claims. We operationalize them using computational measures such as established reading scores and interpretative analysis. Our results indicate that responses were mostly (but not always) truthful, legitimate, and comprehensible and aligned with the developmental phases, but with one important exception: responses for two-year-olds. Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Computational linguistics

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Performance Variations in Digital Talent: Gender-Based Insights from STEM and AI Education Backgrounds 4

Performance Variations in Digital Talent: Gender-Based Insig...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Halim, Erwin Sari, Azani Cempaka Wairooy, Irma Kartika Ario, Muhamad Keenan Achmad, Said Fitrianah, Devi School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331506490

In the digital era, digital talent development has become increasingly vital for organizations and economies to thrive. While much focus has been placed on technical skills, this report emphasizes the often-overlooked role of soft skills in developing digital talent. Through an extensive literature review and data analysis, the study identifies that soft skills are crucial in enhancing digital talent performance. This research explores the factors that influence Digital Talent Performance in Indonesia, specifically comparing the performance levels of male and female digital talents. Data were collected from college students in the Jabodetabek area (cities surrounding Jakarta, Indonesia) using a purposive sampling method, with an online questionnaire distributed to 378 respondents. The study utilized Structural Equation Modeling (SEM) to analyze the collected data using SmartPLS 4.1. software. Key variables examined include Communication Skills, Teamwork, Emotional Intelligence, Academic Achievement, Problem-Solving, Digital Readiness, Digital Skills, Digital Technology, and Digital Talent Performance. Out of the nine hypotheses tested, one was not statistically significant. The results suggest that a combination of both technical and soft skills significantly affects digital talent performance, with important gender-based differences in performance levels emerging from the analysis. This research contributes to understanding Indonesia's complex dynamics of digital talent development. © 2024 IEEE.

关键词： digital skill digital talent soft skills STEM AI talent performance

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Anti-Laundering Approach for Bitcoin Transactions 14

Anti-Laundering Approach for Bitcoin Transactions

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14th International Conference on information and Communication systems, ICICS 2023

作者： Alkhatib, Khalid Abualigah, Sayel Jordan University of Science and Technology Computer Information Systems Irbid Jordan

ISBN: (纸本)9798350307870

Money laundering has significantly increased in recent years as a result of the quick growth of financial systems and the emergence of cryptocurrencies. These crimes threaten the stability of both economic and social systems and impact the security of public and private financial institutions. In response, detecting and preventing money laundering has become a top priority for financial systems, which rely on deep analysis of assets and the nature of funds to uncover illegitimate sources. With the rise of cryptocurrencies like Bitcoin, criminals have adopted new technologies and channels to cover up their illegal activities. This research aims to develop a new deep learning architecture for detecting illegitimate Bitcoin transactions in order to prevent Bitcoin laundering crimes. In this study, a deep investigation of the previous works will be conducted for better understanding of the problem statement. Besides, a new framework will be designed to exceed the performance of current best practices with regard to accuracy. Ultimately, the findings of this research will aid the financial sector in combating Bitcoin laundering activities and strengthening the security of digital financial transactions. An elliptic dataset used for bitcoin transactions that belong to real entities, where it consists of 203,769 nodes and 234,355 edges for illicit and licit transactions. The licit transaction families are licit services, exchanges, miners, and wallet providers. While the illicit transaction families are malware, frauds, Ponzi (fraud) schemes, ransomware, and terrorist organizations. The proposed model semi-supervised generative adversarial network (SGAN) utilized to complete the labelling process of the unknown entities. Based on the experimental results, the proposed model significantly improved upon previous methods, achieving a 98% success rate in accurately predicting illicit transactions. Thus, we consider it as a competitive stand in terms of anti-laundering for the

关键词： Laundering

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