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9th Edition of IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2021

作者： Senevirathne, Gayathri Manathunga, Kalpani Sri Lanka Institute of Information Technology Faculty of Graduate Studies and Research Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665432405

Interface design is a mandatory aspect influencing the success of an e-Learning system implementation. User interface (UI) design of e-learning is a point of interaction between user and computer software. Users prefer more attractive and simpler interface designs rather than dull or complex designs. This study aims to outline the impact of UI design on the satisfaction of learners. Specifically, this study will be evaluating different user interaction design strategies such as ease of navigation, ease of resource discoverability, ease of configuring integrated tools etc. in e-learning platforms such as learning management systems, and massive open online courses (MOOCs). Further, this research aims to find answers for the challenges and issues faced by students and teachers when using e-learning platforms. A comprehensive questionnaire was distributed among teachers and students. Collected data was analyzed to get an idea about main interface design problems that frustrate the learners and teachers and distract them from educational tasks. Using this statistical analysis results, a model is proposed indicating success factors and failure factors that may affect to e-learning system interface designing. Moreover, this research also results in a set of guidelines or suggestions that can be followed to improve UI designing in e-learning platforms. Finally, an initial prototype implementation capable of recommending suggestions intelligently for e-learning platform designers and users is proposed after modelling the user satisfaction factors. © 2021 IEEE.

关键词： User interfaces

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Understanding Research Trends in Android Malware Research Using information Modelling Techniques

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computers, Materials & Continua 2021年第3期66卷 2655-2670页

作者： Jaiteg Singh Tanya Gera Farman Ali Deepak Thakur Karamjeet Singh Kyung-sup Kwak Chitkara University Institute of Engineering&Technology Chitkara UniversityPunjab140401India Department of Software Sejong UniversitySeoul05006South Korea Department of Computer Science and Engineering Thapar UniversityPatiala147001India Department of Information&Communication Engineering Inha UniversityIncheon22212South Korea

Android has been dominating the smartphone market for more than a decade and has managed to capture 87.8%of the market *** popularity of Android has drawn the attention of cybercriminals and malware *** malicious applications can steal sensitive information like contacts,read personal messages,record calls,send messages to premium-rate numbers,cause financial loss,gain access to the gallery and can access the user’s geographic *** surveys on Android security have primarily focused on types of malware attack,their propagation,and techniques to mitigate *** the best of our knowledge,Android malware literature has never been explored using information modelling ***,promulgation of contemporary research trends in Android malware research has never been done from semantic point of *** paper intends to identify intellectual core from Android malware literature using Latent Semantic Analysis(LSA).An extensive corpus of 843 articles on Android malware and security,published during 2009–2019,were processed using ***,the truncated singular Value Decomposition(SVD)technique was used for dimensionality ***,machine learning methods were deployed to effectively segregate prominent topic solutions with minimal *** to observed term and document loading matrix values,this five core research areas and twenty research trends were ***,potential future research directions have been detailed to offer a quick reference for information *** study concludes to the fact that Android security is crucial for pervasive Android *** analysis is the most widely investigated core area within Android security research and is expected to remain in trend in near *** trends indicate the need for a faster yet effective model to detect Android applications causing obfuscation,financial attacks and stealing user information.

关键词： Android security research trends latent semantic analysis vulnerabilities malware machine learning clustering

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A systematic literature review to implement large language model in higher education: issues and solutions

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Discover Education 2025年第1期4卷 1-25页

作者： Guizani, Sghaier Mazhar, Tehseen Shahzad, Tariq Ahmad, Wasim Bibi, Afsha Hamam, Habib College of Engineering Alfaisal University Riyadh 11533 Saudi Arabia School of Computer Science National College of Business Administration and Economics Lahore 54000 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah 31200 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal 57000 Pakistan Department of Computing School of Arts and Creative Technology University of Greater Manchester Manchester BL15AB United Kingdom Department of Computer and Software Technology University of Swat Swat 19200 Pakistan Faculty of Engineering Uni de Moncton Moncton E1A3E9 NB Canada School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence - Spectrum Tunis Tunisia

Artificial intelligence-driven Chatbots, especially large language models (LLMs) like GPT-4, represent significant progress in digital education. These models excel in mimicking human-like text and transforming learning and teaching methods. This study examines the development, application, and impact of LLMs in education. It highlights their role in automating instructional tasks and promoting personalized learning experiences. Despite integration concerns and ethical debates, LLMs showcase the potential of AI to improve educational practices. Our research concludes that LLMs offer transformative opportunities for education. However, their incorporation requires careful ethical considerations, data privacy measures, and a balance between human educators and AI technologies. The findings suggest strategies for integrating LLMs into educational frameworks to enhance learning outcomes while preserving educational integrity. © The Author(s) 2025.

关键词： Artificial intelligence Education Large language models Learning systems Natural language processing systems Neural networks

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Shift from Texture-bias to Shape-bias: Edge Deformation-based Augmentation for Robust Object Recognition

Shift from Texture-bias to Shape-bias: Edge Deformation-base...

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International Conference on computer Vision (ICCV)

作者： Xilin He Qinliang Lin Cheng Luo Weicheng Xie Siyang Song Feng Liu Linlin Shen Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Shenzhen Institue of Artificial Intelligence & Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing University of Leicester

Recent studies have shown the vulnerability of CNNs under perturbation noises, which is partially caused by the reason that the well-trained CNNs are too biased toward the object texture, i.e., they make predictions mainly based on texture cues. To reduce this texture-bias, current studies resort to learning augmented samples with heavily perturbed texture to make networks be more biased toward relatively stable shape cues. However, such methods usually fail to achieve real shape-biased networks due to the insufficient diversity of the shape cues. In this paper, we propose to augment the training dataset by generating semantically meaningful shapes and samples, via a shape deformation-based online augmentation, namely as SDbOA. The samples generated by our SDbOA have two main merits. First, the augmented samples with more diverse shape variations enable networks to learn the shape cues more elaborately, which encourages the network to be shape-biased. Second, semantic-meaningful shape-augmentation samples could be produced by jointly regularizing the generator with object texture and edge-guidance soft constraint, where the edges are represented more robustly with a self information guided map to better against the noises on them. Extensive experiments under various perturbation noises demonstrate the obvious superiority of our shape-bias-motivated model over the state of the arts in terms of robustness performance. Code is available at https://***/C0notSilly/-ICCV-23-Edge-Deformation-based-Online-Augmentation.

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Large Capacity Image Watermarking Model using SWT and Laplacian Pyramid

Large Capacity Image Watermarking Model using SWT and Laplac...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Baowei Wang Fan Yang Yufeng Wu Changyu Dai Wenjue Huang Xingyuan Zhao Engineering Research Center of Digital Forensics Ministry of Education China Waterford Institute Nanjing University of Information Science and Technology Nanjing China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

Image watermarking algorithms have developed rapidly in recent years. Most of the Deep Neural Network(DNN)-based image watermarking algorithms embed only 30 bits or 64 bits messages in 128 × 128 images, while few algorithms embed large capacity messages. This is mainly because embedding large capacity messages will significantly influence the performance of algorithms. However, the limited capacity of these messages inhibits their effectiveness in certain data protection scenarios. Therefore, we propose a DNN-based large capacity image water-marking model. By using Stationary Wavelet Transform(SWT) and Laplacian pyramid transform, we expand the embedding capacity and achieve embedding 128 bits messages in 128 × 128 images. Experimental results show that under the same conditions, the proposed model is superior to state-of-the-art models, and has excellent performance.

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Secure Location-based Authenticated Key Establishment Scheme for Maritime Communication

Secure Location-based Authenticated Key Establishment Scheme...

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IEEE International Conference on Communications (ICC)

作者： Saurabh Agrawal Anusha Vangala Ashok Kumar Das Neeraj Kumar Sachin Shetty Sajal K. Das Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India Department of Computer Science Missouri University of Science and Technology Rolla Missouri USA Department of Computer Science and Engineering Thapar University Patiala India Department of Computational Modeling and Simulation Engineering Old Dominion University Suffolk Virginia USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Maritime communication helps vessels and ports plan their movements, exchange environmental information, and communicate among themselves. The vessels' movement and changing location are critical to keep them secure from data interception and data tampering by unauthorized parties during transmission. To secure maritime communication, we propose a novel lightweight authentication scheme sensitive to the current ship location. We assess the effectiveness of the proposed protocol in defending against a range of security threats while keeping communication and computation costs low, and meeting the desired security and functional requirements of anonymity and untraceability. The detailed security analysis using the widely accepted Scyther tool demonstrates that location-based keys as proposed in our protocol are secure against location inference and spoofing attacks among others.

关键词： Resistance Protocols Authentication Seaports Data aggregation Security Servers

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An Exploratory Comparative Study on the Impacts of Technical Support on Student Successes in Computing Project-Based Learning

An Exploratory Comparative Study on the Impacts of Technical...

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Frontiers in Education (FIE) Conference

作者： Ahmad D. Suleiman Jan DeWaters Daqing Hou Yu Liu David Shepherd Computing and Information Sciences Rochester Institute of Technology Rochester NY USA Institute for STEM Education Clarkson University Potsdam NY USA Software Engineering Department Rochester Institute of Technology Rochester NY USA Electrical & Computer Engineering Clarkson University Potsdam NY USA Department of Computer Science Louisiana State University Baton Rouge LA USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

In this research paper, we conducted a comparative study to measure the effectiveness of the provided technical support in computing project-based learning (PjBL) courses. Students learn much better by solving authentic real-world problems through PjBL. PjBL in computing education has proven to boost student motivation and engagement while enhancing academic performance. Crucial to PjBL in computing is the technical support that the instructors can provide to students, which is required for sustained, successful learning during project tasks. Without adequate support, PjBL will fall short of accomplishing its goals, leading to a rise in student frustration, a loss of motivation and engagement, and compromised learning outcomes. Measuring the impacts and effectiveness of the provided support is imperative for fostering continuous improvement, informed decision-making, and student success. It enables instructors to assess the impacts of their strategies, improve their approaches, and utilize their resources more effectively. To measure the impacts of technical support on students during PjBL, we performed a comparative study on two undergraduate computing courses in Spring 2024, Fundamentals of software engineering and Database Systems. In both courses, students work on two assigned projects, one with little and inadequate support and the other with adequate support. We administered a post-survey after each project was completed. We analyzed students' selfreflection responses across four sub-scales, support satisfaction, motivation, self-efficacy, and project satisfaction. The results show a statistically significant increase in the supported project in the Fundamentals of software engineering course and no difference in the Database Systems course. This finding is likely due to other differences between the two projects for the Database Systems course beyond support, such as project scale. Qualitative analysis of students' responses also indicates the need for suppor

关键词： Surveys Statistical analysis Education Decision making Database systems software measurement Continuous improvement Springs software engineering

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Relaxed Rotational Equivariance via G-Biases in Vision

arXiv

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arXiv 2024年

作者： Wu, Zhiqiang Liu, Yingjie Sun, Licheng Yang, Jian Dong, Hanlin Lin, Shing-Ho J. Tang, Xuan Mi, Jinpeng Jin, Bo Wei, Xian Software Engineering Institute East China Normal University China School of Geospatial Information Information Engineering University China School of Artificial Intelligence University of Chinese Academy of Sciences China School of Communication and Electronic Engineering East China Normal University China Institute of Machine Intelligence University of Shanghai for Science and Technology China School of Computer Science and Technology Tongji University China

Group Equivariant Convolution (GConv) can capture rotational equivariance from original data. It assumes uniform and strict rotational equivariance across all features as the transformations under the specific group. However, the presentation or distribution of real-world data rarely conforms to strict rotational equivariance, commonly referred to as Rotational Symmetry-Breaking (RSB) in the system or dataset, making GConv unable to adapt effectively to this phenomenon. Motivated by this, we propose a simple but highly effective method to address this problem, which utilizes a set of learnable biases called G-Biases under the group order to break strict group constraints and then achieve a Relaxed Rotational Equivariant Convolution (RREConv). To validate the efficiency of RREConv, we conduct extensive ablation experiments on the discrete rotational group Cn. Experiments demonstrate that the proposed RREConv-based methods achieve excellent performance compared to existing GConv-based methods in both classification and 2D object detection tasks on the natural image datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

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Deep Unfolding 3D Non-Local Transformer Network for Hyperspectral Snapshot Compressive Imaging

Deep Unfolding 3D Non-Local Transformer Network for Hyperspe...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zheng Zhou Zongxin Liu Yongyong Chen Bingzhi Chen Biqing Zeng Yicong Zhou School of Electronics and Communication Engineering Guangzhou University Guangzhou China School of Software South China Normal University Foshan China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Department of Computer and Information Science University of Macau Macau China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Hyperspectral compressive imaging has shown remarkable advancements through the adoption of deep unfolding frameworks, which integrate the proximal mapping prior into the data fidelity term to formulate the reconstruction problem. However, existing technologies still face challenges in effectively capturing spatial-spectral features during the iterative deep prior learning stage, leading to unsatisfactory performance degradation. To address this issue, we propose a deep unfolding 3D non-local transformer (3DNLT) network for hyperspectral compressive imaging. A learnable half-quadratic splitting (HQS) algorithm is utilized to iteratively update the linear projection. Furthermore, a 3D non-local attention ushaped transformer is presented as the deep proximal mapping prior module to obtain the spatial-spectral long-range dependency features, leading to enhance the network’s ability to capture fine-grained hyperspectral and spatial details. Experimental results on both synthetic and real hyperspectral image reconstruction have demonstrated the superior performance of the 3DNLT network compared to state-of-the-art methods.

关键词： Degradation Three-dimensional displays Image coding Imaging Transformers Robustness Iterative methods

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Corrigendum to ‘Device-to-device communication in 5G heterogeneous network based on game-theoretic approaches: A comprehensive survey’ [J. Netw. Comput., Volume 238, June 2025, 104152] (Journal of Network and computer Applications (2025) 238, (S1084804525000499), (10.1016/***.2025.104152))

引用

Journal of Network and computer Applications 2025年 239卷

作者： Ahmad, Rana Zeeshan Rizwan, Muhammad Yousuf, Muhammad Jehanzaib Khan, Mohammad Bilal Almadhor, Ahmad Gadekallu, Thippa Reddy Abbas, Sidra Department of Information Technology University of Sialkot Sialkot Pakistan College of Science and Engineering University of Derby United Kingdom Software Research Institute Athlone Institute of Technology Ireland School of Electronic Engineering Xidian University Xidian China Department of Computer Engineering and Networks College of Computer and Information Science Jouf University Sakaka Saudi Arabia The College of Mathematics and Computer Science Zhejiang A&F University Hangzhou311300 China Division of Research and Development Lovely Professional University Phagwara India Centre of Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Department of Computer Science COMSATS University Islamabad Sahiwal Pakistan

The authors regret the mistake in affiliation of Author Sidra Abbas. The correct affiliation is as follows: Sidra AbbasDepartment of computer science, COMSATS University Islamabad, Sahiwal, Pakistansidraabbas@***:sidr...

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