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International Journal of Interactive Mobile Technologies 2025年第5期19卷 233-257页

作者： Ghai, Anupriya Sharma Altinay, Zehra Altinay, Fahriye Jasola, Sanjay Dagli, Gokmen Yikici, Betul School of Computing Graphic Era Hill University Uttarakhand Dehradun India Faculty of Education Societal Research and Development Center Near East University Mersin 10 Nicosia99138 Cyprus Department of Computer Science DBS Global University Uttarakhand Dehradun India Faculty of Education University of Kyrenia Mersin 10 Kyrenia99138 Cyprus

Adopting new technologies in higher education is essential for enhancing students’ skills and knowledge. Blended learning, an innovative approach integrating traditional classroom instruction with online components, has emerged as an effective educational solution. Incorporating online elements into blended learning models has shown great promise in enriching the learning experience. This study employs a structural equation model to investi-gate the key factors influencing students’ adoption of blended learning in higher educational institutions. The study identifies significant relationships among various factors, highlight-ing their roles in shaping student experiences and intentions. These findings provide prac-tical recommendations for educators and institutions aiming to optimize blended learning environments, ultimately fostering active student participation and satisfaction. This study contributes to the continuous improvement of educational practices and student outcomes within blended learning models, particularly with the integration of massive open online courses (MOOCs). © 2025 by the authors of this article. Published under CC-BY.

关键词： Contrastive Learning

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KSKV:Key-Strategy for Key-Value Data Collection with Local Differential Privacy

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computer Modeling in Engineering & sciences 2024年第6期139卷 3063-3083页

作者： Dan Zhao Yang You Chuanwen Luo Ting Chen Yang Liu Artificial Intelligence Development Research Center Institute of Scientific and Technical Information of ChinaBeijing100038China Industry Development Department NSFOCUS Inc.BeijingChina School of Information Science and Technology Beijing Forestry UniversityBeijing100083China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu610054China Institute of Computing Technology China Academy of Railway Sciences Corporation LimitedBeijing10081China

In recent years,the research field of data collection under local differential privacy(LDP)has expanded its focus fromelementary data types to includemore complex structural data,such as set-value and graph ***,our comprehensive review of existing literature reveals that there needs to be more studies that engage with key-value data *** studies would simultaneously collect the frequencies of keys and the mean of values associated with each ***,the allocation of the privacy budget between the frequencies of keys and the means of values for each key does not yield an optimal utility *** the importance of obtaining accurate key frequencies and mean estimations for key-value data collection,this paper presents a novel framework:the Key-Strategy Framework forKey-ValueDataCollection under ***,theKey-StrategyUnary Encoding(KS-UE)strategy is proposed within non-interactive frameworks for the purpose of privacy budget allocation to achieve precise key frequencies;subsequently,the Key-Strategy Generalized Randomized Response(KS-GRR)strategy is introduced for interactive frameworks to enhance the efficiency of collecting frequent keys through group-anditeration *** strategies are adapted for scenarios in which users possess either a single or multiple key-value ***,we demonstrate that the variance of KS-UE is lower than that of existing *** claims are substantiated through extensive experimental evaluation on real-world datasets,confirming the effectiveness and efficiency of the KS-UE and KS-GRR strategies.

关键词： Key-value local differential privacy frequency estimation mean estimation data perturbation

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HiHiSpMV: Sparse Matrix Vector Multiplication with Hierarchical Row Reductions on FPGAs with High Bandwidth Memory

HiHiSpMV: Sparse Matrix Vector Multiplication with Hierarchi...

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Annual IEEE Symposium on Field-Programmable Custom computing Machines (FCCM)

作者： Abdul Rehman Tareen Marius Meyer Christian Plessl Tobias Kenter Department of Computer Science Paderborn University Paderborn Center for Parallel Computing Paderborn Germany

ISBN: (数字)9798350372434

ISBN: (纸本)9798350372441

The multiplication of a sparse matrix with a dense vector is a vital operation in linear algebra, with applications in numerous contexts. After earlier research on FPGA acceleration of this operation had shown the potential to achieve a high bandwidth efficiency, this workload has received renewed attention with the introduction of high bandwidth memory on FPGA platforms. However, previous designs fell short of scaling to the full bandwidth potential of current FPGA platforms with high bandwidth memory. In this work, we present HiHiSpMV with a novel design approach around hierarchical accumulation, which allows us to overcome several limitations of the related work. Our design is completely implemented with high-level synthesis and compiled with Vitis, and instantiates 16 independent compute units, each processing up to 16 matrix elements per clock cycle. The reduction of these elements is performed without any latency or resource overhead and the subsequent accumulation uses the well-established shift-register design pattern. Due to the independent nature of compute units, our design can connect to all of the 32 high bandwidth memory pseudo-channels in 512-bit interface mode on an Alveo U280 FPGA board. In our tests, we reach up to 86% of the theoretical available bandwidth of this FPGA platform, enabling a computational throughput of up to 98 GFLOPS. This is about 1.5 × faster than the peak throughput of the best related work in that regard, Serpens. On average, the throughput of HiHiSpMV is even 2.7× higher than Serpens.

关键词： Spectral efficiency Memory management Bandwidth Throughput Vectors Sparse matrices Field programmable gate arrays

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Integrating a Sequential Model Into GNN-Based Social Recommendation for Relieving Over-Smoothing Problem

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IEEE Access 2025年 13卷 100219-100233页

作者： Janekhwan Kitsupapaisan Saranya Maneeroj Atsuhiro Takasu Department of Mathematics and Computer Science Faculty of Science Advanced Virtual and Intelligent Computing Center Chulalongkorn University Bangkok Thailand National Institute of Informatics Tokyo Japan

In recent years, social recommendation systems have increasingly integrated Graph Neural Networks (GNNs) to capture social relationships among users from both direct and higher-order neighbors. However, these state-of-the-art models face limitations regarding the number of iterations they can propagate across the graph. Messages from higher-order neighbors often dilute the unique characteristics of the target node. This causes node representations to become similar and indistinguishable, leading to a phenomenon known as the over-smoothing problem. Several works have attempted to address this problem with the expectation of preserving user characteristics. They add auxiliary information to the GNN-based social recommendation models without considering the dynamic preferences of users, which frequently change over time. Therefore, those works still struggle with the same problem after propagating in a few iterations. To mitigate this phenomenon, we propose a novel approach to integrate user characteristics extracted from users’ historical interactions using a sequential model to slow down node convergence. Integrating user characteristics into the GNN-based social recommendation will allow the model to capture user preferences that dynamically change over time. Consequently, the proposed model will be able to leverage informative data from higher-order neighbors over social networks for user representation learning, leading to better performance and effectiveness of the recommendations. Our experimental results demonstrate that the proposed model outperforms state-of-the-art social recommendation models across three benchmark datasets.

关键词： Representation learning Graph neural networks Social networking (online) Convergence Redundancy Recommender systems Diffusion processes Smoothing methods Nickel Feature extraction

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Hybrid cloud storage system with enhanced multilayer cryptosystem for secure deduplication in cloud

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International Journal of Intelligent Networks 2023年第1期4卷 301-309页

作者： Mageshkumar, Nagappan Swapna, J. Pandiaraj, A. Rajakumar, R. Krichen, Moez Ravi, Vinayakumar Department of Computer Science and Technology Madanapalle Institute of Technology and Science Madanapalle India Department of Computer Science & Engineering Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computing Technology SRM Institute of Science and Technology Kattankulathur chennai India Al-Baha University Alaqiq 65779-7738 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3038 Tunisia Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar34754 Saudi Arabia Centre for Automation School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India

Data deduplication is a crucial technique in the field of data compression that aims to eliminate redundant copies of recurring data. This technique has gained significant popularity in the realm of cloud storage due to its ability to effectively reduce storage requirements and optimize bandwidth utilization. To ensure the safeguarding of sensitive data while simultaneously facilitating deduplication, researchers have put forth the concept of convergent encryption as a potential solution. This technique involves encrypting the data prior to its outsourcing, thereby enhancing the confidentiality of the information. In this work, an earnest endeavor is undertaken to formally tackle the issue of authorized data deduplication, with the aim of enhancing data security. Our approach combines the Diffie-Hellman algorithm and symmetrical external decision to protect and popularize information, ensuring end-to-end encryption to encourage user adoption of cloud storage. The proposed model employs block-level deduplication and guarantees the randomness of ciphertexts by generating encryption keys using the Diffie-Hellman algorithm. This method effectively counters both internal and external brute-force attacks, enhancing data security while reducing computational costs. An extensive experimentation is carried out to demonstrate that our approach is particularly beneficial in scenarios with multiple privilege sets. Overall, the proposed model offers an elaborate framework that maintains data privacy and strengthens security measures, contributing to a more efficient and secure cloud-based document search. © 2023 The Author(s)

关键词： Cryptography

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3L-AODV: Three Layer Security Protocol for Grayhole Attack Mitigation in MANET 8th

3L-AODV: Three Layer Security Protocol for Grayhole Attack ...

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8th International Congress on Information and Communication Technology, ICICT 2023

作者： Kamel, Mohammed B. M. Abdullah, Wisam Dawood Hamoud, Alaa Khalaf Valadares, Dalton C. G. Shareiyat, Ammar Ligeti, Peter Department of Computer Algebra Eotvos Lorand University Budapest Hungary Institute for Data Science Cloud Computing and IT Security Furtwangen University of Applied Sciences Furtwangen im Schwarzwald Germany Department of Computer Science University of Kufa Najaf Iraq Department of Computer Science University of Tikrit Tikrit Iraq Department of Computer Information Systems University of Basrah Basrah Iraq Virtus RDI Center Federal University of Campina Grande Campina Grande Brazil Federal Institute of Pernambuco Pernambuco Caruaru Brazil Department of CSS University of Stockholm Stockholm Sweden Department of Vulnerability Operations Center Orange Cyberdefense Stockholm Sweden

ISBN: (纸本)9789819930425

As the mobile ad hoc networks infrastructure has no centralized points of connections, the traffic from a source to a destination passes through number of intermediate nodes. When more than one neighbor node is available, the traffic passes through one of them based on some criteria. If a node behaves maliciously in all of the connections and drops the traffic, it creates a security blackhole, while a malicious node that behaves normally is some of the connections creates a security grayhole. Since the malicious node behaves normally in some connections, mitigating this attack is challenging. In this paper, we proposed 3L-AODV, a security protocol that uses three layers of protection and detection to mitigate the grayhole attack. As it does not require any modification on the AODV protocol, it can be implemented on any network that its routing is based on AODV. The analysis of our results shows that 3L-AODV could mitigate the grayhole attack without affecting the performance of the network. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mobile ad hoc networks

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Portability and Scalability of OpenMP Offloading on State-of-the-Art Accelerators 38th

Portability and Scalability of OpenMP Offloading on State...

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38th International Conference on High Performance computing, ISC High Performance 2023

作者： Fridman, Yehonatan Tamir, Guy Oren, Gal Department of Computer Science Ben-Gurion University of the Negev Be’er Sheva Israel Department of Physics Nuclear Research Center - Negev Be’er Sheva Israel Scientific Computing Center Nuclear Research Center - Negev Be’er Sheva Israel Intel Corporation Santa Clara United States Department of Computer Science Technion - Israel Institute of Technology Haifa Israel

ISBN: (纸本)9783031408427

Over the last decade, most of the increase in computing power has been gained by advances in accelerated many-core architectures, mainly in the form of GPGPUs. While accelerators achieve phenomenal performances in various computing tasks, their utilization requires code adaptations and transformations. Thus, OpenMP, the most common standard for multi-threading in scientific computing applications, introduced offloading capabilities between host (CPUs) and accelerators since v4.0, with increasing support in the successive v4.5, v5.0, v5.1, and the latest v5.2 versions. Recently, two state-of-the-art GPUs – the Intel Ponte Vecchio Max 1100 and the NVIDIA A100 GPUs – were released to the market, with the oneAPI and NVHPC compilers for offloading, correspondingly. In this work, we present early performance results of OpenMP offloading capabilities to these devices while specifically analyzing the portability of advanced directives (using SOLLVE’s OMPVV test suite) and the scalability of the hardware in representative scientific mini-app (the LULESH benchmark). Our results show that the coverage for version 4.5 is nearly complete in both latest NVHPC and oneAPI tools. However, we observed a lack of support in versions 5.0, 5.1, and 5.2, which is particularly noticeable when using NVHPC. From the performance perspective, we found that the PVC1100 and A100 are relatively comparable on the LULESH benchmark. While the A100 is slightly better due to faster memory bandwidth, the PVC1100 reaches the next problem size (4003 ) scalably due to the larger memory size. The results are available at: https://***/Scientific-computing-Lab-NRCN/Accel-OpenMP-Portability-Scalability. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Scalability

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Learning to Rank Patches for Unbiased Image Redundancy Reduction

Learning to Rank Patches for Unbiased Image Redundancy Reduc...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yang Luo Zhineng Chen Peng Zhou Zuxuan Wu Xieping Gao Yu-Gang Jiang Shanghai Collaborative Innovation Center of Intelligent Visual Computing School of Computer Science Fudan University University of Maryland College of Information Science and Engineering Hunan Normal University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Images suffer from heavy spatial redundancy because pixels in neighboring regions are spatially correlated. Existing approaches strive to overcome this limitation by reducing less meaningful image regions. However, current leading methods rely on supervisory signals. They may compel models to preserve content that aligns with labeled categories and discard content belonging to unlabeled categories. This categorical inductive bias makes these methods less effective in real-world scenarios. To address this issue, we propose a self-supervised framework for image redundancy reduction called Learning to Rank Patches (LTRP). We observe that image reconstruction of masked image modeling models is sensitive to the removal of visible patches when the masking ratio is high (e.g., 90%). Building upon it, we implement LTRP via two steps: inferring the semantic density score of each patch by quantifying variation between reconstructions with and without this patch, and learning to rank the patches with the pseudo score. The entire process is self-supervised, thus getting out of the dilemma of categorical inductive bias. We design extensive experiments on different datasets and tasks. The results demonstrate that LTRP outperforms both supervised and other self-supervised methods due to the fair assessment of image content. Code is available at https://***/irsLu/1trp.

关键词： visualization Accuracy Semantic segmentation Redundancy Semantics Self-supervised learning Object detection

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Hyper Elliptic Curve Based Certificateless Signcryption Scheme for Secure IIoT Communications

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computers, Materials & Continua 2022年第5期71卷 2515-2532页

作者： Usman Ali Mohd Yamani Idna Idris Jaroslav Frnda Mohamad Nizam Bin Ayub Roobaea Alroobaea Fahad Almansour Nura Modi Shagari Insaf Ullah Ihsan Ali Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur50603Malaysia Department of Computer Science University of SwatSaidu Sharif19130Pakistan Center for Research in Mobile Cloud Computing University of MalayaKuala Lumpur50603Malaysia Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026 ZilinaSlovakia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Sciences and Arts in RassQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science Hamdard Institute of Engineering and TechnologyIslamabad44000Pakistan

Industrial internet of things (IIoT) is the usage of internet of things(IoT) devices and applications for the purpose of sensing, processing andcommunicating real-time events in the industrial system to reduce the unnecessary operational cost and enhance manufacturing and other industrial-relatedprocesses to attain more profits. However, such IoT based smart industriesneed internet connectivity and interoperability which makes them susceptibleto numerous cyber-attacks due to the scarcity of computational resourcesof IoT devices and communication over insecure wireless channels. Therefore, this necessitates the design of an efficient security mechanism for IIoTenvironment. In this paper, we propose a hyperelliptic curve cryptography(HECC) based IIoT Certificateless Signcryption (IIoT-CS) scheme, with theaim of improving security while lowering computational and communicationoverhead in IIoT environment. HECC with 80-bit smaller key and parameterssizes offers similar security as elliptic curve cryptography (ECC) with 160-bitlong key and parameters sizes. We assessed the IIoT-CS scheme security byapplying formal and informal security evaluation techniques. We used Realor Random (RoR) model and the widely used automated validation of internet security protocols and applications (AVISPA) simulation tool for formalsecurity analysis and proved that the IIoT-CS scheme provides resistance tovarious attacks. Our proposed IIoT-CS scheme is relatively less expensivecompared to the current state-of-the-art in terms of computational cost andcommunication overhead. Furthermore, the IIoT-CS scheme is 31.25% and 51.31% more efficient in computational cost and communication overhead,respectively, compared to the most recent protocol.

关键词： IoT security authentication protocols hyperelliptic curve cryptography certificateless public key cryptography

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Towards Context-aware Support for Color Vision Deficiency: An Approach Integrating LLM and AR 13

Towards Context-aware Support for Color Vision Deficiency: A...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Morita, Shogo Zhang, Yan Yamauchi, Takuto Chen, Sinan Li, Jialong Tei, Kenji Tokyo Institute of Technology School of Computing Tokyo Japan University of Tokyo International Research Center for Neurointelligence Tokyo Japan Waseda University Department of Computer Science and Engineering Tokyo Japan Kobe University Center of Mathematical and Data Sciences Kobe Japan

ISBN: (纸本)9798350355079

People with color vision deficiency often face challenges in distinguishing colors such as red and green, which can complicate daily tasks and require the use of assistive tools or environmental adjustments. Current support tools mainly focus on presentation-based aids, like the color vision modes found in iPhone accessibility settings. However, offering context-aware support, like indicating the doneness of meat, remains a challenge since task-specific solutions are not cost-effective for all possible scenarios. To address this, our paper proposes an application that provides contextual and autonomous assistance. This application is mainly composed of: (i) an augmented reality interface that efficiently captures context;and (ii) a multi-modal large language model-based reasoner that serves to cognitize the context and then reason about the appropriate support contents. Preliminary user experiments with two color vision deficient users across five different scenarios have demonstrated the effectiveness and universality of our application. © 2024 IEEE.

关键词： Color vision

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