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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Jeba, Samiha Maisha Aurpa, Tanjim Taharat Alom, Md Samiul Joy, Md. Wadud Mahmud Ahamed, Asm Shakil Dash, Satya Ranjan Ahmed, Md Shoaib International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Bangladesh Bangabandhu Sheikh Mujibur Rahman Digital University Department of Data Science Bangladesh Kiit University School of Computer Applications Odisha India Boise State University Department of Computer Science BoiseID United States

ISBN: (纸本)9798350381931

We often open our eyes in the morning with the appalling news of data breaches of different popular companies. This is a significant threat not only to giant companies but also to the general people's privacy. Various methods are maneuvered for data breaches, and identifying these methods in a short can help understand the loss and take proper steps against these incidents. Researchers nowadays focus on the data breaches issue to reduce its threats to the tech industry. In this research, we have used advanced deep learning methods(e.g., BERT, XLNet, and Electra) to predict data breach methods using the data breach story. The proposed classifier has provided an accuracy of 92.86%. © 2024 IEEE.

关键词： Deep learning

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Massive Parallel Simulation for Tensor Network Contraction Ordering Solving 4

Massive Parallel Simulation for Tensor Network Contraction O...

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4th International Conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Xu, Wei Tong, Weiqin Liu, Xiaoyang Shanghai University School of Computer Engineering and Science Shanghai China Rensselaer Polytechnic Institute Department of Computer Science New York United States

ISBN: (纸本)9798350394375

Tensor network contraction is a powerful mathematical tool for dealing with complex systems and high-dimensional data, which is widely used in classical simulations of molecules and quantum circuits. To improve the performance of simulations, existing methods are implemented by searching the optimal contraction orderings for tensor networks. In particular, some reinforcement learning (RL) methods show a greater potential for solving. However, with the increasing scale of simulation, the number of tensors in the corresponding tensor network is huge, the existing RL methods suffer from sampling bottlenecks and slow solving speed, which make it difficult to efficiently solve for high-quality contraction orderings. In this paper, we design a massive parallel simulation environment for tensor network contraction ordering solving on a single GPU. We experimented with multiple numbers of parallel environments on the tensor-train network, and achieved the highest speedup when the number of environments was 26. We also have experimented on large-scale tensor-train networks and tree tensor networks, and compared to Kahypar and RL-Baseline, our method achieves average speedup of 8.96× and 7.48× on tensor-train networks, and 10.03× and 7.48× on tree tensor networks. © 2024 IEEE.

关键词： Tensors

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Regression-Based Exploration of Factors Influencing Movie Profits

Regression-Based Exploration of Factors Influencing Movie Pr...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Lindawaty Veronica Tifara Beata Wibowo Alexander Agung Santoso Gunawan Karli Eka Setiawan Computer Science Department Data Science Program School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350366648

ISBN: (纸本)9798350366655

Accurate box office prediction is crucial for managing financial risks in film production. The internet has transformed consumer behavior, affecting marketing strategies. Critical online reviews, more than early revenue, predict a film's profit, while establishing film critics as the predictors. Hence, this study investigates the intricate relationship between ratings and movies profit. While also finding the best model to predict those variables. This research was conducted through a comprehensive process encompassing data collection and processing, feature selection, prediction modeling, and a detailed discussion of the conducted analysis. Several manual interventions were applied to the data to support further discoveries and analysis in this research. These interventions were carried out based on a solid foundation to enhance the depth of exploration and understanding within the study. The findings reveal that the random forest regression emerged as the most effective in this study, achieving the Mean Squared Error (MSE) of 768.40. Leveraging a dataset of 500 movies from ***, incorporating critics' ratings from ***. Through this research, industry professionals not only enhance their understanding but also identify factors that can be considered to increase a movie’s revenue.

关键词： Seminars Runtime Reviews Profitability Production Predictive models Motion pictures Solids Robustness Random forests

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Resource Allocation in Edge Computing Environment Using Deterministic Policy Gradient Algorithm 10

Resource Allocation in Edge Computing Environment Using Dete...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Shabariram, C.P. Shanthi, N. Ponnuswamy, P. Priya Subramaniyaswamy, V. Sathana, V. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Erode India School of Computing Sastra University Department of Computer Science and Engineering Thanjavur India

ISBN: (纸本)9798350384369

In recent years, the edge computing paradigm enables the movement of processing units and storage nearer to the data available locations. The mechanism completes the computation in a short span of time in minimum bandwidth. Edge ecosystem is a type of distributed computing that is sensitive to topology and geography;the Internet of Things is a prime instance of this. Rather than referring to a single technology, Edge computing refers to an architecture. This paper proposes a resource allocation methodology that will enliven the situation between users and edge servers. By creating continuous control at the edge servers to determine resource allocation, edge computing improves reaction time, provides high security with decreased risk, scalability, lowers transmission costs, and versatility (offload targets, migration bandwidth and computing resources). The Deterministic Policy Gradient, Deep learning and Quality Network concepts are combined in the proposed system. The continuous action space is achieved by a deterministic policy gradient. The experience relay includes a quality network. In the proposed system, the actor-critic network produces a single continuous action instead of resulting probability based actions. The critic-part uses Q-value from a quality network based on current status and activity. The goal of the proposed system is to develop a Deep Deterministic Policy Gradient methodology to allocate servers for mobile users with the help of the Edge computing while taking computation resources, offloading goals and migration bandwidth into consideration. The simulation result indicates that deterministic policy gradients integrated deep learning models improve the system performance compared with Game theory. © 2024 IEEE.

关键词： Mobile edge computing

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CE-CDNet:A Transformer-Based Channel Optimization Approach for Change Detection in Remote Sensing

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computers, Materials & Continua 2025年第4期83卷 803-822页

作者： Jia Liu Hang Gu Fangmei Liu Hao Chen Zuhe Li Gang Xu Qidong Liu Wei Wang School of Computer Science and Technology Zhengzhou University of Light IndustryZhengzhou450002China Department of Computing Xi’an Jiaotong-Liverpool UniversitySuzhou215123China

In recent years,convolutional neural networks(CNN)and Transformer architectures have made significant progress in the field of remote sensing(RS)change detection(CD).Most of the existing methods directly stack multiple layers of Transformer blocks,which achieves considerable improvement in capturing variations,but at a rather high computational *** propose a channel-Efficient Change Detection Network(CE-CDNet)to address the problems of high computational cost and imbalanced detection accuracy in remote sensing building change *** adaptive multi-scale feature fusion module(CAMSF)and lightweight Transformer decoder(LTD)are introduced to improve the change detection *** CAMSF module can adaptively fuse multi-scale features to improve the model’s ability to detect building changes in complex *** addition,the LTD module reduces computational costs and maintains high detection accuracy through an optimized self-attention mechanism and dimensionality reduction *** test results on three commonly used remote sensing building change detection data sets show that CE-CDNet can reduce a certain amount of computational overhead while maintaining detection accuracy comparable to existing mainstream models,showing good performance advantages.

关键词： Remote sensing change detection attention mechanism channel optimization multi-scale feature fusion

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Intention to Participate in MOOCs: Case of University Students in Northwestern Nigeria During COVID-19 Pandemic

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第7期17卷 97-117页

作者： Ahmed, Abubakar Mu’azu Abdullah, Nor Athiyah Husin, Mohd Heikal Bello, Hassan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia Department of Computer Science Kaduna State University Kaduna Nigeria

Massive open online courses (MOOCs) have been introduced over the past few decades to account for the twenty-first-era education and recent COVID-19 pandemic trials driven by the spread of internet-based technology on the internet. This research seeks to give an extended model investigating the intention to use MOOCs based on students at public universities in northwestern Nigeria. The extended TAM model was tested via PLS structural equation modelling using data collected from 451 students at public universities in northwestern Nigeria. The research findings indicated that the proposed extended model delivers a 72.0% descriptive effect. The creative technology acceptance TAM model establishes a strong signal for the effects of PU, PR, PEOU, SN, and TA on intentions to practice MOOCs technology among students in Nigerian public universities during the COVID-19 pandemic. The outcomes present practical and theoretical implications that MOOC developers can use to justify why MOOCs are not high within public universities in Northern-western Nigeria. The findings also indicated that PU, PEOU, PR SN, and TA significantly impact students' intention to use MOOCs. The research has provided insight into extended TAM in Nigerian learning environments to discover the issues influencing students' intention to use MOOCs. Dissimilar to preceding empirical reviews, this research broadly investigated the intention to participate in MOOCs of public University students in northwestern Nigeria during the COVID-19 pandemic, delivering crucial discoveries and commendations for impending research openings. The findings could practically enlighten administrators, instructors, developers, and policymakers in making informed decisions © 2023, International Journal of Interactive Mobile *** Rights Reserved.

关键词： COVID-19

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Applying an Information Retrieval Approach to Retrieve Relevant Articles in the Legal Domain

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Annals of data science 2024年第5期11卷 1563-1580页

作者： Kanapala, Ambedkar Pal, Sukomal Dara, Suresh Jannu, Srikanth Department of Computer Science and Engineering Geethanjali College of Engineering and Technology TS Hyderabad501301 India Varanasi India School of Technology Woxsen University TS Hyderabad502345 India Department of Computer Science and Engineering Vaagdevi Engineering College TS Warangal506005 India

Retrieving legal texts is an important step for building a question answering system on law domain, which needs relevant articles to answer a query. Remarkable research has been done on legal information retrieval. However, retrieving relevant articles for a question is an extremely challenging task. In this paper, we describe a novel approach to retrieve relevant civil law article for a question from legal bar exams. We used three models Hiemstra, BM25 and PL2F implemented within Terrier. Our system retrieves top-ranked document from the collection according to the models specified and it outputs one single document per query. The best model has been selected on the basis of voting algorithm. Appropriate civil law articles are then retrieved using a mapping between document pair-id and the articles. The system achieved an accuracy of over 71.16% of correct civil law articles on training data and moderate scores on test data. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Information retrieval

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Towards More Robust and Scalable Deep Learning Systems for Medical Image Analysis

Towards More Robust and Scalable Deep Learning Systems for M...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Yenumala, Akshaj Zhang, Xinyue Lo, Dan School of Computer Science Georgia Institute of Technology Atlanta United States Kennesaw State University Department of Computer Science Kennesaw United States

ISBN: (纸本)9798350362480

Deep learning (DL) has attracted interest in healthcare for disease diagnosis systems in medical imaging analysis (MedIA) and is especially applicable in Big data environments like federated learning (FL) and edge computing. However, there is little research into mitigating the vulnerabilities and robustness of such systems against adversarial attacks, which can force DL models to misclassify, leading to concerns about diagnosis accuracy. This paper aims to evaluate the robustness and scalability of DL models for MedIA applications against adversarial attacks while ensuring their applicability in FL settings with Big data. We fine-tune three state-of-the-art transfer learning models, DenseNet121, MobileNet-V2, and ResNet50, on several MedIA datasets of varying sizes and show that they are effective at disease diagnosis. We then apply the Fast Gradient Sign Method (FGSM) to attack the models and utilize adversarial training (AT) and knowledge distillation to defend them. We provide a performance comparison of the original transfer learning models and the defended models on the clean and perturbed data. The experimental results show that the defensive techniques can improve the robustness of the models to the FGSM attack and be scaled for Big data as well as utilized for edge computing environments. © 2024 IEEE.

关键词： Adversarial machine learning

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Extensive analysis of IoT Architecture and Access Control Policies in Fog Enabled Environment 2

Extensive analysis of IoT Architecture and Access Control Po...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Lalit, Mohit Maurya, Sant Kumar Khurana, Shiraz Gupta, Nishant Computer Science and Engineering Department Chandigarh University Punjab Mohali India Computer Science and Engineering Department School of ICT Gautam Buddha University Greater Noida India Computer Science and Engineering Department Sharda University Greater Noida India

ISBN: (纸本)9798331541217

Cloud technology provides solutions to a variety of software platform and data accessibility issues. However, as the industrial revolution 4.0 progresses, innovations such as healthcare 4.0 smart buildings, IoT enabled transportation services, and many other new paradigms necessitate more secure data management solutions. Meanwhile, Fog Computing (FC) arises as a supplement to cloud computing, providing a variety of solutions to issues such as low latency and real-time data transfer. However, security concerns remained, and to address such issues, a regressive access control mechanism approach study was necessary. This article addresses methods to access control mechanisms for preserving privacy and confidentiality during communication in a cloud-fog computing environment. The research goal is to investigate various access control approaches used in the recent past for fog computing enabled environments. © 2024 IEEE.

关键词： Cloud platforms

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Analysis on unit maximum capacity of orthogonal multiple watermarking for multimedia signals in B5G wireless communications

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Digital Communications and Networks 2024年第1期10卷 38-44页

作者： Mianjie Li Senfeng Lai Jiao Wang Zhihong Tian Nadra Guizani Xiaojiang Du Chun Shan School of Electronics and Information Guangdong Polytechnic Normal UniversityGuangzhou510665China Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China Schoolof Electrical Engineering and Computer Science Washington State UniversityPullmanWA99164USA Department of Computer and Information Science Temple UniversityPhiladelphiaPA19122USA

Beyond-5G(B5G)aims to meet the growing demands of mobile traffic and expand the communication *** that intelligent applications to B5G wireless communications will involve security issues regarding user data and operational data,this paper analyzes the maximum capacity of the multi-watermarking method for multimedia signal hiding as a means of alleviating the information security problem of *** multiwatermarking process employs spread transform dither *** the watermarking procedure,Gram-Schmidt orthogonalization is used to obtain the multiple spreading ***,multiple watermarks can be simultaneously embedded into the same position of a multimedia ***,the multiple watermarks can be extracted without affecting one another during the extraction *** analyze the effect of the size of the spreading vector on the unit maximum capacity,and consequently derive the theoretical relationship between the size of the spreading vector and the unit maximum capacity.A number of experiments are conducted to determine the optimal parameter values for maximum robustness on the premise of high capacity and good imperceptibility.

关键词： B5G Multimedia information security Spread transform dither modulation Spreading vector measurement Unit maximum capacity

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