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arXiv 2023年

作者： Fuchs, Martin Hermann Paul Byju, Akshara Preethy Walda, Alisa Rasti, Behnood Demir, Begüm Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Germany Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India BIFOLD - Berlin Institute for the Foundations of Learning and Data Germany

Deep learning-based hyperspectral image (HSI) compression has recently attracted great attention in remote sensing due to the growth of hyperspectral data archives. Most of the existing models achieve either spectral or spatial compression and do not jointly consider the spatio-spectral redundancies present in HSIs. To address this problem, in this paper, we propose High Fidelity Compression (HiFiC)-based models for spatio-spectral compression of HSIs. In detail, we introduce two new models: i) HiFiC using Squeeze and Excitation (SE) blocks (denoted as HiFiCSE);and ii) HiFiC with 3D convolutions (denoted as HiFiC3D) in the framework of compression of HSIs. We analyze the effectiveness of HiFiCSE and HiFiC3D in compressing the spatio-spectral redundancies with channel attention and inter-dependency analysis. Experimental results show the efficacy of the proposed models in performing spatio-spectral compression, while reconstructing images at reduced bitrates with higher reconstruction quality. The code of the proposed models is publicly available at https://***/rsim/HSI-SSC. © 2023, CC BY.

关键词： Generative adversarial networks

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Legal-Onto Model for Efficient Land Law Updates in Vietnam 11th

Legal-Onto Model for Efficient Land Law Updates in Vietnam

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11th International Conference on Future data and Security Engineering, FDSE 2024

作者： D. T. Do, Huy D. Nguyen, Hien T. Pham, Vuong Nguyen, Tri-Hai Faculty of Computer Science University of Information Technology Ho Chi Minh City Viet Nam Faculty of Mathematics and Computer Science University of Science Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Computer Science Department New Mexico State University Las Cruces United States Institute of Data Science and Artificial Intelligence Sai Gon University Ho Chi Minh City Viet Nam Faculty of Information Technology School of Technology Van Lang University Ho Chi Minh City Viet Nam

ISBN: (纸本)9789819604333

In the rapidly evolving economic and social landscape of Vietnam, land remains a pivotal resource, crucial for economic growth and social stability. The recent enactment of the Land Law 2024, which supersedes the 2013 legislation, necessitates continuous updates for land users, investors, and regulatory authorities to ensure compliance and optimize land utilization. Traditional legal information retrieval systems are constrained by keyword-based searches and pre-set responses, which are inadequate for addressing specific user queries or rapidly integrating new legislative updates. This paper presents an advanced technological solution employing the Legal-Onto model to represent and update legal knowledge. The Legal-Onto model, inspired by relational ontology and keyphrase graphs, captures the semantic content of legal documents. By employing natural language processing techniques, the system automatically compares old and new legal texts, identifying differences at the article, point, and clause levels. This enables efficient updates and provides a valuable tool for legal professionals. It demonstrates the potential of artificial intelligence in legal contexts, offering a more effective and accessible approach to legal information. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Information Retrieval Intelligent System Knowledge-Based Systems Legal Document Ontology Vietnamese Land Law

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Enhancement of Security in IoT using Secure Hash Cryptography Algorithm with Double Encryption (SHCA-DE) 9

Enhancement of Security in IoT using Secure Hash Cryptograph...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Uganya, G. Rajalakshmi, D. Vangapandu, Ramarao Dinesh, M. Giri, Jayant Odeh, Mahmoud Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India Vignan's Institute of Engineering for Women Department of Computer Science and Engineering Vishakhapatnam India Saveetha University Department of Vlsi Microelectronics Saveetha School of Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (纸本)9798350377972

The 'Internet of things' is the developing technology to transfer the world into smart innovation. 'IoT' security is the most problematic issue in recent applications due to its properties including 'centralized architecture', 'limited capacity devices', and 'ambiguity'. So, blockchain with 'IoT' applications provides secure data communication compared to traditional 'IoT' applications. Blockchain is a recent growing technology that has features including 'decentralized structure', 'transparency', 'immutable records', 'increased capacity', and 'quick backtracks'. The 'cryptography hash algorithm' is very important to provide secure data transmission. It converted the different size input to fixed-size unalterable output to enhance the security. But still, it needs efficient algorithm to secure data transmission with safeguard of private key. The objective of this paper is to propose a secure hash cryptography algorithm with double encryption (SHCA-DE) to protect the private key message. Then, this proposed algorithm has been analyzed with MD5, SHA-256, and SHA3-512. From this analysis, the SHA3-512 performs well with proposed algorithm. © 2024 IEEE.

关键词： Encryption algorithms

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ICGNN: Graph Neural Network Enabled Scalable Beamforming for MISO Interference Channels

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IEEE Transactions on Mobile Computing 2025年

作者： He, Changpeng Lu, Yang Ai, Bo Dobre, Octavia A. Ding, Zhiguo Niyato, Dusit Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China Memorial University Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi127788 United Arab Emirates Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore

This paper investigates the graph neural network (GNN)-enabled beamforming design for interference channels. We propose a model termed interference channel GNN (ICGNN) to solve a quality-of-service constrained energy efficiency maximization problem. The ICGNN is two-stage, where the direction and power parts of beamforming vectors are learned separately but trained jointly via unsupervised learning. By formulating the dimensionality of features independent of the transceiver pairs, the ICGNN is scalable with the number of transceiver pairs. Besides, to improve the performance of the ICGNN, the hybrid maximum ratio transmission and zero-forcing scheme reduces the output ports, the feature enhancement module unifies the two types of links into one type, the subgraph representation enhances the message passing efficiency, and the multi-head attention and residual connection facilitate the feature extracting. Furthermore, we present the over-the-air distributed implementation of the ICGNN. Ablation studies validate the effectiveness of key components in the ICGNN. Numerical results also demonstrate the capability of ICGNN in achieving near-optimal performance with an average inference time less than 0.1 ms. The scalability of ICGNN for unseen problem sizes is evaluated and enhanced by transfer learning with limited fine-tuning cost. The results of the centralized and distributed implementations of ICGNN are illustrated. © 2002-2012 IEEE.

关键词： Unsupervised learning

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Efficient Evaluation Methods for Neural Architecture Search: A Survey

arXiv

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arXiv 2023年

作者： Song, Xiaotian Xie, Xiangning Lv, Zeqiong Yen, Gary G. Ding, Weiping Lv, Jiancheng Sun, Yanan College of Computer Science Sichuan University Chengdu610065 China School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74078 United States School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China Faculty of Data Science City University of Macau 999078 China

Neural Architecture Search (NAS) has received increasing attention because of its exceptional merits in automating the design of Deep Neural Network (DNN) architectures. However, the performance evaluation process, as a key part of NAS, often requires training a large number of DNNs. This inevitably makes NAS computationally expensive. In past years, many Efficient Evaluation Methods (EEMs) have been proposed to address this critical issue. In this paper, we comprehensively survey these EEMs published up to date, and provide a detailed analysis to motivate the further development of this research direction. Specifically, we divide the existing EEMs into four categories based on the number of DNNs trained for constructing these EEMs. The categorization can reflect the degree of efficiency in principle, which can in turn help quickly grasp the methodological features. In surveying each category, we further discuss the design principles and analyze the strengths and weaknesses to clarify the landscape of existing EEMs, thus making easily understanding the research trends of EEMs. Furthermore, we also discuss the current challenges and issues to identify future research directions in this emerging topic. In summary, this survey provides a convenient overview of EEM for interested users, and they can easily select the proper EEM method for the tasks at hand. In addition, the researchers in the NAS field could continue exploring the future directions suggested in the paper. Copyright © 2023, The Authors. All rights reserved.

关键词： Network architecture

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Gamification using Octalysis Framework in Knowledge Management System for Vocational High Schools during the Covid-19 Pandemic

Gamification using Octalysis Framework in Knowledge Manageme...

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International Conference on Informatics and Computing (ICIC)

作者： Mgs. Afriyan Firdaus Dwi Rosa Indah Yoppy Sazaki Eka Prasetyo Ariefin Muhammad Fachri Nuriza Muhammad Rafly Information System Department Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia Data Structure and Accounting Information System Laboratory Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia Informatics Management Department Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia

ISBN: (纸本)9798350345728

Knowledge management systems enable teachers and students to interact directly in the knowledge transfer process during the Covid-19 pandemic. In addition, applying the gamification concept in vocational disciplines could enhance the quality of teaching to increase motivation and student learning outcomes which tend to decline. Octalysis Framework, which supports the gamification concept, consists of eight cores that focus on creativity, self-expression, social dynamics, logic, thinking, and ownership depicted in an octagon making an application more fun and helpful to motivate users to enjoy and be involved in an activity. This condition is crucial and urgent in the Covid-19 pandemic that requires online and student-centered learning. This study discussed the application of Octalysis Framework-based Gamification in a computer Learning Knowledge Management system in Vocational High Schools during the Covid-19 Pandemic. The Gamification concept with the Octalysis Framework was implemented in the Knowledge Management System. Some game mechanics in a non-game context consisting of four phases, namely the Discovery, Onboarding, Scaffolding, and Endgame was applied to improve student performance and motivation in learning. Meanwhile, The Knowledge Management System software achieved an overall quality score of 5.14 out of 6 (85.6 percent of quality achieved) covering functionality, reliability, usability, efficiency, maintainability, and portability characteristics of the ISO 9126 quality indicator.

关键词： COVID-19 Pandemics ISO Standards Games Knowledge management System software Software reliability

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KA$^{2}$2SE: Key-Aggregation Authorized Searchable Encryption Scheme for data Sharing in Wireless Sensor Networks

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IEEE Transactions on Services Computing 2024年第1期18卷 226-238页

作者： Haijiang Wang Jianting Ning Wei Wu Chao Lin Kai Zhang School of Information and Electronic Engineering Zhejiang University of Science and Technology Zhejiang China School of Cyber Science and Engineering Wuhan University Wuhan China Faculty of Data Science City University of Macau Macau China College of Education Sciences Hong Kong University of Science and Technology Guangzhou China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fuzhou China Guangdong Key Laboratory of Blockchain Security Guangzhou University Guangzhou China College of Computer Science and Technology Shanghai University of Electric Power Shanghai China

As a promising technology, key-aggregation searchable encryption with constant computation overhead is especially suitable for sensor nodes with limited computation resources in wireless sensor networks. However, in most of the existing key-aggregation searchable encryption schemes, the authorized aggregation key is generated in a deterministic way. As a result, these schemes suffer from “ Key Forge Attack ” and “ Trapdoor Forge Attack ” that we proposed and hence fail to support the security property as they claimed (which is an important goal to be achieved in key-aggregation searchable encryption schemes). To fix these flaws, in this paper, we identify the security challenges related to key-aggregation searchable encryption and propose a lightweight key-aggregation authorized searchable encryption scheme based on attribute-based encryption, called KA $^{2}$ SE. It enables a data owner to share encrypted data with an authorized query user by issuing only a single authorized aggregation key, and the authorized query user only needs to submit a single trapdoor to the cloud server to perform keyword search. We formulate the security definitions for KA $^{2}$ SE and prove its security. Finally, empirical evaluations demonstrate that KA $^{2}$ SE is computationally efficient in comparison with existing schemes.

关键词： Encryption Cryptography Keyword search Wireless sensor networks Servers Privacy Cloud computing Security Probabilistic logic Authorization

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DeepMind Trainer: Harnessing Cognitive Neural Networks on Raspberry Pi for Advanced Cognitive Enhancement Games 2

DeepMind Trainer: Harnessing Cognitive Neural Networks on Ra...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Abirami, P. Antony Joseph Rajan, D. Vivekanandan, T. Saillaja, V. Mohankumar, N. Rajmohan, M. Velammal Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sakunthala Engineering College Vel Tech Multi Tech Dr Rangarajan Dr Department of Mathematics Tamil Nadu Chennai India SRM Institute of Science and Technology Faculty of Management Studies Tamil Nadu Kattankulathur Chennai India B.V. Raju Institute of Technology Department of Electronics and Communication Engineering Telangana India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350371314

The presentation summarizes a groundbreaking cognitive training program designed specifically for those who suffer from cognitive impairments or neurodevelopmental abnormalities. The work creates a dynamic platform for immersive and adaptable cognitive training games by combining cutting-edge Convolutional Neural Networks (CNNs) with Raspberry Pi technology. Memory, focus, and ability to think creatively are all qualities directly targeted by these games. The CNN model, trained on a broad dataset, interprets real-time user inputs, delivering quick feedback and altering game complexity depending on an individual's performance. The incorporation of Raspberry Pi guarantees a solution that is both affordable and easily accessible. The capacity of the system to redefine cognitive rehabilitation by providing a solution that is individualizable, interactive, and broadly deployable is the potential source of the system's influence. The goal of the work is to make a substantial contribution to the field, which will eventually be to the advantage of users, caregivers, and healthcare professionals and ultimately improve the cognitive well-being of those coping with cognitive issues. © 2024 IEEE.

关键词： Convolutional neural networks

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An Exploration of Interpolation - Machine Learning Model for Climate Model Downscaling Under the Limitation of data Quantity

An Exploration of Interpolation - Machine Learning Model for...

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International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Chotirose Prathom Paskorn Champrasert Data Science Consortium Faculty of Engineering Chiang Mai University Chiang Mai Thailand Department of Computer Engineering Faculty of Engineering Chiang Mai University Chiang Mai Thailand

In order to response to climate change, climate model is essential for future prediction, which is advantage for adaptation planning. With the fact that the output of the climate model is in a coarse spatial resolution, it is poorly representing data on local level and requires a downscaling process to increase its resolution. To perform the downscaling, high amount of historical observed data is necessary for calibrating the model output. However, the data may not be available in some area, also Thailand. To overcome the limitation of data Quantity, four combination methods between two interpolation (Inverse Distance Weight and Triangular Irregular Network) and two machine learning models (Artificial Neural Network and Gradient Boosting Regression Tree) are experimented to explore the proper one for downscaling data on Thailand area. By considering the performance of both interpolation and machine learning model, the combination between Inverse Distance Weight and Artificial Neural Network shows the best performance in down scaling process under data Quantity limitation.

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Design an Ensemble Pretrained Deep Learning Model for Classification of Melanoma Skin Cancer Images

Design an Ensemble Pretrained Deep Learning Model for Classi...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Dheeb Albashish Nedaa Almansour Azizi Abdullah Hossam M. J. Mustafa Manar Rizik AlSayyed Oraib Alrashdan Computer Science Department Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Salt Jordan Center for Artificial Intelligence Technology (CAIT) Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Center For Artificial Intelligence Technology (CAIT) Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Malaysia Department of Data Science and Artificial Intelligence Faculty of Information Technology University of Petra Amman Jordan Pathology Department University of Jordan Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The recent global outbreak of melanoma skin cancer has prompted increased attention toward computer-aided diagnosis systems (CAD). The primary application of CAD in melanoma is to discriminate between benign and malignant skin medical images. Additionally, the survival rate improves with early cancer detection. This study presents a CAD diagnosis method for melanoma skin diagnosis, which utilizes deep learning and medical image processing. To elaborate, the proposed model comprises two stages: Firstly, fine-tune three powerful pretrained models, namely VGG16, NASNetMobile, and ResNet50V2, to address the current task. Secondly, we leverage their decisions through ensemble learning and hard voting techniques. The evaluation of the proposed ensemble model on a skin cancer dataset yielded a notable accuracy of 91.5% on a test portion of the dataset. This method reveals the potential for diagnosing melanoma while also illustrating the efficacy of the ensemble model in augmenting diagnostic performance.

关键词： Deep learning Solid modeling Design automation Computational modeling Melanoma Skin computer aided diagnosis Ensemble learning Medical diagnostic imaging Computational intelligence

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