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4th International Conference on Novel and Intelligent Digital Systems, NiDS 2024

作者： Lorico, Hans Daniel Q. Zamuco, Amiel David P. Caro, Jaime D. L. Juayong, Richelle Ann B. Service Science and Software Engineering Laboratory Department of Computer Science College of Engineering University of the Philippines Diliman Quezon City1101 Philippines

ISBN: (纸本)9783031733437

Blockchain is a developing and promising field in transaction and identity management. Recent efforts have been underway to address issues of data insecurity and inefficiency presented by centralized systems. Philippine government agencies, like the Land Transportation Office (LTO), traditionally store information in centralized databases, which can be susceptible to unauthorized access. This study makes the case of decentralizing certain LTO online procedures, starting with driver’s license application, as well as identity management using blockchain technology. With an immutable, distributed ledger system, it aims to introduce security, transparency, and efficiency into the system through a proof-of-concept web application. It demonstrates that not only certain LTO procedures can be decentralized, but also the management of license ID, more specifically the selective disclosure of information to requesting parties. The design presented here lays the groundwork for developing a decentralized application through Hyperledger Fabric and Indy, leveraging features like consensus mechanisms, verifiable credentials (VCs), and self-sovereign identity (SSI). The initial evaluation of the design using Hyperledger Caliper sets the stage for further research on decentralization, scalability, and efficiency. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Efficiency

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A Survey of Integrating Generative Artificial Intelligence and 6G Mobile Services: Architectures, Solutions, Technologies and Outlooks

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IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1334-1356页

作者： Liu, Yi-Jing Du, Hongyang Xu, Xinyi Zhang, Ruichen Feng, Gang Cao, Bin Niyato, Dusit Kim, Dong In Jamalipour, Abbas Letaief, Khaled B. Tafazolli, Rahim University of Electronic Science and Technology of China National Key Lab on Wireless Communications Chengdu China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of University of Sydney School of Electrical and Computer Engineering Sydney Australia Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong University of Surrey Institute for Communication Systems School of Computer Science and Electronic Engineering United Kingdom

Generative artificial intelligence (GenAI) is rapidly driving a new phase of artificial intelligence revolution, marked by various applications such as ChatGPT, Sora and DeepSeek. With powerful capabilities in content generation, personalization and user interaction, GenAI can drive significant enhancements in various mobile services to satisfy individual user preferences. Meanwhile, mobile services offer substantial support for accessibility, functionality and intelligence of GenAI applications. However, current research on integrating GenAI and mobile services remains nascent, lacking necessary theoretical and technical support. In this survey, we break down a new vision to guide research on integrating GenAI and mobile services, aiming to improve 6G intelligence levels and enable 6G mobile networks to support the integration of GenAI and mobile services effectively. We begin by examining the necessity of integrating GenAI with 6G mobile services and exploring potential learning architectures of GenAI models within mobile networks. Next, we analyze potential solutions from two perspectives: how 6G mobile networks empower GenAI and how GenAI enables mobile services. We subsequently explore a promising use case, along with essential features and techniques, to facilitate the integration of GenAI and mobile services. Finally, we discuss future research directions, aiming at realizing intelligent 6G networks. © 2015 IEEE.

关键词： Generative adversarial networks

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Reassessing Glove Embeddings in Deep Learning: A Comparative Study with Classical ML Approaches 15

Reassessing Glove Embeddings in Deep Learning: A Comparative...

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15th International Conference on Emerging Ubiquitous Systems and Pervasive Networks / 14th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, EUSPN/ICTH 2024

作者： Labd, Zakia Bahassine, Said Housni, Khalid Aadi, Fatima Zahrae Aithamou Laboratory of research in informatics LatRI Department of Computer Science Faculty of Sciences Ibn Tofail University Kenitra Morocco Laboratory of Artificial Intelligence and Complex Systems Engineering Department of Computer Science National Higher School of Arts and Crafts Hassan II University Casablanca Morocco

The importance of text classification algorithms has increased due to the growing availability of large-scale data. This has led to a greater demand for efficient classification techniques and encoding algorithms. Word embedding techniques, like Glove, have shown significant success in encoding semantic relationships between words. This research paper aims to reassess the effectiveness of Glove embeddings coupled with deep learning algorithms. The impact of Glove embedding on two widely used deep learning models: Recurrent Neural Networks (RNN) and Recurrent Convolutional Neural Networks (RCNN) is analyzed. The results highlight the impact of Glove embeddings on deep learning models, showcasing significant performance enhancements in some cases while having minimal effects in others. By examining the impact of Glove embeddings on traditional ML algorithms in a previous study, valuable context for understanding the performance differences between the two approaches is obtained. © 2024 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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A Low-Cost Pipelined Architecture Based on a Hybrid Sorting Algorithm

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IEEE Transactions on Circuits and Systems I: Regular Papers 2024年第2期71卷 717-730页

作者： Chen, You-Rong Ho, Chien-Chia Chen, Wei-Ting Chen, Pei-Yin The Digital Integrated Circuit Design Laboratory Department of Computer Science and Information Engineering National Cheng Kung University Tainan70101 Taiwan MediaTek Inc. Hsinchu30078 Taiwan

In this paper, a low-cost pipelined architecture based on a hybrid sorting algorithm is proposed. The proposed architecture is constructed with a bitonic sorter and several cascaded bidirectional insertion sorting units. The bidirectional insertion sorting unit uses the segmented sorted subsequence generated by the bitonic sorter as input, and records the maximum and minimum values of the subsequence. After all segmented subsequences are processed through the cascaded bidirectional insertion sorting units, a sorted sequence is obtained. The proposed architecture is implemented using the Verilog hardware description language (HDL) and synthesized using the Synopsys Design Compiler with a TSMC 90-nm cell library. The experimental results indicate that the proposed architecture can not only shorten sorting cycles but also reduce hardware area costs. Moreover, sorting cycles can be further shortened by increasing the parallelism of the proposed architecture. Under the configuration that 2048 32-bit data to be sorted and 16 data have to be processed simultaneously, the proposed architecture can improve the throughput-to-gate-count ratio by 16%, and throughput-to-power-consumption-ratio by 25% compared to the existing sorting design. The proposed architecture makes the most efficient use of hardware resources. © 2023 IEEE.

关键词： Costs

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Task Placement and Resource Allocation for Edge Machine Learning: A GNN-Based Multi-Agent Reinforcement Learning Paradigm

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IEEE Transactions on Parallel and Distributed Systems 2023年第12期34卷 3073-3089页

作者： Li, Yihong Zhang, Xiaoxi Zeng, Tianyu Duan, Jingpu Wu, Chuan Wu, Di Chen, Xu Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Peng Cheng Laboratory Department of Communications Shenzhen518066 China Southern University of Science and Technology Institute of Future Networks Shenzhen518055 China University of Hong Kong Department of Computer Science Hong Kong Hong Kong

Machine learning (ML) tasks are one of the major workloads in today's edge computing networks. Existing edge-cloud schedulers allocate the requested amounts of resources to each task, falling short of best utilizing the limited edge resources for ML tasks. This paper proposes TapFinger, a distributed scheduler for edge clusters that minimizes the total completion time of ML tasks through co-optimizing task placement and fine-grained multi-resource allocation. To learn the tasks' uncertain resource sensitivity and enable distributed scheduling, we adopt multi-agent reinforcement learning (MARL) and propose several techniques to make it efficient, including a heterogeneous graph attention network as the MARL backbone, a tailored task selection phase in the actor network, and the integration of Bayes' theorem and masking schemes. We first implement a single-task scheduling version, which schedules at most one task each time. Then we generalize to the multi-task scheduling case, in which a sequence of tasks is scheduled simultaneously. Our design can mitigate the expanded decision space and yield fast convergence to optimal scheduling solutions. Extensive experiments using synthetic and test-bed ML task traces show that TapFinger can achieve up to 54.9% reduction in the average task completion time and improve resource efficiency as compared to state-of-the-art schedulers. © 1990-2012 IEEE.

关键词： Reinforcement learning

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Machine learning integrated graphene oxide‐based diagnostics, drug delivery, analytical approaches to empower cancer diagnosis

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BMEMat(BioMedical Engineering Materials) 2025年第1期3卷 158-181页

作者： Suparna Das Hirak Mazumdar Kamil Reza Khondakar Ajeet Kaushik Department of Computer Science and Engineering BVRIT HYDERABAD College of Engineering for WomenHyderabadIndia School of Engineering and Technology Department of Computer Science and EngineeringAdamas UniversityKolkataIndia School of Technology Woxsen UniversityHyderabadIndia NanoBiotech Laboratory Department of Environmental EngineeringFlorida Polytechnic UniversityLakelandUSA

Machine learning(ML)and nanotechnology interfacing are exploring opportunities for cancer treatment *** improve cancer therapy,this article investigates the synergistic combination of Graphene Oxide(GO)‐based devices with ML *** production techniques and functionalization tactics used to modify the physicochemical characteristics of GO for specific drug delivery are explained at the outset of the *** is a great option for treating cancer because of its natural biocompatibility and capacity to absorb medicinal ***,complicated biological data are analyzed using ML algorithms,which make it possible to identify the best medicine formulations and individualized treatment plans depending on each patient's particular *** study also looks at optimizing and predicting the interactions between GO carriers and cancer cells using *** modeling helps ensure effective payload release and therapeutic efficacy in the design of customized drug delivery ***,tracking treatment outcomes in real time is made possible by ML algorithms,which permit adaptive modifications to therapy *** optimizing medication doses and delivery settings,the combination of ML and GO in cancer therapy not only decreases adverse effects but also enhances treatment accuracy.

关键词： artificial intelligence cancer diagnosis graphene oxide machine learning real‐time monitoring sensing

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Predictive Health: Chronic Kidney Disease Case Study 23

Predictive Health: Chronic Kidney Disease Case Study

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7th International Conference on Information System and Data Mining, ICISDM 2023

作者： Boukenze, Basma Quafafou, Mohammed Department of Computing Computer Science and Systems Laboratory-CNRS Aix-Marseille University France

ISBN: (纸本)9798400700637

Kidney failure is a silent chronic disease, without symptoms, and develops into an acute stage quickly. Unfortunately, the majority of patients discover this disease in its advanced stages where hemodialysis or transplant becomes a necessity. Predicting kidney failure will make controlling the progression of the disease possible and can even stabilize the state of patients. Data Analytics and data mining techniques, as well as machine learning proved a great success in information and knowledge discovery from medical big data to help decision making. Indeed, all the fields of the medical sector are paramount by the application of these technologies, given the useful knowledge extracted from collected data. Such knowledge will contribute to improve decision-making and save human lives. In this paper, we will present the big role of data mining technics in medical field in matters of predicting chronic diseases. Also we will show how these technics are very crucial to extract useful knowledge, and that by using linear regression model to predict glomerular filtration rate values (GFR) as the best indicator of kidney failure. The main purpose is to allow doctors to help patients, predict kidney failure and control this unwanted disease. © 2023 ACM.

关键词： Multiple linear regression

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A Linked List Encryption Scheme for Image Steganography without Embedding

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computer Modeling in Engineering & sciences 2024年第10期141卷 331-352页

作者： Pengbiao Zhao Qi Zhong Jingxue Chen Xiaopei Wang Zhen Qin Erqiang Zhou Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of ChinaChengdu610054China Faculty of Data Science City University of MacaoMacao999078China Department of Computer Science and Engineering University of CaliforniaRiversideCA 92521USA

Information steganography has received more and more attention from scholars nowadays,especially in the area of image steganography,which uses image content to transmit information and makes the existence of secret information *** enhance concealment and security,the Steganography without Embedding(SWE)method has proven effective in avoiding image distortion resulting from cover *** this paper,a novel encrypted communication scheme for image SWE is *** reconstructs the image into a multi-linked list structure consisting of numerous nodes,where each pixel is transformed into a single node with data and pointer *** employing a special addressing algorithm,the optimal linked list corresponding to the secret information can be *** receiver can restore the secretmessage fromthe received image using only the list header position *** scheme is based on the concept of coverless steganography,eliminating the need for any modifications to the cover *** boasts high concealment and security,along with a complete message restoration rate,making it resistant to ***,this paper proposes linked-list construction schemeswithin theproposedframework,which caneffectively resist a variety of attacks,includingnoise attacks and image compression,demonstrating a certain degree of *** validate the proposed framework,practical tests and comparisons are conducted using multiple *** results affirm the framework’s commendable performance in terms of message reduction rate,hidden writing capacity,and robustness against diverse attacks.

关键词： Steganography encryption steganography without embedding coverless steganography

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USR: Unrolled Super-Resolution with Deep Priors for Structured Illumination Microscopy

USR: Unrolled Super-Resolution with Deep Priors for Structur...

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3D Image Acquisition and Display: Technology, Perception and Applications, 3D, COSI, DH, FLatOptics, IS, pcAOP 2023 - Part of Imaging and Applied Optics Congress 2023

作者： Dajkhosh, S. Parisa Hossain, Mazharul Preza, Chrysanthe Computational Imaging Research Laboratory Electrical and Computer Engineering Department United States Computer Science Department The University of Memphis MemphisTN United States

We introduce a method for enhancing the resolution of Structured Illumination Microscopy images. It uses a deep prior network, unrolled with a model-based analytical process to improve performance, maintain structural... 详细信息

ISBN: (纸本)9781957171289

关键词： Image enhancement

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TinyML on Arduino Nano 33 BLE for Disabled Person 1

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International Conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2023

作者： Bouh, Youssef Baslam, Mohamed Ouhda, Mohamed TIAD Laboratory Computer Science Department Faculty of Sciences and Technics Sultan Moulay Slimane University Beni Mellal Morocco TIAD Laboratory Department of Computer and Mathematics Higher School of Technology Sultan Moulay Slimane University Khenifra Morocco

ISBN: (数字)9783031542886

ISBN: (纸本)9783031542879

In recent years, the embedded machine learning topic has become very popular in AI. With the help of different techniques, it is possible to run pre-trained models or training neural networks directly on embedded machine learning, for example, smart products such as voice assistants and smart sensors. All that techniques are needed in the robotics fields. And all people like to use smart devices in different popular devices every time. However, to use smart objects everywhere requires the use of small components for ease of use and handling. This requires the running of machine learning models on microcontrollers. Disabled people can take advantage of new artificial intelligence technologies to control devices. In this paper, we propose to run a model on the Arduino Nano 33 BLE Sens for disabled people to switch lamp using voice recognition. This method can be used to start washing machine, pomp, and other situations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Disabled persons

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