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A privacy and compliance in regulated anonymous payment system based on blockchain

Journal of Ambient Intelligence and Humanized Computing 2024年第8期15卷 3141-3157页

作者： Elfadul, Issameldeen Wu, Lijun Elhabob, Rashad Elkhalil, Ahmed School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China School of Computer and Information Engineering Qilu Institute of Technology Shandong Jinan250200 China Faculty of Computer Science and Information Technology Karary University Khartoum Sudan

Decentralized Anonymous Payment Systems (DAP), often known as cryptocurrencies, stand out as some of the most innovative and successful applications on the blockchain. These systems have garnered significant attention in the financial industry due to their highly secure and reliable features. Regrettably, the DAP system can be exploited to fund illegal activities such as drug dealing and terrorism. Therefore, governments are increasingly worried about the illicit use of DAP systems, which poses a critical threat to their security. This paper proposes Privacy and Compliance in Regulated Anonymous Payment System Based on Blockchain (PCRAP), which provides government supervision and enforces regulations over transactions without sacrificing the essential idea of the blockchain, that is, without surrendering transaction privacy or anonymity of the participants. The key characteristic of the proposed scheme is using a ring signature and stealth address to ensure the anonymity of both the sender and receiver of the transaction. Moreover, a Merkle Tree is used to guarantee government supervision and enforce regulations. Our proposed scheme satisfies most of the stringent security requirements and complies with the standards of secure payment systems. Additionally, while our work supports government regulations and supervision, it guarantees unconditional anonymity for users. Furthermore, the performance analysis demonstrates that our suggested scheme still remains applicable and effective even when achieving complete anonymity. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Blockchain

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Determining the Critical Factors of Antenatal Care Follow-Up Using Two-Phase Machine Learning 27

Determining the Critical Factors of Antenatal Care Follow-Up...

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27th International Conference on Soft Computing and Measurements, SCM 2024

作者： Bessmertny, Igor A. Enkomaryam, Bidru C. Mamuye, Adane L. Itmo University Faculty of Software Engineering and Computer Systems Saint Petersburg Russia University of Gondar Department of Computer Science Gondar Ethiopia

ISBN: (纸本)9798350363708

Determining the critical factors affecting antenatal visits will greatly contribute to reducing maternal and infant mortality. This study, thus attempted to construct a cluster-based predictive model to determine the critical factors of antenatal care follow-up among women in Ethiopia using two-step machine learning techniques. The 5,990 preprocessed obstetric dataset with 13 selected attributes were used that was collected from the Dabat Research Center at the University of Gondar, Ethiopia. To solve the study problem, six cluster models were developed using K-means, Expectation Maximization and Make Density clustering algorithms, which are well-suited for analyzing health datasets, providing scalability, flexibility, robustness to noise, and the ability to handle uneven cluster sizes. K-means out-formers Density based and Expectation Maximization clustering algorithms with minimum number of sum of square errors (17094) and number of iteration (3). Then, output of the K-means cluster model was used as input to segment the dataset and build a total of 21 predictive models using the tree-based J48 classifier, PART and JRip rule generators while adjusting their parameters. The model for predicting antenatal care follow-up is constructed by induction of the truncated PART rule (accuracy of 99.93%) and extracts attributes of interest such as antenatal care attendance, antenatal care location, antenatal care frequency, current vaccination, woman's age, and HIV testing to predict antenatal care follow-up in the study area. © 2024 IEEE.

关键词： Machine learning

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RSChat:Intelligent Question Answering Model for Railway Safety Knowledge 24

RSChat:Intelligent Question Answering Model for Railway Safe...

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24th IEEE/ACIS International Conference on computer and Information Science, ICIS 2024

作者： Li, Jinyu Li, Chao Niu, Sen Dai, Bingrong School of Computer and Information Engineering Shanghai Polytechnic University Shanghai China Shanghai Development Center of Computer Software Technology Shanghai China

ISBN: (纸本)9798350376647

The release of ChatGPT heralds a new era in artificial intelligence, as large language models (LLMs) empower myriad industries. This paper introduces RSChat, a model based on supervised fine-tuning of multiple large language models (LLMs), exploring a novel paradigm for applying knowledge in railway safety. Initially, we perform prompt engineering using collected domain data, including key information extraction and knowledge expansion. Subsequently, leveraging the constructed fine-tuning datasets, we conduct supervised fine-tuning using multiple large language models (LLMs) as base models. Finally, experimental evaluations demonstrate that RSChat can generate more professional responses in the railway safety domain, exhibiting robust potential for application. © 2024 IEEE.

关键词： Railroad accidents

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A Review of 3D Indoor Positioning and Navigation in Geographic Information Systems 8th

A Review of 3D Indoor Positioning and Navigation in Geogr...

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8th International Conference on Smart City Applications, SCA 2023

作者： Kazangirler, Buse Yaren Karas, Ismail Rakip Ozcan, Caner Department of Computer Technologies Kastamonu University Kastamonu Turkey Department of Computer Engineering Karabuk University Karabuk Turkey Department of Software Engineering Karabuk University Karabuk Turkey

ISBN: (纸本)9783031543753

Geographic information system (GIS) has become widespread with the management of geographically-based information, constituting a large part of the new data obtained and produced today in electronic platform. Three-dimensional (3D) models provide an overview of the more rapid establishment of new ideas that a two-dimensional (2D) visualization cannot present. Thus, 3D GIS demands data to be comprehensive and continious. 3D indoor positioning and 3D navigation are technologies that provide the ability to position and navigate indoors using these systems. The 3D navigation system is a field that can show covering objects in 3D along the route to the goal when precision is involved. In this study, navigation and indoor positioning studies in 3D GIS applications are mentioned, and particularly cutoff edge studies are included. It contains research on the use and benefits of 3D indoor modeling and navigation techniques. The literature examined for the study were evaluated, and it was concluded that 3D indoor positioning and navigation operations were generally performed with algorithms such as map-matching, location-based service, or ML/DL models. We comprehensively review positioning and navigation techniques in the 3D GIS field by combining existing studies, findings, and different approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Geographic information systems

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Loop Subgraph-Level Greedy Mapping Algorithm for Grid Coarse-Grained Reconfigurable Array

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Tsinghua Science and Technology 2023年第2期28卷 330-343页

作者： Naijin Chen Fei Cheng Chenghao Han Jianhui Jiang Xiaoqing Wen School of Computer and Information Science Anhui Polytechnic UniversityWuhu 241000China School of Software Engineering Tongji UniversityShanghai 201804China Department of Computer Science and Networks Kyushu Institute of TechnologyFukuoka 820-8502Japan

To solve the problem of grid coarse-grained reconfigurable array task mapping under multiple constraints,we propose a Loop Subgraph-Level Greedy Mapping(LSLGM)algorithm using parallelism and processing element *** the constraint of a reconfigurable array,the LSLGM algorithm schedules node from a ready queue to the current reconfigurable cell array *** mapping a node,its successor’s indegree value will be dynamically *** its successor’s indegree is zero,it will be directly scheduled to the ready queue;otherwise,the predecessor must be dynamically *** the predecessor cannot be mapped,it will be scheduled to a blocking *** dynamically adjust the ready node scheduling order,the scheduling function is constructed by exploiting factors,such as node number,node level,and node *** with the loop subgraph-level mapping algorithm,experimental results show that the total cycles of the LSLGM algorithm decreases by an average of 33.0%(PEA44)and 33.9%(PEA_(7×7)).Compared with the epimorphism map algorithm,the total cycles of the LSLGM algorithm decrease by an average of 38.1%(PEA_(4×4))and 39.0%(PEA_(7×7)).The feasibility of LSLGM is verified.

关键词： Grid Coarse-Grained Reconfigurable Array(GCGRA) mapping loop subgraph scheduling

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Brain magnetic resonance image (MRI) segmentation using multimodal optimization

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Multimedia Tools and Applications 2025年第16期84卷 16971-17020页

作者： Akan, Taymaz Oskouei, Amin Golzari Alp, Sait Bhuiyan, Mohammad Alfrad Nobel Department of Medicine Louisiana State University Health Sciences Center ShreveportLA71103 United States Software Engineering Department Istanbul Topkapi University Istanbul34020 Turkey Faculty of Information Technology and Computer Engineering Azarbaijan Shahid Madani University Tabriz Iran Department of Software Engineering İstinye University İstanbul Turkey Department of Artificial Intelligence Engineering Trabzon University Trabzon61335 Turkey

One of the highly focused areas in the medical science community is segmenting tumors from brain magnetic resonance imaging (MRI). The diagnosis of malignant tumors at an early stage is necessary to provide treatment for patients. The patient’s prognosis will improve if it is detected early. Medical experts use a manual method of segmentation when making a diagnosis of brain tumors. This study proposes a new approach to simplify and automate this process. In recent research, multi-level segmentation has been widely used in medical image analysis, and the effectiveness and precision of the segmentation method are directly tied to the number of segments used. However, choosing the appropriate number of segments is often left up to the user and is challenging for many segmentation algorithms. The proposed method is a modified version of the 3D Histogram-based segmentation method, which can automatically determine an appropriate number of segments. The general algorithm contains three main steps: The first step is to use a Gaussian filter to smooth the 3D RGB histogram of an image. This eliminates unreliable and non-dominating histogram peaks that are too close together. Next, a multimodal particle swarm optimization method identifies the histogram’s peaks. In the end, pixels are placed in the cluster that best fits their characteristics based on the non-Euclidean distance. The proposed algorithm has been applied to a Cancer Imaging Archive (TCIA) and brain MRI Images for brain Tumor detection dataset. The results of the proposed method are compared with those of three clustering methods: FCM, FCM_FWCW, and FCM_FW. In the comparative analysis of the three algorithms across various MRI slices. Our algorithm consistently demonstrates superior performance. It achieves the top mean rank in all three metrics, indicating its robustness and effectiveness in clustering. The proposed method is effective in experiments, proving its capacity to find the proper clusters. © The Auth

关键词： Image segmentation

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Pairwise tagging framework for end-to-end emotion-cause pair extraction

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Frontiers of computer Science 2023年第2期17卷 111-120页

作者： Zhen WU Xinyu DAI Rui XIA National Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210023China

Emotion-cause pair extraction(ECPE)aims to extract all the pairs of emotions and corresponding causes in a *** generally contains three subtasks,emotions extraction,causes extraction,and causal relations detection between emotions and *** works adopt pipelined approaches or multi-task learning to address the ECPE ***,the pipelined approaches easily suffer from error propagation in real-world *** multi-task learning cannot optimize all tasks globally and may lead to suboptimal extraction *** address these issues,we propose a novel framework,Pairwise Tagging Framework(PTF),tackling the complete emotion-cause pair extraction in one unified tagging *** prior works,PTF innovatively transforms all subtasks of ECPE,i.e.,emotions extraction,causes extraction,and causal relations detection between emotions and causes,into one unified clause-pair tagging *** this unified tagging task,we can optimize the ECPE task globally and extract more accurate emotion-cause *** validate the feasibility and effectiveness of PTF,we design an end-to-end PTF-based neural network and conduct experiments on the ECPE benchmark *** experimental results show that our method outperforms pipelined approaches significantly and typical multi-task learning approaches.

关键词： emotion-cause pair extraction pairwise tagging framework end-to-end neural network

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ALQ: An Efficient Active Learning Recommendation Algorithm 6

ALQ: An Efficient Active Learning Recommendation Algorithm

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6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Song, Ao Lei, Zhou Chen, Shengbo Shanghai University College of Computer Engineering and Science Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai China

ISBN: (纸本)9798350316520

With the rapid development of the Internet, the problem of information overload is becoming more and more serious, which makes it difficult for users to choose the content they are interested in from a large number of information. With the emergence of recommendation systems, the historical interaction information between users and information and items is used to provide users with topics of interest, which not only alleviates the problem of information overload, but also brings great satisfaction to users. However, training a model that can make accurate recommendations is not an easy task. In addition to an appropriate training strategy, it also requires a large amount of data support. Getting data, especially labeled data, takes a lot of time and money. Therefore, this paper proposes a new active learning algorithm. Firstly, the recommendation problem was transformed into a PU learning problem, and then the method was introduced in the positive sample learning process to achieve the purpose of increasing positive sample data, thereby reducing the cost of data labeling. Through experiments, the proposed method reduces the cost of data labeling in the recommendation task and further verifies the rationality of transforming the recommendation problem into a PU learning problem. © 2024 IEEE.

关键词： Recommender systems

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PDC-ViT: source camera identification using pixel difference convolution and vision transformer

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Neural Computing and Applications 2025年第9期37卷 6933-6949页

作者： Elharrouss, Omar Akbari, Younes Almadeed, Noor Al-Maadeed, Somaya Khelifi, Fouad Bouridane, Ahmed Department of Computer Science and Software Engineering United Arab Emirates University Al Ain United Arab Emirates Department of Computer Science and Engineering Qatar University Doha Qatar Northumbria University Newcastle Newcastle upon Tyne United Kingdom University of Sharjah Sharjah United Arab Emirates

Source camera identification has emerged as a vital solution to unlock incidents involving critical cases like terrorism, violence, and other criminal activities. The ability to trace the origin of an image/video can aid law enforcement agencies in gathering evidence and constructing the timeline of events. Moreover, identifying the owner of a certain device narrows down the area of search in a criminal investigation where smartphone devices are involved. This paper proposes a new pixel-based method for source camera identification, integrating Pixel Difference Convolution (PDC) with a Vision Transformer network (ViT), and named PDC-ViT. While the PDC acts as the backbone for feature extraction by exploiting Angular PDC (APDC) and Radial PDC (RPDC). These techniques enhance the capability to capture subtle variations in pixel information, which are crucial for distinguishing between different source cameras. The second part of the methodology focuses on classification, which is based on a Vision Transformer network. Unlike traditional methods that utilize image patches directly for training the classification network, the proposed approach uniquely inputs PDC features into the Vision Transformer network. To demonstrate the effectiveness of the PDC-ViT approach, it has been assessed on five different datasets, which include various image contents and video scenes. The method has also been compared with state-of-the-art source camera identification methods. Experimental results demonstrate the effectiveness and superiority of the proposed system in terms of accuracy and robustness when compared to its competitors. For example, our proposed PDC-ViT has achieved an accuracy of 94.30%, 84%, 94.22% and 92.29% using the Vision dataset, Daxing dataset, Socrates dataset and QUFVD dataset, respectively. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolution

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Comparative Analysis of Camera Calibration Algorithms for Football Applications 4

Comparative Analysis of Camera Calibration Algorithms for Fo...

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4th International Workshop on Information Technologies: Theoretical and Applied Problems, ITTAP 2024

作者： Sorokivskyi, Oleksandr Hotovych, Volodymyr Nazarevych, Oleg Shymchuk, Grygorii Department of Computer Science Faculty of Computer Information Systems and Software Engineering Ternopil Ivan Puluj National Technical University Ternopil Ukraine

In solving the problem of automated analysis of football match video recordings, special video cameras are currently used. This work presents a comparative characterization of known algorithms and methods for video camera calibration, including those utilizing machine learning and neural networks, with the aim of identifying their shortcomings and forming a theoretical foundation for developing modern, more effective methods and algorithms. Specifically, it examines both algorithms that require more input data but operate quickly [1, 2] and more accurate algorithms using machine learning [3, 4, 5, 6, 5]. It is demonstrated that their main drawback is either accuracy or speed. More accurate algorithms using machine learning often do not specify the algorithm's operational speed, which precludes their use in real-time applications. The examined works that emphasize speed frequently lack the accuracy necessary for practical use in real-life scenarios. Copyright © 2024 for this paper by its authors.

关键词： Semantic Segmentation

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