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IEEE International Conference on data science in Cyberspace (DSC)

作者： Hanjin Tong Tianqing Zhu Faqian Guan Wanlei Zhou School of Computer Science China University of Geosciences China Faculty of Data Science City University of Macau

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Graph neural networks (GNNs) have shown outstanding performance in graph node classification. However, as a deep learning model, GNNs can be influence by adversarial attacks, such as graph injection attacks or graph modification attacks, which modify edges or node features on the original graph. This paper focus on Graph Injection Attacks (GIA), which adds a small number of nodes and edges to the original graph to change the prediction results. GIA has stronger attack potential and it can cause more damage to the homogeneity of graphs. To tackle this problem, this paper proposes a novel defense strategy. We observe that real graphs are normally sparse, so that a link prediction model may be adopted to tell reliable edges from adversarial edges. By increasing the interaction information of edges, and reducing the sensitivity of vulnerable nodes to adversarial edges, the proposed method can increase the prediction acccuracy. Meanwhile, we designe a homogeneous filtering to help to identify adversarial edges, reducing the interference of the adversarial to the model. Experiments show our method has better defense performance than other baseline defense methods.

关键词： Deep learning Sensitivity Filtering Cyberspace Closed box Interference Predictive models data science Graph neural networks Reliability

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data Wrangling and Generation for Machine Learning Models in Medication Adherence Analytics: A practical Standpoint using Patient-Level and Medical Claims data

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data Intelligence 2025年第2期7卷 485-526页

作者： Wellington Kanyongo Absalom El-Shamir Ezugwu Tsitsi Moyo Jean Vincent Fonou Dombeu School of Mathematics Statistics and Computer Science University of Kwa Zulu-Natal Pietermaritzburg Campus Department of Computer Science Faculty of Science Engineering Bindura University of Science Education Unit for Data Science and Computing North-West University Cimas Medical Aid Society Borrowdale Office Park

Even though data wrangling(DW) accounts for more than half of the machine learning(ML) process, there is a dearth of research on data wrangling and dataset preparation. Consequently, we present a valuable state-of-the-art dataset prepared based on medical and pharmaceutical claims, as well as patient-level data collected from the transactional processing system of a Zimbabwean health insurance company. We were given two initial raw sub-datasets(one for the diabetics and one for the hypertensive patients) that had been retrieved and divided based on the International Classification of Diseases 10th Revision Code(ICD-10). Since adherence metrics measure the percentage of patients covered by prescription claims for the same medication in the same therapeutic class, within the measurement year, the created dataset only comprised data for 2022, which spanned from January 1 until December 31, 2022. We acknowledge, however, that there is no universally approved compendium of DW stages; yet certain building blocks and functionalities with considerable overlaps are commonly accepted and can be considered typical of DW. In light of this, we take a pragmatic approach to generating a dataset,incorporating nine essential DW building blocks and tasks, employed iteratively and incrementally, akin to an agile approach to dataset generation. Thus, we used some of the DW tasks iteratively and incrementally throughout the DW process without restricting them to a single stage. Following a rigorous DW procedure, the created dataset was then used to build an ML model, which then demonstrates a generally good degree of performance, as evidenced by the 81% accuracy. The outcomes, techniques, and insights proffered in this article inform future researchers, data scientists, and analysts on how to conduct data mining, dataset enrichment, and DW. The created dataset can stimulate further study in areas such as evaluating medication adherence(MA) and performing ML tasks such as classifying, pr

关键词： dataset dataset generation Diabetes Hypertension Non-communicable disease (NCD), Patient(s)

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Privacy-Preserving Access Control Model for E-Medical Systems 2

Privacy-Preserving Access Control Model for E-Medical System...

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2nd International Conference on Big data and Privacy Computing, BDPC 2024

作者： Cai, Rongsheng Chen, Lanxiang Zhu, Yizhao City University of Macau Faculty of Data Science 999078 China College of Computer and Cyber Security Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou China

ISBN: (纸本)9798350307597

With the high speed development of information technology, the healthcare system increasingly relies on cloud servers for data storage and complex computation, patient data privacy issues stem from healthcare systems' vulnerability to breaches, despite data encryption emphasis, insufficient focus on preventing unauthorized access risks compromises patient information confidentiality. Consequently, the need to protect privacy within the healthcare system has gained significant attentions. However, existing research primarily concentrates on data encryption processes, but there is comparatively less attention given to addressing potential privacy leaks concerning data visitors. In this paper, we propose a privacy-preserving access control model for healthcare cloud data visitors. Our model combines the RBAC (Role-Based Access Control) model with the PSI (Private Set Intersection) technique to ensure the privacy and security of data visitors. The performance analysis of our proposed solution demonstrates its relative efficiency and practicality when applied to E-medical systems. © 2024 IEEE.

关键词： Differential privacy

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A Secured and Optimized Broadcast Authentication Scheme for the Internet of Medical Things 4

A Secured and Optimized Broadcast Authentication Scheme for ...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Riaz, Syed Waleed Khan, Fazlullah Ullah, Insaf Alshawi, Bandar Alturki, Ryan Wedyan, Mohammad Abdul Wali Khan University Mardan Computer Science Department KPK Pakistan School of Computer Science University of Nottingham Ningbo China Faculty of Science and Engineering Zhejiang Ningbo315104 China Institute for Analytics and Data Science University of Essex ColchesterCO4 3SQ United Kingdom College of Computing Umm Al-Qura University Department of Computer and Network Engineering Makkah Saudi Arabia College of Computing Umm Al-Qura University Department of Software Engineering Makkah Saudi Arabia Faculty of Information Technology and Computer Sciences Department of Computer Sciences Irbid21163 Jordan

ISBN: (纸本)9798350395914

This Internet of Medical Things (IoMT), facilitates the medical stop regarding real-time monitoring of patients, medical emergency management, remote surgery, patient information management, medical equipment, drug monitoring, etc. However, IoMT devices communicate with each other in an open environment that makes them vulnerable to a wide range of malicious threats from malicious entities. To protect the devices and their associated data, we designed a broadcast authentication scheme for IoMT devices using identity-based public key cryptography that exploits the lightweight features of the Hyper-Elliptic Curve (HEC). We then performed the security analysis based on the Random Oracle Model (ROM), in which we have proved that the proposed scheme is unforgeable under the hardiness of the hyperelliptic curve discrete logarithm problem. The proposed scheme is analyzed in terms of computational and communication overhead and the experimental results justify the superiority of the proposed work in comparison to the existing schemes. © 2024 IEEE.

关键词： Authentication

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EQCNN: Enhanced Remote Sensing Imagery Classification with Circuit-Based Error-Corrected Quantum Convolutional Neural Networks 25

EQCNN: Enhanced Remote Sensing Imagery Classification with C...

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25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Zaman, Muhammad Kehkashan, Tanzila Akhunzada, Adnan Alaidaros, Hashem Uddin, Mueen Azeem, Muhammad University of Lahore Faculty of Computer Science Punjab Sargodha40100 Pakistan Universiti Teknologi Malaysia Faculty of Computing Johor Bahru Malaysia College of Computing & It University of Doha for Science & Technology Department of Data and Cybersecurity Doha Qatar Dar Al-Hekma University Department of Cybersecurity Jeddah Saudi Arabia

ISBN: (纸本)9798350379037

Quantum information processing is more complex than classical counterpart because of the No-Cloning theorem, decoherence, and issues detecting quantum states. Building a quantum computer without error detection and correction module is challenging. To overcome this challenge, qubit error correction codes are implemented. Inadequate error detection and correction codes have been the downfall of earlier attempts to encode quantum information, leading to disrupted qubit transmission. Qubit information is decohered by circuit noise. Unfortunately, the principal qubit error, phase flip error is not corrected by existing qubit error correction methods. In this study, we proposed Quantum Error Detection and Correction scheme (QEDC) to keep qubit in a coherent state. Used S gates, unitary gates, 10 CNOT gates, and Hadamard gates spanning on 4 physical and 1 logical qubits over the IBM Quantum Experience Machine. We found that the error probability is reduced to 7.91% owing to the efficiency of the quantum information encoding and phase flip control. Furthermore, we proposed Error Corrected Quantum Convolution Neural Network (EQCNN) for Image Classification. EQCNN combat and correct qubit errors that occur during the image processing. EQCNN outperformed over Earth Observatory (EO) dataset, EuroSAT and achieved an impressive results of 99.3% accuracy, 98.9% F1-score, 96.73% precision, and 98.4% recall. © 2024 IEEE.

关键词： Qubits

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Customer Segmentation Using Clustering Analysis

Customer Segmentation Using Clustering Analysis

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Jiet, Moses Makuei Kamble, Aahash Puri, Chetan Yesankar, Prajyot Verma, Prateek Rewatkar, Rajendra Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Data Science Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Biomedical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798350382693

This research focuses on the crucial role of the clustering technique in data mining, specifically in market forecasting and planning. The study presents a comprehensive report on utilizing the k-means clustering technique and data visualization tools to develop a real- time system for a supermarket, predicting sales across various annual cycles. An open-source dataset of a mall with n = 200 customers divided into k = 3 groups, demonstrated high accuracy in predicting day-to-day sales statistics when compared with actual results. The research highlights the effectiveness of the clustering technique in real-time sales prediction for supermarkets. © 2024 IEEE.

关键词： Sales

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Clarifying the Concept of Enterprise Architecture & System of Systems: A Systematic Literature Review 7

Clarifying the Concept of Enterprise Architecture & System o...

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7th International Conference on Sustainable Information Engineering and Technology, SIET 2022

作者： Diana, Lupna Kurniawan, Tri Astoto Priharsari, Diah Prabowo, Wahyu Setyo Brawijaya University Faculty of Computer Science Indonesia National Research and Innovation Agency Center for Data and Information

ISBN: (纸本)9781450397117

Enterprise-level system development requires a grand design to effectively achieve the system development objectives, which may involve more than one application. Currently, two approaches are available for creating such a grand design, i.e. Enterprise Architecture (EA) and System of Systems (SoS). In practice, EA plays a significant role in such development. However, it is not easy to choose correctly between the two because comparative studies do not yet exist in the literature, which, in turn, confuses. Thus, this research aims to develop a comparative study between EA and SoS using the Systematic Literature Review (SLR) approach. This study used four major digital libraries, i.e. Cambridge, Emerald Publishing, IEEE Xplore, and science Direct, resulting in 48 primary studies published in the last decade. The comparison relied on three main criteria, *** definition, the characteristics, and the framework. As a result, we have successfully clarified that EA and SoS have specific purposes. Finally, the result may become one of the knowledge resources for academia, researchers, and practitioners to adopt in creating a system development grand design at the enterprise level. © 2022 ACM.

关键词： computer architecture

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Multimodal Deep Representation Learning for Quantum Cross-Platform Verification

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Physical Review Letters 2024年第13期133卷 130601页

作者： Yang Qian Yuxuan Du Zhenliang He Min-Hsiu Hsieh Dacheng Tao School of Computer Science Faculty of Engineering College of Computing and Data Science JD Explore Academy Beijing 101111 China *** Beijing 101111 China Taipei 114699 Taiwan

Cross-platform verification, a critical undertaking in the realm of early-stage quantum computing, endeavors to characterize the similarity of two imperfect quantum devices executing identical algorithms, utilizing minimal measurements. While the random measurement approach has been instrumental in this context, the quasiexponential computational demand with increasing qubit count hurdles its feasibility in large-qubit scenarios. To bridge this knowledge gap, here we introduce an innovative multimodal learning approach, recognizing that the formalism of data in this task embodies two distinct modalities: measurement outcomes and classical description of compiled circuits on explored quantum devices, both containing unique information about the quantum devices. Building upon this insight, we devise a multimodal neural network to independently extract knowledge from these modalities, followed by a fusion operation to create a comprehensive data representation. The learned representation can effectively characterize the similarity between the explored quantum devices when executing new quantum algorithms not present in the training data. We evaluate our proposal on platforms featuring diverse noise models, encompassing system sizes up to 50 qubits. The achieved results demonstrate an improvement of 3 orders of magnitude in prediction accuracy compared to the random measurements and offer compelling evidence of the complementary roles played by each modality in cross-platform verification. These findings pave the way for harnessing the power of multimodal learning to overcome challenges in wider quantum system learning tasks.

关键词： Artificial neural networks Deep learning Quantum benchmarking Quantum computation

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Privacy-Preserving Responsible Gaming Systems 2

Privacy-Preserving Responsible Gaming Systems

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2nd International Conference on Big data and Privacy Computing, BDPC 2024

作者： Zhu, Yizhao Chen, Lanxiang City University of Macau Faculty of Data Science 999078 China College of Computer and Cyber Security Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou China

ISBN: (纸本)9798350307597

Responsible gaming aims at maintaining the integrity and sustainability of the gaming industry. However, The exchange of sensitive data, such as player and gaming related information, among different parties raises significant privacy concerns, and safeguarding privacy becomes a top priority consideration throughout the design and implementation of responsible gaming systems. In this paper, we introduce an innovative scheme that utilizes Oblivious Transfer (OT) and Private Set Intersection (PSI) protocols to address privacy concerns of responsible gaming systems. Our scheme proposes a system model that addresses privacy concerns related to player global limits and banned player list query and response, without requiring infrastructure changes, unlike blockchain-based solutions;Our proposed scheme not only addresses privacy concerns between the query user and the centralized database server but also mitigates privacy concerns between the query user and certificate authority (CA) by incorporating blind RSA signature scheme;Our proposed scheme achieves linear complexity in computation and communication, while remaining resilient against malicious adversaries under the random oracle model. © 2024 IEEE.

关键词： Differential privacy

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Partial optimality in the linear ordering problem 24

Partial optimality in the linear ordering problem

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Proceedings of the 41st International Conference on Machine Learning

作者： David Stein Bjoern Andres Faculty of Computer Science TU Dresden Germany Faculty of Computer Science TU Dresden Germany and Center for Scalable Data Analytics and AI Dresden/Leipzig

The linear ordering problem consists in finding a linear order < on a finite set A so as to minimize the sum of costs associated with pairs of elements a, b for which a < b. The problem is NP-hard and APX-hard. We introduce algorithms for solving the problem partially by deciding efficiently for some pairs (a, b) whether a < b is in an optimal solution. To do so, we construct maps from the feasible set of orders to itself and establish efficiently testable conditions on the cost function of the problem for which these maps are improving. We examine the effectiveness and efficiency of these conditions and algorithms empirically, on two data sets.

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