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arXiv 2023年

作者： Zhao, Qiang Mao, Haokun Qiao, Yucheng Abd El-Latif, Ahmed A. Li, Qiong Information Countermeasure Technique Institute School of Cyberspace Science Faculty of Computing Harbin Institute of Technology Harbin150001 China Department of Computer Science and Engineering The Chinese University of Hong Kong 999077 Hong Kong School of Information Engineering Guilin University of Electronic Science and Technology Guilin541004 China EIAS Data Science Laboratory College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Department of Mathematics and Computer Science Faculty of Science Menoufia University Shibin Al Kawm32511 Egypt

The blind quantum computation (BQC) protocol allows for privacy-preserving remote quantum computations. In this paper, we introduce a remote quantum error correction code preparation protocol for BQC using a cluster state and analyze its blindness in the measurement-based quantum computation model. Our protocol requires fewer quantum resources than previous methods, as it only needs weak coherent pulses, eliminating the need for quantum memory and limited quantum computing. The results of our theoretical analysis and simulations show that our protocol requires fewer quantum resources compared to non-coding methods with the same qubit error rate. © 2023, CC BY.

关键词： Error correction

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The Evolution of Zero Trust Architecture (ZTA) from Concept to Implementation

arXiv

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arXiv 2025年

作者： Nasiruzzaman, Md. Ali, Maaruf Salam, Iftekhar Miraz, Mahdi H. School of Computing & Data Science Xiamen University Malaysia Malaysia Faculty of Computer Science IT Universiteti Metropolitan Tirana Albania The Doctoral College University of Wales Trinity Saint David Wales United Kingdom School of Computing Faculty of Arts Science & Technology Wrexham University United Kingdom Faculty of Computing Engineering & Science University of South Wales United Kingdom

Zero Trust Architecture (ZTA) is one of the paradigm changes in cybersecurity, from the traditional perimeter-based model to perimeterless. This article studies the core concepts of ZTA, its beginning, a few use cases and future trends. Emphasising the always-verify and least privilege access, some key tenets of ZTA have grown to be integration technologies like Identity Management, Multi-Factor Authentication (MFA) and real-time analytics. ZTA is expected to strengthen cloud environments, education, work environments (including from home) while controlling other risks like lateral movement and insider threats. Despite ZTA’s benefits, it comes with challenges in the form of complexity, performance overhead and vulnerabilities in the control plane. These require phased implementation and continuous refinement to keep up with evolving organisational needs and threat landscapes. Emerging technologies, such as Artificial Intelligence (AI) and Machine Learning (ML) will further automate policy enforcement and threat detection in keeping up with dynamic cyber threats. Copyright © 2025, The Authors. All rights reserved.

关键词： Machine learning

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Streamlining Eye-Tracking and Observational data for Field Study Visual Analysis 25

Streamlining Eye-Tracking and Observational Data for Field S...

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Proceedings of the 2025 Symposium on Eye Tracking Research and Applications

作者： Yidan Zhang Nethara Athukorala Ziying Liang Yidan Qiao Simran - Yu Xuan Yio Lee Lawrence Benjamin Tag Mor Vered Michael Wybrow Sarah Goodwin Department of Human-Centred Computing Monash University Melbourne Australia Monash University Melbourne Victoria Australia Faculty of Information Technology Monash University Melbourne Victoria Australia Monash University Melboure Victoria Australia Faculty of Information Technology Monash University Melboure Victoria Australia Monash Business School Monash University Melbourne Australia School of Computer Science and Engineering University of New South Wales Sydney New South Wales Australia Dept of Data Science & AI Monash University Clayton VIC Australia Department of Human-Centred Computing Monash University Melbourne VIC Australia Monash University Melbourne Australia

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Forecast Evaluation for data Scientists: Common Pitfalls and Best Practices

arXiv

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arXiv 2022年

作者： Hewamalage, Hansika Ackermann, Klaus Bergmeir, Christoph School of Computing Technologies RMIT University Melbourne Australia SoDa Labs Dept of Econometrics & Business Statistics Monash University Melbourne Australia Dept of Data Science and AI Faculty of IT Monash University Melbourne Australia Dept of Data Science and AI Faculty of IT Monash University Melbourne Australia Faculty of Information Technology Monash University Clayton Campus 20 Exhibition Walk Wellington Road ClaytonVIC3800 Australia

Machine Learning (ML) and in particular Deep Learning (DL) methods nowadays are increasingly replacing traditional methods in many different domains involved with important decision making activities. Sophisticated DL techniques tailor-made for specific tasks such as image recognition, signal processing, or speech analysis are being introduced at a fast pace with many improvements. However, for the domain of time series forecasting, the current state in the ML community is perhaps where other domains such as Natural Language Processing and Computer Vision were at several years ago. The field of forecasting has mainly been fostered by statisticians/econometricians;consequently the related concepts are not the mainstream knowledge among general ML practitioners. The different forms of non-stationarities associated with time series challenge the capabilities of data-driven ML models. Nevertheless, recent trends in the domain have demonstrated that with the availability of massive amounts of time series, ML and DL techniques are quite competent in time series forecasting, when related pitfalls are properly handled. Therefore, in this work we provide a tutorial-like compilation of the details of one of the most important steps in the overall forecasting process, namely the evaluation. This way, we intend to impart the information associated with forecast evaluation to fit the context of ML, as means of bridging the knowledge gap between traditional methods of forecasting and current state-of-the-art ML techniques. We elaborate the details of the different problematic characteristics of time series such as non-normalities and non-stationarities and how they are associated with common pitfalls in forecast evaluation. Best practices in forecast evaluation are outlined with respect to the different steps such as data partitioning, error calculation, statistical testing, and others. Further guidelines are also provided along selecting valid and suitable error measures dependi

关键词： Time series

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Implementation of Artificial Intelligence In Engineering to Achieve Sustainable Development Goals (SDGs)

Implementation of Artificial Intelligence In Engineering to ...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Muhammad D. Zakaria Azwa Abdul Aziz Azrul Amri Jamal Wan Azani Mustafa Laith H. Alzubaidi A.H. A. Hussein Faculty of Informatics & Computing Universiti Sultan Zainal Abidin (UniSZA) Tembila Campus Terengganu Malaysia Data Science & Analytics (DASA) Special Interest Group (SIG) Universiti Sultan Zainal Abidin (UniSZA) Gong Badak Terengganu Malaysia Centre of Excellence Advanced Computing (CoE AdvComp) Universiti Malaysia Perlis Arau Perlis Malaysia College of Pharmacy Ahl Al Bayt University Karbala Iraq Faculty of Engineering The Islamic University Najaf Iraq

Artificial Intelligence (AI) is implicit to revolutionize the engineering industry by automating tasks, minimizing costs, improving quality, and creating new opportunities for innovation. The article explores the implementation of AI in engineering as a means to achieve the Sustainable Development Goals (SDGs) set forth by the United Nations. AI technologies hold significant potential in optimizing resource utilization, enabling data-driven decision-making, and fostering innovation in various engineering domains. This study provides an overview of AI applications highlighting their alignment with specific SDGs. It examines the advantages, challenges, and ethical considerations connected to AI integration in engineering practices. Moreover, the article provides recommendations for policymakers, researchers, and practitioners to ensure responsible and inclusive AI development. By harnessing the transformative power of AI in engineering, we can increase the process towards SDGs, creating a more sustainable and prosperous future for all.

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Research on Optimization and Scheduling of MPTCP data Networks in GNSS Network Reference Stations

Research on Optimization and Scheduling of MPTCP Data Networ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Fengqi Hao Yuxin Wang Hoiio Kong Dexin Ma Xiyuan Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Data Science City University of Macau Macau China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Communication College Qingdao Agricultural University Qingdao China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The Global Navigation Satellite System Network Reference Stations (GNSS-NRS) are pivotal in modern po-sitioning and navigation applications. However, GNSS-NRS face significant communication challenges, including instability during rapid user movement and inconsistent coverage by different operators, which often results in data transmission interruptions and delays. Moreover, the conventional Multipath Transmission Control Protocol (MPTCP) scheduling strategy does not accommodate the diversity of the data types managed by GNSS-NRS. This default approach treats all data uniformly, even under deteriorating network conditions. To overcome these limitations, this paper introduces a ‘Multiple Reception’ strategy based on MPTCP that ensures timely and reliable GNSS data transmission. This novel strategy differentiates between observational and control data based on their real-time criticality and applies tailored transmission techniques. Under adverse conditions, it utilizes multiple transmission paths and selectively receives control data, thus prioritizing the transfer of critical information. We conducted simulation experiments using MiniNet to compare the ‘Multiple Reception’ method against conventional and redundant strategies, demonstrating that it significantly reduces data loss rates in GNSS-NRS.

关键词： Global navigation satellite system Protocols Real-time systems Delays MPTCP Reliability data communication Kernel Optimization Faces

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Artificial Intelligence-based Service Chains Scheduling for Medical Emergency in Healthcare 7

Artificial Intelligence-based Service Chains Scheduling for ...

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7th International Conference on Contemporary computing and Informatics, IC3I 2024

作者： Soni, Mukesh Rusho, Maher Ali Byeon, Haewon Alghamdi, Azzah Hannan, Shaikh Abdul Dave, Parth Rameshchandra Dr. D. Y. Patil School of Science & Technology Tathawade Dr. D. Y. Patil Vidyapeeth Marashtra Pune India University of Colorado Department of Lockheed Martin Engineering Management BoulderCO80309 United States Inje University Department of Ai and Software & Medical Big Data Research Centre Gimhae50834 Korea Republic of Imam Abdalrhman Bin Faisal University College of Computer Science and Information Technology Computer Information Systems Department Khobar Saudi Arabia AlBaha University Faculty of Computing and Information AlBaha Saudi Arabia L. D. College of Engineering Department of Computer Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798350350067

This study analyses the weekly scheduling challenge of health and medical information network service function chains to meet medical emergency categorization and grading needs. Emergency services are divided into four sorts by urgency and service area, with priorities and weights. The health and medical information network architecture, service model, and operation method are built accordingly. To reduce weighted completion time, a service function chain scheduling model is created. The function chain scheduling scheme is solved by matching game and Q -learning reinforcement learning algorithms for different issue scales. Finally, simulation studies demonstrate that this approach can accomplish graded medical emergency service, build health and medical information networks, and progress smart healthcare. © 2024 IEEE.

关键词： Electronic health record

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Machine learning-based classification of medication adherence among patients with noncommunicable diseases

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Informatics in Medicine Unlocked 2025年 52卷

作者： Kanyongo, Wellington Ezugwu, Absalom E. Moyo, Tsitsi Fonou Dombeu, Jean Vincent Department of Computer Science Faculty of Science Engineering Bindura University of Science Education Bindura Zimbabwe Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom 2520 South Africa Cimas Medical Aid Society Borrowdale Office Park Borrowdale Road P. O Box 1243 Harare Zimbabwe School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Pietermaritzburg Campus Pietermaritzburg 3201 South Africa

Non-adherence to medication among individuals with non-communicable diseases (NCDs) leads to increased morbidity, mortality, and healthcare costs. The integration of electronic drug prescription and dispensation systems enables comprehensive analysis of medication adherence (MA). Patient-level and medical claims data for 8141 diabetic and hypertensive patients in Harare, Zimbabwe, were analysed. Non-adherence was defined as medication refills falling below 75 % of the intended 12 monthly claims, while adherence required at least 75 % of the refills. Classification employed multiple machine learning algorithms, including SVM, KNN, DT, Naïve Bayes, DNN, LR, and RF in Python 3.11.3. Significant variables for MA were identified through the Random Forest (RF) feature importance mechanism and the information gain technique. These included the annual quantity of medical supplies, annual claim amount, patient age, wellness program subscription, medical aid cover, contribution towards medical aid cover, comorbidity, diagnosis, hospital cover type, complications development, gender, and medical aid scheme. The total units of medical supplies dispensed annually emerged as the most significant predictor of MA. Considering the 8-feature subset, which consistently produced the most robust machine learning models, the classification accuracy of the ML classifiers ranged from 84.9 % to 88.2 %, while the AUC values varied between 0.857 and 0.934. RF, an ensemble learning technique, was the most robust classifier, achieving 88.2 % accuracy, an AUC of 0.935, and superior precision, recall, and F1-score. This model shows promise as a prognostic tool for enhancing MA, aiding in identifying adherence levels among patients. These findings contribute to addressing disparities in medication refilling and adherence rates among patients with NCDs. The ML model holds potential for the development of intelligent MA and intervention applications to improve patient adherence to medication in the

关键词： Chronic disease Decision trees Deep neural network Diabetes Hypertension k-NN Logistic regression Machine learning Medication adherence Naïve bayes Non-communicable diseases Random forest SVM

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Topic-based Multi-layer Knowledge Filtering for Emotion Recognition in Conversation

Topic-based Multi-layer Knowledge Filtering for Emotion Reco...

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International Conference on Computer Supported Cooperative Work in Design

作者： Xiaoyang Li Zhenyu Yang Yiwen Li Zhijun Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Emotion recognition in conversation (ERC) is a prominent research topic in natural language processing, widely applicable in various scenarios. However, accessing external commonsense knowledge and related dialogue topics, along with the complexity of fusing utterances, presents significant challenges. In this paper, we introduce a Topic-based Multilayer Knowledge Filtering (TMKF) model to enhance dialog emotion recognition accuracy. TMKF acquires commonsense knowledge for each utterance and employs two Variational Autoencoder (VAE) modules to extract global and speaker-level dialogue topics. We utilize Global Knowledge Filtering (GKF) and Local Knowledge Filtering (LKF) to obtain topic-specific knowledge representations after filtering commonsense information through global and local topic hierarchies. Subsequently, we leverage relational graphs to convolve commonsense knowledge with utterances, resulting in knowledge-constrained utterance representations for classification. Our proposed model is evaluated through extensive experiments on four widely used benchmark datasets for conversational emotion recognition. The results underscore the effectiveness of TMKF, which significantly outperforms other methods in standard metric evaluations.

关键词： Measurement Emotion recognition Filtering Convolution Oral communication Benchmark testing Nonhomogeneous media

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CAFI: News Recommendation with Candidate Perception of Fine-Grained Interaction Information

CAFI: News Recommendation with Candidate Perception of Fine-...

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International Conference on Computer Supported Cooperative Work in Design

作者： Yiwen Li Zhenyu Yang Xiaoyang Li Zhijun Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The most critical task in personalized news recommendation is to perform exact matching between candidate news and users’ interests. Existing news recommendation methods usually model users’ interests from historical clicked news items without considering candidate news, which makes it difficult to precisely match candidate news with users’ interests. Moreover, it is challenging to distinguish similar news when forming a vector representation. Simply adding candidate news to the user interest modeling process without considering word-level information does not provide sufficient discrimination for recommending appropriate news. In this paper, we propose a news recommendation model with candidate-aware fine-grained interaction information (CAFI). In our approach, we propose a fine-grained interaction module that matches candidate news items and text fragments of each historical news item at each semantic granularity to achieve interaction between the two at the word level and help similar historical news form more specific and accurate representations. In addition, our proposed gated self-attention mechanism utilizes the candidate news features as channel moderation gates to implement the process of filtering out information that is irrelevant to the candidate news for learning user interest representations, thereby better matching the candidate news to specific user interests. Experiments conducted on the large real-world Microsoft News dataset (MIND) show that our model significantly outperforms the previously developed models in terms of all metrics. Our code is published at the following URL: https://***/liyiweneven/CAFI.

关键词： Uniform resource locators Measurement Codes Filtering Federated learning Semantics Focusing

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