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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Wijaya Barus, Effrina Yuli Zarlis, Muhammad Situmorang, Zakarias It University of North Sumatra Faculty of Computer Science Medan Indonesia Bina Nusantara University Dept. of Information Systems Management Jakarta Indonesia University of Katholik Santo Thomas Faculty of Computer Science and It Medan Indonesia

ISBN: (纸本)9798350367492

This study explores the application of Fuzzy Tsukamoto and Simple Additive Weighting (SAW) methods to enhance scholarship admissions. Implemented using Python, the system evaluates multiple criteria including GPA, semester, parental income, parental allowance, age, and certificates by assigning appropriate weight values to each variable. The Fuzzy Tsukamoto method, known for its capability to handle uncertainty, combined with the SAW method for its straightforward additive approach, is employed to streamline the selection process and recommend suitable scholarship types for students. The findings demonstrate that integrating these methods significantly improves the accuracy and efficiency of scholarship selection, providing a robust tool for decision-making in academic funding. © 2024 IEEE.

关键词： Fuzzy logic

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Cloud Video System Availability Assessment Using Markov and Semi-Markov Models 16

Cloud Video System Availability Assessment Using Markov and ...

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16th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and computer Engineering, TCSET 2022

作者： Ivanchenko, Oleg Kharchenko, Vyacheslav Udovyk, Iryna Ponochovnyi, Yuriy Dnipro University of Technology Dept. of Computer Systems and Software Dnipro Ukraine National Aerospace University Dept. of Computer Systems Networks and Cybersecurity Kharkiv Aviation Institute Kharkiv Ukraine Dept. of Information Systems and Technologies Poltava Ukraine

ISBN: (纸本)9781665468619

Comparative analysis of Markovian and Semi-Markovian modelling for cloud video system availability is performed considering its architecture and dependability characteristics of various components. Initial data for modelling is based on review of research and operation data of cloud systems. The results of modelling provide decision making related to choice of technique of modelling and availability assessment. Besides, they contributes to ensuring the high availability of cloud systems and can be used by developers in order to address the issues into DevOps engineering area. © 2022 IEEE.

关键词： Availability

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Technique for Evaluating the Effectiveness of the information Security Department 16

Technique for Evaluating the Effectiveness of the Informatio...

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16th IEEE International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering, APEIE 2023

作者： Belov, Viktor M. Pestunova, Tamara M. Belova, Nadezhda V. Kosov, Dmitry L. Information Security Dept. Novosibirsk State Technical University Novosibirsk Russia Computer Systems Dept. Novosibirsk State University Novosibirsk Russia Economics and Planning Dept. PJSC Rosneft Oil Company Moscow Russia

ISBN: (纸本)9798350330885

This article proposes a technique that establishes the procedure for evaluating the level of efficiency of the information security department (an employee performing information security functions). The technique uses performance evaluation criteria based on the apparatus of fuzzy logic, the composition of fuzzy relations. The technique describes the procedure for evaluating the effectiveness of the information security department (information security officer) during audits in the area of "Organization and state of work on information protection", self-assessment of the effectiveness of work. The method of assessing the level of efficiency consists in presenting with the help of a set of measurements (both at the quantitative and qualitative level) the features collected to build a classification of the effectiveness of the information security department (information security officer). Based on a set of measurements of signs, the decision-maker must determine (classify) the effectiveness of work using the criteria for assessing the quality of their work. In the future, the methodology can be expanded for additional purposes of predicting the level of security of informatization objects. © 2023 IEEE.

关键词： Classification (of information)

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Children and information Access: Fostering a Sense of Belonging

Children and Information Access: Fostering a Sense of Belong...

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Joint Workshops of IUI 2023: HAI-GEN - Human-AI Co-Creation with Generative Models, ITAH - Workshop on Interactive Technologies for AI in Healthcare, MILC - Workshop on Intelligent Music Interfaces for Listening and Creation, SHAI - Workshop on Designing for Safety in Human-AI Interactions, SketchRec - Workshop on Sketch Recognition and SOCIALIZE - Social and Cultural Integration with Personalized Interfaces

作者： Pera, Maria Soledad Wright, Katherine Landau Kennington, Casey Fails, Jerry Alan Web Information Systems TU Delft Netherlands Dept. of Literacy Language and Culture Boise State University United States Dept. of Computer Science Boise State University United States

In this vision paper, we spotlight children as often underserved users in the digital ecosystem. With online search as a use case, we discuss the need for a multi-perspective approach to designing interactive interfaces and technologies that can enable information access systems to better respond to children's requirements while respecting the cultural and social norms impacting their upbringing. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： information retrieval

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A Comprehensive Threat Model for Autonomous Vehicle Communication Security 6

A Comprehensive Threat Model for Autonomous Vehicle Communic...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Meegammana, Niranjan W. Fernando, Harinda Shilpa Sayura Foundation Cyber Security Research Lab Kandy Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

The rapid advancement of autonomous vehicles (AVs) promises to enhance transportation efficiency, reliability, and safety. However, these systems, particularly multi-channel vehicular communications, are vulnerable to cyber attacks that threaten authenticity, authorization, confidentiality, integrity, availability, and non-repudiation. The 2014 Chrysler Jeep hack highlights the urgent need for robust security measures in vehic-ular communications. This study utilizes the STRIDE, DREAD, and MITRE ATT&CK security frameworks to develop a comprehensive threat model that identifies and assesses critical threats and risks to AV communications. By detailing specific attack vectors and their multidimensional impacts and criticality, the study establishes a baseline of threats for communication security in vehicular networks. It produces visual threat maps, impact graphs of various attacks, and a color-coded threat grid to guide stakeholders in developing effective cyber security measures for protecting AV communications, thereby help stakeholders to improve the reliability and safety of AV navigation for fostering growth in the AV industry. © 2024 IEEE.

关键词： Cyber attacks

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SiloFuse: Cross-silo Synthetic Data Generation with Latent Tabular Diffusion Models 40

SiloFuse: Cross-silo Synthetic Data Generation with Latent T...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Shankar, Aditya Brouwer, Hans Hai, Rihan Chen, Lydia Distributed Systems TU Delft Delft Netherlands BlueGen.ai The Hague Netherlands Web Information Systems TU Delft Delft Netherlands University of Neuchatel/TU Delft Dept. of Computer Science Neuchatel Switzerland

ISBN: (纸本)9798350317152

Synthetic tabular data is crucial for sharing and augmenting data across silos, especially for enterprises with proprietary data. However, existing synthesizers are designed for centrally stored data. Hence, they struggle with real-world scenarios where features are distributed across multiple silos, necessitating on-premise data storage. We introduce SiloFuse, a novel generative framework for high-quality synthesis from cross-silo tabular data. To ensure privacy, SiloFuse utilizes a distributed latent tabular diffusion architecture. Through autoencoders, latent representations are learned for each client's features, masking their actual values. We employ stacked dis-tributed training to improve communication efficiency, reducing the number of rounds to a single step. Under SiloFuse, we prove the impossibility of data reconstruction for vertically partitioned synthesis and quantify privacy risks through three attacks using our benchmark framework. Experimental results on nine datasets showcase SiloFuse's competence against centralized diffusion-based synthesizers. Notably, SiloFuse achieves 43.8 and 29.8 higher percentage points over GANs in resemblance and utility. Experiments on communication show stacked training's fixed cost compared to the growing costs of end-to-end training as the number of training iterations increases. Additionally, SiloFuse proves robust to feature permutations and varying numbers of clients. © 2024 IEEE.

关键词： Data privacy

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Iterative Detection Schemes for Uplink Highly Loaded User-Centric Hybrid Beamforming Based Frequency Selective Massive MIMO Networks

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IEEE Transactions on Communications 2024年

作者： Karatas, Metehan Guvensen, Gokhan M. Dept. of Information Technology Division of Computer Systems in Uppsala University Sweden Ankara Turkey

—In cell-free (CF) networks, access points (APs) jointly serve the user equipments (UEs). In user-centric (UC) variant, only a handful of APs serve a particular UE, namely the ones with the best channels. In this paper, we propose a hybrid beamforming aided uplink receiver scheme for highly loaded UC massive MIMO (mMIMO) networks, operating in millimeter-wave(mmWave) frequencies, employing single carrier modulation under frequency selective channels. Every AP employs its own analog beamformer (AB) called the modified eigen-beamformer (MEB), which promotes a trend of fairness. In the digital domain, two detectors are proposed;Cell-Free Iterative Detector (CFI-D) and User-Centric Iterative Subset-Detector (UCIS-D). CFI-D introduces the iterative block decision feedback equalization (IB-DFE) method for CF networks. UCIS-D is an alternative to CFI-D. UCIS-D is UC, has low computational complexity (CCP) and is scalable. UCIS-D relies on parallel interference cancellation (PIC) aided IB-DFE. Interfering UEs are divided into three sets. Weak interferers are processed with PIC, strong interferers with IB-DFE which considers the residual interference from PIC. Weakest interferers are ignored. Both PIC and IB-DFE utilize the soft decisions from previous iterations. Simulation results show that these receivers can handle ultra high loads in terms of bit-error-rate (BER) and achievable information rate (AIR). © 2024 IEEE.

关键词： Bit error rate

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Detecting and Mitigating Adversarial Perturbations to Improve E-Commerce Security 11

Detecting and Mitigating Adversarial Perturbations to Improv...

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11th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2024

作者： Tariq, Usman Jaafar, Fehmi Malik, Yasir Prince Sattam Bin Abdulaziz University College of Business Administration Dept. of Management Information Systems Saudi Arabia Université du Québec à Chicoutimi Dept. d'Informatique et de mathématique Canada Bishop's University Dept. of Computer Science Canada

ISBN: (纸本)9798350367300

E-commerce platforms face the critical challenge of adversary events, including fraudulent transactions and fake reviews, which can lead to significant financial and reputational damage. Addressing this, our research introduces a hybrid Deep Learning model, tailored for the detection of such adversarial activities. This innovative approach leverages spatial and sequential data processing capabilities, enhancing the identification of subtle adversarial manipulations across diverse e-commerce contexts. Our findings indicate a high detection rate of 93 percent for adversarial attacks, with precision, recall, and Matthews Correlation Coefficient metrics underscoring the model's efficacy. This work significantly contributes to e-commerce security by advancing the robustness of detection systems against a spectrum of adversarial threats, including account takeovers and deceptive reviews. While demonstrating a notable improvement over existing methods, our research also acknowledges the potential for evasion by sophisticated attacks, highlighting areas for future work in enhancing model resilience. This balance of innovation and critical insight provides a solid foundation for further advancements in the field of e-commerce security © 2024 IEEE.

关键词： Adversarial machine learning

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Increase Patients' Survivability During Emergency Care Using Blockchain-Based Digital Twin Technology 21

Increase Patients' Survivability During Emergency Care Using...

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21st IEEE/ACIS International Conference on Software Engineering Research, Management and Applications, SERA 2023

作者： Hasavari, Shirin Song, Yeong Tae Lawner, Benjamin School of Business & Management Morgan State University Dept. Information Science & Systems Baltimore United States Towson University Dept. Computer& Information Science Towson United States School of Medicine University of Maryland Dept. Emergency Medicine Baltimore United States

ISBN: (纸本)9798350345889

Effective communication of patient clinical and care information between pre-hospital services and Emergency Departments (EDs) is crucial for the rapid and effective treatment of patients, potentially saving lives. Recent research indicates a frequent lack of patient data metrics, such as vital signs, which represents a potential limitation in the comprehensiveness of handoffs received by ED physicians from pre-hospital providers. To address this challenge, we propose a solution that utilizes a patient's digital twin and permissioned blockchain technology to ensure complete, real-time, secure, and shareable access to patients' vital signs and other data metrics for Emergency Departments. This solution seeks to answer the following questions: Which data model for the digital twin best represents the patient during transport, meeting the requirements of the receiving facility? How can patient digital twin data, including the history of clinical information and patient care, be shared in real-time with the receiving facility? How can EHR-compliant digital twin data be produced to satisfy the reconciliation between the Patient Digital Twin (PDT) and the Electronic Health Record (EHR)? Method: We conducted a comprehensive literature review and a series of interviews with both pre-hospital and hospital care providers to identify the problem and develop a model based on it. To address the second question, we designed an architecture that includes all parties in the care team, during and after the transport. For the third question, we explored NEMSIS data exchange standards and a Natural Language Processing (NLP) Module. Objective: Our goal is to improve the survivability of patients in emergency care through secure, effective, and real-time sharing of patient metric data between Emergency Medical Services (EMS) and Emergency Departments or Trauma Centers. This approach also aims to reduce the waiting time for patients upon arrival at the receiving facility. © 2023 IEEE.

关键词： Patient treatment

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Arabic Sentiment Analysis of Mobile Banking Services Reviews 10

Arabic Sentiment Analysis of Mobile Banking Services Reviews

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10th International Conference on Social Networks Analysis, Management and Security, SNAMS 2023

作者： Khabour, Safaa M. Mustafa, Dheya Althebyan, Qutaibah Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Al Ain University Dept. of Cybersecurity Al Ain United Arab Emirates Jordan University of Science and Technology Irbid Jordan

ISBN: (纸本)9798350318906

Online customer reviews have developed into a significant source of information about a business's performance. Due to shifting consumer expectations and growing internet penetration, the Middle East, especially Jordan, is seeing an increase in the usage of online banking. This propensity toward digital transition has been accelerated by the COVID-19 pandemic. Mobile banking service companies aim to effectively use customers' feedback to identify areas for improvement in customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on mobile banking reviews in Arabic, exploiting machine learning, natural language processing, and resampling methods to obtain subjective feedback, determine polarity, and identify customers' feelings in the banking domain. This work walks the reader through detailed steps of cleaning and preprocessing a manually annotated dataset of online banking reviews in Arabic, followed by features extraction and modeling, dataset splitting, and balancing training data. Finally, we examined classification algorithms including Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Random Forest (RF). Ensemble hard and soft voting ML methods were also evaluated. We achieved a maximum accuracy of about 94% using the LR classifier and a maximum precision of 94% using the LR and hard voting methods. Our methodology will enable banks to acquire insights on how to develop their online presence and meet the demands of customers and stakeholders. © 2023 IEEE.

关键词： Sentiment analysis

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