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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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Multi-Task Bias-Variance Trade-Off Through Functional Constraints 48

Multi-Task Bias-Variance Trade-Off Through Functional Constr...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Cervino, Juan Bazerque, Juan Andrés Calvo-Fullana, Miguel Ribeiro, Alejandro University of Pennsylvania Dept. of Electrical and Systems Engineering Philadelphia United States University of Pittsburgh Dept. of Electrical and Computer Engineering Pittsburgh United States Universitat Pompeu Fabra Dept. of Information and Communication Technologies Barcelona Spain

ISBN: (纸本)9781728163277

Multi-task learning aims to acquire a set of functions, either regressors or classifiers, that perform well for diverse tasks. At its core, the idea behind multi-task learning is to exploit the intrinsic similarity across data sources to aid in the learning process for each individual domain. In this paper we draw intuition from the two extreme learning scenarios - a single function for all tasks, and a task-specific function that ignores the other tasks dependencies - to propose a bias-variance trade-off. To control the relationship between the variance (given by the number of i.i.d. samples), and the bias (coming from data from other task), we introduce a constrained learning formulation that enforces domain specific solutions to be close to a central function. This problem is solved in the dual domain, for which we propose a stochastic primal-dual algorithm. Experimental results for a multi-domain classification problem with real data show that the proposed procedure outperforms both the task specific, as well as the single classifiers. © 2023 IEEE.

关键词： Constrained Learning Multi-task Learning

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CapDC: A new capuchin search based algorithm for efficient data collection in Wireless Sensor Networks 20

CapDC: A new capuchin search based algorithm for efficient d...

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20th International Multi-Conference on systems, Signals and Devices, SSD 2023

作者： Pravija Raj, P.V. Al Ali, Amel Khedr, Ahmed M. University of Sharjah Department of Computer Science Sharjah United Arab Emirates University of Sharjah Dept. of Information Systems Sharjah United Arab Emirates

ISBN: (纸本)9798350332568

Despite the widespread applications of Wireless Sensor Network (WSN) in different domains, several challenges such as energy, throughput and load balancing that substantially impact the network's lifetime exist. Devising efficient clustering and routing strategy is treated as a viable solution to these challenges. Previous research has proven that meta-heuristic algorithms are successful in providing an optimal or near-optimal outcome with limited computational resources in an acceptable time period. In view of this, we introduce a new capuchin search based algorithm for efficient data collection in WSNs (CapDC). CapDC is a hybrid Capuchin Search (CapSA) and Ant Colony optimization (ACO) algorithms, which is an integration of CapSA and ACO. CapSA identifies the best cluster heads while ACO accomplishes energy-efficient routing, which reduces energy usage and enhances WSN lifetime. The results reveal that the CapDC improves residual energy usage, throughput and WSN lifetime significantly. © 2023 IEEE.

关键词： Wireless sensor networks

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Arabic Sentiment Analysis of Food Delivery Services Reviews

Arabic Sentiment Analysis of Food Delivery Services Reviews

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Mustafa, Dheya Khabour, Safaa M. Shatnawi, Ahmed S. Taqieddin, Eyad The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan Jordan University of Science & Technology Dept. of Software Engineering Irbid21110 Jordan School of Computing and Informatics Dept. of Cyber Security Al-Hussein Technical University Amman Jordan

ISBN: (纸本)9798350335590

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of 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 FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier. © 2023 IEEE.

关键词： Sentiment analysis

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iSFA: Intelligent SF Allocation Approach for LoRa-Based Mobile and Static End Devices

iSFA: Intelligent SF Allocation Approach for LoRa-Based Mobi...

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IEEE Conference on Wireless Communications and Networking

作者： Anakhi Hazarika Nikumani Choudhury Dept. of Electrical & Electronics Engineering India Dept. of Computer Science & Information Systems India

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

LoRaWAN (Long Range Wide Area Network) is a low-power, wide-area wireless communication protocol designed specifically for the Internet of Things (IoT) and machine-to-machine (M2M) applications that enable long-range, bidirectional communication between low-power devices. Lo-RaWAN employs Adaptive Data Rate (ADR) technology to dynamically adjust the data rate for each device based on its signal quality and distance from the gateway. ADR enables improved network performance, extends device battery life, and simplifies network management, making LoRaWAN suitable for various IoT deployments. However, the end devices' inefficient utilization of radio resources (e.g., spreading factor and transmission power) significantly degrades network performance, device battery life, and adaptability to changing network conditions. Machine Learning (ML) algorithms analyze and optimize the real-time network conditions to enhance network performance. This work aims to develop an ML-based approach that adaptively selects the most suitable Spreading Factor (SF) for end devices (ED). Two independent ML algorithms such as K-means and Reinforcement Learning (RL) have been applied to EDs and Gateways, respectively, to dynamically allocate SF for both static and mobile EDs. Through simulations, the performance of the proposed mechanism is analyzed in terms of packet success rate, convergence time, energy consumption, latency, and throughput.

关键词： Performance evaluation Wireless communication Wide area networks Machine learning algorithms Reinforcement learning Logic gates Throughput

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Scalable, Cost Effective IoT Based Medical Oxygen Monitoring System for Resource Constrained Hospital Environment 6

Scalable, Cost Effective IoT Based Medical Oxygen Monitoring...

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6th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022

作者： Rodrigues, Joel Chandra Shekar, R.K. School of Engineering Dept. of Computer Science and Engg. Bengaluru India BITS Pilani Pilani Campus Dept. of Computer Science and Information Systems India

ISBN: (数字)9781665487160

ISBN: (纸本)9781665487160

Oxygen therapy is one of the critical treatments employed in epidemics, pandemics, and natural calamities. Recent covid pandemic worldwide witnessed many deaths due to improper management, delayed delivery, and wastage of medical oxygen. Therefore, efficient utilization of available oxygen is very important. To monitor and manage oxygen, several hospitals employ IoT-based systems. Scalability is an essential feature in such monitoring systems in order to cater to the needs of a sudden surge in the number of patients requiring oxygen. The most commonly employed technique to monitor and manage an oxygen cylinder uses a pressure sensor where scaling up is an issue. Therefore, in this paper, a scalable solution that efficiently measures and monitors the available oxygen in the cylinder is proposed. The approach measures oxygen level using a weight sensor module and raises alerts during critical conditions such as low oxygen level and blockage or leakage of oxygen. The proposed system is a cost-effective, plug-and-play system that aids rapid deployment thereby providing timely care to the patients. Also, it does not require any change in the existing infrastructure making it suitable for a resource-constrained environment. The proposed system supports a web-based dashboard and mobile app that can be remotely accessed. © 2022 IEEE.

关键词： Oxygen

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