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International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA)

作者： Ansh Mishra Jia Qi Yip Eng Siong Chng Department of Physics IIT Delhi New Delhi India Interdisciplinary Graduate Programme Nanyang Technological University (NTU) Singapore School of Computer Science and Engineering Nanyang Technological University (NTU) Singapore

ISBN: (数字)9798331520311

ISBN: (纸本)9798331520328

Heart auscultation remains a valuable approach for early detection of valvular heart disease, but its efficacy depends on physician skill. The rise of telehealth services necessitates consideration of heart sound transmission and classification through compressed formats. While neural audio codecs offer impressive compression rates, they may introduce distortions that could impact classification accuracy. We propose a novel approach to perform heart sound classification directly on the continuous representations of the codec, rather than on reconstructed audio. Our method demonstrates good performance on the heart sound classification task while leveraging the encoder of the audio codec, potentially improving robustness and efficiency in telehealth applications. Using the Descript Audio Codec encoder combined with a modified M5 convolutional neural network, our approach achieves a Classification Error Rate of 0.78 on the Yaseen Dataset, outperforming models trained on transmitted audio and comparing favorably with state-of-the-art methods. This performance, coupled with the method's inherent robustness to transmission artifacts, highlights its potential for improving remote cardiac diagnostics in telehealth settings.

关键词： Heart Codecs Error analysis Telemedicine Computational modeling Medical services Robustness Real-time systems Time-domain analysis Informatics

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Model Predictive Control of Five-Level Current Source Converter with Optimized Weighting Factors 3

Model Predictive Control of Five-Level Current Source Conver...

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3rd IEEE International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023

作者： Chen, Muyu Yuan, Zhige Faraji, Faramarz Ghias, Amer M. Y. M. Cha, Honnyong Nicholas, Vun Chan Hua School of Electrical and Electronic Engineering Nanyang Technological University Collab. Initiative Interdisciplinary Graduate Programme Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Energy Engineering Kyungpook National University Daegu Korea Republic of School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350324754

This paper presents the finite control set model predictive control (FCS-MPC) with improved control objectives using optimized weighting factors. The Pareto curve obtained from the Particle Swarm optimizes the weighting factors. The optimal weighting factors are then used in the FCS-MPC algorithm to enhance the control objective performance of a three-phase five-level current source converter (5L-CSC). In 5L-CSC, two identical three-level CSC (3L-CSC) modules are connected in parallel with one common current source supply to provide uniform five-level output current waveform. This CSC requires a current balance to operate the converter safely. Therefore, the control objective of the MPC is to control the output voltage and balance the internal currents. The weighting factors are optimized based on the converter's total harmonics distortion (THD) performance and inductor current ripple. The performance enhancement of 5L-CSC, utilizing optimal weighting factors, is confirmed through MATLAB simulations when compared to the model employing arbitrarily assigned weighting factors. The simulation outcomes additionally indicate that the integration of Particle Swarm Optimization (PSO) with FCS-MPC exhibits resilience across various condition settings. © 2023 IEEE.

关键词： Model predictive control

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A Sensor Network-Based Data Stream Clustering Algorithm for Pervasive Computing

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Chinese Journal of Electronics 2023年第2期18卷 255-258页

作者： Ning Ye Ruchuan Wang Institute of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Department of Information Science Nanjing College for Population Programme Management Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Pervasive computing is characterized by the integration with communication and digital media technology embedded to the people's living space. People can transparently access the digital service anywhere. Wireless sensor networks are a novel technology and have broad application prospects. With the maturity of the wireless sensor networks technology, pervasive computing is becoming a reality. It is become a new technology challenge to process the data streams of sensor networks for pervasive environment efficiently and to find useful knowledge in these data streams. A k-means data stream clustering algorithm based on sensor networks is presented. The main idea of this algorithm is to select the initial centroids according to the aggregation gain of the node, then to cluster the data stream using the average square error. The experimental results are showed that this algorithm is effective and efficient.

关键词： Pervasive computing Performance evaluation Knowledge engineering Wireless sensor networks Sociology Clustering algorithms Media

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On the Privacy Preservation and Secure Communication in Fog-Based Mobile Crowdsensing

SSRN

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SSRN 2024年

作者： Ogundoyin, Sunday Oyinlola Kamil, Ismaila Adeniyi Ojedokun, Isaac Adewale Babalola, John Oluwaseun Nyangaresi, Vincent Omollo Electrical and Electronic Engineering Programme Bowen University Iwo Nigeria Department of Electrical and Electronic Engineering University of Ibadan Ibadan Nigeria Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and Technology Bando 40601 Kenya

The rapid growth of mobile crowdsensing (MCS) presents significant opportunities for large-scale data collection through the collaborative use of smart devices. However, the increasing volume of sensitive data generated by MCS services poses critical challenges in terms of privacy and security, particularly latency-sensitive applications. While some solutions exist, many still lack the scalability, robustness, efficiency, and privacy preservation required by MCS services. This paper proposes a privacy-preserving and secure fog-based MCS (FB-MCS) scheme, comprising a four-tier fog computing architecture that incorporates dynamic lower-tier fog (LTF) and static upper-tier fog (UTF) systems to enable efficient data aggregation, privacy protection, and secure communication. The proposed scheme utilizes secret sharing and homomorphic MAC techniques for efficient and verifiable data aggregation over multi-dimensional data, allowing a data requester to verify the accuracy and integrity of the aggregated results. The scheme employs a lightweight elliptic curve cryptography (ECC) to ensure secure authentication without overburdening resource-constrained devices. An adaptive fog node selection strategy, based on trust and mobility, is proposed for reliable real-time task allocation. Extensive security analysis demonstrates that the scheme not only guarantees privacy preservation, integrity of aggregation results, strong anonymity, un-linkability, traceability, and resistance to well-known attacks but also achieves data confidentiality and unforgeability in the random oracle model under Type I and Type II adversaries, assuming the Computational Diffie-Hellman Problem (CDHP) and Discrete Logarithm Problem (DLP) are intractable. Moreover, performance assessments indicate that the proposed scheme surpasses previous advanced solutions, achieving a 48\% - 280\% improvement in efficiency. © 2024, The Authors. All rights reserved.

关键词： Fog computing

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CAUSAL DISCOVERY AND CLASSIFICATION USING LEMPEL-ZIV COMPLEXITY

arXiv

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arXiv 2024年

作者： Dhruthi Nagaraj, Nithin Harikrishnan, N.B. Department of Computer Science and Information Systems BITS Pilani K K Birla Goa Campus Goa 403726 India Complex Systems Programme National Institute of Advanced Studies Indian Institute of Science Campus Karnataka Bengaluru560012 India

Inferring causal relationships in the decision-making processes of machine learning algorithms is a crucial step toward achieving explainable Artificial Intelligence (AI). In this research, we introduce a novel causality measure and a distance metric derived from Lempel-Ziv (LZ) complexity. We explore how the proposed causality measure can be used in decision trees by enabling splits based on features that most strongly cause the outcome. We further evaluate the effectiveness of the causality-based decision tree and the distance-based decision tree in comparison to a traditional decision tree using Gini impurity. While the proposed methods demonstrate comparable classification performance overall, the causality-based decision tree significantly outperforms both the distance-based decision tree and the Gini-based decision tree on datasets generated from causal models. This result indicates that the proposed approach can capture insights beyond those of classical decision trees, especially in causally structured data. Based on the features used in the LZ causal measure based decision tree, we introduce a causal strength for each features in the dataset so as to infer the predominant causal variables for the occurrence of the outcome. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Joint Task Decomposition and Allocation in Tactile Internet FiWi Enhanced Mobile Networks

Joint Task Decomposition and Allocation in Tactile Internet ...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Amin Ebrahimzadeh Martin Maier Roch H. Glitho CIISE Concordia University Montreal Canada Optical Zeitgeist Laboratory INRS Montreal Canada Computer Science Programme University of Western Cape Capetown South Africa

In this paper, we shed light on how and to what extent formation of robotic teams may help reduce the average task completion time in a dynamic multi-robot task allocation scenario. Specifically, we develop an analytical framework to estimate the end-to-end packet delay as well as task completion time in a fiber-wireless (FiWi)-based Tactile Internet networking infrastructure. After formulating the problem of joint decompo-sition and allocation of physical tasks in a multi-robot scenario, we propose our water filling-based multi-robot allocation (WF-MRA) algorithm to coordinate robotic teams to minimize the task completion time as well as the waiting time for task execution to begin. We show that formation of robotic teams does not always help reduce the average task completion time, thus mandating the need for designing a proper coordination strategy. The results indicate that our proposed WF-MRA algorithm can achieve an up to 75.2 % reduction of the average task completion time compared to the benchmark nearest robot allocation (NRA) algorithm.

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Intent-Based Service Graph Selection for Cost-Effective Cloud Deployment

Intent-Based Service Graph Selection for Cost-Effective Clou...

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International Conference on Intelligence in Next Generation Networks (ICIN)

作者： Sasan Sabour Amin Ebrahimzadeh Mbarka Soualhia Fetahi Wuhib Roch. H. Glitho Telecommunication Service Engineering Research Lab Concordia University Montréal QC Canada Ericsson Research Montréal QC Canada Computer Science Programme University of Western Cape Capetown South Africa

ISBN: (数字)9798331542399

ISBN: (纸本)9798331542405

Cloud computing offers a diverse range of virtualized services for users. However, selecting and composing these services to generate optimal service graphs that meet user requirements is complex because it requires an expert with extensive knowledge of the cloud applications and their service composition, to manually generate service graphs. This, however, cannot often guarantee that all the requirements are met. A potentially promising approach to address this complexity is to develop a system that enables cloud users to specify their intent and then to automatically select a service graph that meets all their requirements. In this paper, we address the problem of service graph selection while considering both functional and non-functional requirements derived from user intent. Our objective is to minimize the total deployment cost of the service graph in a data center network and to determine its placement within the distributed data center network. We formulate the problem as an Integer Linear Programming (ILP), taking into account the bandwidth and latency requirements of user intent. To solve this, we propose our Service Graph Selection (SGS) solution, which aims to achieve a near-optimal solution in a computationally efficient manner. Our results demonstrate that the proposed solution achieves a deployment cost that is only 4–6% larger than the lower bound of the optimal deployment cost.

关键词： Cloud computing Data centers Technological innovation Lower bound Costs Bandwidth Integer linear programming Computational efficiency Complexity theory

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Data Labeling for Fault Detection in Cloud: A Test Suite-Based Active Learning Approach

Data Labeling for Fault Detection in Cloud: A Test Suite-Bas...

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IEEE Conference on Network Softwarization (NetSoft)

作者： Prateek Bagora Amin Ebrahimzadeh Fetahi Wuhib Roch H. Glitho CIISE Concordia University Montréal Canada Ericsson Research Montréal Canada Computer Science Programme University of Western Cape Capetown South Africa

Ensuring the quality of service of applications deployed in inherently complex and fault-prone cloud environments is of utmost concern. While machine learning based fault management solutions help attain the desired reliability, they require labeled cloud metrics data for training and evaluation. Furthermore, high dynamicity of cloud environments brings forth emerging data distributions, which necessitate frequent labeling of data for model adaptation. We propose a test suite-based active learning framework for automated labeling of cloud metrics data with the corresponding cloud system state while accounting for emerging fault patterns and data or concept drifts. We have implemented our solution on a cloud testbed and introduced various emerging data distribution scenarios to evaluate the proposed framework’s labeling efficacy over known and emerging data distributions. According to our results, the proposed framework achieves a 41% higher weighted Fl-score and a 34% higher average AUC score than a system without any adaptation for emerging data distributions.

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Causal-Temporal Analysis-Based Feature Selection for Predicting Application Performance Degradation in Edge Clouds

Causal-Temporal Analysis-Based Feature Selection for Predict...

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IEEE International Conference on Communications (ICC)

作者： Behshid Shayesteh Chunyan Fu Amin Ebrahimzadeh Roch H. Glitho CIISE Concordia University Montréal Canada Ericsson Research Montréal Canada Computer Science Programme University of Western Cape Capetown South Africa

Next-generation networks will enable applications that are expected to be highly reliable, always available, with guaranteed Quality-of-Service (QoS). Distributed, heterogeneous edge clouds are key enablers for these applications. However, applications deployed in such networks may suffer from performance degradation caused by various infrastructure-related faults. Preventing performance degradations by predicting them using Machine Learning (ML)-based analytics and handling them proactively is thus critical for maintaining the application QoS. Predicting performance degradations can be challenging due to the diversity of the underlying causes. In this paper, we propose an automated feature selection system that uses causal-temporal analysis to find the infrastructure metrics that have causal relationships with application metrics. The selected features are further used to train ML models for predicting application performance degradation. We have validated a proof-of-concept of our system on a Kubernetes testbed, where it is demonstrated that the proposed feature selection system is up to 17.9 times faster than Recursive Feature Elimination (RFE). Using the features selected by the proposed system, the ML models could predict performance degradation with up to 12.7% higher F1-score compared to using historical values of application Key Performance Indicator (KPI) to forecast its future values.

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Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization 37

Trusted-Maximizers Entropy Search for Efficient Bayesian Opt...

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37th Conference on Uncertainty in Artificial Intelligence, UAI 2021

作者： Nguyen, Quoc Phong Wu, Zhaoxuan Low, Bryan Kian Hsiang Jaillet, Patrick Department of Computer Science National University of Singapore Singapore Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Institute of Data Science National University of Singapore Singapore NUSGS Integrative Sciences and Engineering Programme National University of Singapore Singapore

Information-based Bayesian optimization (BO) algorithms have achieved state-of-the-art performance in optimizing a black-box objective function. However, they usually require several approximations or simplifying assumptions (without clearly understanding their effects on the BO performance) and/or their generalization to batch BO is computationally unwieldy, especially with an increasing batch size. To alleviate these issues, this paper presents a novel trusted-maximizers entropy search (TES) acquisition function: It measures how much an input query contributes to the information gain on the maximizer over a finite set of trusted maximizers, i.e., inputs optimizing functions that are sampled from the Gaussian process posterior belief of the objective function. Evaluating TES requires either only a stochastic approximation with sampling or a deterministic approximation with expectation propagation, both of which are investigated and empirically evaluated using synthetic benchmark objective functions and real-world optimization problems, e.g., hyperparameter tuning of a convolutional neural network and synthesizing 'physically realizable' faces to fool a black-box face recognition system. Though TES can naturally be generalized to a batch variant with either approximation, the latter is amenable to be scaled to a much larger batch size in our experiments. © 2021 37th Conference on Uncertainty in Artificial Intelligence, UAI 2021. All Rights Reserved.

关键词： Entropy

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