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arXiv 2024年

作者： Le, Thao Miller, Tim Zhang, Ruihan Sonenberg, Liz Singh, Ronal School of Computing and Information Systems The University of Melbourne Australia School of Electrical Engineering and Computer Science The University of Queensland Australia CSIRO/Data61 Australia

This paper presents Visual Evaluative AI, a decision aid that provides positive and negative evidence from image data for a given hypothesis. This tool finds high-level human concepts in an image and generates the Weight of Evidence (WoE) for each hypothesis in the decision-making process. We apply and evaluate this tool in the skin cancer domain by building a web-based application that allows users to upload a dermatoscopic image, select a hypothesis and analyse their decisions by evaluating the provided evidence. Further, we demonstrate the effectiveness of Visual Evaluative AI on different concept-based explanation approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision making

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DDPS: Dynamic Differential Pricing-Based Edge Offloading System With Energy Harvesting Devices

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IEEE Transactions on Network Science and engineering 2025年

作者： Xue, Hai Xia, Yun Xiong, Neal N. Zhang, Di Pei, Songwen University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Sul Ross State University Department of Computer Science and Mathematics AlpineTX79830 United States Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Zhengzhou University Henan International Joint Laboratory of Intelligent Health Information System Zhengzhou450001 China Zhengzhou University National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China

Mobile edge computing (MEC) mitigates the energy and computation burdens on mobile users (MUs) by offloading tasks to the network edge. To optimize MEC server utilization through effective resource allocation, a well-designed pricing strategy is indispensable. In this paper, we propose a dynamic differential pricing scheme (DDPS) for an edge offloading scenario with energy harvesting devices, which determines prices based on computing resource usage to enhance edge server (ES) utilization. First, an offloading decision algorithm is proposed to balance harvested and consumed energy, determining whether and how much data to offload. Second, a Stackelberg game-based differential pricing algorithm is proposed to optimize computing resource allocation for MUs and reallocate surplus resources to delay-sensitive devices. Extensive simulations are conducted to demonstrate the effectiveness of the proposed DDPS scheme. Specifically, in comparison to the existing best-performing pricing scheme, for different task arrival rates, DDPS can achieve a 5.3% decrease in average execution delay, a 1.7% increase in ES utility (Userver, which represents the payment from MUs minus penalties for discarded tasks), and a 2.1% increase in the average ratio of service for MUs. In addition, DDPS also improves 2.8% Userver on average with different ES computation capacities. © 2025 IEEE. All rights reserved.

关键词： Mobile edge computing

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On the Capacity of Vehicular Communications 31

On the Capacity of Vehicular Communications

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2023 31st National Conference with International Participation, TELECOM 2023

作者： Sofotasios, Paschalis C. Yoo, Seong Ki Simmons, Nidhi Cotton, Simon L. Valkama, Mikko Khalifa University Center for Cyber Physical Systems Department of Electrical Engineering and Computer Science Abu Dhabi127788 United Arab Emirates Tampere University Department of Electrical Engineering TampereFI-33101 Finland Coventry University Centre for Future Transport and Cities Faculty of Engineering Environment and Computing United Kingdom Queen's University Belfast Institute of Electronics Communications and Information Technology BelfastBT3 9DT United Kingdom

ISBN: (纸本)9798350303292

The μ / inverse gamma and μ / inverse gamma distributions constitute two of the most distinct composite fading models. The present work offers novel results on the achievable ergodic capacity over these composite multipath/shadowing conditions. The derived expressions are corroborated with respective simulation results and are shown to be useful in practical wireless scenarios in the context of device to device communications, such as off-body, and vehicle-to-vehicle communications. © 2023 IEEE.

关键词： Vehicle to vehicle communications

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Incremental-Dissipativity-Based Synchronization of Interconnected systems with MIMO Nonlinear Operators

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IFAC-PapersOnLine 2023年第2期56卷 1853-1858页

作者： Tao Liu David J. Hill Jun Zhao Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong SAR Shenzhen Institute of Research and Innovation University of Hong Kong Shenzhen China Department of Electrical and Computer Systems Engineering Monash University Melbourne Australia College of Information Science and Engineering Northeastern University Shenyang China

This paper considers a type of dynamical networks with incrementally dissipative multiple inputs and multiple outputs nonlinear operators that are interconnected both internal and external interconnections. A novel quadratic form of incremental dissipativity is used to investigate synchronization of such a network from an input-output approach point of view. It is shown that the synchronization problem is equivalent to a relative stability issue of a large-scale interconnected nonlinear system with dissipative subsystems. Therefore, traditional dissipativity theory is used for the synchronization analysis.

关键词： Incremental dissipativity synchronization multiple-input multiple-output operator interconnected systems stability of nonlinear systems

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False Data Injection Attack Detection Based on Wavelet Packet Decomposition and Random Forest in Smart Grid 23

False Data Injection Attack Detection Based on Wavelet Packe...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Chen, Zhenyu Yuan, Shuai Wu, Longfei Guan, Zhitao Du, Xiaojiang School of Control and Computer Engineering North China Electric Power University China Brock University Department of Finance Operations and Information Systems ON Canada Fayetteville State University Department of Mathematics and Computer Science Fayetteville United States Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States

ISBN: (纸本)9781665494571

As one of the critical infrastructures, the safety and reliability of the smart grid are directly associated with the development and stability of society. However, studies have shown that the power grid is at risk when the parameters are manipulated and cyber-attacks are generated against the state estimation, i.e., under false data injection attack (FDIA). Currently, a rich body of literature has studied on the FDIA defense methods, but most of them focus on the direct current (DC) scenario. This paper proposes a novel detection model that combines the wavelet packet decomposition (WPD) technique with the random forest (RF) algorithm. The WPD is able to capture the deviation of parameters from the normal conditions, whereas the RF is developed to classify these features and effectively identify the malicious data. The proposed model is also evaluated using real-world data on IEEE 118-bus power system. The results demonstrate excellent performance on precision rate and recall rate under varying scenarios. © 2021 IEEE.

关键词： Radio frequency Training data Interference Power system stability Feature extraction Data models Wavelet packets

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ROBUSTLY STABLE ACCELERATED MOMENTUM METHODS WITH A NEAR-OPTIMAL L2 GAIN AND H∞ PERFORMANCE

arXiv

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arXiv 2023年

作者： Gürbüzbalaban, Mert Department of Management Science and Information Systems Department of Electrical and Computer Engineering Department of Statistics Rutgers University NJ United States

We consider the problem of minimizing a strongly convex smooth function where the gradients are subject to additive worst-case deterministic errors that are square-summable. We study the trade-offs between the convergence rate and robustness to gradient errors when designing the parameters of a first-order algorithm. We focus on a general class of momentum methods (GMM) with constant stepsize and two momentum parameters which can recover gradient descent (GD), Nesterov’s accelerated gradient (NAG), the heavy-ball (HB) and the triple momentum methods (TMM) as special cases. We measure the robustness of an algorithm in terms of the cumulative suboptimality over the iterations normalized by the squared 2 norm of the gradient errors. This quantity can be interpreted as the (squared) 2 gain of a dynamical system that represents the GMM iterations where the input is the gradient error sequence and the output is a weighted distance to the optimum. For quadratic objectives, we compute the 2 gain explicitly leveraging its representation as the H∞ norm of the GMM system in the frequency domain and construct gradient errors that lead to worst-case performance explicitly. We also study the stability of GMM with respect to multiplicative errors by characterizing the structured real and stability radius of the GMM system through their connections to the H∞ norm. This allows us to compare GD, HB, NAG methods in terms of robustness, and argue that HB is not as robust as NAG despite being the fastest in terms of the rate. We then develop the robustly stable heavy ball method that can be faster than NAG while being at the best robustness level possible. We also propose the robustly stable gradient descent that is the fastest version of GD with constant stepsize while being at the best robustness level. Finally, we extend our framework to general strongly convex smooth objectives, providing non-asymptotic rate results for inexact GMM methods and bounds on the 2 gain where we can choos

关键词： Momentum

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Automatic Diagnosis of Pectus Excavatum from CT Images Using a Joint CNN-LSTM Model 9

Automatic Diagnosis of Pectus Excavatum from CT Images Using...

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9th International Conference on Control, Decision and information Technologies, CoDIT 2023

作者： Liao, Yizhi Zhou, Haiyu Xie, Longhan Cai, Siqi School of Computing and Information Systems University of Melbourne Melbourne Australia Guangdong Lung Cancer Institute Guangdong Provincial People's Hospital Division of Thoracic Surgery Guangzhou China Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangzhou China National University of Singapore Department of Electrical and Computer Engineering Singapore

ISBN: (纸本)9798350311402

Pectus excavatum (PE) is one of the most common congenital sternal deformities. Accurate preoperative diagnosis of PE is of great significance for subsequent correction and improvement of the patient's quality of life. However, current diagnostic methods rely on the calculation of some PE indices, which is a heavy workload for physical therapists and suffers from measurement errors. To address this issue, we propose an end-to-end automatic assessment of PE, which features a cascaded structure of CNN and LSTM. Specifically, the high-level feature representations of CT images are extracted by the pretrained CNN and then processed through the LSTM layers for classification. In addition, we build up a medical image dataset for PE diagnosis by collecting chest CT images of 42 subjects. Results on this dataset show that the proposed CNN-LSTM framework achieves a relatively high accuracy of 90.20%, which provides a new perspective for the automatic diagnosis of PE in clinics. © 2023 IEEE.

关键词： computerized tomography

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Efficient displacement convex optimization with particle gradient descent

arXiv

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arXiv 2023年

作者： Daneshmand, Hadi Lee, Jason D. Jin, Chi Laboratory for Information and Decision Systems MIT United States Department of Electrical Engineering and Computer Science Princeton Universit United States

Particle gradient descent, which uses particles to represent a probability measure and performs gradient descent on particles in parallel, is widely used to optimize functions of probability measures. This paper considers particle gradient descent with a finite number of particles and establishes its theoretical guarantees to optimize functions that are displacement convex in measures. Concretely, for Lipschitz displacement convex functions defined on probability over Rd, we prove that O(1/ϵ2) particles and O(d/ϵ4) computations are sufficient to find the ϵ-optimal solutions. We further provide improved complexity bounds for optimizing smooth displacement convex functions. We demonstrate the application of our results for function approximation with specific neural architectures with two-dimensional inputs. © 2023, CC BY-NC-ND.

关键词： Convex optimization

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CHARLIE: A Chatbot That Recommends Daily Fitness and Diet Plans

CHARLIE: A Chatbot That Recommends Daily Fitness and Diet Pl...

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2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023

作者： Chowdhury, Deepanjali Roy, Ahana Ramamurthy, Sreenivasan Ramasamy Roy, Nirmalya Texas A&m University Department of Electrical and Computer Engineering College Station United States Howard County Public School System MD United States Bowie State University Department of Computer Science Bowie United States University of Maryland Baltimore County Department of Information Systems Baltimore United States

ISBN: (纸本)9781665453813

Managing a work-life balance has always been challenging, especially after the recent trend of working from home, which has made maintaining one's fitness and diet regime strenuous. Failing to adhere to a fitness and diet plan has been shown to cause long-term effects on a person's health including obesity and shortened lifespans. Currently, people plan their fitness and diet plans around their schedule, but these tasks are challenging to maintain. Users tend to give up these plans due to a lack of time and planning. To help with better planning for a fitness and diet regime, we propose, CHARLIE, a chatbot that works around a user's schedule to intelligently recommend diets and fitness goals based on their calendar. CHARLIE is a recommendation system that suggests the users' a certain meal and fitness activities based on the amount of time they have left in their day and the number of calories they lose. © 2023 IEEE.

关键词： Recommender systems

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Efficient Drinking Water Quality Analysis using Machine Learning Model with Hyper-Parameter Tuning 7

Efficient Drinking Water Quality Analysis using Machine Lear...

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7th International Conference on Intelligent Computing and Control systems, ICICCS 2023

作者： Maheswari, M Uma Sudharsanan, R. Arthy, M. Jenefer, Anne Oormila, L. Samuthira Pandi, V. RMD Engineering College Department of Electrical and Electronics Engineering Tamil Nadu India SRM Institute of Science and Technology Department of Information Technology Tamil Nadu Ramapuram India SRM Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu India Agni College of Technology Department of Computer Science Engineering Tamil Nadu India SRM Institute of Science and Technology Department of Electronics Ad Communication Engineering Tamilnadu Ramapuram India

ISBN: (纸本)9798350397253

The conventional approaches to determining the quality of water include costly and lengthy statistical and laboratory testing;hence, the concept of real-time monitoring is no longer applicable in the modern day. The disastrous implications of poor water quality demand that there be a remedy that is both more expedient and more realistic. This research study examines a variety of supervised machine learning algorithms in order to identify the quality of the water. A variety of variables, such as the pH value, the hardness, the solids, the chloramines, the sulfates, the conductivity, the organic carbon, the trihalomethanes, the turbidity, and the potability of the water, are essential for determining the quality of the water. Random Forest (RF) and Decision Tree (DT) are used in order to ascertain the caliber of the water that is suitable for human consumption. The standard laboratory approach for testing the quality of water is one that takes a lot of time and may be somewhat expensive at times. In a very short length of time, the algorithms that are proposed in this study are able to provide an estimation of the quality of drinking water. DT has a height accuracy F1 score of 99%, whereas RF has a score of 87.86% and a precision of 82.36%. The difference in these two scores is due to the fact that DT's precision is lower. The suggested method demonstrates its potential for usage in real-time water quality monitoring systems by achieving appropriate accuracy with a minimal set of parameters. To prove the usefulness of this program, this is essential. © 2023 IEEE.

关键词： Water quality

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