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arXiv 2023年

作者： Grosse, Leonhard Saeidian, Sara Oechtering, Tobias J. The Division of Information Science and Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden

Data publishing under privacy constraints can be achieved with mechanisms that add randomness to data points when released to an untrusted party, thereby decreasing the data's utility. In this paper, we analyze this privacy-utility tradeoff for the pointwise maximal leakage (PML) privacy measure and provide optimal privacy mechanisms for a general class of convex utility functions. PML was recently proposed as an operationally meaningful privacy measure based on two equivalent threat models: An adversary guessing a randomized function and an adversary aiming to maximize a general gain function. We prove a cardinality bound, showing that output alphabets of optimal mechanisms in this context need not to be larger than the size of their inputs. Then, we characterize the optimization region as a (convex) polytope. We derive closed-form optimal privacy mechanisms for arbitrary priors in the high privacy regime (when the privacy parameter is sufficiently small) and uniform priors for all ranges of the privacy parameter using tools from convex analysis. Furthermore, we present a linear program that can compute optimal mechanisms for PML in a general setting. We conclude by demonstrating the performance of the closed-form mechanisms through numerical simulations. © 2023, CC BY.

关键词： Differential privacy

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State-Space Piece-Wise Affine System Identification with Online Deterministic Annealing

State-Space Piece-Wise Affine System Identification with Onl...

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European Control Conference (ECC)

作者： Christos N. Mavridis Aris Kanellopoulos John S. Baras Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Department of Electrical and Computer Engineering and the Institute for Systems Research University of Maryland College Park USA

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

We propose an online identification scheme for discrete-time piece-wise affine state-space models based on a system of adaptive algorithms running in two timescales. A stochastic approximation algorithm implements an online deterministic annealing scheme at a slow timescale, estimating the partition of the augmented state-input space that defines the switching signal. At the same time, an adaptive identification algorithm, running at a higher timescale, updates the parameters of the local models based on the estimate of the switching signal. Identifiability conditions for the switched system are discussed and convergence results are given based on the theory of two-timescale stochastic approximation. In contrast to standard identification algorithms for piece-wise affine systems, the proposed approach progressively estimates the number of modes needed and is appropriate for online system identification using sequential data acquisition. This progressive nature of the algorithm improves computational efficiency and provides real-time control over the performance-complexity trade-off, desired in practical applications. Experimental results validate the efficacy of the proposed methodology.

关键词： Switched systems Annealing Stochastic processes Switches Approximation algorithms Real-time systems System identification

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Fuzzy Inference System Based Intelligent Food Recommender System 3

Fuzzy Inference System Based Intelligent Food Recommender Sy...

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3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Panwar, Minakshi Sharma, Ashish Mahela, Om Prakash Khan, Baseem Maulana Azad University Department of Computer Science Rajasthan Jodhpur India Rvpn Power System Planning Division Jaipur India Hawassa University Department of Electrical Engineering Ethiopia University of Johannesburg Department of Eeet South Africa

ISBN: (纸本)9798350302288

The personalized suggestions using the intelligent methods are becoming promising ways to change eating habits to-ward more desirable diets. This has necessitated the requirements of intelligent food recommender system (FRS). This paper has designed a FRS using the Fuzzy Inference System (FIS) to suggest the food preference (FP). This is achieved using the different system blocks such as Rule Base, Database, Decision-making Unit, Fuzzification Interface Unit (FIU), and Defuzzification Interface Unit (DIU). The FP is decided by processing the input variables such as Age, Income and Calorific values. This is achieved with an accuracy of higher than 95%. Efficacy of proposed FRS is established by comparing the performance with the rule based FRS reported in literature. Study is performed using the coding environment of MATLAB. © 2023 IEEE.

关键词： Recommender systems

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Intelligent Automated Interference for the Protection of Road Safety 1

Intelligent Automated Interference for the Protection of Roa...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Pant, Gargi Bahuguna, Rajesh Pandey, Shweta Gehlot, Anita Yadav, Satya Prakash Pachauri, Rupendra Kumar Uttaranchal University Law College Dehradun Dehradun India Uttaranchal University Lcd Division of Research &innovation Dehradun India Uttaranchal University Uit Division of Research &innovation Dehradun India Gl Bajaj Institute of Technology and Management Department of Computer Science and Engineering Noida India University of Petroleum and Energy Studies School of Engineering Electrical Cluster Dehradun India

ISBN: (纸本)9798350338027

Development in Industry 4.0 is ongoing and as a result, the inculcation of technology has enhanced that use of state-of-the-art technology in different spheres of society. The road safety is an essential component of a life longingprocess in modern era. This makes it important to use technology for making the road transport smooth and efficient. The safety in road transporthas also included the use of technology for providing an efficient model and smooth functioning. The paper reviews different models of road safety with the use of Al including ANN (Artificial Neural Network), Fuzzy Logic model (FLM), Vehicular Ad-hoc Network (VANET) and the use of CNN (Convolutional Neural Network). Additionally, the paper reviews the models of IoT including an app in smartphone which canshow the cracks in the road so that accidents can be prevented, use of mobile application to find parking with the use of infrared sensors and Raspberry Pi model to get the real time traffic condition's data. Lastly, the paper after discussing the challenges of the different models in road safety, provide suggestions to refine and enhance the present road safety models using AI and IoT. © 2023 IEEE.

关键词： Vehicular ad hoc networks

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Teaching in student-centred active learning spaces: How relational, pedagogical, spatial, and technological aspects intertwine and affect the learning environment 51

Teaching in student-centred active learning spaces: How rela...

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51st Annual Conference of the European Society for engineering Education, SEFI 2023

作者： Korpås, G.S. Andersen, T.H. Kahrs, M.S. Hansen, G. Røren, K.A.F. Department of Physics Norwegian University of Science and Technology Trondheim Norway Department of Computer Science Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim Norway Section for Teaching and Learning Support Education Quality Division Norwegian University of Science and Technology Trondheim Norway

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Quantum Multi - Agent Reinforcement Learning Software Design and Visual Simulations for Multi - Drone Mobility Control

Quantum Multi - Agent Reinforcement Learning Software Design...

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IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS)

作者： Soohyun Park Gyu Seon Kim Sovi Jung Joongheon Kim Division of Computer Science Sookmyung Women's University Seoul Republic of Korea Department of Electrical and Computer Engineering Korea University Seoul Republic of Korea Department of Electrical and Computer Engineering Ajou University Suwon Republic of Korea

ISBN: (数字)9798350361704

ISBN: (纸本)9798350361711

Quantum machine learning (QML) has received a lot of attention according to its light training parameter numbers and speeds; and the advances of QML lead to active research on quantum multi-agent reinforcement learning (QMARL). Existing classical multi-agent reinforcement learning (MARL) features non-stationarity and uncertain properties. Therefore, this paper presents a simulation software framework for novel QMARL to control autonomous multi-drone mobility, i.e., quantum multi-drone reinforcement learning. Our proposed framework ac-complishes reasonable reward convergence and service quality performance with fewer trainable parameters. Furthermore, it shows more stable training results. Lastly, our proposed software allows us to analyze the training process and results.

关键词： Training Wireless communication Visualization Software design Asia Reinforcement learning Software

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Machine Learning and Deep Learning Approach for Predicting Neonatal Mortality in Indonesia

Machine Learning and Deep Learning Approach for Predicting N...

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International Conference on Applied System Innovation (ICASI)

作者： Nur Hamid Fidya Rumiati Rizal Dwi Prayogo Hidetaka Nambo Division of Electrical Engineering and Computer Science Kanazawa University Japan Department of Epidemiology Faculty of Public Health Universitas Indonesia Indonesia

ISBN: (数字)9798350394924

ISBN: (纸本)9798350394931

This research explores machine learning approaches to determine the most significant features related to neonatal mortality in Indonesia. We create prediction tasks with deep learning models including MLP, LSTM, and CNN. We found that low birth weight and early breastfeeding becomes the most significant features related to neonatal mortality in Indonesia. For prediction task, implementing feature importance task as feature selection can improve prediction performance and reduce algorithm complexity. LSTM and CNN achieved the best prediction model with 90.91% accuracy.

关键词： Deep learning Pediatrics Technological innovation Predictive models Feature extraction Prediction algorithms Complexity theory

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Risk-Averse Learning with Delayed feedback

arXiv

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arXiv 2024年

作者： Wang, Siyi Wang, Zifan Johansson, Karl H. Hirche, Sandra Department of Electrical and Computer Engineering Technical University of Munich Munich80333 Germany Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden

In real-world scenarios, the impacts of decisions may not manifest immediately. Taking these delays into account facilitates accurate assessment and management of risk in real-world environments, thereby ensuring the efficacy of strategies. In this paper, we investigate risk-averse learning using Conditional Value at Risk (CVaR) as risk measure, while incorporating delayed feedback with unknown but bounded delays. We develop two risk-averse learning algorithms that rely on one-point and two-point zeroth-order optimization approaches, respectively. The regret achieved by the algorithms is analyzed in terms of the cumulative delay and the number of total samplings. The results suggest that the two-point risk-averse learning achieves a smaller regret bound than the one-point algorithm. Furthermore, the one-point risk-averse learning algorithm attains sublinear regret under certain delay conditions, and the two-point risk-averse learning algorithm achieves sublinear regret with minimal restrictions on the delay. We provide numerical experiments on a dynamic pricing problem to demonstrate the performance of the proposed algorithms. © 2024, CC0.

关键词： Adversarial machine learning

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Machine Learning Methods For Classification of Individuals With Coronary Artery Calcification

Machine Learning Methods For Classification of Individuals W...

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Annual IEEE Symposium on computer-Based Medical Systems

作者： Jakob Svane Tomasz Wiktorski Trygve Eftestøl Stein Ørn Department of Electrical Engineering and Computer Science University of Stavanger Stavanger Norway Division of Cardiology Stavanger University Hospital Stavanger Norway

ISBN: (数字)9798350384727

ISBN: (纸本)9798350384734

Coronary artery calcification (CAC) due to coronary artery disease (CAD) poses significant risks of heart attack, sudden cardiac death, and other cardiac complications. CAC reflects progressive CAD, which in some individuals may be accelerated by high-intensity exercise. Identifying individuals with CAC and determining safe levels of physical exercise is therefore important. The most common way to identify CAC is by coronary computed tomography angiography (CCTA), but due to limited capacity for CCTA assessment, it is highly valuable to develop tools to proactively identify individuals that may benefit from this *** studies have demonstrated significant differences in the physiological response to exercise between individuals with and without CAC. In the present study, we applied machine learning methods to physiological data acquired in relation to prolonged high-intensity exercise in individuals without symptoms or signs of CAD. All individuals were assessed by CCTA after exercise. Various dimensionality reduction methods and classification algorithms were assessed, applying nested cross-validation for hyperparameter optimization and model testing. The best performing model predicted the presence of CAC with an accuracy of 84%, correctly identifying 86% of the individuals with *** subset selection was also carried out to determine the most important input parameters, highlighting the most important physiological parameters as age and blood pressure measured directly after high-intensity *** present findings support the use of machine learning methods on physiological measurements and sensor data to identify individuals who may benefit from CCTA assessment. The best-performing model showed strong predictive power of presence of CAC using only age, blood pressure, body mass index and heart rate variability as input features.

关键词： Solid modeling Predictive models Hyperparameter optimization Physiology Blood pressure Pressure measurement Object recognition

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On the Diagnosis of Nonalcoholic Steatohepatitis Using Dielectric Properties: Feasibility in Mouse Model

On the Diagnosis of Nonalcoholic Steatohepatitis Using Diele...

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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Cherukuri, Akhila Sai Sree Samaddar, Poulami Carlos, Alejos Aguero Gopalakrishnan, Keerthy Gaddam, Sunil Perez, Lucia T. Valenzuela Lee, Hyun Se Kim Dey, Shuvashis Roy, Sayan Mitra, Dipankar Hirsova, Petra Arunachalam, Shivaram P. Division of Gastroenterology and Hepatology Mayo Clinic RochesterMN United States Division of Gastroenterology and Hepatology Mayo Clinic RochesterMN United States Department of Electrical and Computer Engineering North Dakota State University FargoND58105 United States Department of Electrical Engineering and Computer Science South Dakota Mines Rapid CitySD57701 United States Department of Computer Science and Computer Engineering University of Wisconsin-La Crosse La CrosseWI54601 United States Department of Medicine Mayo Clinic RochesterMN United States Department of Radiology Mayo Clinic RochesterMN United States

ISBN: (纸本)9781665493765

Nonalcoholic steatohepatitis (NASH) is a chronic liver disease that can progress to cirrhosis, liver failure, and hepatocellular cancer. Currently, liver biopsy is the gold standard for diagnosis, but it is invasive and risky. Therefore, there is a need for non-invasive diagnostic tools. Recent studies have shown that microwaves and dielectric spectroscopy can differentiate between healthy and diseased liver tissue and can provide information on the severity of NASH. In this study, we used ex vivo NASH mouse model tissues to diagnose NASH using dielectric spectroscopy, a non-invasive technique. According to our study results, the novelty of the study lies in differentiating the various NASH diseased tissue and analyzing them using cole-cole parameters, suggesting the potential development of a noninvasive diagnostic and treatment monitoring tool for NASH. If successful, the use of microwaves could revolutionize the field of NASH diagnosis and monitoring, providing a safer and more convenient alternative to liver biopsy. However, additional research is required to validate this approach and optimize its sensitivity and specificity. © 2023 IEEE.

关键词： Dielectric spectroscopy

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