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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Regis Donald Hontinfinde Amavi G. Hounkpe Houenou Mahugnon G. Azehoun Pazou Faith Y. P. J. Houessou K. B. Audace Didavi Macaire B. Agbomahena Laboratory of Science Engineering and Mathematics University of Abomey (UNSTIM) Abomey Republic of Benin Laboratory of Electronics Telecommunication and Applied Computer Science University of Abomey Calavi (UAC) Lokossa Republic of Benin

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

In this paper, an indirect model for predicting wind turbine output power is proposed, based on a LSTM and a mathematical model for wind power estimation. The LSTM model is used to forecast future changes in wind speed and direction, and the linearized segmented model to estimate wind turbine output power based on forecast wind speeds. To train the LSTM model, NASA meteorological data were used for a period from January 1,2015 to December 31, 2022 for a coastal site in Benin. The results obtained showed that the forecasts made by the LSTM model are fairly close to the actual values. Values of 0.36 m/s and 8.1° for the Mean Square Error (RMSE) and 0.92 and 0.71 for the Coefficient of Determination (R2) were obtained for wind speed and direction forecasts respectively. The predicted speeds were then used to estimate the power output of the NE-200S wind turbine.

关键词： Wind speed Estimation Predictive models Wind power generation Mathematical models Generators Wind turbines Wind forecasting Forecasting Long short term memory

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Towards Efficient and Practical Multi-party Computation under Inconsistent Trust in TEEs

Towards Efficient and Practical Multi-party Computation unde...

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IEEE Symposium on Security and Privacy

作者： Xuanwei Hu Rujia Li Yi Liu Qi Wang Department of Computer Science and Engineering Southern University of Science and Technology Institute for Advanced Study Tsinghua Univesity College of Cyber Security Jinan University National Center for Applied Mathematics Shenzhen Southern University of Science and Technology

ISBN: (数字)9798331522360

ISBN: (纸本)9798331522377

Secure multi-party computation (MPC) allows joint computations on sensitive data while guaranteeing privacy and correctness. In recent years, a series of MPC protocols assisted by trusted execution environments (TEEs) have been proposed to reduce overhead brought by costly cryptographic techniques. However, existing protocols either generally assume consistent trust in TEEs among all participating parties, or require dedicated designs for different applications. This prevents the protocols from being deployed in practice. To address these challenges, in this work, we propose a generic MPC protocol without assuming consistent trust in TEEs while fully utilizing heterogeneous TEEs to improve efficiency. To this end, we propose a security model to capture parties' inconsistent trust in TEEs and prove the security of our protocol under a simpler variant of the UC framework (SUC framework). In addition, we instantiate our protocol for secure aggregation based on a state-of-the-art information-theoretically secure protocol SwiftAgg+. Evaluation results among 64 parties deployed on Azure virtual machines show that our protocol reduces the running time of SwiftAgg+ by 66%. The running time of parties in our protocol is reduced by at most 91% compared to that required in SwiftAgg+.

关键词： Analytical models Privacy Data privacy Protocols Computational modeling Multi-party computation Virtual machines Security

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A spatiotemporal datamining approach for road profile estimation using low-cost device

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Procedia computer science 2022年 207卷 2767-2781页

作者： Mariem TAKTAK Slim TRIKI Higher Institute of Applied Sciences and Technologies of Sousse Department of Computer Science and Applied Mathematics Sousse Tunisia National Engineering School of Sfax Tunisia

In this paper, we address the problem of the spatiotemporal data mining in the field of the road conditions estimation. We demonstrate that road condition estimation can be defined as a problem of dynamic discovering of different classes of road profile during training phase. We focus on the road profile classification from time series data collected by a low-cost inertial sensor embedded into smartphone devices. Data used in this work are composed of three datasets which treat real asphalt pavement problems. The first, called Asphalt-obstacle, address the identification problem of obstacles in the pavement where data are collected. The second, called Asphalt pavement-type, aims at identifying three types of pavements namely flexible pavement, cobblestone street and dirt roads. The third and final dataset, called Asphalt pavement-regularity, treat the pavement quality effect on the driver comfortability. In order to estimate the road conditions, we conduct an evaluation of four spatiotemporal algorithms which combine concepts from machine learning and data-driven. With uniform parameters setting and on-line implementation property, we find that Markov spatiotemporal dynamic model achieve the best average classification accuracy of 90.5%±0.01 in 4-class Asphalt obstacles, 84%±0.006 in 3-class Asphalt pavement-type and 94.9%±0.005 in 2-class Asphalt pavement-regularity.

关键词： temporal data mining smartphone asphalt pavement classification

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Highly Efficient Actively Secure Two-Party Computation with One-Bit Advantage Bound

Highly Efficient Actively Secure Two-Party Computation with ...

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IEEE Symposium on Security and Privacy

作者： Yi Liu Junzuo Lai Peng Yang Qi Wang Anjia Yang Siu-Ming Yiu Jian Weng College of Cyber Security Jinan University Guangzhou China Department of Computer Science The University of Hong Kong Hong Kong SAR China Department of Computer Science and Engineering National Center for Applied Mathematics Shenzhen Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798331522360

ISBN: (纸本)9798331522377

Secure two-party computation (2PC) enables two parties to jointly evaluate a function while maintaining input privacy. Despite recent significant progress, a notable efficiency gap remains between actively secure and passively secure protocols. In S&P'12, Huang, Katz, and Evans formalized the notion of active security with one-bit leakage, providing a promising approach to bridging this gap. Protocols derived from this notion have become foundational in designing highly efficient actively secure 2PC protocols. However, a critical challenge identified by Huang, Katz, and Evans remains unexplored: these protocols face significant weaknesses in ensuring fairness for honest parties when employed in standalone settings rather than as components within larger protocols. While the authors proposed two potential solutions to mitigate this issue, both approaches are prohibitively expensive and lack formalization of security guarantees. In this paper, we first formally define an enhanced notion called active security with one-bit-advantage bound, in which the adversaries' advantages are strictly bounded to at most one bit beyond what honest parties obtain. This bound is enforced through a progressive revelation mechanism, where the evaluation result is disclosed incrementally bit by bit. In addition, we propose a novel approach leveraging label structures within garbled circuits to design a highly efficient constant-round 2PC protocol that achieves active security with one-bit advantage bound. Our protocol demonstrates runtime performance nearly identical to that of passively secure garbled-circuit counterparts in duplex networks (e.g., 1.033 × for the SHA256 circuit in LAN), with low overhead for output progressive revelation (only 80 communicated bytes per bit release). With its strengthened security guarantees and minimal overhead, our protocol is highly suitable for practical 2PC applications.

关键词： Privacy Protocols Runtime Circuits Computational efficiency Security Local area networks Faces

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NEURAL ENTROPIC GROMOV-WASSERSTEIN ALIGNMENT

arXiv

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arXiv 2023年

作者： Wang, Tao Goldfeld, Ziv Applied Mathematics and Computational Science University of Pennsylvania United States School of Electrical and Computer Engineering Cornell University United States

The Gromov-Wasserstein (GW) distance, rooted in optimal transport (OT) theory, provides a natural framework for aligning heterogeneous datasets. Alas, statistical estimation of the GW distance suffers from the curse of dimensionality and its exact computation is NP hard. To circumvent these issues, entropic regularization has emerged as a remedy that enables parametric estimation rates via plug-in and efficient computation using Sinkhorn iterations. Motivated by further scaling up entropic GW (EGW) alignment methods to data dimensions and sample sizes that appear in modern machine learning applications, we propose a novel neural estimation approach. Our estimator parametrizes a minimax semi-dual representation of the EGW distance by a neural network, approximates expectations by sample means, and optimizes the resulting empirical objective over parameter space. We establish non-asymptotic error bounds on the EGW neural estimator of the alignment cost and optimal plan. Our bounds characterize the effective error in terms of neural network (NN) size and the number of samples, revealing optimal scaling laws that guarantee parametric convergence. The bounds hold for compactly supported distributions, and imply that the proposed estimator is minimax-rate optimal over that class. Numerical experiments validating our theory are also provided. Copyright © 2023, The Authors. All rights reserved.

关键词： Alignment

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NETWORK INPAINTING VIA OPTIMAL TRANSPORT

arXiv

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arXiv 2024年

作者： Facca, Enrico Nordbotten, Jan Martin Hanson, Erik Andreas Department of Mathematics University of Bergen Bergen Norway Department of Computer science Electrical engineering and Mathematical sciences Western Norway University of Applied Sciences Bergen Norway

In this work, we present a novel tool for reconstructing networks from corrupted images. The reconstructed network is the result of a minimization problem that has a misfit term with respect to the observed data, and a physics-based regularizing term coming from the theory of optimal transport. Through a range of numerical tests, we demonstrate that our suggested approach can effectively rebuild the primary features of damaged networks, even when artifacts are *** Codes 49-04, 49K20, 49N99, 65N21, 65N30, 65Z05 © 2024, CC BY.

关键词： Inverse problems

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Price Prediction of Seasonal Items Using Time Series Analysis

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computer Systems science & engineering 2023年第7期46卷 445-460页

作者： Ahmed Salah Mahmoud Bekhit Esraa Eldesouky Ahmed Ali Ahmed Fathalla Faculty of Computers and Informatics Zagazig UniversitySharkeya44523Egypt Information Technology Department the University of Technology and Applied SciencesIbriOman School of Electrical and Data Engineering University of Technology SydneySydney2007Australia College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Computer Science Department Faculty of Computers and InformaticsSuez Canal UniversityIsmailia41522Egypt Higher Future Institute for Specialized Technological Studies Cairo3044Egypt Department of Mathematics Faculty of ScienceSuez Canal UniversityIsmailia41522Egypt

The price prediction task is a well-studied problem due to its impact on the business *** are several research studies that have been conducted to predict the future price of items by capturing the patterns of price change,but there is very limited work to study the price prediction of seasonal goods(e.g.,Christmas gifts).Seasonal items’prices have different patterns than normal items;this can be linked to the offers and discounted prices of seasonal *** lack of research studies motivates the current work to investigate the problem of seasonal items’prices as a time series *** proposed utilizing two different approaches to address this problem,namely,1)machine learning(ML)-based models and 2)deep learning(DL)-based ***,this research tuned a set of well-known predictive models on a real-life *** models are ensemble learning-based models,random forest,Ridge,Lasso,and Linear ***,two new DL architectures based on gated recurrent unit(GRU)and long short-term memory(LSTM)models are ***,the performance of the utilized ensemble learning and classic ML models are compared against the proposed two DL architectures on different accuracy metrics,where the evaluation includes both numerical and visual comparisons of the examined *** obtained results show that the ensemble learning models outperformed the classic machine learning-based models(e.g.,linear regression and random forest)and the DL-based models.

关键词： Deep learning price prediction seasonal goods time series analysis

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RL-Based Control of UAS Subject to Significant Disturbance

RL-Based Control of UAS Subject to Significant Disturbance

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Kousheek Chakraborty Thijs Hof Ayham Alharbat Abeje Mersha Smart Mechatronics and Robotics research group Saxion University of Applied Science Enschede The Netherlands Robotics and Mechatronics research group Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Enschede The Netherlands

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

This paper proposes a Reinforcement Learning (RL)-based control framework for position and attitude control of an Unmanned Aerial System (UAS) subjected to significant disturbance that can be associated with an uncertain trigger signal. The proposed method learns the relationship between the trigger signal and disturbance force, enabling the system to anticipate and counteract the impending disturbances before they occur. We train and evaluate three policies: a baseline policy trained without exposure to the disturbance, a reactive policy trained with the disturbance but without the trigger signal, and a predictive policy that incorporates the trigger signal as an observation and is exposed to the disturbance during training. Our simulation results show that the predictive policy outperforms the other policies by minimizing position deviations through a proactive correction maneuver. This work highlights the potential of integrating predictive cues into RL frameworks to improve UAS performance.

关键词： Training Attitude control Simulation Force Reinforcement learning Autonomous aerial vehicles Stability analysis Maintenance Dynamical systems Logistics

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Exact algebraic blind source separation using side information 28

Exact algebraic blind source separation using side informati...

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28th European Signal Processing Conference, EUSIPCO 2020

作者： Weiss, Amir Yeredor, Arie Dept. of Computer Science and Applied Mathematics Weizmann Institute of Science Israel School of Electrical Engineering Tel-Aviv University Israel

ISBN: (纸本)9789082797053

Classical Blind Source Separation (BSS) methods rarely attain exact separation, even under noiseless conditions. In addition, they often rely on particular structural or statistical assumptions (e.g., mutual independence) regarding the sources. In this work we consider a (realistic) "twist" in the classical linear BSS plot, which, quite surprisingly, not only enables perfect separation (under noiseless conditions), but is also free of any assumptions (except for regularity assumptions) regarding the sources or the mixing matrix. In particular, we consider the standard linear mixture model, augmented by a single ancillary, unknown linear mixture of some known linear transformations of the sources. We derive a closed-form expression for an exact algebraic solution, free of any statistical considerations whatsoever, attaining perfect separation in the noiseless case. In addition, we propose a well-behaved solution for the same model in the presence of noise or other measurement inaccuracies. Our derivations are corroborated by several simulation results. © 2021 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Blind source separation

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Accident Severity Prediction using Machine Learning: A case study on the US Accidents Dataset

Accident Severity Prediction using Machine Learning: A case ...

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International IEEE Conference on Signal-Image Technologies and Internet-Based System

作者： Ennaji Fatima Zohra Knouzi Maryam El Kabtane Hamada Elalaoui Elabdallaoui Hasna Laboratory of Process Engineering Computer Science and Mathematics (LIPIM) National School of Applied Science USMS Khouribga Morocco SMARTE Systems and Applications (SSA) National School of Applied Science UCA Marrakech Morocco Computer Science Engineering Laboratory Faculty of Sciences Semlalia UCA Marrakech Morocco

In recent years, road accidents have caused numerous victims, injuries, and deaths every minute, not to mention economic damages. Due to the enormous number of traffic accidents, understanding and predicting their severity becomes a necessity. In this work, the “US Accidents (2016 - 2023)” dataset has been used. The data exploration task was applied before the training phase. Subsequently, and after splitting data into training and testing subsets, experiments were conducted using ANN(Artificial Neural Network), Random Forest Classifier, and Logistic Regression. We achieved an accuracy of 81.1 %, 90.7 %; and 87.03 % respectively.

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