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Approximated Coded Computing: Towards Fast, Private and Secure Distributed Machine Learning

IEEE Transactions on Emerging Topics in Computing 2025年

作者： Qiu, Houming Zhu, Kun Luong, Nguyen Cong Niyato, Dusit Jiangxi University of Finance and Economics School of Software and IoT Engineering Jiangxi 330032 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Phenikaa University Faculty of Computer Science Hanoi12116 Viet Nam Nanyang Technological University School of Computer Science and Engineering 639798 Singapore

In a large-scale distributed machine learning system, coded computing has attracted wide-spread attention since it can effectively alleviate the impact of stragglers. However, several emerging problems greatly limit the performance of coded distributed systems. Firstly, an existence of colluding workers who collude results with each other leads to serious privacy leakage issues. Secondly, there are few existing works considering security issues in data transmission of distributed computing systems/or coded distributed machine learning systems. Thirdly, the number of required results for which need to wait increases with the degree of decoding functions. In this paper, we design a secure and private approximated coded distributed computing (SPACDC) scheme that deals with the above-mentioned problems simultaneously. Our SPACDC scheme guarantees data security during the transmission process using a new encryption algorithm based on elliptic curve cryptography. Especially, the SPACDC scheme does not impose strict constraints on the minimum number of results required to be waited for. An extensive performance analysis is conducted to demonstrate the effectiveness of our SPACDC scheme. Furthermore, we present a secure and private distributed learning algorithm based on the SPACDC scheme, which can provide information-theoretic privacy protection for training data. Our experiments show that the SPACDC-based deep learning algorithm achieves a significant speedup over the baseline approaches. © 2013 IEEE.

关键词： Parallel processing systems

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LDA-UNet: A Lightweight UNet with Multi-Dimensional Selective Attention for Efficient Skin Lesion Segmentation

LDA-UNet: A Lightweight UNet with Multi-Dimensional Selectiv...

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Artificial Intelligence and computer engineering (ICAICE), International Conference on

作者： Qingyang Gao Jingjing Ma Faculty of Software and Computer Engineering ITMO University Saint Petersburg Russian Federation College of Science Sichuan Vocational College of Finance and Economics Chengdu China

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

Different UNet models do well in tasks like semantic segmentation. Yet, these models still do not use detailed features enough and have weak global feature understanding. UNet links the encoder and decoder with skip connections. This lets the decoder get detailed info from the encoder, which helps the model work better. But having skip connections for just one set of encoder and decoder is not enough to use all the multi-level detailed features. Plus, UNet builds the encoder and decoder with convolution methods. Because convolutional operations have a small view of the data, the model's ability to understand the whole set of data is still low. To solve the problems of weak feature use and poor overall modeling, we suggest a simple fix: a light Multi-Dimensional Selective Attention Enhanced UNet (LDA-UNet). To get around the small view of the UNet design, we add the Multi-Dimensional Selective Attention Block. This part boosts the model's global skill and widens the view while using fewer parameters. To fix the issue of low use of multi-level features, we use a multi-level skip connection method. This helps the decoder reach features at different sizes and improves detail capture. We tested the model on the ISIC-2018 dataset, and the results show that, when compared to other methods, this algorithm does a better job at segmentation while using fewer parameters.

关键词： Training Databases Convolution Semantic segmentation Skin Data models Decoding Lesions Artificial intelligence Diseases

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Pipeline Representation Using Generic Parallel Algorithms in Heterogeneous Parallel Computing. A comparative analysis 36

Pipeline Representation Using Generic Parallel Algorithms in...

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36th European Modeling and Simulation Symposium, EMSS 2024, Held at the 21st International Multidisciplinary Modeling and Simulation Multiconference, I3M 2024

作者： Rossainz-López, Mario Sánchez-Rinza, Bárbara López-Sosa, Zuriel Capel-Tuñon, Manuel Faculty of Computer Science Autonomous University of Puebla Av. San Claudio and 14 Sur Street San Manuel PueblaC.P. 72570 Mexico Software Engineering Department College of Informatics and Telecommunications ETSIIT University of Granada Daniel Saucedo Aranda s/n Granada18071 Spain

ISBN: (纸本)9791281988026

Using Parallel Objects and Structured Parallel Programming, the parallel representation of the Communication Pattern between Processes called Pipeline is shown, whose implementation is carried out through different models of Generic Parallel Algorithms within Heterogeneous Parallel Computing (HPC): As a Parallel Design Pattern or PDP, as High-Level Parallel Composition or HLPC and as Flow Graph Interfaces or FGI through programming with message passing. An original and effective development methodology for new programmers in parallelism is proposed for each of them. In these three proposals for Generic Parallel Algorithms, the same problem-example is solved as a case study, and a comparative analysis of their models and designs is carried out, as well as the performance obtained in the execution of said proposals and demonstrate their usefulness, programmability, and performance. The objective of this work is to provide the programmer and/or novice user with different multicore programming approaches so that without much effort they can develop their programs according to a sequential programming style, obtaining automatically, easily and the counterpart parallelization of your code with the help of a specific programming environment like the one proposed. © 2024 The Authors.

关键词： Structured programming

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Evolutionary Approach to Feature Elimination in House Price Estimation 2nd

Evolutionary Approach to Feature Elimination in House Pric...

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2nd International Conference on Advanced engineering, Technology and Applications, ICAETA 2023

作者： Günaydın, Yusuf Şevki Mintemur, Ömer Ankara Yıldırım Beyazıt University Computer Engineering Faculty of Engineering and Natural Sciences Ankara Turkey Ankara Yıldırım Beyazıt University Software Engineering Faculty of Engineering and Natural Sciences Ankara Turkey

ISBN: (纸本)9783031509193

One of the most basic human needs is the need for shelter. Since ancient times, people have been looking for a house that is both safe and affordable. However, in modern times, although safety is no longer an issue, the definition of an affordable house has changed. In the past, affordability did not depend on many parameters as it does today. However, today, this definition depends on different features, such as the location of the house, the year of construction, the number of rooms, etc. These features affect the level of affordability, and consequently the price of the house. Since houses are also used as an investment option, correct estimation of house prices is an important issue. The determination of features that have a significant impact on the price of a house is a subjective notion and, therefore, requires an objective approach. Thanks to technological developments, Artificial Intelligence algorithms remove the human factor in most of the decision-making processes. In this study, a naive approach was proposed to estimate house prices by selecting the most effective features of a house (https://***/OmerMintemur/Feature-Elimination-Using-GA.). To select the most effective features, Genetic Algorithm approach was utilized. For estimation, LightGBM was used. The AmesHouse data set was used for the experiments. The results suggested that the proposed method both reduced the features and produced lower estimation errors than other proposed methods that used the same dataset. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature Reduction Genetic Algorithm House Prices Prediction

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WiFi-based Positioning System with k-means Clustering and Outlier Removal: Evidence from Multiple Datasets 23

WiFi-based Positioning System with k-means Clustering and Ou...

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12th International Symposium on Information and Communication Technology, SOICT 2023

作者： Pham-Hai, Dang Duong-Bao, Ninh He, Jing Nguyen Thi, Luong Lee, Seon-Woo Nguyen-Huu, Khanh Department of Electronics and Telecommunications Dalat University Dalat Viet Nam College of Computer Science and Electronic Engineering Hunan University Changsha China Faculty of Information Technology Dalat University Dalat Viet Nam Division of Software Hallym University Chuncheon Korea Republic of

ISBN: (纸本)9798400708916

Indoor positioning systems are receiving much attention due to their practical applications in our daily lives. WiFi Fingerprinting is the technique that is commonly used for indoor positioning due to its utilization of the WiFi infrastructure which is ubiquitously deployed in modern buildings. However, this technique suffers from the computational burden when determining the user's position in big areas that consist of lots of fingerprints and reference points. To reduce the search space as well as computing time, the clustering algorithm can be applied. In this paper, we evaluate the efficacy of k-means clustering in the WiFi Fingerprinting-based positioning system. Moreover, k-means clustering with outlier removal is applied in several public datasets, in which the WiFi data was collected in different environmental conditions, to show how much the clustering algorithm can help in reducing the computing time as well as improving the positioning accuracy. From the experimental results, compared to popular deterministic-based algorithms, the clustering algorithm can significantly reduce the computing time by up to 79.07%. Nevertheless, it cannot surpass the positioning accuracy of the compared algorithms. © 2023 ACM.

关键词： Indoor positioning systems

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Synthetic Minority Over-sampling Technique for Student Performance Prediction: A Comparative Analysis of Ensemble and Linear Models

Synthetic Minority Over-sampling Technique for Student Perfo...

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International Conference on computer and Information Technology (ICCIT)

作者： Khaled Mahmud Sujon Rohayanti Hassan Nusrat Jahan Department of Software Engineering Faculty of Computing Universiti Teknologi Malaysia (UTM) Johor Malaysia Faculty of Computing Universiti Teknologi Malaysia (UTM) Johor Malaysia Department of Computer Science and Engineering Chittagong University of Engineering & Technology(CUET) Chittagong Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

This study explores the impact of the Synthetic Minority Oversampling Technique (SMOTE) on predictive accuracy in student performance prediction using both linear (Logistic Regression, LR) and ensemble (Random Forest, RF) models. We compare the performance of these models, using a real-time dataset from Universiti Teknologi Malaysia (UTM). While previous studies have examined these models separately, this research uniquely investigates the simultaneous application of SMOTE to both. Our results show that LR’s accuracy improves from 91% to 95% with SMOTE, while RF maintains a consistent 98% accuracy. These findings demonstrate that SMOTE significantly enhances the performance of linear models in handling imbalanced data, providing useful insights for curriculum development in higher education. This research contributes to the literature by guiding higher education institutions in selecting the most effective predictive models.

关键词： Radio frequency Analytical models Logistic regression Accuracy Computational modeling Education Predictive models Data models Real-time systems Information technology

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Service Migration for Delay-Sensitive IoT Applications in Edge Networks

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IEEE Transactions on Services Computing 2025年第3期18卷 1782-1797页

作者： Li, Xiaocui Zhou, Zhangbing Wang, Yasha Deng, Shuiguang Hung, Patrick C. K. School of Information Engineering China University of Geosciences Beijing100083 China Peking University National Research & Engineering Center of Software Engineering Beijing100871 China Peking University New Generation Information Technology Research Institute Tianjin300380 China School of Information Engineering Frontiers Science Center for Deep-Time Digital Earth Beijing100083 China Télécom SudParis Institut Polytechnique de Paris Palaiseau91120 France Zhejiang University College of Computer Science and Technology Hangzhou310012 China Ontario Tech University Faculty of Business and Information Technology OshawaONL1G 0C5 Canada

The proliferation of Internet of Things (IoT) applications prompts extraordinary demands for the collaboration of large amounts of computational resources provided by IoT devices in edge networks, and these applications are mostly delay-sensitive. Generally, these resources are encapsulated as IoT services. Thereafter, IoT applications can be performed, such that the collaboration of their sub-tasks is achieved through the composition of functionally complementary and geographically contiguous IoT services. The status of computational resources in IoT devices may change continuously along with their occupancy and release by IoT services. Considering the resource-scarceness of IoT devices, when the workload of IoT devices increases due to more services to be processed, certain IoT devices may hardly have enough remaining resources to co-host more instances of certain IoT services prescribed by forthcoming IoT applications with strict constraints. As a result, the delay satisfaction of both on-running and forthcoming IoT applications may be negatively impacted, or even hardly be satisfied any longer. To solve this issue, this paper proposes a rEsource-Efficient service Configuration (E2rC) mechanism, which aims to optimize the configuration of computational resources provided by IoT devices with respect to complex requirements prescribed by IoT applications, through service migration techniques. This service migration problem is formulated as markov multi-phases decisions, which is solved through our enhanced Deep Reinforcement Learning (DRL) approach with a two-layer Q-network. Extensive experiments have been conducted upon the dataset of our testbed system. Evaluation results show that our E2rC is more efficient than the state-of-art counterparts in satisfying delay constraints of IoT applications, while reducing the energy consumption and improving the resource utilization efficiency of IoT devices. © 2008-2012 IEEE.

关键词： Markov processes

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An Asymmetrical Amplitude Digitized Modulation Scheme for TP-IBMC in Unbalanced Three-Phase IPT Systems

An Asymmetrical Amplitude Digitized Modulation Scheme for TP...

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IEEE Annual Southern Power Electronics Conference (SPEC)

作者： Zhihao He Duleepa J. Thrimawithana Martin Neuburger Grant A. Covic Department of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand Faculty of Mobility and Technology Esslingen University of Applied Sciences Goppingen Germany

ISBN: (数字)9798350351156

ISBN: (纸本)9798350351163

This paper proposes a three-phase asymmetrical amplitude digitized modulation (TP-AADM) scheme for a three-phase integrated boost multi-level converter (TP-IBMC) utilized on an inductive power transfer (IPT) system with a three-to-three coupler and the double-sided LCL tuning network. It can control these output voltages driving the coupler independently in order to generate balanced currents in the secondary coils or the rectifier with the misalignment situation while the conventional solutions require three separated H-bridge converters. A simulation model in accordance with the SAEJ2954 WPT3/Z3 indicates the performance in the misaligned IPT system.

关键词： Multilevel converters Modulation Rectifiers Couplers Wireless power transfer Rendering (computer graphics) Regulation Voltage control Tuning Standards

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On the Stability of Strategic Energy Storage Operation in Wholesale Electricity Markets (Extended Version)

arXiv

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arXiv 2024年

作者： Navon, Aviad Belikov, Juri Orda, Ariel Levron, Yoash The Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering Technion—Israel Institute of Technology Israel The Department of Software Science Tallinn University of Technology Estonia

High shares of variable renewable energy necessitate substantial energy storage capacity. However, it remains unclear how to design a market that, on the one hand, ensures a stable and sufficient income for storage firms, and, on the other hand, maintains stable and affordable electricity costs for the consumers. Here, we use a game theoretic model to study storage competition in wholesale electricity markets. A main result is that these types of games are not necessarily stable. In particular, we find that under certain conditions, which imply a combination of a high share of variable renewable energy sources and low flexibility of conventional power plants, the system will not converge to an equilibrium. However, we demonstrate that a price cap on storage price bids can ensure convergence to a stable solution. Moreover, we find that when the flexibility of conventional power plants is low, while the storage usage for energy balancing increases with renewable energy generation, the profitability of using storage for the sole purpose of energy arbitrage decreases. Copyright © 2024, The Authors. All rights reserved.

关键词： Game theory

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Optimized Flame Detection Using a YOLOv5 Neural Network

Optimized Flame Detection Using a YOLOv5 Neural Network

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2024 International Conference on Computing, Robotics and System Sciences, ICRSS 2024

作者： Karim, Shahid Ibrar, Muhammad Bilal, Anas Qadir, Akeel Bibi, Irfana School of Information Engineering Xi'an Eurasia University Xi'an710065 China Software College Shenyang Normal University Shenyang China College of Information Science and Technology Hainan Normal University Haikou571158 China Faculty of Computing and Information Technology University of Punjab Department of Computer Science Punjab Lahore Pakistan

ISBN: (纸本)9798331511296

With the rising prevalence of smart homes, there's an increasing demand for comprehensive automation solutions to mitigate fire risks, especially when homeowners are absent or in homes with elderly residents. This study focuses on enhancing flame detection techniques, particularly for smaller, hard-to-detect fire sources. A customized Yolov5 neural network was developed to improve detection precision. This enhancement was facilitated using a dataset of over 2,000 images from lab camera observations and online platforms. These images were then manually labeled and clustered for anchor dimensions using the K-means++ algorithm. The CIOU and diou - nms techniques were integrated to optimize the network's performance. Upon training, the refined model was tested on the Jet Nano, revealing its high efficacy in flame detection. © 2024 IEEE.

关键词： Smart homes

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