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共 6977 条记录，以下是4871-4880 订阅

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Efficient Node Selection for Coding-based Timely Computation over Heterogeneous Systems

Efficient Node Selection for Coding-based Timely Computation...

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IEEE international conference on Big data and Cloud computing (BdCloud)

作者： Yuxuan Lin Bin Tang Siyuan Zhou Zaipeng Xie Baoliu Ye Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China School of Computer and Information Hohai University Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

In large-scale distributed systems, computational nodes often experience random slowdown which can degrade the performance of timely computation tasks significantly. Recently, coded computing has emerged as a promising approach where coded subtasks are generated for parallel execution over different nodes. In this paper, we focus on exploring the tradeoff between the performance of coding-based timely computation tasks and the computation cost in distributed systems with heterogeneous nodes. We introduce a node selection problem (NSP), which aims at minimizing the task failure probability by selecting proper participating nodes under a total cost constraint. We show that NSP is NP-hard due to the total cost constraint, and is more challenging since the task failure probability is a complicated multivariate polynomial. By approximating the task failure probability using central limit theorem and establishing relationships with certain integer linear programming problems, we then propose a linear programming relaxation based algorithm with performance guarantee, as well as an efficient Lagrangian relaxation inspired heuristic algorithm. The effectiveness of the proposed node selection algorithms is further demonstrated via both trace-driven and model-driven simulations.

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High-Speed data Transfer using AXI Protocol for Image Detection

High-Speed Data Transfer using AXI Protocol for Image Detect...

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Innovation in Technology (INOCON), IEEE international conference for

作者： Rajat Raj Sharat Kumar Miskin Naman Bhatia Electronics and Communication Engineering RV College of Engineering Bengaluru India

Convolutional neural networks (CNN) due to their excellent accuracy, have emerged as the leading machine learning method. However, it becomes difficult to deploy CNN algorithms on hardware platforms because of their high memory bandwidth, computational complexity, and power consumption. Hardware accelerators such as Field Programmable Gate Arrays (FPGA), Graphics processing Unit (GPU), etc. are excellent platforms for modelling CNN algorithms. The purpose of the paper is to run a standard software process in hardware and exploit natural parallelism in hardware to speed up neural network prediction. The paper presents a rule-based synthesizable Advanced Microcontroller Bus Architecture (AMBA) Advanced eXtensible Interface (AXI) protocol for high-speed data transmission. The paper develops a computer vision system on an FPGA that could recognize the numbers 0 through 9 written by hand for the capstone design project. The findings show that 91% accuracy is achieved through Rectified linear activation unit (ReLU) which can be increased to 98% by implementing the Sigmoid activation function with a depth of 10 bits. The method provides good accuracy and clock performance while using few resources.

关键词： Protocols Machine learning algorithms Computational modeling Software algorithms Neural networks Graphics processing units Parallel processing

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Towards Industry data Governance: Construction of An Industrial data Decentralized Distributed Symbiotic Sharing Space Based on Tensor

Towards Industry Data Governance: Construction of An Industr...

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international conference on Automation and computing (ICAC)

作者： Gang Chen Kai Zhao Minghao Wang Xufei Wang Yi Shen Shanghai Open Source Big Data Institute Shanghai China ZyllionData Technology (Shanghai) Co. Ltd. shanghai China

One of the focuses of smart manufacturing technology is data management and processing. However, there are issues which is difficult to integrate the data within the enterprise, share industry chain data and obtain open data in the management and processing of existing industrial data. To address this challenge, this paper proposes a construction method of industrial data decentralized distributed symbiotic sharing space (iDS3) based on tensor. The proposed method consists of three core steps. First, metadata is extracted from multi-source heterogeneous raw data; Second, a knowledge graph is constructed using industrial mechanism to enrich the metadata; Finally, tensors are used to store metadata and its corresponding unique identifier ID. A metadata encoding rule based on industrial mechanisms is adopted in this work, which can support tensor queries in different application scenarios, thereby achieving ubiquitous storage of data by solving the distributed symbiotic sharing of multi-source heterogeneous raw data. At present, the industrial data governance platform (iDGP) developed based on iDS3 has been applied to the data integration of industrial enterprises such as ships, integrated circuits and new energy vehicles, the integration development with various the third-party data processing tools, and application services of industry dataspaces and so on, which shows that iDS3 has been a successful solution for the development of technology-driven Industry 4.0 to value-driven Industry 5.0.

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Restoration of Time-Varying Graph Signals using Deep Algorithm Unrolling

Restoration of Time-Varying Graph Signals using Deep Algorit...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Hayate Kojima Hikari Noguchi Koki Yamada Yuichi Tanaka Department of Electrical Engineering and Computer Science Tokyo University of Agriculture and Technology Department of Electrical Engineering Tokyo University of Science Graduate School of Engineering Osaka University

In this paper, we propose a restoration method of time-varying graph signals, i.e., signals on a graph whose signal values change over time, using deep algorithm unrolling. Deep algorithm unrolling is a method that learns parameters in an iterative optimization algorithm with deep learning techniques. It is expected to improve convergence speed and accuracy while the iterative steps are still interpretable. In the proposed method, the minimization problem is formulated so that the time-varying graph signal is smooth both in time and spatial domains. The internal parameters, i.e., time domain FIR filters and regularization parameters, are learned from training data. Experimental results using synthetic data and real sea surface temperature data show that the proposed method improves signal reconstruction accuracy compared to several existing time-varying graph signal re- construction methods.

关键词： Temperature distribution Sea surface Smoothing methods Finite impulse response filters Signal processing algorithms Training data Time-domain analysis

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Machine Learning algorithms for Filtering data Acquired by Arduino 1

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international conference on Computer Science, Electronics and Industrial Engineering, CSEI 2023

作者： Llerena, Mateo López, Jessica Cumbajin, Myriam Gordón, Carlos GITED Research Group Facultad de Ingeniería en Sistemas Electrónica e Industrial Universidad Técnica de Ambato UTA Ambato180207 Ecuador SISAu Research Center Facultad de Ingeniería Industria y Producción Universidad Tecnológica Indoamérica UTI Ambato180103 Ecuador

ISBN: (数字)9783031709814

ISBN: (纸本)9783031709807

In this paper, we developed an artificial intelligence-based data filtering algorithm to improve the accuracy and reliability of MPU6050 sensor measurements. Using the Edge Impulse platform, we trained and optimized the machine learning model for real-time processing of accelerometer and gyroscope readings. The solution was implemented on the ESP32 with a sampling rate of 600 Hz. The experimental results validated the effectiveness of the solution, highlighting its relevance in applications requiring real-time monitoring. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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ICCVAE: Item Concept Causal Variational Auto-Encoder for top-n recommendation

ICCVAE: Item Concept Causal Variational Auto-Encoder for top...

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6th international conference on Intelligent computing and Signal processing (ICSP)

作者： Jingyun Feng Qianqian Wang Zhejun Huang Lili Yang Department of Statistics and Data Science Southern University of Science and Technology Shenzhen China

Recently, recommendation systems, especially interpretable ones, have become increasingly popular. The recommendation system provides personalized recommendations to users based on their previous behaviour data. Existing approaches often transfer methods in disentangle learning, especially Variational Auto-Encoder (VAE) framework, into the recommendation system. However, VAE is proved sub-optimal due to the independence factor assumption. Unfortunately, few research focuses on the VAE framework without an independent assumption for recommendation system. To escape from sub-optimality from independence assumption and provide more interpretability, in this paper, we propose a new method for top-n recommendation tasks called Item Concept Causal Variational Auto-Encoder (ICCVAE), enabling to build up causal structure for factors, i.e., item concepts in recommendation system. We conduct experiments to prove superiority for ICCVAE. Our framework can reach up to 5.3%, 3.4%, and 6.1% definite improvement over baselines in terms of Recall@20, Recall@50 and NDCG@100.

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Congestion-aware Optimal data rate Selection for Efficient Routing in Wireless Rechargeable Sensor Network

Congestion-aware Optimal Data rate Selection for Efficient R...

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Advances in computing, Communication, Embedded and Secure Systems (ACCESS), international conference on

作者： Siron Anita Susan T B Nithya Abdulla Ameen Sarvesh Rajkumar Computer Science and Engineering National Institute of Technology Tiruchirappalli India

Wireless Rechargeable Sensor Nodes (WRSNs) are widely used to sense, collect, and process data or signals transmitted among people, the environment, and computer systems. However, it faces severe threats such as congestion, resource deprivation, security risk, limited communication speed, high cost, limited bandwidth, and so on. To address some of these issues and to improve the QoS, this paper proposes an Adaptive Rate Determined Congestion Management (ARDCM) approach. It adopts a feedback-based system in which the incoming data rate is managed to increase when congestion is low and decrease when congestion is high. It guarantees congestion free data flow between upstream and downstream in data communication by controlling the data rate and buffer occupancy. The proposed approach is simulated on NS2 with varying numbers of active nodes in a simulated environment. The proposed model’s performance is evaluated by comparing the Packet Delivery Ratio(PDR), Packet Loss Ratio(PLR), delay, and throughput to the existing algorithms.

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The Role of Exploratory data Analysis and Pre-processing in the Machine Learning Predictive Model for Heart Disease

The Role of Exploratory Data Analysis and Pre-processing in ...

引用

Advances in computing, Communication and Applied Informatics (ACCAI), international conference on

作者： K. Mahalakshmi P. Sujatha School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Chennai India Women’s Christian College Chennai Department of Information Technology Vels Institute of Science Technology & Advanced Studies Chennai

The heart is the organ which pumps blood to the human body. There is an important need for early disease prediction systems for heart related problems. The disease prediction system supports the doctors to diagnose critical diseases as early as possible. The disease prediction system is built using field knowledge of the healthcare profession and machine learning (ML), artificial intelligence. This research paper aims to explore the role of Exploratory data Analysis [EDA] and pre-processing of heart disease (HD) data for the prediction of HD. This research explores three ML classifiers, namely Random Forest (RF), Support Vector Machine (SVM) and Decision Tree (DT) with missing value imputers, feature scaling techniques, data analytics, and visualization tools using four benchmark HD datasets from the UCI repository. Imputation methods mean, median, most frequent, constant, KNN imputer and iterative imputer with scaling of features were analysed with the help of three classifiers RF, SVM and DT. Random Forest with an iterative imputer achieved 86.41% of accuracy.

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Evolution of Machine Learning Methods in Linear B-Cell Epitope Prediction

Evolution of Machine Learning Methods in Linear B-Cell Epito...

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international conference on computing for Sustainable Global Development (INDIACom)

作者： Hufsa Manzoor Arif Wani Department of Computer Sciences University of Kashmir Kashmir India

The study of B-cell antigen interactions is pivotal for advances in design of vaccines, antibody production, or structural studies based on epitopes. Epitope determination is a colossal and time-consuming process and many methods and techniques have been implemented for the same. The current generation prediction methods are highly evolved and have incorporated recent advances in machine learning and data processing. This review comprises an evaluation of predictors with a focus on current generation methods that integrate recent techniques such as sequence encoding, fuzzy network, feature maps to aid in design of more comprehensive linear b-cell epitope predictors. Further, the tools, techniques and working of these current generation methods along with details of the methodologies used by each of them is discussed. This study analyzes the performance of these methods and provides comparison for each. It concludes by identifying and highlighting the pathways for further enhancements in the design of high performing linear b-cell epitope predictors.

关键词： Proteins Deep learning Limiting Statistical analysis Prediction methods Production data processing

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Evaluation of Categorical Generative Models - Bridging the Gap Between Real and Synthetic data

Evaluation of Categorical Generative Models - Bridging the G...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Florence Regol Anja Kroon Mark Coates Dept. of Electrical and Computer Engineering McGill University Montréal Québec Canada

The machine learning community has mainly relied on real data to benchmark algorithms as it provides compelling evidence of model applicability. Evaluation on synthetic datasets can be a powerful tool to provide a better understanding of a model’s strengths, weaknesses and overall capabilities. Gaining these insights can be particularly important for generative modeling as the target quantity is completely unknown. Multiple issues related to the evaluation of generative models have been reported in the literature. We argue those problems can be avoided by an evaluation based on ground truth. General criticisms of synthetic experiments are that they are too simplified and not representative of practical scenarios. As such, our experimental setting is tailored to a realistic generative task. We focus on categorical data and introduce an appropriately scalable evaluation method. Our method involves tasking a generative model to learn a distribution in a high-dimensional setting. We then successively bin the large space to obtain smaller probability spaces where meaningful statistical tests can be applied. We consider increasingly large probability spaces, which correspond to increasingly difficult modeling tasks, and compare the generative models based on the highest task difficulty they can reach before being detected as being too far from the ground truth. We validate our evaluation procedure with synthetic experiments on both synthetic generative models and current state-of-the-art categorical generative models.

关键词： Machine learning algorithms Machine learning Probability Signal processing Benchmark testing Aerospace electronics data models

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