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IPL data Analysis and Visualization for Team Selection and Profit Strategy

IPL Data Analysis and Visualization for Team Selection and P...

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international conference on computing Methodologies and Communication (ICCMC)

作者： Saranya G Aravind Swaminathan Joel Benjamin J Surendran R Leema Nelson Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai campus Chennai India MSc student Data Science Middlesex University Dubai Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Chitkara University Institute of Engineering & technology Chitkara University Punjab India

Day by day, the role of data science and machine learning in cricket is increasing due to the large amount of data generated from a single player on a whole line. The field of data science is the intensive study of data to extract insights and knowledge from the data and apply the acquired knowledge and actionable insights. These available data and statistics are used to predict things like the team's first-innings score and the probability of winning the second team, etc. This study works on Indian Premier League (IPL) data Analysis 2022 and data Visualization for the duration (2008–2020) using Python. This application modules are followed by preprocessing, data analysis, and visualization, and finally create a model that predicts the team's overall score and the probability of winning. When building models, python algorithms such as Numpy is used for Scientific computing, Pandas used to perform data Analysis, and finally Matplotlib and Seaborn to enable data Visualization. Finally, this paper helps the trainers and owners of the team during the auction to select an emerging player to win the matches in the entire season and to win the trophy and to get profit to the owners by the profit strategy done in this analysis report.

关键词： Analytical models Scientific computing Computational modeling data visualization Machine learning data science Probability

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Intelligent processing of data Streams on the Edge Using Reinforcement Learning

Intelligent Processing of Data Streams on the Edge Using Rei...

引用

IEEE international conference on Communications Workshops, ICC

作者： Shubham Vaishnav Sindri Magnússon Department of Computer and Systems Sciences Stockholm University Sweden

A key challenge in many IoT applications is to en-sure energy efficiency while processing large amounts of streaming data at the edge. Nodes often need to process time-sensitive data using limited computing and communication resources. To that end, we design a novel R - Learning based Offloading framework, RLO, that allows edge nodes to learn energy optimal decisions from experience regarding processing incoming data streams. In particular, when should the node process data locally? When should it transmit data to be processed by a fog node? And when should it store data for later processing? We validate our results on both real and simulated data streams. Simulation results show that RLO learns with time to achieve better overall-rewards with respect to three existing baseline schemes. Moreover, the proposed algorithm excels the existing baseline schemes when different priorities were set on the two objectives. We also illustrate how to adjust the priorities of the two objectives based on the application requirements and network constraints.

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Exploring Instance Relation for Decentralized Multi-Source Domain Adaptation

Exploring Instance Relation for Decentralized Multi-Source D...

引用

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

作者： Yikang Wei Yahong Han College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Lab of Machine Learning Tianjin University Tianjin China Peng Cheng Laboratory Shenzhen China

Multi-source domain adaptation aims to transfer knowledge from multiple labeled source domains to an unlabeled target domain and reduce the domain shift. Considering data privacy and storage cost, the data from different domains are isolated, which leads to the difficulty of domain adaptation. To reduce the domain shift on the decentralized source domains and target domain, we propose an instance relation consistency method for decentralized multi-source domain adaptation. Specifically, we utilize the models from other domains as bridges to conduct domain adaptation. We impose inter-domain instance relation consistency on the isolated source and target domain to transfer the semantic relation knowledge across different domain models. Meanwhile, we exploit intra-domain instance relation consistency to learn the intrinsic semantic relation across different data views. Experiments on three benchmarks indicate the effectiveness of our method for decentralized multi-source domain adaptation.

关键词： Bridges Adaptation models data privacy Costs Semantics Signal processing Benchmark testing

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BMDP:Blockchain-Based Multi-Cloud Storage data Provenance

BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance

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international conference on Computer Supported Cooperative Work in Design

作者： Feiyu Wang Jian-Tao Zhou Xu Guo College of Computer Science Inner Mongolia University Hohhot Inner Mongolia China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

In a multi-cloud storage system, provenance data records all operations and ownership during its lifecycle, which is critical for data security and audibility. However, recording provenance data also poses some challenging security and storage issues. In this paper, we present a secure and efficient multi-cloud storage data source scheme, BMDP. We use blockchain technology to ensure the secure storage of provenance data and design a smart contract to utilize the provenance data to ensure the proper operation of the multi-cloud storage system. Finally, we analyze the scheme’s safety and do simulation experiments to show that the scheme has practicality.

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A Power-System Economic Dispatching Based on Enhanced Group Search Optimizer 1

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18th international conference on Intelligent Information Hiding and Multimedia Signal processing, IIH-MSP 2022

作者： Pan, Jeng-Shyang Nguyen, Trong-The Nguyen, Trinh-Dong Nguyen, Vinh-Tiep Dao, Thi-Kien College of Computer Science and Engineering Shandong University of Science and Technology Qing-Dao266590 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan University of Information Technology Ho Chi Minh City700000 Viet Nam Vietnam National University Ho Chi Minh City700000 Viet Nam Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China

ISBN: (数字)9789819901050

ISBN: (纸本)9789819901043

Economic dispatch (ED) is one of the critical tasks in a power system’s optimization issues, but its characteristics with non-convex, high-dimensional, non-linear, and non-differentiable, so that caused solutions are more challenging in reality. This paper suggests an enhanced group search optimizer (EGSO) for solving the ED model with the valve-point effect and the different fuels. The EGSO adopts the original group search optimizer (GSO) by changing polar to Cartesian coordinate’s conversion and adding cyclic translation operators to the search behavior of the finder and walker to avoid falling into local optimal and slow convergence of the GSO. In the experiment section, the testing power systems of the ED model are used to validate the EGSO performance. The experimental results reveal that EGSO outperformed the other algorithms regarding convergence speed, the time required to complete calculations, and impacts with lower fuel cost. Additionally, with excellent outcomes in optimization issues involving multiple local optimal values show the EGSO is practical, quick, and efficient in addressing the challenging ED model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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Enabling Surrogate-Assisted Evolutionary Reinforcement Learning via Policy Embedding 1

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17th international conference on Bio-Inspired computing: Theories and Applications, BIC-TA 2022

作者： Tang, Lan Li, Xiaxi Zhang, Jinyuan Li, Guiying Yang, Peng Tang, Ke Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Faculty of Engineering Shenzhen MSU-BIT University Shenzhen518172 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

Evolutionary Reinforcement Learning (ERL) that applying Evolutionary algorithms (EAs) to optimize the weight parameters of Deep Neural Network (DNN) based policies has been widely regarded as an alternative to traditional reinforcement learning methods. However, the evaluation of the iteratively generated population usually requires a large amount of computational time and can be prohibitively expensive, which may potentially restrict the applicability of ERL. Surrogate is often used to reduce the computational burden of evaluation in EAs. Unfortunately, in ERL, each individual of policy usually represents millions of weights parameters of DNN. This high-dimensional representation of policy has introduced a great challenge to the application of surrogates into ERL to speed up training. This paper proposes a PE-SAERL Framework to at the first time enable surrogate-assisted evolutionary reinforcement learning via policy embedding (PE). Empirical results on 5 Atari games show that the proposed method can perform more efficiently than the four state-of-the-art algorithms. The training process is accelerated up to 7x on tested games, comparing to its counterpart without the surrogate and PE. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Batch Normalization Damages Federated Learning on NON-IID data: Analysis and Remedy

Batch Normalization Damages Federated Learning on NON-IID Da...

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

作者： Yanmeng Wang Qingjiang Shi Tsung-Hui Chang School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China School of Software Engineering Tongji University Shanghai China

Batch normalization (BN) has been widely used for accelerating the training of deep neural networks. However, recent findings show that, in the federated learning (FL) scenarios, BN can damage the learning performance when the clients have non-i.i.d. data. While several FL schemes have been proposed to address this issue, they still suffer a significant performance loss compared to the centralized scheme. In addition, none of them have explained how the BN impacts the FL convergence analytically. In this paper, we present the first convergence analysis to show that the mismatched local and global statistical parameters due to non-i.i.d data cause gradient deviation and it leads the algorithm to converge to a biased solution with a slower rate. To remedy this, we further present a new FL algorithm, called FedTAN, based on an iterative layer-wise parameter aggregation procedure. Experiment results are presented to show the superiority of FedTAN.

关键词： Training Deep learning data analysis Federated learning Neural networks Signal processing algorithms Signal processing

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data Leakage Detection Using Secret Key Exchange

Data Leakage Detection Using Secret Key Exchange

引用

international conference on Edge computing and Applications (ICECAA)

作者： D. Jyothirmai B. Vineela R. Pitchai B. Dinesh M. Indhu A.Gaurav Avinash B V Raju Institute of Technology Narsapur Telangana India

data leakage is a significant problem in many industries and institutions. While there are different methods for protecting data using various encryption algorithms, this technology doesn't provide complete security. In some cases, a data distributor may give sensitive data to trusted agents (third parties), and some of the data may be leaked in an unauthorized place. In such cases, the distributor must assess the likelihood that the leaked data came from one or more of the agents, as opposed to having been independently. This paper proposes strategies for allocating data across agents to improve the probability of identifying leaks in the event of data leakage. The proposed methods do not rely on altering the released data like watermarks. Instead, they focus on distributing the data in a way that makes it easier to identify the source of the leak. The paper highlights that data leakage is a significant problem that can lead to a loss of money, damage to reputation, and more. It can occur due to security vulnerabilities, poor data protection practices, or accidentally by a user. Therefore, it's essential to take measures for avoiding data leakage and protect data. Using the proposed strategies, the distributor can improve chances of identifying the source of the leak and taking appropriate action to prevent data leakage.

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Audio-Visual Speech Enhancement with a Deep Kalman Filter Generative Model

Audio-Visual Speech Enhancement with a Deep Kalman Filter Ge...

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

作者： Ali Golmakani Mostafa Sadeghi Romain Serizel Université de Lorraine CNRS Inria LORIA Nancy France

Deep latent variable generative models based on variational autoencoder (VAE) have shown promising performance for audio-visual speech enhancement (AVSE). The underlying idea is to learn a VAE-based audio-visual prior distribution for clean speech data, and then combine it with a statistical noise model to recover a speech signal from a noisy audio recording and video (lip images) of the target speaker. Existing generative models developed for AVSE do not take into account the sequential nature of speech data, which prevents them from fully incorporating the power of visual data. In this paper, we present an audio-visual deep Kalman filter (AV-DKF) generative model which assumes a first-order Markov chain model for the latent variables and effectively fuses audio-visual data. Moreover, we develop an efficient inference methodology to estimate speech signals at test time. We conduct a set of experiments to compare different variants of generative models for speech enhancement. The results demonstrate the superiority of the AV-DKF model compared with both its audio-only version and the non-sequential audio-only and audio-visual VAE-based models.

关键词： Visualization Lips Signal processing algorithms Speech enhancement Signal processing Markov processes data models

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An Improved Scheduling Algorithm Based on Jellyfish Search for Measurement data Process System

An Improved Scheduling Algorithm Based on Jellyfish Search f...

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Computer Science and Automation Technology (CSAT), international conference on

作者： Bin Fang Shi Zhu Qi Yu Li Li Yijia Li Jiayuan Lou Information and Communication Branch of State Grid Hunan Electric Power Company Limited Changsha China Nanjing NARI Information and Communication Technology Nanjing City China School of Computer Science and Engineering Central South University Changsha China

Parallel computing of real-time data is an effective solution to the problem of timely analysis with the rapid increase of real-time data in today's power grid. Currently, most data parallel processing methods do not consider the characteristics of the real-time power measurement data to improve efficiency. This paper proposes an improved jellyfish search method for data process task scheduling, which considers time window segmentation based on data arrival characteristic. The method proposed in this paper can improve the processing speed of realtime power measurement data with resources reduction.

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