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Cricket Performance Analysis System Using Machine Learning Techniques

Cricket Performance Analysis System Using Machine Learning T...

引用

Sustainable Communication Networks and Application (ICSCNA), international conference on

作者： Suraj Mishra Animesh Garg Vansh Batra Devansh Om Saxena Gouthaman. P Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology India Department of Networking and Communications Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

The Cricket Performance Analysis System based on big data tools, like Hadoop and Apache Spark, gives realtime deep insights into performances from players and teams and conditions in matches based on large-scale cricket match data. It scales up speedily and finds patterns using distributed computing capabilities of Spark, which makes it possible to take decisions around performance improvements and strategies. The system applies a set of machine learning techniques including K-means clustering, Logistic Regression, and Random Forest classifiers to be used for the analysis of player performance metrics and match outcomes. Thus, through the conversion of raw data into actionable insights, teams can improve their strategies, make better improvements in performance, and thus gain an upper hand against other competing professional cricket teams. As a result, teams are best positioned to make adjustments in good time and direct finer tactics toward further enhancement of overall performance.

关键词： Measurement Hands Logistic regression Green communications Decision making Cluster computing Big Data Performance analysis Sparks Random forests

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LinuxFP: Transparently Accelerating Linux networking

LinuxFP: Transparently Accelerating Linux Networking

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international conference on distributed computing Systems

作者： Marcelo Abranches Erika Hunhoff Rohan Eswara Oliver Michel Eric Keller University of Colorado Boulder Princeton University

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

This paper introduces transparent acceleration into the Linux networking stack. To do so, we build on years of research in creating high-performance software-based packet processing systems. Rather than treating these technologies as alternative pipelines, we leverage the technology to create explicit fast paths in the Linux kernel. With this, Linux still serves as a complete implementation of all its supported protocols, but frequent operations on the critical path can be transparently han-dled by a fast path. We implement a controller that continuously introspects the Linux kernel to determine exactly what packet-processing functionality is currently configured. The controller then synthesizes and deploys a minimal fast past into the packet processing pipeline that only implements functionality that is currently needed. In this way, common command line tools, such as brctl, control plane software, such as FRRouting (FRR), and higher-level management frameworks such as Kubernetes and Ansible, work without modification and transparently benefit from a faster network data plane. Our system, LinuxFP, includes a controller that can implement IP forwarding, bridging, and IP filtering fast paths that are synthesized on-demand using their specific and current configuration in the kernel. We evaluate performance improvements using Linux management tools and a Kubernetes network plugin. We show performance improvements over Linux for packet forwarding of 77 % and 20 % for an unmodified Kubernetes network plugin.

关键词： Protocols Filtering Linux Pipelines Process control distributed databases Software

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The Insta-Comparison Game: The Relationship between Social Media Use, Social Comparison, and Depression 7

The Insta-Comparison Game: The Relationship between Social M...

引用

7th Information Systems international conference, ISICO 2023

作者： Ahmad, Rahayu Hassan, Syahida Ghazali, Norhasyimatul Naquiah Shahatha Al-Mashadani, Abdul Razak F. School of Computing Universiti Utara Malaysia Sontok Kedah06010 Malaysia Sohar Univerisity 3111 Al Jamiah Street Sohar Sohar Sohar311 Oman

Instagram popularity and extended usage has been associated with several adverse outcomes towards mental health especially among youth. However, despite numerous studies on the matter, most studies often conceptualized usage on Instagram solely through frequency or time spent on Instagram. Therefore, there is a need for a more systematic study of the different types of Instagram use and their relationship with depression. Specifically, examining how active versus passive use of Instagram influence depression. This study, grounded on Social Comparison Theory, proposed a model explaining how active versus passive Instagram use relates to depression through social comparison. An online survey has been distributed on various social media platforms from December 2022 until the end of January 2023. A total of 163 responses were obtained. The model is evaluated using the structural equation modelling method. The results indicate that social comparison, which is associated with depression, is influenced by active usage and passive usage of Instagram. Furthermore, the results show that social comparison has a moderate effect size towards depression. This finding highlights the need to increase awareness among social media users to be more mindful when using social media and realize the psychological process that may detriment users' well-being. © 2023 The Authors. Published by Elsevier B.V.

关键词： Social networking (online)

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Query Performance Comparison of PostgreSQL vs. Neo4j. A Basic distributed Setup on OpenStack 23

Query Performance Comparison of PostgreSQL vs. Neo4j. A Basi...

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23rd RoEduNet conference: networking in Education and Research, RoEduNet 2024

作者： Fotache, Marin Teaca, Nicoleta Esanu, Codrin-Stefan Cluci, Marius-Iulian Pinzaru, Ciprian Gasner, Paul Cuza University of Iasi Alexandru Ioan Dept. of Accounting Information Systems and Statistics Iasi Romania Cuza University of Iasi Alexandru Ioan Iasi Romania Cuza University of Iasi Cegeka Romania Iasi Alexandru Ioan Romania Cuza University of Iasi S.C. HVM SRL IaşIasi Alexandru Ioan Romania Cuza University of Iasi Faculty of Physics Alexandru Ioan Iasi Romania

ISBN: (纸本)9798331540388

Among the NoSQL technologies, Neo4j is one of the most popular solutions for managing graph databases and an early adopter of transactions (contrary to other NoSQL Systems). Neo4j also provides a powerful high-level data processing language-Cypher. Despite its popularity, there are few comprehensive studies about benchmarking the query performance of Neo4j relative to SQL or other NoSQL counterparts. In this paper, we present a module for converting the TPC-H benchmark database from PostgreSQL to Neo4j, and we built a set of 110 SQL queries that were translated into Cypher. For both database servers, the queries were executed with a 10-minute timeout on OpenStack setups following nine scenarios by combining three database scale factors (1 GB, 5 mGB, and 10 GB) with three data distribution variants (with 1,5, and 10 nodes). Results provide support for query performance assessment of these two big data products. © 2024 IEEE.

关键词： Structured Query Language

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Scalable Adaptive PDE Solvers in Arbitrary Domains 21

Scalable Adaptive PDE Solvers in Arbitrary Domains

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international conference for High Performance computing, networking, Storage and Analysis (SC21)

作者： Saurabh, Kumar Ishii, Masado Fernando, Milinda Gao, Boshun Tan, Kendrick Hsu, Ming-Chen Krishnamurthy, Adarsh Sundar, Hari Ganapathysubramanian, Baskar Iowa State Univ Ames IA 50011 USA Univ Utah Salt Lake City UT USA

ISBN: (纸本)9781450384421

Efficiently and accurately simulating partial differential equations (PDEs) in and around arbitrarily defined geometries, especially with high levels of adaptivity, has significant implications for different application domains. A key bottleneck in the above process is the fast construction of :I 'good' adaptively-relined mesh. In this work, we present an efficient novel oclree-based adaptive discrelizalion approach capable of carving out arbitrarily shaped void regions from the parent domain: an essential requirement for fluid simulations around complex objects. Carving out objects produces an incomplete octree. We develop efficient lop -down and bottom -up traversal methods to perform finite element computations on incomplete octrees. We validate the framework by (a) showing appropriate convergence analysis and (b) computing the drag coefficient for flow past a sphere for a wide range of Reynolds numbers (O(1-10(6))) encompassing the drag crisis regime. Finally, we deploy the framework on a realistic geometry on a current. project. 10 evaluate COVID-19 transmission risk in classrooms.

关键词： Incomplete octrees matrix-free finite element method distributed memory parallelism

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A Neural Network and Bloom Filter-Based Name Search Method in Named Data networking

A Neural Network and Bloom Filter-Based Name Search Method i...

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Neural Networks, Information and Communication Engineering (NNICE), international conference on

作者： Yue Liu Xiaoyong Li Xiaotian Si Mengyang Zhang Kaiguo Yuan Pengcheng Li Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China

In a new network architecture like NDN, designing an efficient name lookup method has always been a valuable challenge. The paper studies the name lookup technology based on the Bloom filter and BPNN neural network mapping model in named data networking. In the part of the hybrid model based on the Bloom filter and BPNN, the paper proposes the fusion idea of the Bloom filter and BPNN, and provides a detailed introduction to the construction process of the hybrid model. At the same time, in order to improve the memory utilization of traditional Bloom filters, this article proposes a way to use neural networks to enhance it, and proved its effectiveness through experiments.

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On the Impact of the Traffic Pattern on Vehicular Federated Learning

On the Impact of the Traffic Pattern on Vehicular Federated ...

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IEEE international conference on Wireless and Mobile computing, networking And Communications (WiMob)

作者： Giuliano Fittipaldi Rodrigo S. Couto M. K. Costa Luís Henrique GTA/DEL-Poli/PEE-COPPE Universidade Federal do Rio de Janeiro Rio de Janeiro RJ Brazil

ISBN: (数字)9798350387445

ISBN: (纸本)9798350387452

The emergence of software-defined vehicles has brought machine learning into the vehicular domain. To support these data-driven applications, techniques to incentivize users to share their vehicle data are crucial. Federated learning trains machine learning models in a distributed manner, leveraging client data without compromising its privacy. Nonetheless, in vehicular networks, the dynamic behavior of nodes affects client availability and the global model's performance. Accordingly, this paper evaluates federated learning (FL) in a realistic vehicular network topology, accounting for real vehicle traffic in two Brazilian urban areas. The network simulation covers 3.7 km 2 with road speeds and 1,290 vehicles per hour, based on real data. We observe a performance decay in urban areas with longer vehicle permanence. Interestingly, longer vehicle participation in FL training leads to a biased model, with reduced generalization. We then improve our investigation based on the Dice-Sorensen coefficient to enhance vehicle variability over time. With 47% fewer vehicles per round, we achieve faster learning, higher convergence in the first 15 rounds, and equivalent final accuracy of 93 %.

关键词： Wireless communication Training Federated learning Network topology Roads Urban areas distributed databases Traffic control Vehicle dynamics Mobile computing

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Incorporate Personas and Emotions into Dialog Generation 5

Incorporate Personas and Emotions into Dialog Generation

引用

5th international Academic Exchange conference on Science and Technology Innovation, IAECST 2023

作者： Wu, Yufeng Liu, Chengyu Guo, Wenjian Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Beijing China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

ISBN: (纸本)9798350357738

Since the Turing test, creating believable open-domain dialogues has been a basic problem in the study of artificial intelligence. Achieving success with dialogue models can greatly improve natural human-computer interactions and open up new possibilities for applications such as chatbots for therapy. Incorporating emotional information into dialogue systems and giving them personas can enhance user satisfaction by fostering more natural communication and human-like conversations. Both of these study directions are comparatively well-established, with important accomplishments in them. On the other hand, various roles in real-life discussion scenarios portray emotions in different ways. In order to create more fulfilling and human-like discussions, we investigate in this study the integration of personas and emotions in dialogue creation systems. Through the combination of explicit and implicit persons, we tackle the problem of individualized and emotional conversation. While implicit personas are gleaned from linguistic style, colloquial idioms, and personality attributes, explicit personas are derived from user profiles. Explicit personas and emotional labels are incorporated into the dialogue agent, and implicit personas are captured through the use of latent variables in a pre-trained Transformer model. Our suggested method enhances the authenticity of discussions and user experience by producing responses that are consistent with personas and emotions, hence improving the quality of communication. © 2023 IEEE.

关键词： Human computer interaction

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SDN Security: Taming the Wild West of Network Automation

SDN Security: Taming the Wild West of Network Automation

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Blockchain and distributed Systems Security (ICBDS), IEEE international conference on

作者： Ruchika Dungarani Satish Narayan Gujjar Computer Science & Engineering University of Technology Jaipur

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

Software-Defined networking (SDN) has revolutionized computer networking by enabling unprecedented levels of programmability, flexibility, and centralized control. However, the distinctive architecture of SDN introduces novel security challenges that must be addressed to maintain the integrity and reliability of these networks. This paper offers a detailed analysis of the security issues inherent in SDN environments, focusing on vulnerabilities in the centralized control plane, programmable interfaces, and the separation between control and data *** address these challenges, the paper proposes a multi-layered security strategy, encompassing measures such as access control, secure communication protocols, flow rule verification, and secure application development. Additionally, it explores key security frameworks and tools tailored for SDN, including methods for security monitoring, incident response, and rigorous testing and validation *** case studies and real-world examples, the paper highlights the practical implementation of SDN security measures in various settings, underscoring both the benefits and the complexities involved. The discussion also extends to future research directions, particularly the security implications of emerging technologies like intent-based networking, network slicing, and edge computing. The paper concludes by recommending a holistic approach to SDN security, emphasizing the importance of automation, open standards, and collaborative efforts to ensure a secure and resilient network infrastructure.

关键词： Automation Protocols Standards organizations Collaboration Organizations Security Reliability Software defined networking Centralized control Testing

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Machine Learning Integrated Software Defined networking Architecture for Congestion Control

Machine Learning Integrated Software Defined Networking Arch...

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distributed computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： Akhbar Sha S Madhan Sandeep Neemkar Vanapala Bharath Chandra Varma Lekha S. Nair Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Data center networks rely upon the excellent throughput of the network to ensure data transfer happens at incredible speeds. Many factors can determine the network’s throughput, such as the design, routing mechanism, and congestion control model. The introduction of the cloud made it more challenging as high traffic can happen and disrupt the data transfer for the cloud applications; this is a challenge in hosting cloud services and applications. The introduction of Software Defined networking (SDN) made it possible to program the networks and thus provide scope for enhancing the congestion control mechanisms. Separating the control plane from the data plane helps in independence and extracting and processing the data from different perspectives. This paper proposes a Machine Learning approach implementable in an SDN context that helps reduce congestion control by learning data transfer patterns. We propose a modified version of SDN in which an additional layer is implemented above the control plane called ML plane. ML plane takes care of the trend learning in the network that guides the control plane for better decisions. In this paper, we exhibit the architecture of ML plane for the purpose of congestion control in busy networks.

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