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Revenue Forecasting Using distributed Machine Learning In Online Advertising Systems

Revenue Forecasting Using Distributed Machine Learning In On...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), international conference on

作者： Murugan P Esakkiammal S N. Kanagavalli P. Krishna Priya Jason Elroy Martis P. Kavitha Department of BBA Vels Institute of Science Technology and Advanced Studie (VISTAS) Pallavaram Chennai Tamilnadu India Department of Library Central University of Tamil Nadu Neelakudi Thiruvarur Tamilnadu India Department of CSE Rajalakshmi Institute of Technology Kuthambakkam Chennai Tamilnadu India KL Business School KL Education Foundation Vaddeswaram Guntur Andrha Pradesh India Department of Information Science and Engineering NMAM Institute of Technology Nitte Deemed to be University Karkala Karnataka India Department of Computer Science and Engineering R.M.K. Engineering College Tamilnadu India

This research delves into the utilization of distributed machine learning methods for revenue forecasting in the realm of online advertising systems. As the domain of digital advertising continues to expand rapidly, the accurate prediction of revenue becomes indispensable for optimizing resource allocation and making well-informed decisions. Conventional approaches frequently encounter difficulties in managing the sheer volume and intricacy of advertising data. In contrast, distributed machine learning harnesses the capabilities of parallel processing and distributed computing to efficiently process and analyze large datasets. This investigation explores diverse distributed machine learning algorithms, including gradient boosting, random forests, and neural networks, within the context of revenue forecasting. The research assesses their performance, scalability, and precision in comparison to traditional techniques. Through empirical experiments and case studies, we showcase the potential of distributed machine learning to elevate the accuracy of revenue forecasting in online advertising systems, ultimately contributing to more informed business strategies and enhanced revenue generation.

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Design of RTOS-based Wireless Remote Monitoring System for Indoor Environment 23

Design of RTOS-based Wireless Remote Monitoring System for I...

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Proceedings of the 2023 12th international conference on networks, Communication and computing

作者： Peizhi Zhu Dajun Li Lihui Guo Fanqiang Lin Chengdu University of Technology China

ISBN: (纸本)9798400709265

With the development of low-cost microcontrollers, sensors and wireless communications, intelligent environment monitoring based on the Internet of Things (IoT) has become possible. In this paper, an indoor environment monitoring system constructed by sensor network is realized. The system consists of distributed sensing nodes, which contain temperature and humidity, human infrared, voice recognition and camera sensors, etc. The nodes are connected to STM32 microcontrollers and WIFI modules and are scheduled in real time using free real time operating system(FreeRTOS). The visual and time series sensing data are transmitted to the server via WIFI in real time, and the user can monitor the system remotely by using the PYQT interface program on the PC. The system can also control home appliances by voice. The data interaction uses self-built WIFI module in AP mode for SOCKET communication. Through rigorous testing, the system demonstrates the capability of data communication, low-latency video streaming, and bi-directional control. The sensor network and real-time OS based architecture utilizes parallel execution to balance different temporal tasks and realize an agile cyber-physical system. This paper provides a reference for the underlying software infrastructure of intelligent buildings, embodying the integrated application of real-time scheduling, sensor networks and human-computer interaction.

关键词： Internet of Things Real Time Operating System STM32 data acquisition Sensor networks WIFI

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MetaWBC: POSIX-Compliant Metadata Write-Back Caching for distributed File Systems

MetaWBC: POSIX-Compliant Metadata Write-Back Caching for Dis...

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Supercomputing conference

作者： Yingjin Qian Wen Cheng Lingfang Zeng Marc-André Vef Oleg Drokin Andreas Dilger Shuichi Ihara Wusheng Zhang Yang Wang André Brinkmann Data Direct Networks (DDN) China Huazhong University of Science and Technology (HUST) China Zhejiang Lab China Johannes Gutenberg University Mainz Germany Whamcloud Inc USA Data Direct Networks (DDN) Japan Tsinghua University China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China

ISBN: (纸本)9781665454452

In parallel and distributed file systems, caching can improve data performance and metadata operations. Currently, most distributed file systems adopt a write-back data cache for performance and a write-through metadata cache for simplifying consistency. However, with modern file systems scales and workloads, write-through metadata caching can impact overall file system performance, e.g., through lock contention and heavy RPC loads required for namespace synchronization and transaction serialization. This paper proposes a novel metadata write-back caching (MetaWBC) mechanism to improve the performance of metadata operations in distributed environments. To achieve extreme metadata performance, we developed a fast, lightweight, and POSIXcompatible memory file system as a metadata cache. Further, we designed a file caching state machine and included other performance optimizations. We coupled MetaWbc with Lustre and evaluated that MetaWbc can outperform the native parallel file system by up to 8x for metadata-intensive benchmarks, and up to 7x for realistic workloads in throughput.

关键词： File systems High performance computing distributed databases Metadata Benchmark testing Throughput Synchronization

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NPV: Fast Network Policy Verification for Cloud-Native Networking

NPV: Fast Network Policy Verification for Cloud-Native Netwo...

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international conference on distributed computing Systems

作者： Shunbin Dong Yumin Xie Jin Zhao Kun Qiu School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Shanghai China

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Network policy plays a crucial role in cloud-native networking, especially in multi-tenant scenarios. It provides precise control over connectivity by specifying source and destination endpoints, traffic types, and other criteria to allow or deny traffic. However, manual configuration of these policies introduces the risk of errors, leading to isolation violations or network service unavailability. Therefore, network policy verification is essential for maintaining security and quality of service in cloud-native networking. Currently, a naïve approach involves individually checking each policy within the cluster, which can take over 100s for verification in a cluster size of over 100k. Existing verification frameworks, like Kano and Verikube, improve performance by leveraging pre-filtering and Satisfiability Modulo Theories (SMT) solvers, achieving a 3.12x to 12.99x performance boost over the naïve baseline. However, as network policy changes rapidly within 100ms in real cloud-native networks, both frameworks need over 10s to perform verification for cluster sizes over 100k, which is far from satisfying. To overcome these issues, we propose and implement a novel network policy verification framework NPV, which utilizes the policy-label pre-filter process with bitwise compression. We further enhance the policy verification algorithm with a policy-namespace divide-and-conquer strategy to improve the data-level parallelism. We implement NPV on commodity servers and evaluate its performance using real network policy datasets. Our experiments indicate that, compared with the state-of-the-art methods, NPV can achieve up to 139.00x to 651.06x improvement in verification time compared to Kano and Verikube, with 65% less memory usage.

关键词： Memory management Clustering algorithms Quality of service Manuals parallel processing Servers Security

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Generalized agent for solving higher board states of tic tac toe using Reinforcement Learning

Generalized agent for solving higher board states of tic tac...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Bhavuk Kalra Department of Computer Engineering Thapar Institute of Engineering and Technology Patiala India

ISBN: (纸本)9781665454025

Tic Tac Toe is amongst the most well-known games. It has already been shown that it is a biased game, giving more chances to win for the first player leaving only a draw or a loss as possibilities for the opponent, assuming both the players play optimally. Thus on average majority of the games played result in a draw. The majority of the latest research on how to solve a tic tac toe board state employs strategies such as Genetic Algorithms, Neural networks, Co-Evolution, and Evolutionary Programming. But these approaches deal with a trivial board state of 3X3 and very little research has been done for a generalized algorithm to solve 4X4,5X5,6X6 and many higher states. Even though an algorithm exists which is Min-Max but it takes a lot of time in coming up with an ideal move due to its recursive nature of implementation. A Sample has been created on this link Min Max Implementation to prove this fact. This is the main problem that this study is aimed at solving i.e providing a generalized algorithm(Approximate method, Learning-Based) for higher board states of tic tac toe to make precise moves in a short period. Also, the code changes needed to accommodate higher board states will be nominal. The idea is to pose the tic tac toe game as a well-posed learning problem. The study and its results are promising, giving a high win to draw ratio with each epoch of training. This study could also be encouraging for other researchers to apply the same algorithm to other similar board games like Minesweeper, Chess, and GO for finding efficient strategies and comparing the results.

关键词： Training Codes Neural networks Games Reinforcement learning Programming Grid computing

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1st international conference on Emerging Networking Architecture and Technologies, ICENAT 2022

1st International Conference on Emerging Networking Architec...

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1st international conference on Emerging Networking Architecture and Technologies, ICENAT 2022

ISBN: (纸本)9789811996962

The proceedings contain 50 papers. The special focus in this conference is on Emerging Networking Architecture and Technologies. The topics include: CQCF: A Cyclic Queuing Cluster Forwarding Based Transmission Mechanism for Large-Scale Deterministic Network;a Task-Resource Joint Optimization Model in distributed computing;cache Strategy for Information Center Satellite networks Based on Node Importance;intelligent Online Traffic Optimization Based on Deep Reinforcement Learning for Information-Centric networks;detection and Defense Schemes for Cache Pollution Attack in Content-Centric Network;SBR-based User Intent Extraction Mechanism for Intent-Based Network;SPOF-NDN: A POF-Based NDN Forwarding Scheme;customizable Service Identification Method and System Design for Smart Integration Identifier Network;a Dynamic Live Streaming Service Architecture Integrated Sensing and Control;Detection of SDN Flow Rule Conflicts Based on Knowledge Graph;Risk-Aware SFC Placement Method in Edge Cloud Environment;preface;An In-Network computing Service Placement Mechanism for NUMA-based Software Router;network Traffic Identification Method Based on Temporal and Multi-scale Spatial Fusion;a Trustworthy Content Moderation Scheme Based on Permissioned Blockchain;multi-agent Deep Reinforcement Learning-Based Content Caching in Cache-Enabled networks;mutual Authentication Protocol in a distributed Heterogeneous Environment: A Blockchain-Based Approach;performance Evaluation of Transmission Protocols Based on Satellite networks;optimizing Communication Topology for Collaborative Learning Across Datacenters;design of Reliable parallel Transmission System in Complex Heterogeneous Network;design of Privacy-Preserving in Multi-link Vehicle-Ground Communication;smart Mobile Router Wireless Heterogeneous Network Access Mechanism;A Comparative Study on Routing Convergence of IP-Based and ICN-Based Routing Protocols in Satellite networks;implementation and Deployment of Digital Twin in Cloud-Nativ

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D-KAP: A Deep Learning-based Kashmiri Apple Plant Disease Prediction Framework

D-KAP: A Deep Learning-based Kashmiri Apple Plant Disease Pr...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Himanshu Sharma Devanand Padha Nahida Bashir Department of Computer Science and Information Technology Central University of Jammu Rahya-Suchani Bagla

ISBN: (纸本)9781665454025

Apple is one of the most popular plants in the Indian-origin Kashmir valley, where it is grown on about half of the horticultural land. Every year, the apples from Kashmir are exported to other areas of the globe, creating a substantial amount of revenue. However, apple trees are prone to diseases like apple scab, alternaria leaf blotch, and apple rot, which devastate apple yields and cause major losses for apple growers. Disease in apple plants mostly originates in the leaves and causes significant losses to apple farmers. Consequently, the prompt detection or prediction of such diseases is essential in a country like India, where half of the population does farming. The early detection of apple plant diseases may enable apple producers to take the necessary precautions immediately to save the fruits from illness. The conventional methods of apple plant disease prediction are time-consuming and laborious, involving lab assistance to diagnose the apple leaves for possible diseases. With the advent of machine learning and deep learning, it is now possible to quickly determine if a plant is infected or not with reliable accuracy. In this article, we introduce D-KAP, a deep learning-based Kashmiri apple plant disease prediction framework capable of detecting the above-described diseases. For feature extraction and prediction, our model employs the advanced deep learning capabilities of Convolutional Neural networks (CNN). In conclusion, our framework produces state-of-the-art results in identifying apple plant diseases with an accuracy of 92 percent over testing samples. In addition, we also introduce a novel Kashmiri apple plant leaf dataset containing samples of three distinct diseases along with healthy leaves.

关键词： Deep learning Plant diseases Sociology Predictive models Feature extraction Grid computing Convolutional neural networks

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GANDPS: Ensuring Both Detection Precision and Stability in GAN-based Anomaly Detection for Network Traffic

GANDPS: Ensuring Both Detection Precision and Stability in G...

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

作者： Liuhuan Li Ximin Yang Xiao Zhang Yumeng Yang Xuefeng Chen Wan Tang School of Computer Science South-Central Minzu University Wuhan China Key Laboratory of Cyber-Physical Fusion Intelligent Computing State Ethnic Affairs Commission China China Telecom Group GuiZhou Co. Ltd Guiyang China

With the ever-growing network traffic and the vast amount of abnormal traffic being created, anomaly detection methods have attracted close attention in the cybersecurity domain. Generative adversarial networks (GANs) have been applied to network traffic anomaly detection, but obtaining high-precision and consistently stable detection results is still challenging for current methods. In the paper, we propose an improved GAN-based anomaly detection method called GANDPS for network traffic. The improved GAN introduces an encoder based on the original GAN, and a specialized layer is integrated into the encoder to process abnormal data. This improvement enables the encoder to effectively capture the features of normal data while distinguishing the features of abnormal data. In addition, an analogous reconstruction training strategy is employed between the generator and the encoder to balance the adversarial training between the generator and the discriminator, thereby improving the detection precision and stability of the GAN-based model. This comprehensive enhancement not only effectively elevates the detection performance of our method, but also ensures stable detection results. Experimental results on public datasets demonstrate that the proposed GANDPS outperforms multiple GAN-based methods in terms of detection precision, recall, and F1-score, and the detection results are stabilized within acceptable ranges.

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Big Data Analytics Based Recommender System for Tele-Communication Industry

Big Data Analytics Based Recommender System for Tele-Communi...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Devika Rani Roy Sitesh Kumar Sinha S. Veenadhari Rabindranath Tagore University Madhya Pradesh India

ISBN: (纸本)9781665454025

In recent times, the amount of data sent and received through wireless networks has grown quickly. Smartphones and the growth of Internet access around the world are two big reasons for this volume. Due to the current state of global health, which is mostly caused by Covid-19, telecommunications companies have a great chance to find new ways to make money by using Big Data Analytics (BDA) solutions. This is because data traffic has gone up. After all, more customers are using telecommunications services. As most of the world's data is now made by smartphones and sent through the telecom network, telecom operators are facing an information explosion that makes it harder to make decisions based on the data they need to predict how people will act. This problem was solved by making a system that sorts through information and makes suggestions based on how people have behaved in the past. Content-based filtering, collaborative filtering, and a hybrid approach are the three main ways that recommender systems filter data to solve the problem of too much data and give users relevant recommendations based on their interests and the data that is being created in real-time. Distance algorithms like Cosine, Euclidean, Manhattan, and Minkowski are at the heart of the suggested recommender system, which aims to research and design an effective recommendation strategy. The suggested model suggests different telecom packages to meet the needs of users to increase revenue per subscriber and get consumers, telecom providers, and corporations to sign long-term contracts.

关键词： Wireless networks Software algorithms Taxonomy Filtering algorithms Big Data Information filters Telecommunications

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FERI: Feature Enhancement and Relational Interaction for Image-text Matching

FERI: Feature Enhancement and Relational Interaction for Ima...

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international conference on parallel and distributed Systems (ICPADS)

作者： Yu Zhang Jianqiang Zhang Gongpeng Song Qin Lu Shuo Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method.

关键词： Computer vision Fuses Image matching Semantics Feature extraction Natural language processing

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