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检索条件"任意字段=IEEE International Conference on Distributed Computing Systems"

共 150545 条记录，以下是811-820 订阅

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Real-Time Sentiment Analysis Pipeline for Yelp Customer Feedback using ChatGPT’s LLM and distributed systems 6

Real-Time Sentiment Analysis Pipeline for Yelp Customer Feed...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Revathy, S. Shahid, J. Mohammad Salih, S. Computer Science and Engineering St.Joseph’s Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798331522667

This work proposes a real-time sentiment analysis pipeline on customer feedback using Yelp and addresses the high-volume dynamic user-generated contents processing problem. The proposal integrates state-of-the-art machine learning models and distributed systems, making use of Large Language Models within a Docker container called ChatGPT for the real-time sentiments analysis. The system ingests live Yelp data streams through TCP/IP, processes them in a scalable Apache Spark cluster, and ensures that data flows seamlessly through Apache Kafka. Enriched feedback is indexed into Elasticsearch, providing powerful analytics and visualizations through tools like Kibana, Tableau, and Power BI. The low-latency design of the pipeline gives enterprises actionable insights for real-time decision-making, enabling enhanced customer sentiment analysis and business intelligence. Scalable and efficient, this architecture fits all large volume Enterprise Feedback Management (EFM) needs when looking to achieve insights fast from customer sentiment. © 2025 ieee.

关键词： Sales

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Accelerating Time to Science using CRADLE: A Framework for Materials Data Science 30

Accelerating Time to Science using CRADLE: A Framework for M...

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30th international conference on High Performance computing, Data, and Analytics (HiPC)

作者： Nihar, Arafath Ciardi, Thomas G. Chawla, Rounak Akanbi, Olatunde Chaudhary, Vipin Wu, Yinghui French, Roger H. Case Western Reserve Univ Dept Comp & Data Sci Cleveland OH 44106 USA Case Western Reserve Univ Dept Mat Sci Cleveland OH USA

ISBN: (纸本)9798350383225

Modern materials science research problems present a challenge to data science and analytics as experiments generate Petabyte-scale spatiotemporal datasets that span a number of modalities and formats. Creating computing infrastructure and frameworks that support the scale and diversity of materials science data while remaining accessible for materials scientists to use is a non-trivial task. We have developed the Common Research Analytics and Data Lifecycle Environment (CRADLE) to solve the challenges of materials data science through a scalable research computing framework and cyber infrastructure that can (1) handle large-scale, heterogeneous datasets (2) provide a flexible toolbox for building machine learning pipelines that span from ingestion to model deployment (3) be accessible to research scientists with limited to extensive computational backgrounds and (4) utilize a myriad of low performance to high performance computer systems. CRADLE is a framework that integrates distributed systems like Hadoop and High-Performance computing (HPC) infrastructure to handle materials data at scale. This all enables the general materials data scientist to query Petabytes of data and train thousands of models in a parallel, distributed environment. We demonstrate three use cases for CRADLE to benchmark its capability to ingest and analyze spatiotemporal materials data at scale. These tasks span three data modalities: transforming 2.6 billion Photovoltaic time-series power measurements, training hundreds of deep learning models on Atomic Force Microscopy images, and ingesting 27 billion geospatial data points. CRADLE exemplifies an overarching framework that accelerates time to science, extends to other domains with similar challenges, and expands the horizon of data science and research.

关键词： scalable infrastructure distributed computing Hadoop AI4Science materials data science

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DDoS-FOCUS: A distributed DoS Attacks Mitigation using Deep Learning Approach for a Secure IoT Network 7

DDoS-FOCUS: A Distributed DoS Attacks Mitigation using Deep ...

引用

7th ieee international conference on Edge computing and Communications (ieee EDGE) / ieee World Congress on Services (SERVICES)

作者： Al-khafajiy, Mohammed Al-Tameemi, Ghaith Baker, Thar Univ Lincoln Sch Comp Sci Lincoln England Univ Northampton Fac Art Sci & Technol Northampton England Univ Brighton Sch Architecture Technol & Engn Brighton E Sussex England

ISBN: (纸本)9798350304831

The fast growth of the Internet of Things devices and communication protocols poses equal opportunities for lifestyle-boosting services and pools for cyber attacks. Usually, IoT network attackers gain access to a large number of IoT (e.g., things and fog nodes) by exploiting their vulnerabilities to set up attack armies, then attacking other devices/nodes in the IoT network. The distributed Denial of Service (DDoS) flooding-attacks are prominent attacks on IoT. DDoS concerns security professionals due to its nature in forming sophisticated attacks that can be bandwidth-busting. DDoS can cause unplanned IoT-services outages, hence requiring prompt and efficient DDoS mitigation. In this paper, we propose a DDoS-FOCUS;a solution to mitigate DDoS attacks on fog nodes. The solution encompasses a machine learning model implanted at fog nodes to detect DDoS attackers. A hybrid deep learning model was developed using Conventional Neural Network and Bidirectional LSTM (CNN-BiLSTM) to mitigate future DDoS attacks. A preliminary test of the proposed model produced an accuracy of 99.8% in detecting DDoS attacks.

关键词： DDoS IoT CNN-BiLSTM distributed fog/edge

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Proceedings - 2024 ieee 27th international Symposium on Real-Time distributed computing, ISORC 2024

Proceedings - 2024 IEEE 27th International Symposium on Real...

引用

27th ieee international Symposium on Real-Time distributed computing, ISORC 2024

ISBN: (纸本)9798350371284

The proceedings contain 42 papers. The topics discussed include: hierarchical heterogeneous cluster systems for scalable distributed deep learning;streamlining CPS validation: using interoperable UML tools for seamless model exchange;multivariate LSTM for execution time prediction in HPC for distributed deep learning training;multi-criteria optimization of distributed real-time network topologies;securing real-time systems using schedule reconfiguration;an explainable method for cost-efficient multi-view fall detection;enhanced RF-based 3D UAV outdoor geolocation: from trilateration to machine learning approaches;real-time embedded monitoring technologies in modern healthcare systems: a survey;security assessment solutions for IoT devices;and intrusion detection schemes based on synthetic minority oversampling technique and machine learning models.

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Passively Measuring IPFS Churn and Network Size 42

Passively Measuring IPFS Churn and Network Size

引用

42nd ieee international conference on distributed computing systems (ICDCS)

作者： Daniel, Erik Tschorsch, Florian Tech Univ Berlin Distributed Secur Infrastruct Berlin Germany

ISBN: (数字)9781665488792

ISBN: (纸本)9781665488792

The InterPlanetary File System (IPFS) is a popular decentralized peer-to-peer network for exchanging data. While there are many use cases for IPFS, the success of these use cases depends on the network. In this paper, we provide a passive measurement study of the IPFS network, investigating peer dynamics and curiosities of the network. With the help of our measurement, we estimate the network size and confirm the results of previous active measurement studies.

关键词： Network Measurement IPFS P2P Networks

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A Quantum Model for the Added Noise in SNAIL Traveling-Wave Parametric Amplifiers

A Quantum Model for the Added Noise in SNAIL Traveling-Wave ...

引用

ieee international conference on Microwaves, Communications, Antennas, Biomedical Engineering and Electronic systems (COMCAS)

作者： Haider, Michael Yuan, Yongjie Jirauschek, Christian Tech Univ Munich TUM Sch Computat Informat & Technol Garching Germany

ISBN: (纸本)9798350348194;9798350348187

Broadband cryogenic traveling-wave parametric amplifiers are vital components for the readout of superconducting qubits. This is due to their large gain and quantum-limited noise performance. In this paper, we present analytic solutions for the equivalent added input fluctuations of state-of-the-art traveling-wave parametric amplifiers based on superconducting nonlinear asymmetric elements derived from a quantum mechanical model, including losses and dispersion.

关键词： quantum computing qubit readout Josephson parametric amplifier traveling-wave parametric amplifier SNAIL

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Perfect Subset Privacy in Polynomial Computation

Perfect Subset Privacy in Polynomial Computation

引用

ieee international Symposium on Information Theory (ISIT)

作者： Deng, Zirui Ken Ramkumar, Vinayak Raviv, Netanel Washington Univ St Louis Dept Comp Sci & Engn Washington DC USA Tel Aviv Univ Dept Elect Engn Tel Aviv Israel

ISBN: (纸本)9798350382853;9798350382846

Delegating large-scale computations to service providers is a common practice which raises privacy concerns. This paper studies information-theoretic privacy-preserving delegation of data to a service provider, who may further delegate the computation to auxiliary worker nodes, in order to compute a polynomial over that data at a later point in time. We study techniques which are compatible with robust management of distributed computation systems, an area known as coded computing. Privacy in coded computing, however, has traditionally addressed the problem of colluding workers, and assumed that the server that administrates the computation is trusted. This viewpoint of privacy does not accurately reflect real-world privacy concerns, since normally, the service provider as a whole (i.e., the administrator and the worker nodes) form one cohesive entity which itself poses a privacy risk. This paper aims to shift the focus of privacy in coded computing to safeguarding the privacy of the user against the service provider as a whole, instead of merely against colluding workers inside the service provider. To this end, we leverage the recently defined notion of perfect subset privacy, which guarantees zero information leakage from all subsets of the data up to a certain size. Using known techniques from Reed-Muller decoding, we provide a scheme which enables polynomial computation with perfect subset privacy in stragglerfree systems. Furthermore, by studying information super-sets in Reed-Muller codes, which may be of independent interest, we extend the previous scheme to tolerate straggling worker nodes inside the service provider.

关键词： Perfect subset privacy distributed systems coded computing Reed-Muller codes

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Efficient Differentially Private Tensor Factorization in the Parallel and distributed computing Paradigm 21

Efficient Differentially Private Tensor Factorization in the...

引用

21st ieee international Symposium on Parallel and distributed Processing with Applications, 13th ieee international conference on Big Data and Cloud computing, 16th ieee international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhang, Feng Wang, Hao Guo, Ruixin Xue, Erkang Qu, Guangzhi China University of Geosciences School of Computer Science Wuhan China Oakland University Department of Computer Science and Engineering Rochester United States

ISBN: (纸本)9798350329223

Tensor factorization plays a fundamental role in multiple areas of AI research. Nevertheless, it encounters significant challenges related to privacy breaches and operational efficiency. In this study, we propose a novel approach that addresses both of these issues simultaneously by integrating differential privacy with parallel and distributed computing. To accommodate diverse scenarios, we introduce two models: DPTF-SVRG and ADMM-DPTF, each leveraging specific techniques. DPTF-SVRG is designed for single-GPU environments and utilizes a unique strategy to reduce gradient variance, enabling faster convergence compared to SGD. Moreover, it achieves parallelism on the GPU through a lock-free asynchronous approach. On the other hand, ADMM-DPTF utilizes distributed ADMM to parallelize DPTF-SVRG, enabling multi-GPU parallelism. Experimental results demonstrate that our algorithms outperform existing benchmarks while maintaining differential privacy. © 2023 ieee.

关键词： Tensors

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A Big Data Sharing Architecture Based on Federal Learning in State Grid 22

A Big Data Sharing Architecture Based on Federal Learning in...

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ieee 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Na, Liu Yang, Rui Zhang, Zhicheng Wang, Yu Wu, Chao Li, Xiaofei Li, Zhendong Li, Meng Beijing Smartchip Microelect Technol Co Ltd Beijing Peoples R China State Grid Leshan Elect Power Supply Co Leshan Peoples R China State Grid Meishan Elect Power Supply Co Meishan Peoples R China

ISBN: (纸本)9798350381993;9798350382006

Along with constructing new power systems and the Energy Internet, much power data is generated and stored at the edge devices, which may contain customer privacy and be challenging to use. Besides, the lack of computing power and the untrustworthy environment in the edge layer prevent further development of the data. How to effectively and securely utilize the accumulated power data and facilitate the construction of new power systems and power business transformation is now a significant challenge for the State Grid Corporation of China (SGCC). This paper proposes a new data sharing architecture based on federated learning and blockchain technology, which exchanges the data model instead of the original data to ensure data security with a privacy protection protocol and a lightweight consensus for edge devices. Meanwhile, an incentive mechanism is established to encourage high-quality data share and training. In conclusion, the architecture has approximate accuracy with acceptable privacy cost compared to traditional methods with high privacy preservation It can be widely used in big data scenarios in smart distribution or metering areas with medium-sized network scenarios.

关键词： data share privacy protection federated learning power big data edge computing distribution area

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AI-Driven Health Monitoring of distributed computing Architecture: Insights from XGBoost and SHAP 4

AI-Driven Health Monitoring of Distributed Computing Archite...

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4th international conference on Communication Technology and Information Technology, ICCTIT 2024

作者： Sun, Xiaoxuan Yao, Yue Wang, Xiaoye Li, Pochun Li, Xuan Mountain View United States Northeastern University PortlandME United States Western University LondonON Canada Northeastern University BostonMA United States Columbia University New YorkNY United States

ISBN: (纸本)9798331528973

With the rapid development of artificial intelligence technology, its application in the optimization of complex computer systems is becoming more and more extensive. Edge computing is an efficient distributed computing architecture, and the health status of its nodes directly affects the performance and reliability of the entire system. In view of the lack of accuracy and interpretability of traditional methods in node health status judgment, this paper proposes a health status judgment method based on XGBoost and combines the SHAP method to analyze the interpretability of the model. Through experiments, it is verified that XGBoost has superior performance in processing complex features and nonlinear data of edge computing nodes, especially in capturing the impact of key features (such as response time and power consumption) on node status. SHAP value analysis further reveals the global and local importance of features, so that the model not only has high-precision discrimination ability but also can provide intuitive explanations, providing data support for system optimization. Research shows that the combination of AI technology and computer system optimization can not only realize the intelligent monitoring of the health status of edge computing nodes but also provide a scientific basis for dynamic optimization scheduling, resource management and anomaly detection. In the future, with the in-depth development of AI technology, model dynamics, cross-node collaborative optimization and multimodal data fusion will become the focus of research, providing important support for the intelligent evolution of edge computing systems. © 2024 ieee.

关键词： Electronic health record

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