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Dynamic Tensor Linearization and Time Slicing for Efficient Factorization of Infinite data Streams 37

Dynamic Tensor Linearization and Time Slicing for Efficient ...

引用

37th IEEE international Parallel and Distributed processing Symposium (IPDPS)

作者： Soh, Yongseok Helal, Ahmed E. Checconi, Fabio Laukemann, Jan Tithi, Jesmin Jahan Ranadive, Teresa Petrini, Fabrizio Choi, Jee W. Univ Oregon Eugene OR 97403 USA Intel Labs Hillsboro OR USA Univ Erlangen Niirnberg Erlangen Germany Lab Phys Sci College Pk MD USA

ISBN: (纸本)9798350337662

Streaming tensor factorization is an effective tool for unsupervised analysis of time-evolving sparse data, which emerge in many critical domains such as cybersecurity and trend analysis. In contrast to traditional tensors, time-evolving tensors demonstrate extreme sparsity and sparsity variation over time, resulting in irregular memory access and inefficient use of parallel computing resources. Additionally, due to the prohibitive cost of dynamically generating compressed sparse tensor formats, the state-of-the-art approaches process streaming tensors in a raw form that fails to capture data locality and suffers from high synchronization cost. To address these challenges, we propose a new dynamic tensor linearization framework that quickly encodes streaming multi-dimensional data on-the-fly in a compact representation, which has substantially lower memory usage and higher data reuse and parallelism than the original raw data. This is achieved by using a spatial sketching algorithm that keeps all incoming nonzero elements but remaps them into a tensor sketch with considerably reduced multi-dimensional image space. Moreover, we present a dynamic time slicing mechanism that uses variable-width time slices (instead of the traditional fixed-width) to balance the frequency of factor updates and the utilization of computing resources. We demonstrate the efficacy of our framework by accelerating two high-performance streaming tensor algorithms, namely, CP-stream and spCP-stream, and significantly improve their performance for a range of real-world streaming tensors. On a modern 56-core CPU, our framework achieves 10.3- 11x and 6.4- 7.2x geometric-mean speedup for the CP-stream and spCP-stream algorithms, respectively.

关键词： Sparse tensors tensor factorization streaming data multi-core CPU

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Predicting Next Application Most Likely Used with Word Embedding and Time-Series data Encoding

Predicting Next Application Most Likely Used with Word Embed...

引用

IEEE international conference on Big data and Smart computing (BigComp)

作者： Song, Taieui Gweon, Gahgene Seoul Natl Univ Dept Intelligence & Informat Seoul South Korea

ISBN: (纸本)9781665475785

Predicting Next Application Most Likely Used, referred to as NAMLU, is a problem predicting what application a user will use at the next using prediction model. In this study, we propose the Time Series Deep learning (TED) model, which has two components that yield better model performance for the NAMLU task than the NAP (Natural App processing), which is the existing state-of-the-art model. The TED model proposes the following architectural changes to address two challenges (1) To reduce data representation building cost, we use word embedding and time-series data encoding (2) To increase the model's inference speed, we use CNN-based deep learning architecture. Two experiments were conducted to measure the best data embedding and encoding representation as well as the TED model performance. The experiments showed that using Doc2Vec for embedding and using Gramian Angular Field (GAF) for encoding yields the best performance at 84.5% (Recall@5). This result is about 5% points higher than the SOTA model, NAP. In addition, through experiments, it was confirmed that using the transfer learning model improves prediction performance by 4% compared to the user independent model.

关键词： Next App Most Likely Used Word Embedding Time-series data Encoding Convolutional Neural Network Mobile

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A learning platform for basic courses of design major in colleges and universities based on data mining intelligent algorithms

A learning platform for basic courses of design major in col...

引用

2023 international conference on Computer Application and Information Security, ICCAIS 2023

作者： Lin, Cun School of Design and Innovation China Academy of Art Zhejiang Hangzhou China

ISBN: (纸本)9781510675032

With the arrival of a learning society, information networking has brought about a fundamental change in the way of knowledge dissemination. It has made modern education methods break away from restrictions in time and space. online learning platform (OLP) learning platform plays an increasingly important role in China's education and has gradually become a widely accepted and adopted learning method. Therefore, based on data mining and intelligent algorithms, this paper conducts an in-depth study on the learning platform (LP) of basic courses for college design majors. This paper first introduces the data processing method and resource recommendation algorithm design, then analyzes the functional requirements of the LP and designs the overall architecture of the platform and the learning resource recommendation system, and finally analyzes the use of the LP and the learning resource recommendation system and draws the corresponding conclusions. The research results show that the LP and learning resource recommendation system meet the needs of users for basic course learning. © 2024 SPIE.

关键词： data mining

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Optimization of data Asset Management and Sharing in Yantian Power Supply Bureau Based on Blockchain Smart Contracts

Optimization of Data Asset Management and Sharing in Yantian...

引用

2023 international conference on Computer Simulation and Modeling, Information Security, CSMIS 2023

作者： Yang, Hongshan Zhang, Chong Zhu, Kai Huang, Zhanhua Hu, Minghui Ou, Yuhang Shenzhen Power Supply Co. Ltd. Guangdong Shenzhen518081 China Electric Power Research Institute Beijing100055 China

ISBN: (纸本)9798350343724

Management and secure exchange of power data assets are favorable to fostering value mining of power data assets, which is crucial to the growth of the power business. In order to prevent data leaking, safeguard the rights and interests of data providers, and offer appropriate rates to the demand side, it is sufficient for power data transactions to involve simply the exchange of the original data computation and analysis findings. In order to facilitate the trading and sharing of the results of calculations and analyses performed on the original data, the blockchain data sharing system must offer a data computing service. In this piece, we propose a homomorphic encryption smart contract-based system for exchanging electrical data, and we show how to put that system into action with the Fabric super ledger. The data computing services defined by smart contracts are carried out in protected Docker containers, achieving a trustworthy data computing environment. This article explores the selection method of an algorithm, taking the implementation of aggregate addition calculation function as an example. Paillier and integer vector homomorphic algorithms have superior encryption and computational efficiency compared to CKKS algorithm, so the first two algorithms are more suitable for implementing aggregate addition calculation function. Finally, this article completed the design and implementation of a prototype system for power data security sharing. The system's sharing operation performance was tested using a blockchain testing framework, and the performance of four functional transactions was tested at the second level, demonstrating the usability of the solution. © 2023 IEEE.

关键词： Smart contract

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Automating Big data Analysis Using Artificial Intelligence 3

Automating Big Data Analysis Using Artificial Intelligence

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3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Bhimanpallewar, Ratnmala N. Jangid, Satish Kumar Yadav, Rakesh Kumar Arunkumar, D.T. Dehankar, Pooja Geetha, B. Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India School of Engineering Ajeenkya D. Y. Patil University Pune India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai128 India

ISBN: (纸本)9798350319125

Huge information evaluation, the use of synthetic Intelligence, is a manner of using AI structures to automate the system of extracting meaningful insights from massive sets of statistics. A combination of AI-driven algorithms and hardware may be used to quickly perceive styles in unlabeled information that could otherwise take vast quantities of time to extract. This paper presents a method and framework for Automating Big data Analysis using Artificial Intelligence (AI). With the increasing volume and complexity of big data, traditional manual analysis methods are becoming inefficient and often result in inaccurate or incomplete insights. To address this challenge, we propose a systematic approach that leverages AI techniques such as machine learning and natural language processing to automate the process of data analysis. Our framework includes measures for data preprocessing, feature selection, model building, and result interpretation, and adapts to different types and sources of big data. We demonstrate the effectiveness of our approach through experiments on real-world datasets and compare the results with manual analysis methods. The proposed automated approach not only improves the speed and accuracy of big data analysis, but also reduces the human effort and expertise required. This has the potential to greatly enhance decision-making processes in various industries and domains that deal with large amounts of data. These talents can be used to permit higher choice-making and create extra efficient, automatic approaches to conducting huge-scale data analysis. © 2023 IEEE.

关键词： Permit processing Quantities Similarly Visualization

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Deduction of efficient algorithm to anatomize job insights 14

Deduction of efficient algorithm to anatomize job insights

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14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Shareef, Sk. Khaja Sanjana, Dadi Sandeepa, Digumarthy Amrutha, Chamarthi Reddy, Annam Mahatej Mlr Institute of Technology Department of Information Technology Hyderabad India

ISBN: (纸本)9798350335095

In this model, freshers, job seekers, as well as other professionals, are given the opportunity to know what is new in the market through a platform that enables them to keep up with the newest technology. We will be extracting data from job portals like Naukri, LinkedIn, etc., which analyzes and optimizes the data using machine learning algorithms. This optimized data will provide better insights and will be helpful in different ways. Beautiful Soup and Selenium web drivers are used to scrape and extract data from an open website. In order to extract the precise data, we are comparing the classification algorithms and choosing the most accurate one to implement. This will help in analyzing web data and providing better insights. We also added an additional section called Find-Jobs, which helps people to find jobs based on their location, experience, and designation. © 2023 IEEE.

关键词： data mining

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Leveraging Federated Learning and XAI for Privacy-Aware and Lightweight Edge Training in Network Traffic Classification

Leveraging Federated Learning and XAI for Privacy-Aware and ...

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2023 IEEE international conference on computing, ICOCO 2023

作者： Ariffin, Azizi Zaki, Faiz Anuar, Nor Badrul Universiti Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia

ISBN: (纸本)9798350302684

The exponential growth of internet traffic causes significant challenges for network traffic classification, such as maintaining data privacy and requiring more computing resources. To address such challenges, moving the processing to data instead of the opposite shows some promise. Related technologies, such as edge computing, present a potential solution as they reduce latency and bandwidth and consider data privacy. However, edge computing often runs on resource-constraint devices, thus making complex model training like deep learning challenging. As such, this paper proposes a distributed and lightweight edge training method for network traffic classification using federated learning and XAI, which are currently underexplored in the domain. In doing so, we managed to run deep learning on edge devices and preserve data privacy. We evaluate the proposed method using the ISCXVPN2016 dataset on three NVIDIA Jetson devices to emulate edge devices. The results show a 36.71% improvement in F1 score, training time, memory, and GPU utilization when benchmarked against the more conventional centralized training. © 2023 IEEE.

关键词： data privacy

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Node-Aware Adaptive data processing Method Based on Att-BiGRU-RNN 4

Node-Aware Adaptive Data Processing Method Based on Att-BiGR...

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4th international conference on Smart Grid and Energy Engineering, SGEE 2023

作者： Sun, Qiao Xu, Fang Chai, Chao Liu, Ke Beijing FibrLink Communications Co. Ltd. Beijing China Tianjin University of Science & Technology Tianjin China

ISBN: (纸本)9798350359619

In the realm of edge computing for the Internet of Things (IoT) within the power industry, the efficient processing, accurate classification, and rapid response to massive IoT data have become urgent challenges. Traditional centralized data processing methods based on cloud computing are inadequate to handle the current vast data volumes. To address these issues, this paper proposes an intelligent edge data processing framework based on the combination of Bidirectional Gated Recurrent Units- Recurrent Neural Network with Attention Mechanism (Att-BiGRU-RNN) and Bloom filters. This framework integrates an attention mechanism to capture temporal dependencies in data from edge nodes, enabling preliminary prediction and categorization of data types. It dynamically adjusts the parameter structure of Bloom filters, reducing collisions and minimizing data false positives. This framework dynamically and adaptively selects appropriate data for processing at the edge nodes, significantly reducing the amount of data transmitted to the cloud, thereby conserving communication bandwidth and reducing time delays. It effectively addresses real-time identification methods for terminal devices in access networks and facilitates fast access to the network. © 2023 IEEE.

关键词： Internet of things

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Machine Learning in Soil Borne Diseases, Soil data Analysis & Crop Yielding: A Review

Machine Learning in Soil Borne Diseases, Soil Data Analysis ...

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2023 IEEE international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2023

作者： Kiruthiga, C. Dharmarajan, K. School of Computer Science Vels University Chennai India Vels University Department of Information Technology Chennai India

ISBN: (纸本)9781665462631

The agricultural study is critically important from a financial perspective. Research areas in agriculture are demand prediction, crop yielding prediction, soil classification, diseases, and weed detection Another dimension of agriculture is horticulture, it provides economic development, and healthy foods to society. Soil is critical for increasing crop yields, the quality of food, and its healthiness and nutritive quality. But with the right use of organic fertilizers and other modern tools and techniques, it may be enhanced. Experts use data mining and machine learning algorithms like support vector machines (SVMs), Naive Bayes (NBs), decision trees (DTs), and linear discriminant analyses (LDAs) to make predictions and draw conclusions from agricultural data (CNN). This paper comprises a review of the Machine Learning Algorithm in the soil data analysis and soil-borne diseases and Crop yielding. © 2023 IEEE.

关键词： Horticulture

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Homomorphic Encryption Enables data and Algorithm Confidentiality for Remote Monitoring and Control: An Application to data Center Systems 14

Homomorphic Encryption Enables Data and Algorithm Confidenti...

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14th ACM international conference on Future Energy Systems (e-Energy)

作者： Forsgren, Henrik Brannvall, Rickard Vesterlund, Mattias Minde, Tor-Bjorn Res Inst Sweden RISE Dept Comp Sci Lulea Sweden

ISBN: (纸本)9798400702273

The design of intelligent algorithms used for device monitoring and control can be costly and is an investment that must be protected against reverse engineering by competitors. An algorithm can be safeguarded by running remotely from the cloud instead of locally on the equipment hardware. However, such a setup requires that sensitive data is sent from the device to the cloud. Fully Homomorphic Encryption (FHE) is an emerging technology that offers a solution to this problem since it enables computation on encrypted data. A cloud service using FHE can protect its proprietary algorithms while simultaneously offering customer data confidentiality. The computational overhead for the technology is, however, still very high. This work reports on a practical investigation of using FHE for data center remote control problems: What applications are feasible today? And at what cost?

关键词： remote monitoring remote control neural networks confidential computing homomorphic encryption

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