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ieee symposium on large data analysis and visualization (LDAV)

作者： Luca Reichmann David Hägele Daniel Weiskopf University of Stuttgart

ISBN: (数字)9798331516925

ISBN: (纸本)9798331516932

Dimensionality reduction (DR) is a well-established approach for the visualization of high-dimensional data sets. While DR methods are often applied to typical DR benchmark data sets in the literature, they might suffer from high runtime complexity and memory requirements, making them unsuitable for large data visualization especially in environments outside of high-performance computing. To perform DR on large data sets, we propose the use of out-of-sample extensions. Such extensions allow inserting new data into existing projections, which we leverage to iteratively project data into a reference projection that consists only of a small manageable subset. This process makes it possible to perform DR out-of-core on large data, which would otherwise not be possible due to memory and runtime limitations. For metric multidimensional scaling (MDS), we contribute an implementation with out-of-sample projection capability since typical software libraries do not support it. We provide an evaluation of the projection quality of five common DR algorithms (MDS, PCA, t-SNE, UMAP, and autoencoders) using quality metrics from the literature and analyze the trade-off between the size of the reference set and projection quality. The run-time behavior of the algorithms is also quantified with respect to reference set size, out-of-sample batch size, and dimensionality of the data sets. Furthermore, we compare the out-of-sample approach to other recently introduced DR methods, such as PaCMAP and TriMAP, which claim to handle larger data sets than traditional approaches. To showcase the usefulness of DR on this large scale, we contribute a use case where we analyze ensembles of streamlines amounting to one billion projected instances.

关键词： Measurement Dimensionality reduction Runtime Software libraries data analysis High performance computing Software algorithms Memory management data visualization Principal component analysis

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Effects of Motion-Relevant Knowledge From Unlabeled Video to Human-Object Interaction Detection

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ieee TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第9期34卷 5760-5773页

作者： Lin, Xue Zou, Qi Xu, Xixia Huang, Yaping Ding, Ding Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing 100044 Peoples R China

The existing works on human-object interaction (HOI) detection usually rely on expensive large-scale labeled image datasets. However, in real scenes, labeled data may be insufficient, and some rare HOI categories have few samples. This poses great challenges for deep-learning-based HOI detection models. Existing works tackle it by introducing compositional learning or word embedding but still need large-scale labeled data or extremely rely on the well-learned knowledge. In contrast, the freely available unlabeled videos contain rich motion-relevant information that can help infer rare HOIs. In this article, we creatively propose a multitask learning (MTL) perspective to assist in HOI detection with the aid of motion-relevant knowledge learning on unlabeled videos. Specifically, we design the appearance reconstruction loss (ARL) and sequential motion mining module in a self-supervised manner to learn more generalizable motion representations for promoting the detection of rare HOIs. Moreover, to better transfer motion-related knowledge from unlabeled videos to HOI images, a domain discriminator is introduced to decrease the domain gap between two domains. Extensive experiments on the HICO-DET dataset with rare categories and the V-COCO dataset with minimum supervision demonstrate the effectiveness of motion-aware knowledge implied in unlabeled videos for HOI detection.

关键词： Task analysis Training visualization Feature extraction Semantics Dynamics Streaming media Domain adaptation human-object interaction (HOI) multitask learning (MTL) self-supervised learning (SSL)

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Unsupervised clustering of disturbances in power systems via deep convolutional autoencoders

Unsupervised clustering of disturbances in power systems via...

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ieee-Power-and-Energy-Society General Meeting (PESGM)

作者： Islam, Md Maidul Faruque, Md Omar Butterfield, Joshua Singh, Gaurav Cooke, Thomas A. FAMU FSU Coll Engn Elect & Comp Engn Tallahassee FL 32310 USA Elect Power Res Inst Palo Alto CA USA

ISBN: (纸本)9781665464413

Power quality (PQ) events are recorded by PQ meters whenever anomalous events are detected on the power grid. Using neural networks with machine learning can aid in accurately classifying the recorded waveforms and help power system engineers diagnose and rectify the root causes of problems. However, many of the waveforms captured during a disturbance in the power system need to be labeled for supervised learning, leaving a large number of data recordings for engineers to process manually or go unseen. This paper presents an autoencoder and K -means clustering-based unsupervised technique that can be used to cluster PQ events into categories like sag, interruption, transients, normal, and harmonic distortion to enable filtering of anomalous waveforms from recurring or normal waveforms. The method is demonstrated using three-phase, field -obtained voltage waveforms recorded in a distribution grid. First, a convolutional autoencoder compresses the input signals into a set of lower feature dimensions which, after further processing, is passed to the K -means algorithm to identify data clusters. Using a small, labeled dataset, numerical labels are then assigned to events based on a cosine similarity analysis. Finally, the study analyzes the clusters using the t-distributed stochastic neighbor embedding (t-SNE) visualization tool, demonstrating that the technique can help investigate a large number of captured events in a quick manner.

关键词： Convolutional autoencoder data visualization power quality unsupervised clustering waveform clustering

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Privacy-Preserving Learning Model Using Lightweight Encryption for Visual Sensing Industrial IoT Devices

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ieee TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年

作者： Deebak, B. D. Hwang, Seong Oun Gachon Univ Dept Comp Engn Seongnam 13120 South Korea

Technological convergence in visual sensing with industrial IoT (VSI-IoT) can bring numerous advances to large-scale crowd management systems like visual crowdsensing. VSI-IoT has significant features, including sensing, computing, analyzing, and storing, to address the issues of bearing failures, such as unplanned outages, increased downtime, and reduced operational efficiency. By contrast, providing privacy to the IIoT environments is a challenging task. Thus, this paper presents a novel privacy-preserving learning (PPL) mechanism that senses the defect rate of bearing failures using lightweight model aggregation at edge computing systems to preserve the privacy features. This convergence model synthesizes shape features comprehensively to transform the feature vectors into predictive functions that examine the categorization models using a two-dimensional convolution neural network (2D-CNN). Using security analysis, we demonstrate that the proposed PPL can achieve better privacy protection and model accuracy to preserve the learning features without additional verifiability. Further, the examination results showed that the proposed 2D-CNN with BN and LN consumed less computation complexity to achieve better detection accuracy (similar to 87.91.9% to similar to 99.98%) and communication cost $ (similar to 21.09MB to 23.92MB) over three bearing datasets (i.e., IMS-Rexnord, CWRU, and Paderborn) than other state-of-the-art approaches. Above all, the privacy preserving based AlexNet was implemented using CryptoNet and LoLa to show different sets of efficiencies such as processing time, privacy, and integrity checks to preserve system performance following time-sensitive application scenarios like supply-chain optimization.

关键词： Computational modeling Sensors Privacy data models visualization Computer architecture data privacy Accuracy Hidden Markov models Edge computing Communication cost convolution neural network Industrial Internet of Things machine-to-machine privacy-preserving visual sensing

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Denoising Diffusion Probabilistic Models for High-Fidelity fMRI Intrinsic Connectivity Network data Generation

Denoising Diffusion Probabilistic Models for High-Fidelity f...

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2024 ieee-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Ajith, Meenu Calhoun, Vince D. @ Georgia State AtlantaGA30303 United States

ISBN: (纸本)9798350351552

The emergence of diffusion models such as Glide, Dalle-2, Imagen, and Stable Diffusion marks a significant breakthrough in generative AI-based image generation. This paper introduces an rs-fMRI image synthesis framework that leverages the nonlinear capabilities of denoising diffusion probabilistic models (DDPMs) to overcome the limitations of linear methods like independent component analysis (ICA) in neuroimaging analysis. Unlike ICA, which assumes linearity, DDPMs capture the intricate and complex patterns inherent in neuroimaging data. Our approach advances from 2D to 3D representations, providing a comprehensive visualization of intrinsic connectivity networks (ICNs). This framework also addresses the challenge of sparse training datasets commonly encountered in deep learning applications for neuroimaging. Trained on a large database, our model captures the intricate variability of different ICNs, generating realistic connectivity patterns. The proposed method is evaluated quantitatively to compare the synthesized ICNs against ground truth data. Results demonstrate that the proposed DDPM-based framework showcases competitive performance accuracy in reflecting the true complexity of neural connectivity patterns. © 2024 ieee.

关键词： diffusion model generative modeling image synthesis rs-fMRI data

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analysis of Embedding Space of Speech Synthesis System as High-Dimension, Low-Sample-Size data 12

Analysis of Embedding Space of Speech Synthesis System as Hi...

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12th ieee Global Conference on Consumer Electronics, GCCE 2023

作者： Tachioka, Yuuki Denso It Laboratory Tokyo Japan

ISBN: (纸本)9798350340181

Principal component analysis (PCA) is often used to visualize and cluster embedding vectors of deep-learning models;however large noise can degrade the results of PCA when the dimension of the embedding vectors is much larger than the data size. These types of data are referred to as high-dimension, low-sample-size (HDSS) data, where the number of data dimension is much larger than the sample size, and they must be treated differently than typical data, because the eigenvalues of a HDSS data covariance matrix are impacted by large noise. Previous studies on HDSS data proposed the noise reduction methodology (NRM) and cross-data matrix methodology (CDM) to reduce such effects. In this study, we apply NRM and CDM to visualize the embedding vectors of an end-to-end speech synthesis system. Experimental results show that the NRM can estimate reduced eigenvalues by eliminating the large noise and that the CDM can provide a more reasonable visualization, although the results are dependent on the initial CDM clusters. In addition, we propose a method for estimating the power exponent of a general spiked model to assess whether the estimated eigenvalues are consistent. © 2023 ieee.

关键词： Noise abatement

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data Variability Study of Advanced 3D NAND Memory using Python 35

Data Variability Study of Advanced 3D NAND Memory using Pyth...

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35th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)

作者： Agam, Moshe Mebrahtu, Henok T. Dey, Himadri Intel Foundry Dev Rio Rancho NM 87124 USA

ISBN: (纸本)9798350384567;9798350384550

The methodology for data analysis with large variability based on Python is discussed and demonstrated. As NAND memory transitioned from 2D to 3D, a significant new source of variability emerged due to the stacking of several hundred layers of memory cells on top of each other. This 3D construction demands a novel approach to efficient analysis that allows for a more general overview without sacrificing the details of individual memory cells. The authors explore how to achieve this by identifying sources of variation and systematically parsing and manipulating the data file into smaller files. This methodology enables straightforward visualization of parametric variability in relation to test and process conditions and facilitating the creation of accessible documentation for future reference. The use of Python provides an open-source and user-friendly interface that can be further customized to enhance consistency and reusability.

关键词： 3D NAND NVM Memory Array Python Variability study data analysis

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Transforming Big data Challenges Into Opportunities: An In-Depth analysis of Microsoft Power BI for Analytics and visualization

Transforming Big Data Challenges Into Opportunities: An In-D...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Charan, Piyush Latha, K. Hema Neelamegam, G. Verma, Rajiv Pal, Subharun Teltumbade, Ganesh Ramesh Manav Rachna University Department of Electronics and Communication Engineering Haryana Faridabad India Muffakham Jah College of Engg. and Tech. Dept. of Mechanical Engg Telangana India V.S.B College of Engg. Dept.of Cse Technical Campus Coimbatore India School of Management Sir Padampat Singhania University Rajasthan Udaipur India Iit Jammu Department of Cse Jammu India MGV's Sph College of Management & Tech Dept. of Management Maharashtra India

ISBN: (纸本)9798350379716

In the modern big data-driven business environment, organizations face the dual challenge and opportunity of managing and utilizing large volumes of data. Microsoft Power BI offers robust solutions for these challenges, providing advanced analytics and comprehensive visualization tools that transform complex data sets into actionable intelligence. This paper explores the capabilities of Power BI to integrate seamlessly with existing data systems, enhancing operational efficiency and strategic decision-making across various industries. To demonstrate how Power BI aids businesses in data visualization and analytics, this article employs theoretical analysis and experimental research to demonstrate how Power BI simplifies the understanding and implementation of complex analytics. The study also examines future trends in business intelligence tools, highlighting the integration of artificial intelligence and machine learning technologies that continue to enhance the predictive capabilities of Power BI. © 2024 ieee.

关键词： data Analytics

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Predictability of LoRaWAN Link Quality based on Weather data: Insights from a Long-Term Study 25

Predictability of LoRaWAN Link Quality based on Weather Data...

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25th ieee International symposium on a World of Wireless, Mobile and Multimedia Networks (ieee WoWMoM)

作者： Szafranski, Daniel Tech Univ Clausthal Dept Informat Clausthal Zellerfeld Germany

ISBN: (纸本)9798350394665;9798350394672

WSNs are often deployed outdoors and thus exposed to a variety of different weather conditions. Due to the properties of radio waves, their propagation can be negatively impacted by the prevailing weather conditions. The sender and receiver hardware, including the antennas, can also be affected, potentially leading to variations in signal quality and link degradation. In order to assess the impact of individual weather components on the quality of WSN links, we analyze a large scale LoRaWAN deployment consisting of multiple nodes and gateways over a period of several months. The WSN is used for environmental monitoring, which means that the weather data is directly measured by the nodes and available in a high spatial and temporal resolution. Our results indicate significant dependencies between different weather components like precipitation, temperature, humidity but also time of day and the link quality as measured by RSSI and SNR. We further find that every link is affected to a different degree and has its own individual characteristic. Our analysis also shows significant correlations of the link quality within clusters of nodes that are deployed geographically close to each other. Finally, we evaluate the predictability of the link quality based on the prevailing weather conditions using different supervised learning models. Our results show that the trend of the link quality is indeed predictable, especially periodic patterns and trend changes can be recognized by the models and predicted within the test sets. The best model performance as measured by RMSE ranges from 1.79dBm to 6.39dBm for RSSI and 1.15 dB to 6.06 dB for SNR.

关键词： LoRa LoRaWAN weather conditions link quality prediction

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From Bag-of-Words to Transformers: A Comparative Study for Text Classification in Healthcare Discussions in Social Media

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ieee TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第1期9卷 1063-1077页

作者： De Santis, Enrico Martino, Alessio Ronci, Francesca Rizzi, Antonello Univ Roma La Sapienza Dept Informat Engn Elect & Telecommun I-00184 Rome Italy LUISS Univ Dept Business & Management Rome Italy

One notable paradigm shift in Natural Language Processing has been the introduction of Transformers, revolutionizing language modeling as Convolutional Neural Networks did for Computer Vision. The power of Transformers, along with many other innovative features, also lies in the integration of word embedding techniques, traditionally used to represent words in a text and to build classification systems directly. This study delves into the comparison of text representation techniques for classifying users who generate medical topic posts on Facebook discussion groups. Short and noisy social media texts in Italian pose challenges for user categorization. The study employs two datasets, one for word embedding model estimation and another comprising discussions from users. The main objective is to achieve optimal user categorization through different pre-processing and embedding techniques, aiming at high generalization performance despite class imbalance. The paper has a dual purpose, i.e., to build an effective classifier, ensuring accurate information dissemination in medical discussions and combating fake news, and to explore also the representational capabilities of various LLMs, especially concerning BERT, Mistral and GPT-4. The latter is investigated using the in-context learning approach. Finally, data visualization tools are used to evaluate the semantic embeddings with respect to the achieved performance. This investigation, focusing on classification performance, compares the classic BERT and several hybrid versions (i.e., employing different training strategies and approximate Support Vector Machines in the classification layer) against LLMs and several Bag-of-Words based embedding (notably, one of the earliest approaches in text classification). This research offers insights into the latest developments in language modeling, advancing in the field of text representation and its practical application for user classification within medical discussions.

关键词： Social networking (online) Transformers Semantics Vectors Training Task analysis Support vector machines Embedding techniques healthcare natural language processing social network analysis text categorization text mining transformers mistral GPT-4 large language models

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