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检索条件"任意字段=IEEE Symposium on Large Data Analysis and Visualization"

共 15522 条记录，以下是181-190 订阅

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Brain State Transition Disruptions in Alzheimer's Disease: Insights from EEG State Dynamics

Brain State Transition Disruptions in Alzheimer's Disease: I...

引用

ieee International symposium on Biomedical Imaging

作者： Seyed Majid Razavi Triet M. Tran Steven E. Wilson Sina Khanmohammadi Data Science And Analytics Institute University of Oklahoma Norman OK USA School of Computer Science University of Oklahoma Norman OK USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Alzheimer's Disease (AD) has been associated with reduced neuroplasticity, but the extent to which such reduction impacts large-scale neural dynamics is not yet fully understood. Here, we use a network dynamics analysis framework based on Laplacian Eigenmaps to study the disruptions of brain state transitions associated with AD. Specifically, we utilize the Normalized Variability Index (NVI) to quantify the consistency of nonlinear brain state transitions in resting state electroencephalogram (EEG). Our results show that AD patients tend to have significantly lower NVI values compared to control subjects, reflecting more consistent brain state transitions. The difference was most significant in the Beta band, which has been associated with memory encoding and retrieval. Overall, our results suggest a reduced flexibility in brain state transitions among Alzheimer's patients. These results could provide new insights into large-scale neural dynamics in Alzheimer's disease and pave the path for developing new biomarkers for early detection and diagnosis.

关键词： Neuroplasticity Laplace equations Biomarkers Electroencephalography Encoding Indexes Alzheimer's disease Biomedical imaging

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Enhancing Cryptocurrency Trading Strategies: A Deep Reinforcement Learning Approach Integrating Multi-Source LLM Sentiment analysis

Enhancing Cryptocurrency Trading Strategies: A Deep Reinforc...

引用

Computational Intelligence for Financial Engineering and Economics (CiFer), ieee symposium on

作者： Nanjiang Du Yida Zhao Jintao Wang Yicheng Zhu Siyu Xie Luyao Yang Yiru Tong Shengzhe Xu Wangying Zhang Zecheng Tang Kai Xu Jianfeng Ren Tianxiang Cui School of Computer Science University of Nottingham Ningbo China Ningbo China School of Computer Science University of Nottingham Nottingham UK

ISBN: (数字)9798331508319

ISBN: (纸本)9798331508326

Recent advancements in large language models (LLMs) have demonstrated their potential to significantly impact finance trading, particularly through sentiment analysis. The cryptocurrency market, known for its volatility and unpredictability, often renders price-based trading approaches inadequate. This necessitates the adoption of more sophisticated techniques such as market sentiment analysis, which can benefit from the insights provided by LLMs. This study introduces an innovative method that integrates sentiment analysis derived from five distinct LLMs with deep reinforcement learning to devise a cryptocurrency trading strategy. Recognizing that LLM outputs cannot be guaranteed to be infallibly accurate, which contributing to the LLM hallucinations, this paper details the implementation of a stringent outlier detection and removal process. By adopting a “Trust-The-Majority” strategy, the research aims to ensure that trading decisions are informed by reliable sentiment data. In addition, sentiment scores are traditionally timestamped to the publication of news or social media posts. To more accurately reflect the actual impact of such information on market sentiment, this study applies the Ebbinghaus Forgetting Curve to model the waning influence of information over time. This allows for a more nuanced understanding of how news affects market dynamics. The enhanced sentiment scores, in conjunction with traditional market data such as OHLCV (Open, High, Low, Close, Volume), are utilized by a deep reinforcement learning model to make trading decisions. Experimental results demonstrate that the proposed multi-LLM sentiment-driven framework improves trading performance in the fast-paced cryptocurrency market. The methodology outlined in this paper offers a solid foundation for incorporating real-time market sentiment analysis into financial applications.

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Multivariate Time Series data Mining for Failure Prediction & Root Cause analysis

Multivariate Time Series Data Mining for Failure Prediction ...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Naman Agarwal Gagan Raj Gupta Vishwesh Jatala Aniket Saha Radcom Department of CSE IIT Bhilai Chennai Mathematical Institute

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper presents an analysis of a large-scale, high-dimensional industrial dataset containing over 2 million data points collected over several months. The dataset includes more than 200 failures of various types, each resulting from complex causes. Utilizing state-of-the-art unsupervised multivariate anomaly detection algorithms, a system was developed that predicts failures several minutes in advance, introducing a new evaluation metric termed lead time. This system also identifies the location and potential causes of these failures. Initial application of current anomaly detection algorithms yielded low F1 scores, due to either low recall or low precision. To address this issue, a visual reasoning framework was created to reduce false positives by analyzing motifs and discords in the top-N anomalous signals contributing to the anomaly. We find that the human-in-the-loop approach enhances the precision and F1-score of multivariate algorithms by leveraging human judgment for final decision-making. Another key finding is that the LSTM-based anomaly detection algorithm achieves sufficient lead time for the industrial failure prediction studied in this paper.

关键词： Measurement visualization Root cause analysis Time series analysis Signal processing algorithms Reinforcement learning Signal processing Prediction algorithms Speech processing Anomaly detection

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Conditional Diffusion Model for Abdominal CT Image Synthesis

Conditional Diffusion Model for Abdominal CT Image Synthesis

引用

ieee International symposium on Biomedical Imaging

作者： Xu Wang Dinglun He Baoming Zhang Yao Hao Deshan Yang Ye Duan Department of Electrical Engineering and Computer Science University of Missouri Columbia USA Department of Computer Science The University of Texas Dallas Richardson USA Department of Radiation Oncology Washington University in St. Louis USA Department of Radiation Oncology Duke University Durham USA School of Computing Clemson University Clemson USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Deep neural networks have revolutionized medical image analysis, with Generative Adversarial Networks (GANs) being prominent in synthesizing realistic images. However, GANs face challenges like mode collapse. As an alternative, Denoising Diffusion Probabilistic Models (DDPM) have emerged, showing promise in generating high-fidelity images, though their application to medical imaging is still nascent. In this study, we propose using DDPM to generate realistic computed tomography (CT) images guided by segmentation labels to ensure anatomical precision. We performed data preprocessing and downsampling on CT images from the TotalSegmentator dataset, trained our DDPM-based model, and subsequently applied super-resolution techniques to produce high-resolution images. Evaluation metrics such as Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) confirm the effectiveness of our approach in generating CT images that meet clinical standards while maintaining visual fidelity. We demonstrate that the generated images can augment training datasets for deep learning-based medical image segmentation models, thereby enhancing segmentation accuracy. Additionally, these synthetic images hold significant potential for applications in medical education.

关键词： Measurement Training Image segmentation visualization PSNR Uncertainty Image synthesis Computed tomography Diffusion models Medical diagnostic imaging

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Multi-Dimensional Vector ISA Extension for Mobile In-Cache Computing

Multi-Dimensional Vector ISA Extension for Mobile In-Cache C...

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ieee symposium on High-Performance Computer Architecture

作者： Alireza Khadem Daichi Fujiki Hilbert Chen Yufeng Gu Nishil Talati Scott Mahlke Reetuparna Das University of Michigan Institute of Science Tokyo

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

In-cache computing technology transforms existing caches into long-vector compute units and offers low-cost alternatives to building expensive vector engines for mobile CPUs. Unfortunately, existing long-vector Instruction Set Architecture (ISA) extensions, such as RISC-V Vector Extension (RVV) and Arm Scalable Vector Extension (SVE), provide only onedimensional strided and random memory accesses. While this is sufficient for typical vector engines, it fails to effectively utilize the large Single Instruction, Multiple data (SIMD) widths of incache vector engines. This is because mobile data-parallel kernels expose limited parallelism across a single dimension. Based on our analysis of mobile vector kernels, we introduce a long-vector Multi-dimensional Vector ISA Extension (MVE) for mobile in-cache computing. MVE achieves high SIMD resource utilization and enables flexible programming by abstracting cache geometry and data layout. The proposed ISA features multi-dimensional strided and random memory accesses and efficient dimension-level masked execution to encode parallelism across multiple dimensions. Using a wide range of data-parallel mobile workloads, we demonstrate that MVE offers significant performance and energy reduction benefits of $2.9 \times$ and $8.8 \times$, on average, compared to the SIMD units of a commercial mobile processor, at an area overhead of 3.6%.

关键词： Single instruction multiple data Instruction sets Memory management Layout Transforms Programming Vectors Resource management Kernel Engines

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A Comparative analysis of IoT Platform Tools: Security and Scalability Preferences in Device Management Using AHP

A Comparative Analysis of IoT Platform Tools: Security and S...

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2025 International Conference on Visual Analytics and data visualization, ICVADV 2025

作者： Maheswari, S. Rajkumar, N. Geetha, R. Theja, Kalakuntla Malleswari, M. Anand, Jose Simats Engineering Saveetha Institute of Medical and Technical Sciences Department of Ece TamilNadu Chennai602105 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Tamil Nadu Tiruvallur600062 India Srm Institute of Science and Technology Department of Computing Technologies TamilNadu Chennai603203 India Maisammaguda Dulapally Department of Cse Telangana Rangareddy500100 India Vel Tech High Tech Dr Rangarajan Dr Sakunthala Engineering College Department of It Tamil Nadu 600062 India Kcg College of Technology Department of Ece Tamil Nadu Karapakkam Chennai600097 India

ISBN: (纸本)9798331521394

This study evaluates four prominent Internet of Things (IoT) platforms-AWS IoT Core, Microsoft Azure, Particle, and Losant-using the Analytic Hierarchy Process (AHP) to determine their suitability for various project requirements. The evaluation criteria, derived from a comprehensive review of existing literature, emphasize security and scalability. The findings indicate that AWS IoT Core excels in data security, making it ideal for large-scale projects where security is paramount. Microsoft Azure offers exceptional scalability, benefiting from its integration within the Microsoft ecosystem. Particle provides cost-effective solutions suitable for smaller projects, while Losant offers flexible pricing tailored to customer needs. Given the rapid evolution of IoT technologies, the relevance of these platforms may change over time, necessitating ongoing evaluation to ensure alignment with future technological advancements. © 2025 ieee.

关键词： Scalability

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Optimizing Moving Target Selection in VR by Integrating Proximity-Based Feedback Types and Modalities

Optimizing Moving Target Selection in VR by Integrating Prox...

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ieee Annual International symposium Virtual Reality

作者： Xuning Hu Wenxuan Xu Yushi Wei Hao Zhang Jin Huang Hai-Ning Liang Xi’an Jiaotong-Liverpool University Suzhou China The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Dartmouth College Hanover USA Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331536459

ISBN: (纸本)9798331536466

Proximity-based feedback provides users with real-time guidance as they approach an interaction goal. This type of feedback is particularly useful for tasks that require guidance during the interaction process, such as selecting moving targets. This work explores proximity-based feedback types and modalities to improve the selection of moving targets in VR by leveraging three feedback types that combine visual, auditory, and haptic modalities. We evaluated the performance of these mechanisms through two user studies, analyzing both objective data (e.g., selection time, error rate) and subjective data (e.g., user experience, preferences) to explore the characteristics of feedback types across different modalities and to examine the roles of various modalities within multimodal combinations. Our findings suggest optimal selection mechanisms for developers and should be tailored to different goals: achieving user precision, enabling quick movement to a target, considering task duration, and enhancing entertainment value. We also discuss applications that correspond to these different perspectives.

关键词： visualization Three-dimensional displays Error analysis Entertainment industry Virtual reality User interfaces User experience Real-time systems Haptic interfaces

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Efficient Caching with A Tag-enhanced DRAM

Efficient Caching with A Tag-enhanced DRAM

引用

ieee symposium on High-Performance Computer Architecture

作者： Maryam Babaie Ayaz Akram Wendy Elsasser Brent Haukness Michael R. Miller Taeksang Song Thomas Vogelsang Steven C. Woo Jason Lowe-Power University of California Davis Rambus Labs Rambus Inc Samsung Electronics Davis

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

As SRAM-based caches are hitting a scaling wall, manufacturers are integrating DRAM-based caches into system designs to continue increasing cache sizes. While DRAM caches can improve the performance of memory systems, existing DRAM cache designs suffer from high miss penalties, wasted data movement, and interference between misses and demands. In this paper, we propose TDRAM, a novel DRAM microarchitecture tailored for caching. TDRAM enhances existing DRAM, such as HBM3, by adding small, low-latency mats to store tags and metadata on the same die as the data mats. These mats enable tag and data access in lockstep, in-DRAM tag comparison, and conditional data response based on the comparison result (reducing wasted data transfers), akin to SRAM cache mechanisms. TDRAM further optimizes hit and miss latencies through opportunistic early tag probing. Moreover, TDRAM introduces a flush buffer to store conflicting dirty data on write misses, eliminating data bus turnaround delays on write demands. We evaluate TDRAM in a full-system simulation using a set of HPC workloads with large memory footprints, showing that TDRAM, on average, provides $2.65 \times$ faster tag checks, $1.23 \times$ speedup, and 21% less energy consumption compared to state-of-the-art commercial and research designs.

关键词： Energy consumption Microarchitecture Memory management Energy conservation Random access memory Interference Metadata Energy efficiency Low latency communication System analysis and design

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Transformer-Based Multivariate Time Series Anomaly Localization

Transformer-Based Multivariate Time Series Anomaly Localizat...

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Computational Intelligence on Engineering/Cyber Physical Systems (CIES), ieee symposium on

作者： Charalampos Shimillas Kleanthis Malialis Konstantinos Fokianos Marios M. Polycarpou KIOS Research and Innovation Center of Excellence Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Department of Mathematics and Statistics University of Cyprus Nicosia Cyprus

ISBN: (数字)9798331508272

ISBN: (纸本)9798331508289

With the growing complexity of Cyber-Physical Systems (CPS) and the integration of Internet of Things (IoT), the use of sensors for online monitoring generates large volume of multivariate time series (MTS) data. Consequently, the need for robust anomaly diagnosis in MTS is paramount to maintaining system reliability and safety. While significant advancements have been made in anomaly detection, localization remains a largely underexplored area, though crucial for intelligent decisionmaking. This paper introduces a novel transformer-based model for unsupervised anomaly diagnosis in MTS, with a focus on improving localization performance, through an in-depth analysis of the self-attention mechanism's learning behavior under both normal and anomalous conditions. We formulate the anomaly localization problem as a three-stage process: time-step, window, and segment-based. This leads to the development of the SpaceTime Anomaly Score (STAS), a new metric inspired by the connection between transformer latent representations and spacetime statistical models. STAS is designed to capture individual anomaly behaviors and inter-series dependencies, delivering enhanced localization performance. Additionally, the Statistical Feature Anomaly Score (SFAS) complements STAS by analyzing statistical features around anomalies, with their combination helping to reduce false alarms. Experiments on real world and synthetic datasets illustrate the model's superiority over state-of-the-art methods in both detection and localization tasks.

关键词： Location awareness Measurement Time series analysis Transformers Sensor systems Safety Internet of Things Reliability Monitoring Synthetic data

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Comparison of Depression Detection Between LLMs and Zero-Shot Learning Using DAD dataset

Comparison of Depression Detection Between LLMs and Zero-Sho...

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International Colloquium on Signal Processing & Its Applications, CSPA

作者： Justin J. D. Leow Hui Na Chua Muhammed Basheer Jasser Bayan Issa Richard T.K. Wong School of Engineering and Technology Sunway University Selangor Darul Ehsan Malaysia Faculty of Informatics Engineering University of Aleppo Syria

ISBN: (数字)9798331522193

ISBN: (纸本)9798331522209

Early and accurate detection of depression is critical for improving mental health outcomes. Yet, selecting an appropriate machine learning approach remains challenging due to differences in data availability, annotation costs, and computational resources. Fine-tuned large Language Models (LLMs) and Zero-Shot Learning (ZSL) represent fundamentally distinct paradigms for natural language understanding tasks. Comparing these approaches is crucial because LLMs rely on large, labeled datasets, while ZSL provides a practical alternative in scenarios with limited or no annotated data. Despite their growing application, these methods are often studied in isolation, leaving a gap in understanding their relative advantages and trade-offs. This study addresses this gap by evaluating the performance of a fine-tuned BERT-LLM and a BART-based ZSL model on our custom-made Depression analysis dataset (DAD). Our research experiments showed that the fine-tuned BERT-LLM achieved accuracy and F1 scores of 98.94% and 99.24 %, respectively, while the BART-ZSL model attained 76.53% and 81.26%. The findings highlight the suitability of LLMs for high-reliability settings and ZSL for early detection in resource-constrained contexts, providing actionable insights for data scientists and mental health practitioners to design effective and accessible depression detection systems.

关键词： visualization Accuracy Explainable AI large language models Zero shot learning Blogs Mental health Writing Depression Multilingual

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