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54th Annual Hawaii International conference on System Sciences

作者： Jekic, Nikolina Schreyer, Manuela Neubert, Steffen Mutlu, Belgin Schreck, Tobias Pro2Future GmbH Graz Graz Austria Austria Metall GmbH Braunau Austria AMAG Casting GmbH Ranshofen Austria Know Ctr GmbH Graz Graz Austria Graz Univ Technol Graz Austria

ISBN: (纸本)9780998133140

Today's manufacturing industry is shaped by the Industry 4.0 vision, which is to increase the number of individual goods produced while minimizing the production costs and time. To increase the production outcome and quality, users need to continuously monitor and adjust the entire process. While the recent advances in sensor technology can help users to collect, produce and exchange data, human beings are often overwhelmed by the amount of data being collected. Still, the human visual system is a powerful tool that can be used to decode and process large datasets. To make intelligent use of this ability, we have developed an interactive visual data analysis tool called ADAM that can support production data exploration in the aluminum industry. Furthermore, we demonstrate the effectiveness of our tool using real production data and present insights which could be gained from use of our tool by domain experts.

关键词： data handling

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Robust Feature Extraction Technique for Hand Gesture Recognition System 6th

Robust Feature Extraction Technique for Hand Gesture Recogni...

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6th International conference on Intelligent Computing and Optimization, ICO 2023

作者： Yadukrishnan, v. Anilkumar, Abhishek Arun, K.S. Madhu, M. Nimal Hareesh, v. Center for Computational Engineering and Networking Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore641112 India Department of Electrical Engineering National Institute of Technology Calicut Kerala Kozhikode India

ISBN: (纸本)9783031503269

Hand gesture recognition (HGR) is a vital and versatile technology widely employed in various applications such as gaming, robotics, and interactive media. Beyond its broad utility, HGR systems play a crucial role in facilitating interactions for individuals with disabilities, empowering them with newfound capabilities. This paper introduces an innovative approach to feature extraction, leveraging Autoencoders to derive essential characteristics from the data. The extracted features are subsequently utilized for a comparative analysis of the accuracy achieved across different data sizes within the same dataset. By mapping the visual vocabulary of images, the Autoencoder generates a unified vector, which is then fed into Support vector Machine (SvM), Naive Bayes, and K-Nearest Neighbor (KNN) classifiers. Through this research, we aim to enhance the efficiency and effectiveness of hand gesture recognition systems, fostering advancements in human-computer interaction and augmenting the quality of life for individuals with disabilities. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Support vector machines

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Optimizing vo-viso: A Modified Methodology to Parallel Computing with Isolating data in Memristor Arrays 20th

Optimizing Vo-Viso: A Modified Methodology to Parallel Comp...

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20th IFIP WG 10.3 International conference on Network and Parallel Computing, NPC 2024

作者： Wang, Wenqing Chen, Yabo Xu, Nuo Hu, Yihong Huang, Chenglong Wei, Shaojun Fang, Liang National University of Defense Technology Changsha410073 China Tsinghua University Beijing100084 China

ISBN: (纸本)9789819628292

The MAGIC NOR gate-based memristor array is a promising processing-in-memory (PIM) approach due to its massive parallelism, flexible logical operation, and non-volatile memory characteristics. The vo-viso MAGIC NOR gate operation mode is proposed to achieve high-throughput parallel computing and isolate other data within a memristor array. However, viso has a cumulative effect on the node voltage of the active row, calling into question the feasibility of vo-viso operation mode. In this paper, circuit analysis defines the necessary circuit parameter conditions for enabling parallel computing and isolation units in the MAGIC NOR gate array. By algorithms in Python and CUDA C++ exploration, we determined that achieving the necessary conditions within the array parameters ranges—including the tolerance margin vΔ, array size, vo, viso and memristor parameters suitable for real-world circuit requirements—is unfeasible. In response to these challenges, we modified the memristor array architecture. The modified architecture employs transistors to activate compute units while safeguarding the rest selectively. The feasibility of this architecture is validated by SPICE with selected transistors fabricated using a 65 nm process, taking into account transistor resistance and the fluctuations in vset and vreset in the 8×8 modified memristor array. To accommodate the design requirement of the state-of-the-art memristor array, the feasibility of 64×64 modified memristor array, taking into account the impact of wire resistance, is further verified. This paper bridges the gap between reliable high-throughput parallel computing operation and MAGIC NOR PIM architecture. © IFIP International Federation for Information Processing 2025.

关键词： SPICE

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15th International Joint conference on Computer vision, Imaging and Computer Graphics Theory and Applications, vISIGRAPP 2020

15th International Joint Conference on Computer Vision, Imag...

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15th International Joint conference on Computer vision, Imaging and Computer Graphics Theory and Applications, vISIGRAPP 2020

ISBN: (纸本)9783030948924

The proceedings contain 25 papers. The special focus in this conference is on Computer vision, Imaging and Computer Graphics Theory and Applications. The topics include: Efficient Range Sensing Using Imperceptible Structured Light;Hierarchical Object Detection and Classification Using SSD Multi-Loss;scene Text Localization Using Lightweight Convolutional Networks;early Stopping for Two-Stream Fusion Applied to Action Recognition;RGB-D Images Based 3D Plant Growth Prediction by Sequential Images-to-Images Translation with Plant Priors;ConvPoseCNN2: Prediction and Refinement of Dense 6D Object Pose;expression Modeling Using Dynamic Kernels for Quantitative Assessment of Facial Paralysis;perceptually-Informed No-Reference Image Harmonisation;On-board UAv Pilots Identification in Counter UAv Images;skeleton-and-Trackball Interactive Rotation Specification for 3D Scenes;Exploring Tele-Assistance for Cyber-Physical Systems with MAUI;On the Use of 3D CNNs for video Saliency Modeling;CNN-Based Deblurring of THz Time-Domain Images;thermal Image Super-Resolution: A Novel Unsupervised Approach;regression-Based 3D Hand Pose Estimation for Human-Robot Interaction;detection and Recognition of Barriers in Egocentric Images for Safe Urban Sidewalks;CSG Tree Extraction from 3D Point Clouds and Meshes Using a Hybrid Approach;intention Understanding for Human-Aware Mobile Robots: Comparing Cues and the Effect of Demographics;tracking Eye Movement for Controlling Real-Time Image-Abstraction Techniques;improving Deep Learning Projections by Neighborhood analysis;scalable visual exploration of 3D Shape databases via Feature Synthesis and Selection;visual analysis of Linked Musicological data with the musiXplora;a Research-Teaching Guide for visual data analysis in Digital Humanities.

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Quaternary Contourites of the Northern Argentine Continental Margin and their Relationship with Subsurface Hydrocarbon exploration 1

Quaternary Contourites of the Northern Argentine Continental...

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1st EAGE conference on South Atlantic Offshore Energy Resources

作者： Principi, S. Acosta, L. Palma, F. Ormazabal, J.P. Isola, J. Tassone, A. University of Buenos Aires Argentina YPF Tecnología S.A. Argentina

ISBN: (纸本)9789462824737

This study focuses on the recent sedimentary deposits in the Colorado basin, which comprise a vast contourite depositional system (CDS) along the continental slope. The formation of this CDS is attributed to the northward flow of modern deep-water masses, resulting in both erosive and depositional features. As part of the "Pampa Azul Initiative," a national program promoting ocean science research in Argentina, a bathymetric dataset covering an area of 13,000 km2 was acquired during the YTEC-GTGM 4 cruise in 2019 aboard the R/v Austral. Additionally, six sediment cores were extracted during the same survey. In this study, we analyze the bathymetric and sediment data alongside multichannel 2D seismics to characterize the contourites on the lower slope that shape the present-day seafloor. Furthermore, we examine the underlying fault system. By investigating the geological processes driving the formation of the CDS and studying subsurface structural features, we aim to gain insights into the dynamics of the Northern Argentine Continental Margin and identify potential seepage systems in the region. © 2023 1st EAGE conference on South Atlantic Offshore Energy Resources. All Rights Reserved.

关键词： Offshore oil well production

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One-Model-Connects-All: A Unified Graph Pre-Training Model for Online Community Modeling

One-Model-Connects-All: A Unified Graph Pre-Training Model f...

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conference on Empirical Methods in Natural Language Processing (EMNLP)

作者： Ma, Ruoxue Xue, Jiarong Zhang, Xinnong Zhang, Haozhe Zhao, Zuyu Zhang, Qi Huang, Xuanjing Wei, Zhongyu Fudan Univ Sch Data Sci Shanghai Peoples R China Fudan Univ Sch Management Shanghai Peoples R China Fudan Univ Sch Comp Sci Shanghai Peoples R China Fudan Univ Res Inst Intelligent Complex Syst Shanghai Peoples R China Huawei Technol Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9798891760615

Online community is composed of communities, users, and user-generated textual content, with rich information that can help us solve social problems. Previous research hasn't fully utilized these three components and the relationship among them. What's more, they can't adapt to a wide range of downstream tasks. To solve these problems, we focus on a framework that simultaneously considers communities, users, and texts. And it can easily connect with a variety of downstream tasks related to social media. Specifically, we use a ternary heterogeneous graph to model online communities. Text reconstruction and edge generation are used to learn structural and semantic knowledge among communities, users, and texts. By leveraging this pre-trained model, we achieve promising results across multiple downstream tasks, such as violation detection, sentiment analysis, and community recommendation. Our exploration will improve online community modeling.

关键词： Sentiment analysis

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Multilingual Conversational AI incorporated with visual Questions Answering and Intelligent Disease Prediction for Healthcare Industry 7

Multilingual Conversational AI incorporated with Visual Ques...

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Sasmitha, N.U.A. Wathasha, H.K.G.v. Guruge, P.P.L. Silva, W.J.T. Rupasinghe, Lakmal Gunarathne, G.W.D.A. Sri Lanka Institute of Information Technology Faculty of Computing Colombo Sri Lanka

ISBN: (纸本)9781665421683

Artificial intelligence (AI) is becoming more active than ever in everyday life and steadily being incorporated to healthcare. AI, with its seemingly limitless power, affirms a promising future to a revolutionized healthcare system. This paper is proposing a conversational AI solution in two different languages, English and Sinhala, to predict diseases through a conversation, a visual question answering solution to generate answers are based on a given question and a medical image and a disease forecasting module. A robust, accurate prediction is a rather difficult task given the availability of data and absence of preprocessed, clean data. With the aid of outlier rejection, data imputation, vectorization, feature selection and data standardization, the proposed framework gets the advantage of latest machine learning advancements such as AI using DIET classifier and NLU pipelines, for the conversational disease diagnosis which uses support vector machine (SvM) achieved an accuracy of 0.93. Moreover, the visual questions answering module with vGG16 preprocessing, GoogleNews vectors, LSTM networks, scores an accuracy of 0.9721. In addition, time series analysis models such as ARIMA and adaptive models using PROPHET library for forecasting diseases, classification using random forest scoring an accuracy of 0.81, logistic regression scoring an accuracy of 0.84 for predicting diseases. The objective of this research is to compare and select the best fitting models to be used for a centralized framework for healthcare industry. © 2022 IEEE.

关键词： Time series analysis

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Development of a Productive Transport Detection System Using Convolutional Neural Networks

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PATTERN RECOGNITION AND IMAGE analysis 2022年第3期32卷 495-500页

作者： Andriyanov, N. A. Dementiev, v. E. Tashlinskiy, A. G. Ulyanovsk State Tech Univ Ulyanovsk 432027 Russia

The paper discusses various approaches used to optimize the convolutional neural network (CNN) model with You Only Look Once (YOLO) version 3 architecture. At the same time, an implementation using graphic processing units (GPU) computing is used as a baseline performance estimate. A separate study was conducted on the Intel Core i5-8500 central processing unit (CPU). Both the classical methods of the neural network approach, such as model pruning, and effective methods for processing video data, in particular, the optical flow and speed prediction, are considered. The proposed algorithm made it possible to overclock the GPU computing of the NvIDIA RTX 2080 Super video card by 4 times, up to almost 30 frames per second. However, processing frames using Intel Open visual Inference and Neural network Optimization (OpenvINO) toolkit allows us to achieve similar performance on the CPU without optical flow and speed extrapolation. In this case, the acceleration reaches almost 30 times.

关键词： neural network optimization pruning fine tuning convolutional neural networks You Only Look Once traffic monitoring video processing object detection Intel Open visual Inference and Neural network Optimization

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Poster Abstract: TCT: Zero-training two staged Contrastive Transformer network for SSvEP classification 23

Poster Abstract: TCT: Zero-training two staged Contrastive T...

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23rd ACM/IEEE International conference on Information Processing in Sensor Networks, IPSN 2024

作者： Wang, Chenlong Zhuo, Yan Li, Han Chen, Xinlei Tsinghua University Shenzhen International Graduate School China Pengcheng Laboratory China RISC-V International Open Source Laboratory China

ISBN: (纸本)9798350362015

Steady-State Evoked Potential (SSvEP) is a brain response to specific frequency visual stimuli, used in brain-computer interfaces due to its robust and easily detectable signals. Researchers have long applied methods like Canonical Correlation analysis and deep learning for SSvEP signal decomposition and classification. However, those methods struggle to classify SSvEP signals without new subject's data, and calibration is time-consuming. In this paper, we propose a two-stage, two-Transformer streams network to address the challenge of classifying SSvEP signals from new subjects. We utilize hierarchical contrastive learning to project features into a more discriminable feature space before classification. The comparative experiment demonstrates that our approach exhibits superior performance relative to alternative methods in processing SSvEP signals from new subjects. © 2024 IEEE.

关键词： Brain computer interface

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The PERISCOPE data Atlas: A Demonstration of Release v1.2 20th

The PERISCOPE Data Atlas: A Demonstration of Release v1.2

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20th International conference on Artificial Intelligence in Medicine (AIME)

作者： Parimbelli, Enea Larizza, Cristiana Urosevic, vladimir Pogliaghi, Andrea Ottaviano, Manuel Cheng, Cindy Benoit, vincent Pala, Daniele Casella, vittorio Bellazzi, Riccardo Giudici, Paolo Univ Pavia Pavia Italy Belit Doo Belgrade Serbia GeneGIS GI Milan Italy Univ Politecn Madrid UPM Madrid Spain Tech Univ Munich Munich Germany AP HP Paris France

ISBN: (纸本)9783031093425;9783031093418

The data Atlas is the centerpiece of the PERISCOPE project's data-driven research. The Atlas constitutes a centralized access point for the exploration, visualization and analysis of the original data produced by PERISCOPE partners, integrated with the most relevant information about the COvID-19 pandemic and its effects on health, economics, policy-making, and society at large. The Atlas interfaces and tools make such data readily available to the research community, decision makers and the general public, providing the means to amplify its reach and impact. The present demo, showcases the features of v1.2 release of the Atlas, 18 months from the project kick-off, and some of the planned enhancements to be delivered until project month 24.

关键词： COvID-19 data warehouse data integration GIS Pandemic Policy Impact Ontology

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