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SymNet: A Simple Symmetric Positive Definite Manifold Deep Learning Method for Image Set Classification

ieee TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第5期33卷 2208-2222页

作者： Wang, Rui Wu, Xiao-Jun Kittler, Josef Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Jiangsu Prov Engn Lab Pattern Recognit & Computat Wuxi 214122 Jiangsu Peoples R China Univ Surrey Ctr Vis Speech & Signal Proc Guildford GU2 7XH Surrey England

By representing each image set as a nonsingular covariance matrix on the symmetric positive definite (SPD) manifold, visual classification with image sets has attracted much attention. Despite the success made so far, the issue of large within-class variability of representations still remains a key challenge. Recently, several SPD matrix learning methods have been proposed to assuage this problem by directly constructing an embedding mapping from the original SPD manifold to a lower dimensional one. The advantage of this type of approach is that it cannot only implement discriminative feature selection but also preserve the Riemannian geometrical structure of the original data manifold. Inspired by this fact, we propose a simple SPD manifold deep learning network (SymNet) for image set classification in this article. Specifically, we first design SPD matrix mapping layers to map the input SPD matrices into new ones with lower dimensionality. Then, rectifying layers are devised to activate the input matrices for the purpose of forming a valid SPD manifold, chiefly to inject nonlinearity for SPD matrix learning with two nonlinear functions. Afterward, we introduce pooling layers to further compress the input SPD matrices, and the log-map layer is finally exploited to embed the resulting SPD matrices into the tangent space via log-Euclidean Riemannian computing, such that the Euclidean learning applies. For SymNet, the (2-D)(2)principal component analysis (PCA) technique is utilized to learn the multistage connection weights without requiring complicated computations, thus making it be built and trained easier. On the tail of SymNet, the kernel discriminant analysis (KDA) algorithm is coupled with the output vectorized feature representations to perform discriminative subspace learning. Extensive experiments and comparisons with state-of-the-art methods on six typical visual classification tasks demonstrate the feasibility and validity of the proposed SymNet.

关键词： Manifolds Kernel Deep learning Principal component analysis visualization Covariance matrices Videos Deep learning image set classification nonsingular covariance matrix symmetric positive definite (SPD) manifold principal component analysis (PCA)

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Integration of large Language Models into Higher Education: A Perspective from Learners 25

Integration of Large Language Models into Higher Education: ...

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25th International symposium on Computers in Education, SIIE 2023

作者： Zdravkova, Katerina Dalipi, Fisnik Ahlgren, Fredrik Ss. Cyril and Methodius University Faculty of Computer Science and Engineering Skopje Macedonia Linnaeus University Faculty of Technology Department of Informatics Kalmar Sweden Linnaeus University Faculty of Technology Department of Computer Science and Media Technology Kalmar Sweden

ISBN: (纸本)9798350329315

large language models (LLMs) are being criticized for copyright infringement, inadvertent bias in training data, a danger to human innovation, the possibility of distributing incorrect or misleading information, and prejudice. Due to their popularity among students, the introduction of many comparable apps, and the inability to resist unfair and fraudulent student usage, their educational use needs to be adapted and harmonized. The incorporation of LLMs should be defined not only by pedagogues and educational institutions, but also by students who will actively utilize them to learn and prepare assignments. In order to find out what students from two universities think and suggest about LLMs use in education, they were asked to give their contribution by answering the survey that was conducted at the beginning of the spring semester of academic 2022/23. Their feedback was quantitatively and qualitatively analyzed, showing in a better light what students think about LLMs and how and why they would use them. Based on the analysis, the authors propose an original strategy for integrating LLMs into education. The proposed approach is also adapted for those students who are not interested in using LLMs and for those who prefer the hybrid mode by combining their own research with LLMs generated recommendations. The authors expect that by implementing the proposed strategy, schools will benefit from a better education in which research, creativity, academic honesty, recognition of false information, and the ability to improve knowledge will prevail. © 2023 ieee.

关键词： Students

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EVRACE: Enhanced Visual Retrieval and analysis for large-Scale E-Bike Charging Curve Exploration

EVRACE: Enhanced Visual Retrieval and Analysis for Large-Sca...

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ieee International Conference on Big data

作者： Saisai Hu Ji Zhang Zhao Li Donghui Ding Dong Liu Zhijun Pan Jiale Dong School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China University of Southern Queensland Towoomba Australia Hangzhou Yugu Technology Co. Ltd. Hangzhou China

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

This Paper introduces a novel Enhanced Visual Retrieval and analysis for Charging Efficiency (EVRACE) system, which presents a novel two-stage framework for visual retrieval and advanced analytics for massive charging curves of electric vehicles. The EVRACE is becoming an important system for enhancing battery performance management, detecting and mitigating charging anomalies, and informing strategic decision-making for stakeholders in the EV ecosystem. EVRACE utilizes deep time-series clustering to categorize charging curves with distinct characteristics and retrieves the most relevant cluster and the top k curves for comprehensive data analysis and risk assessment. This innovative framework substantially enhances retrieval efficiency, mitigates computational complexity, and offers a robust solution for real-time processing of large-scale charging data. Validation using real-world datasets demonstrates that the EVRACE system significantly improves retrieval efficiency and achieves high accuracy in anomaly detection compared to traditional methods.

关键词： Industries visualization Accuracy Pipelines Electric vehicles Real-time systems Safety Risk management Stakeholders Reliability

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SBAS-InSAR analysis of Coastal Subsidence in Kerala, India, to monitor Flood Inundation Risk due to Relative Sea Level Rise 3

SBAS-InSAR Analysis of Coastal Subsidence in Kerala, India, ...

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3rd ieee India Geoscience and Remote Sensing symposium, InGARSS 2023

作者： Aparna, R. Ojha, Chandrakanta Indian Institute of Science Education and Research Department of Physical sciences Punjab Mohali India Indian Institute of Science Education and Research Department of Earth and Environmental Sciences Punjab Mohali India

ISBN: (纸本)9798350325591

The coastal sinking of land in many low-lying regions in the world has a greater potential risk of flooding-inundation hazards due to relative sea level rise. Kerala, being known for its varied geographical features, sharing a long coastline of 590 km in the southern part of India, has been affected by several floods in the past few decades. To develop an effective coastal subsidence monitoring system and predict the hazard risk, this study makes use of the Multi-Temporal Synthetic Aperture Radar (MT-InSAR) technique using Sentinel-1 data to obtain the Vertical Land Motion (VLM) over the Kerala coast and projects till 2100. The VLM results derived from the InSAR technique reveal a large subsidence of more than 20mm/year in the Kuttanad region of Alappuzha district. Further, we incorporate the VLM, the future projection of sea level from the Intergovernmental Panel on Climate Change (IPCC) AR6 report, and the high spatial resolution of the Digital Elevation Model (DEM) to map the low-lying fast subsiding zones that are prone to future flood inundation due to relative sea level rise. We derive future flood inundation maps for the years 2030, 2050, 2070, and 2100 by considering various socio-economic scenarios. © 2023 ieee.

关键词： Floods

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IMAGING COMPLEX FAULT SLIP OF large EARTHQUAKES WITH SENTINEL-1 AND ALOS-2 SAR analysis AND OTHER GEODETIC AND SEISMIC data

IMAGING COMPLEX FAULT SLIP OF LARGE EARTHQUAKES WITH SENTINE...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Fielding, Eric J. Liang, Cunren Huang, Mong-Han Liu, Zhen Ragon, Thea Bekaert, David Simons, Mark CALTECH Jet Prop Lab Pasadena CA 91125 USA CALTECH Seismol Lab Pasadena CA 91125 USA Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781665403696

We study the distribution of slip on faults during earthquakes with integrated analysis of geodetic imaging and seismic data to learn about parameters that control how faults slip and potentially how damaging future earthquakes may be. We mapped complex fault ruptures for a number of large earthquakes in 2015-2020 using analysis of synthetic aperture radar (SAR) data from the Copernicus Sentinel-1A and Sentinel-1B satellites operated by the European Space Agency and the Advanced Land Observation Satellite-2 (ALOS-2) satellite operated by the Japan Aerospace Exploration Agency (JAXA). We used regular SAR interferometry, along-track or multiple-aperture interferometry, and pixel offset tracking to measure surface displacements and combined this with other geodetic and seismic data to infer slip on faults at depth.

关键词： Earthquakes SAR interferometry pixel offsets fault slip

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A Visual Analytics Framework for Reviewing Streaming Performance data 13

A Visual Analytics Framework for Reviewing Streaming Perform...

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ieee Pacific visualization symposium (PacificVis)

作者： Kesavan, Suraj P. Fujiwara, Takanori Li, Jianping Kelvin Ross, Caitlin Mubarak, Misbah Carothers, Christopher D. Ross, Robert B. Ma, Kwan-Liu Univ Calif Davis Davis CA 95616 USA Rensselaer Polytech Inst Troy NY 12181 USA Argonne Natl Lab Argonne IL 60439 USA

ISBN: (数字)9781728156972

ISBN: (纸本)9781728156972

Understanding and tuning the performance of extreme-scale parallel computing systems demands a streaming approach due to the computational cost of applying offline algorithms to vast amounts of performance log data. Analyzing large streaming data is challenging because the rate of receiving data and limited time to comprehend data make it difficult for the analysts to sufficiently examine the data without missing important changes or patterns. To support streaming data analysis, we introduce a visual analytic framework comprising of three modules: data management, analysis, and interactive visualization. The data management module collects various computing and communication performance metrics from the monitored system using streaming data processing techniques and feeds the data to the other two modules. The analysis module automatically identifies important changes and patterns at the required latency. In particular, we introduce a set of online and progressive analysis methods for not only controlling the computational costs but also helping analysts better follow the critical aspects of the analysis results. Finally, the interactive visualization module provides the analysts with a coherent view of the changes and patterns in the continuously captured performance data. Through a multi-faceted case study on performance analysis of parallel discrete-event simulation, we demonstrate the effectiveness of our framework for identifying bottlenecks and locating outliers.

关键词： Human-centered computing visualization visualization application domains Visual analytics

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Research on Computer Simulation Algorithm of Aerobics Formation Transformation Path Based on Matrix analysis

Research on Computer Simulation Algorithm of Aerobics Format...

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2022 International symposium on Computing and Artificial Intelligence, ISCAI 2022

作者： Zong, Jun Huang, Xiaoxuan Jiangxi Science and Technolongy Normal University Nanchang China

ISBN: (纸本)9798350323252

At present, the technology of computer simulation has been paid more and more attention by sports science and technology researchers. Whether computer simulation technology can be more effective for sports workers and athletes. On the basis of exploring the transformation path of large-scale aerobics formation, this paper puts forward a path transformation optimization algorithm-minimum path optimization algorithm, and puts forward the solution scheme when the path is contradictory, which provides the algorithm basis for computer simulation. According to the conclusion of computer simulation of formation transformation path of large-scale aerobics, the path planning of each member during formation transformation of large-scale aerobics can be found more intuitively, which provides a good auxiliary means for large-scale aerobics practice. On the other hand applying big data to analyze college aerobics teaching data may assist teachers in better comprehending students' present learning situations and their personal growth and academic achievement. © 2022 ieee.

关键词： Motion planning

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Distributed data Processing for large-Scale Simulations on Cloud

Distributed Data Processing for Large-Scale Simulations on C...

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ieee International Joint symposium on Electromagnetic Compatibility (EMC), Signal and Power Integrity (SIPI) and EMC Europe

作者： Lu, Tianjian Hoyer, Stephan Wang, Qing Hu, Lily Chen, Yi-Fan Google Res 1600 Amphitheatre Pkwy Mountain View CA 94043 USA

ISBN: (纸本)9781665448888

The computational challenges encountered in the large-scale simulations are accompanied by those from data-intensive computing. In this work, we proposed a distributed data pipeline for large-scale simulations by using libraries and frameworks available on Cloud services. The building blocks of the proposed data pipeline such as Apache Beam and Zarr are commonly used in the data science and machine learning community. Our contribution is to apply the data-science approaches to handle large-scale simulation data for the hardware design community. The data pipeline is designed with careful considerations for the characteristics of the simulation data in order to achieve high parallel efficiency. The performance of the data pipeline is analyzed with two examples. In the first example, the proposed data pipeline is used to process electric potential obtained with a Poisson solver. In the second example, the data pipeline is used to process thermal and fluid data obtained with a computational fluid dynamic solver. Both solvers are in-house developed and finite-difference based, running in parallel on Tensor Processing Unit (TPU) clusters and serving the purpose of data generation. It is worth mentioning that in this work, the focus is on data processing instead of data generation. The proposed data pipeline is designed in a general manner and is suitable for other types of data generators such as full-wave electromagnetic and multiphysics solvers. The performance analysis demonstrates good storage and computational efficiency of the proposed data pipeline. As a reference, it takes 5 hours and 14 mins to convert simulation data of size 7.8 TB into Zarr format and the maximum total parallelism is chosen as 10,000.

关键词： Apache Beam data Pipeline Distributed Computing Simulation Tensor Processing Unit Zarr

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DynPTA: Combining Static and Dynamic analysis for Practical Selective data Protection 42

DynPTA: Combining Static and Dynamic Analysis for Practical ...

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42nd ieee symposium on Security and Privacy (SP)

作者： Palit, Tapti Moon, Jarin Firose Monrose, Fabian Polychronakis, Michalis SUNY Stony Brook Stony Brook NY 11794 USA UNC Chapel Hill Chapel Hill NC USA

ISBN: (纸本)9781728189345

As control flow hijacking attacks become more challenging due to the deployment of various exploit mitigation technologies, the leakage of sensitive process data through the exploitation of memory disclosure vulnerabilities is becoming an increasingly important threat. To make matters worse, recently introduced transient execution attacks provide a new avenue for leaking confidential process data. As a response, various approaches for selectively protecting subsets of critical in-memory data have been proposed, which though either require a significant code refactoring effort, or do not scale for large applications. In this paper we present DynPTA, a selective data protection approach that combines static analysis with scoped dynamic data flow tracking (DFT) to keep a subset of manually annotated sensitive data always encrypted in memory. DynPTA ameliorates the inherent overapproximation of pointer analysis-a significant challenge that has prevented previous approaches from supporting large applications-by relying on lightweight label lookups to determine if potentially sensitive data is actually sensitive. Labeled objects are tracked only within the subset of value flows that may carry potentially sensitive data, requiring only a fraction of the program's code to be instrumented for DFT. We experimentally evaluated DynPTA with real-world applications and demonstrate that it can prevent memory disclosure (Heartbleed) and transient execution (Spectre) attacks from leaking the protected data, while incurring a modest runtime overhead of up to 19.2% when protecting the private TLS key of Nginx with OpenSSL.

关键词： Runtime Sensitivity Instruments Memory management Discrete Fourier transforms data protection Process control

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Robustness of visualization Methods in Preserving the Continuous and Discrete Latent Structures of High-Dimensional Single-Cell data

Robustness of Visualization Methods in Preserving the Contin...

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18th ieee International Conference on Computational Intelligence in Bioinformatics and Computational Biology (ieee CIBCB)

作者： Malepathirana, Tamasha Senanayake, Damith A. Gautam, Vini Halgamuge, Saman K. Univ Melbourne Dept Mech Engn Melbourne Vic Australia Univ Melbourne Dept Biomed Engn Melbourne Vic Australia

ISBN: (纸本)9781665401128

Contemporary single-cell technologies produce data with a vast number of variables at a rapid pace, making large volumes of high-dimensional data available. The exploratory analysis of such high dimensional data can be aided by intuitive low dimensional visualizations. In this work, we investigate how both discrete and continuous structures in single cell data can be captured using the recently proposed dimensionality reduction method SONG, and compare the results with commonly used methods UMAP and PHATE. Using simulated and real-world datasets, we observed that SONG preserves a variety of patterns including discrete clusters, continuums, and branching structures. More importantly, SONG produced more/equally insightful visualizations compared to UMAP and PHATE in all considered datasets. We also quantitatively validate the high-dimensional pairwise distance preservation ability of these visualization methods in the low dimensional space for the generated visualizations.

关键词： Dimensionality Reduction visualization scRNA-Seq UMAP SONG

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