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ieee International symposium on Geoscience and Remote Sensing (IGARSS)

作者： Naoya Sogi Takashi Shibata Makoto Terao Kenta Senzaki Masahiro Tani Royston Rodrigues Visual Intelligence Research Labs NEC Corporation Japan NEC Laboratories Singapore NEC Asia Pacific Pte Ltd Singapore

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

We propose a framework for quickly selecting images that show the disaster situation from many images, estimating their locations with high accuracy, and displaying them on a map. The proposed framework introduces interactive image retrieval based on the Vision and Language Model (VLM), which can retrieve images from many images that show the disaster situation according to the user’s intention. Using the correlation between language and images based on VLM and the similarity between images selected interactively enables more accurate retrieval. Next, for selected images for which the location of the affected area is unknown, the location of the image is estimated with street address-level accuracy by matching it with an overhead image covering a large area of the city and map data and then displayed on a map. We confirmed the effectiveness of the proposed method on publicly available datasets such as CrisisNLP.

关键词： Accuracy Correlation Disasters Image retrieval Urban areas Geoscience and remote sensing data visualization

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GenStore: In-Storage Filtering of Genomic data for High-Performance and Energy-Efficient Genome analysis

GenStore: In-Storage Filtering of Genomic Data for High-Perf...

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ieee-Computer-Society Annual symposium on VLSI (ISVLSI)

作者： Ghiasi, Nika Mansouri Park, Jisung Mustafa, Harun Kim, Jeremie Olgun, Ataberk Gollwitzer, Arvid Cali, Damla Senol Firtina, Can Mao, Haiyu Alserr, Nour Almadhoun Ausavarungnirun, Rachata Vijaykumar, Nandita Alser, Mohammed Mutlu, Onur Swiss Fed Inst Technol Zurich Switzerland Bionano Genom San Diego CA USA KMUTNB Bangkok Thailand Univ Toronto Toronto ON Canada

ISBN: (数字)9781665466059

ISBN: (纸本)9781665466059

Genome sequence analysis, which analyzes the DNA sequences of organisms, is important for many applications in personalized medicine [1]–[8], outbreak tracing [9]–[14], and evolutionary studies [15]–[21]. The information of an organism's DNA is converted to digital data via a process called sequencing. A sequencing machine extracts the sequences of DNA molecules from the organism's sample in the form of strings consisting of four base pairs \n$(bps)$\n, denoted by \n$\\mathrm{A}, \\mathrm{C}, \\mathrm{G}$\n, and T. No current sequencing technology has the capability to read a human DNA molecule in its entirety. Instead, state-of-the-art sequencing machines generate randomly sampled, inexact sub-strings of the original genome, called reads. The information about the corresponding location of each read in the complete genome is lost during sequencing in most technologies. State-of-the-art sequencing machines produce one of two kinds of reads. 1) Short read sequencing technologies, such as Illumina [22], [23], produce reads that are highly accurate (99-99.9%) [24]–[26], but short (e.g., up to a few hundred DNA base pairs [24], [27], [28]). 2) Long read sequencing technologies, such as Pacific Biosciences (PacBio) [29] and Oxford Nanopore Technologies (ONT) [30], produce reads that are less accurate (85-90%) [27,31–33], but long (e.g., lengths ranging from thousands to millions of base pairs [34]).

关键词： Sequential analysis Sequences Filtering Precision medicine Genomics DNA Very large scale integration

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Two Approaches to Survival analysis of Open Source Python Projects 30

Two Approaches to Survival Analysis of Open Source Python Pr...

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30th ieee/ACM International Conference on Program Comprehension (ICPC)

作者： Robinson, Derek Enns, Keanelek Koulecar, Neha Sihag, Manish Univ Victoria Comp Sci Victoria BC Canada

ISBN: (纸本)9781450392983

A recent study applied frequentist survival analysis methods to a subset of the Software Heritage Graph and determined which attributes of an open source software project contribute to its health. This paper serves as an exact replication of that study. In addition, Bayesian survival analysis methods were applied to the same dataset, and an additional project attribute was studied to serve as a conceptual replication. Both analyses focus on the effects of certain attributes on the survival of open-source software projects as measured by their revision activity. Methods such as the Kaplan-Meier estimator, Cox Proportional-Hazards model, and the visualization of posterior survival functions were used for each of the project attributes. The results show that projects which publish major releases, have repositories on multiple hosting services, possess a large team of developers, and make frequent revisions have a higher likelihood of survival in the long run. The findings were similar to the original study;however, a deeper look revealed quantitative inconsistencies.

关键词： data science survival analysis open source python Kaplan Meier Cox Proportional-Hazards model Bayesian analysis frequentist

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Visual Space-Time Complexity analysis of Big Signal data

Visual Space-Time Complexity Analysis of Big Signal Data

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ieee International symposium on Electromagnetic Compatibility (EMC)

作者： Jan Nemec Stanislav Kovar Jan Valouch Milan Adamek Faculty of Applied Informatics Tomas Bata University in Zlin Zlin Czech Republic

ISBN: (数字)9798350360394

ISBN: (纸本)9798350360400

This paper will focus on introducing visual analysis for big signal data suitable for preliminary conclusions and information presented to others. The analysis is to determine uniqueness, focusing on spatial-temporal complexity by the nature of the test data. Alongside introducing the analysis and optimization for programming purposes, the real data will be presented and used to display possible usage of this analysis and to determine the uniqueness of electromagnetic background for the possible use in true random number generators. The data consists of measurements in frequency band increments in many locations using widely available consumer-grade equipment. Visual analyses are especially valuable in situations like search for entropy sources because there is no immediate need for a concrete number or boolean statement of the uniqueness; moreover, presenting work is the daily job of a researcher, and the ability to visualize findings in a simple figure is easier to digest by the wider population.

关键词： visualization Histograms Transforms Programming Real-time systems Generators Complexity theory Frequency measurement Time-domain analysis Optimization

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Interactive Graph Construction for Graph-Based Semi-Supervised Learning

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2021年第9期27卷 3701-3716页

作者： Chen, Changjian Wang, Zhaowei Wu, Jing Wang, Xiting Guo, Lan-Zhe Li, Yu-Feng Liu, Shixia Tsinghua Univ Sch Software BNRist Beijing Peoples R China Cardiff Univ Cardiff CF10 3AT Wales Microsoft Res Asia Beijing 100080 Peoples R China Nanjing Univ Nanjing 210023 Jiangsu Peoples R China

Semi-supervised learning (SSL) provides a way to improve the performance of prediction models (e.g., classifier) via the usage of unlabeled samples. An effective and widely used method is to construct a graph that describes the relationship between labeled and unlabeled samples. Practical experience indicates that graph quality significantly affects the model performance. In this paper, we present a visual analysis method that interactively constructs a high-quality graph for better model performance. In particular, we propose an interactive graph construction method based on the large margin principle. We have developed a river visualization and a hybrid visualization that combines a scatterplot, a node-link diagram, and a bar chart to convey the label propagation of graph-based SSL. Based on the understanding of the propagation, a user can select regions of interest to inspect and modify the graph. We conducted two case studies to showcase how our method facilitates the exploitation of labeled and unlabeled samples for improving model performance.

关键词： Dogs visualization Image edge detection Noise measurement Labeling data visualization Uncertainty Semi-supervised learning unlabeled samples graph quality

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55th Annual ieee/ACM International symposium on Microarchitecture (MICRO)

作者： Jiang, Shizhi Yang, Qiusong Ci, Yiwei Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Software Beijing Peoples R China

ISBN: (数字)9781665462723

ISBN: (纸本)9781665462723

One critical challenge of designing an efficient prefetcher is to strike a balance between performance and hardware overhead. Some state-of-the-art prefetchers achieve very high performance at the price of a very large storage requirement, which makes them not amenable to hardware implementations in commercial processors. We argue that merging memory access patterns can be a feasible solution to reducing storage overhead while obtaining high performance, although no existing prefetchers, to the best of our knowledge, have succeeded in doing so because of the difficulty of designing an effective merging strategy. After analysis of a large number of patterns, we find that the address offset of the first access in a certain memory region is a good feature for clustering highly similar patterns. Based on this observation, we propose a novel hardware data prefetcher, named Pattern Merging Prefetcher (PMP), which achieves high performance at a low cost. The storage requirement for storing patterns is largely reduced and, at the same time, the prefetch accuracy is guaranteed by merging similar patterns in the training process. In the prefetching process, a strategy based on access frequencies of prefetch candidates is applied to accurately extract prefetch targets from merged patterns. According to the experimental results on a wide range of various workloads, PMP outperforms the enhanced Bingo by 2.6% with 30x lesser storage overhead and Pythia by 8.2% with 6x lesser storage overhead.

关键词： cache hardware data prefetching

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Non-Collusive Interest Flooding Attacks (NCIFA) in Named data Networks using Rocketfuel ISP topology 17

Non-Collusive Interest Flooding Attacks (NCIFA) in Named Dat...

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17th ieee International Conference on Advanced Networks and Telecommunications Systems, ANTS 2023

作者： Hurali, Lalitha Chinmayee M. Patil, Annapurna P. Gorahalli, Pavan V. Ramaiah Institute of Technology Department of Cse Bengaluru India Visvesvaraya Technological University Belagavi590018 India

ISBN: (纸本)9798350307672

Non Collusive Interest Flooding Attacks (NCIFA), a class of DDoS attacks have posed significant threat in Information Centric Networking (ICN) Paradigm based Named data Networks (NDNs). NDN have gained much popularity in content distribution due to their named data routing, in-network caching, and intrinsic security features. Applications such as web applications, multimedia, IoT, and vehicular networks have been benefitting from this ICN paradigm. This work performs an empirical analysis to understand the impact of Non-Collusive IFAs (NCIFA) in NDN using large-scale ISP-like topologies (Rocket-fuel) using the ndnSim simulation tool. The analysis is conducted at multi-levels involving the Content Consumers/clients (CCs), Content Routers (CRs) and Content Producers (CPs) using various applicable Quality of Service (QoS) metrics. The holistic study of characteristics of performance metrics under baseline and NCIFA DDoS attack scenarios help devise effective detection and mitigation strategies for NCIFA in NDN at the router and the interface level. © 2023 ieee.

关键词： Topology

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Graph Neural Networks and Fault Tree analysis for Runtime Dynamic Risk Assessment of Autonomous Systems 71

Graph Neural Networks and Fault Tree Analysis for Runtime Dy...

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71st Annual Reliability and Maintainability symposium, RAMS 2025

作者： Grimstad, Joachim Morozov, Andrey Institute of Industrial Automation and Software Engineering University of Stuttgart Pfaffenwaldring 47 Stuttgart70550 Germany

ISBN: (纸本)9798350367744

To our knowledge, this paper offers two novel contributions in this field. First, it addresses the need for runtime dynamic risk assessment in autonomous systems by proposing a framework that enables continuous risk evaluation in complex and dynamic environments at runtime. This framework uses dynamic and inductive graph neural network methods that update and predict during runtime to reflect changes in the environment and can process large amounts of data efficiently. Secondly, it integrates traditional quantitative risk models, to introduce some degree of explainability of the graph neural network by converting the graph into traditional models such as fault trees. The second contribution is the partial proof-of-concept of the proposed concept by training a graph attention network to predict top event probabilities of simple fault trees represented as graphs. The implementation tries simple off-the-shelf algorithms but conducts some initial experimentation with loss functions, dropout layers, and distribution shifted datasets to evaluate the generalization capability of the models. Initial results show promise, demonstrating the potential of using GATs to predict top event probabilities in fault tree analysis. Future research should focus on optimizing dropout probabilities, expanding datasets, and exploring hyperparameter optimization. Additionally, investigating the necessary node features and subdividing graphs for better model handling are key areas for further exploration. Additionally, other methods such as GraphSAGE might be more suitable, and a comparison should be made. This work paves the way for integrating advanced machine learning techniques with traditional risk assessment methodologies, contributing to the development of safer, more reliable, and partially explainable autonomous systems. © 2025 ieee.

关键词： Graph neural networks

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Preliminary Study on Task Division for UAV-Based Visual Inspection of large Structures with Multiple Flights Using 3D Urban Models

Preliminary Study on Task Division for UAV-Based Visual Insp...

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ieee/SICE International symposium on System Integration

作者： Masaya Haneda Yuki Funabora Shinji Doki Department of Information and Communication Engineering Graduate School of Engineering Nagoya University Nagoya Aichi Japan

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

This paper addresses the problem of task division based on 3D urban models for visual inspection using a UAV with multiple flights. When inspecting large structures for damage detection, UAVs are often flown multiple times to obtain high-resolution data from all areas. Research is progressing on coverage path planning (CPP) methods for data collection in a single flight, and the 3D models of the target required for this can be readily obtained. However, the automation of task division for each flight has not been extensively studied. Because the scale of the target is large, it is necessary to execute a task division method that optimizes the performance efficiency of UAVs within a practical calculation time. This paper presents a task division method for data collection by multiple flights of a UAV based on the decomposition of the 3D urban models. Forming a framework in which data collection tasks are divided based on the 3D mesh decomposition, and then the CPP method is applied to each task, this enables efficient inspection of large-scale structures. In this paper, we implement three basic methods based on the strategies of avoiding going far, equalizing the amount of each task, and reducing unnecessary movement as preliminary research. Each method applies to objects on a scale of several hundred meters, and evaluates their performance in automated data collection.

关键词： visualization Solid modeling Three-dimensional displays Urban planning System integration data collection Inspection Autonomous aerial vehicles Planning Spatial resolution

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Fast White Matter Fiber Clustering Using Variational Autoencoder Latent Space

Fast White Matter Fiber Clustering Using Variational Autoenc...

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Medical Information Processing and analysis (SIPAIM), International symposium on

作者： Sebastián Navarrete Jean-François Mangin Cecilia Hernández Pamela Guevara Faculty of Engineering Universidad de Concepción Concepción Chile CEA CNRS Neurospin Baobab Université Paris-Saclay Gif-sur-Yvette France

ISBN: (数字)9798331528652

ISBN: (纸本)9798331528669

Whole brain tractography data contain a large number of streamlines that require algorithms such as clustering to group the data into smaller sets for visualization and analysis. We present a deep-learning clustering algorithm based on the latent space of a variational autoencoder trained on the direct and flipped versions of the streamlines from 10 tractograms (17,294,232 streamlines in total). The model takes advantage of the low-dimensional representation of the data in latent space to apply an HDBSCAN clustering algorithm to perform automatic and fast clustering of the tractography datasets. The proposed method was evaluated in terms of segmentation quality using the Davies-Bouldin index (DB) and execution time against two other state-of-the-art methods, QuickBundles (QB) and FFClust. The results show that the proposed method has the best performance in terms of the DB index, closely followed by QB, and is the second fastest method, only slightly surpassed by FFClust. In addition, the proposed method allows for obtaining meaningful large clusters because it uses metrics based on the density of the groups instead of a distance threshold as the other methods.

关键词： Measurement Shape Clustering algorithms data visualization Information processing data models Indexes White matter

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