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ieee Region 10 symposium (TENSYMP) - Good Technologies for Creating Future

作者： Amin, Farhan Lee, Wai-Kong Mateen, Abdul Hwang, Seong Oun Gachon Univ Dept Comp Engn Gyeonggi Do South Korea Fed Urdu Univ Arts Sci & Technol Dept Comp Sci Islamabad Pakistan

ISBN: (纸本)9781665400268

data science is playing a virtual role in our lives, from the personalization of our experiences to helping us the large and heterogeneous types of data sets in various domains. Based on the theoretical knowledge of network and data sciences, researchers need to be familiar with relevant future technologies. There are several compacting network science approaches that are available quantities research. data science tools and network science approaches offer a unique perspective to tackle complex problems, impenetrable to linear-proportional thinking. So, in this study, we reviewed various network graph-based analytics such as network centrality, closeness centrality as a necessity for the internet of things-based technologies. Also, we discussed a few top data analytics and visualization tools for these technologies as well. Finally, we performed a comparison of network metrics along with suitable platforms.

关键词： Big data Big data Analytics Internet of things Social network analysis smart city data Science Emerging Technologies

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COMPUTATIONALLY-EFFICIENT BANDWISE GBM MODEL FOR HYPERSPECTRAL NONLINEAR UNMIXING

COMPUTATIONALLY-EFFICIENT BANDWISE GBM MODEL FOR HYPERSPECTR...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Ahmad, Touseef Raha, Soumyendu Lyngdoh, Rosly B. Anand, S. S. Gupta, Praveen K. Misra, Arundhati Indian Space Res Org Space Applicat Ctr Ahmedabad Ahmadabad Gujarat India Indian Inst Sci Dept Computat & Data Sci Bangalore Karnataka India

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

Nonlinear Unmixing using the Band-wise Generalized Bilinear Mixing (NU-BGBM) model specifies an acceptable mixing scenario up to the second-order interaction of light rays and also suppresses various types of mixed noise while performing unmixing. However, NU-BGBM requires high computational time and multiple parameter tuning, which could practically limit its application to large HyperSpectral Images (HSIs). In this context, we propose a computationally efficient BGBM as a fast and robust variant of the NU-BGBM method, In this model, the objective function for the non-linear optimization scheme is designed without the sparsity constraint, and an iterative scheme based on the Alternating Direction Method of Multipliers (ADMM) is formulated for solving the proposed model. Extensive analyses have been carried out on synthetic (with simulated mixed noise) and real HSIs. The performance of the proposed method was compared with the NU-BGBM model using signal-to-reconstruction error (SRE), abundance Root-Mean-Square Error (aRMSE), source Root-Mean-Square Error (sRMSE), and Root-Sum-Squared (RSS) error. Results from extensive numerical analysis reveal that the proposed method reduces computation time (on an average six times faster) while being comparable (and often better) than NU-BGBM in terms of accuracy on large data sets.

关键词： Hyperspectral image unmixing bandwise GBM ADMM

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A Shape-Based Similarity Calculation Method for Cost Estimation of large Forged Steel Products 13

A Shape-Based Similarity Calculation Method for Cost Estimat...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Matsuo, Shunga Kondo, Satoshi Imamura, Yusuke Takase, Kenji Watanabe, Shinya Graduate School of Engineering Muroran Institute of Technology Hokkaido Muroran Japan Japan Steel Works M and E Inc. Materials Manufacturing Department Hokkaido Muroran Japan

ISBN: (纸本)9798350373332

This study aims to estimate the cost of large forged steel products by calculating similarity through both shape and numerical factors, such as weight, maximum horizontal length, and maximum diameter. The estimation process intends to simulate expert judgment about manufacturing and fabrication cost similarity, rather than relying solely on geometric and numerical data. We propose an original approach that integrates shape similarity via deep metric learning methods with numerical similarity. To validate our method, the simulation results were compared with the judgments of an experienced professional at an actual steelmaking site. The results of the analysis identified the improvement of reproducibility by emphasizing the complexity of the body shape as a future issue. Specifically, focusing on body shape improved reproducibility within the top three similar products by 36% compared to considering overall shape, and further emphasizing complexity decreased reproducibility by 20%. © 2024 ieee.

关键词： Cost estimating

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Using Microservice Telemetry data for System Dynamic analysis 16

Using Microservice Telemetry Data for System Dynamic Analysi...

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16th ieee International Conference on Service-Oriented System Engineering (SOSE)

作者： Al Maruf, Abdullah Bakhtin, Alexander Cerny, Tomas Taibi, Davide Baylor Univ Dept Comp Sci Waco TX 76798 USA Tampere Univ Software Engn Grp FI-33720 Tampere Finland Tampere Univ CloudSEA AI Grp FI-33720 Tampere Finland

ISBN: (数字)9781665475341

ISBN: (纸本)9781665475341

Microservices bring various benefits to software systems. They also bring decentralization and lose coupling across self-contained system parts. Since these systems likely evolve in a decentralized manner, they need to be monitored to identify when possibly poorly designed extensions deteriorate the overall system quality. For monolith systems, such tasks have been commonly addressed through static analysis. However, given the decentralization and possible language diversity across microservices, static analysis tools are lacking. On the other hand, there are available tools commonly used by practitioners that offer centralized logging, tracing, and metric collection for microservices. In this paper, we assess the opportunity to combine current dynamic analysis tools with anomaly detection in the form of quality metrics and anti-patterns. We develop a tool prototype that we use to assess a large microservice system benchmark demonstrating the feasibility and potential of such an approach.

关键词： Microservices Software Architecture Reconstruction Dynamic analysis Telemetry data

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A Study on a 3D Spatial Mapping System to Investigate Skyscrapers Using Drones 14

A Study on a 3D Spatial Mapping System to Investigate Skyscr...

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Kim, Sang-Su Kang, Ji-Yeon Jung, Hee-Tac Park, Jin-Ho Lee, Seung-Jae Go, Jun-Hui Ictway Corporation Ict Research Laboratory Dae-Jeon Korea Republic of Ictway Corporation Ict Research Laboratory Seoul Korea Republic of

ISBN: (纸本)9798350313277

Recently, as the drone industry develops, related technologies are being used in various fields. In particular, drone technology is being used to search for missing people with large-scale manpower, detect cracks and rust in high-rise buildings, and perform safety inspections. However, there was a downside to using existing drones. First, after shooting the target, check the abnormal point in the original video with the naked eye and save it to the local memory as a file. Also, during AI analysis, it was processed as a cumbersome procedure in which batch-type files had to be analyzed in the analysis module and then the results had to be checked again. Accordingly, this paper proposes a method of visualizing 4K (3840X2160) still images transmitted from drones in real time and AI analysis in real time, and then visualizing the resulting data on a 3D web map. © 2023 ieee.

关键词： 4K images Drone Images visualization High Quality Images search drone visualization

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Exposing the Attraction of data- An Examination and Application of Teaching data visualization in Multimedia Learning Mode From Theory to Practice

Exposing the Attraction of Data- An Examination and Applicat...

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ieee International symposium on Information (IT) in Medicine and Education, ITME

作者： Yue Wang Mingyou Liu Yingxue Zhu Sitong Liu School of Biology and Engineering(Health Medicine Modern Industry College) Guizhou Medical University Guizhou China University of Nottingham Ningbo China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

With the advent of the big data era, data visualization technology has grown in significance and become an essential core technology in the fields of statistical analysis and decision support. However, the current approaches of teaching the data visualization course tend to downplay the significance of theoretical knowledge in favor of developing students' practical skills with various information visualization technologies. When students are faced with complex facts, this teaching style, which prioritizes practice over theory, may result in a lack of deep analytical skills and creative thinking. Meanwhile, in order to reveal the charm of data, the process of transforming data information into visual form can significantly improve the presentation effect of data, and observe and browse the data in an intuitive and interactive way, so that it is easier to find the hidden features, relationships, and patterns in the data, which can provide powerful support for data analysis and *** order to create a naturally occurring combination of knowledge and application and better prepare students for the difficulties of the big data era, teaching data visualization educational programs using a blended learning approach requires maintaining a balance between the two.

关键词： visualization Educational programs data analysis Statistical analysis data visualization Big data Hybrid learning Information technology

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Fuzzing with data Dependency Information 7

Fuzzing with Data Dependency Information

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7th ieee European symposium on Security and Privacy (ieee EuroS and P)

作者： Mantovani, Alessandro Fioraldi, Andrea Balzarotti, Davide EURECOM Biot France

ISBN: (纸本)9781665416146

Recent advances in fuzz testing have introduced several forms of feedback mechanisms, motivated by the fact that for a large range of programs and libraries, edge-coverage alone is insufficient to reveal complicated bugs. Inspired by this line of research, we examined existing program representations looking for a match between expressiveness of the structure and adaptability to the context of fuzz testing. In particular, we believe that data dependency graphs (DDGs) represent a good candidate for this task, as the set of information embedded by this data structure is potentially useful to find vulnerable constructs by stressing combinations of def-use pairs that would be difficult for a traditional fuzzer to trigger. Since some portions of the dependency graph overlap with the control flow of the program, it is possible to reduce the additional instrumentation to cover only "interesting" data-flow dependencies, those that help the fuzzer to visit the code in a distinct way compared to standard methodologies. To test these observations, in this paper we propose DDFUZZ, a new approach that rewards the fuzzer not only with code coverage information, but also when new edges in the data dependency graph are hit. Our results show that the adoption of data dependency instrumentation in coverage-guided fuzzing is a promising solution that can help to discover bugs that would otherwise remain unexplored by standard coverage approaches. This is demonstrated by the 72 different vulnerabilities that our data-dependency driven approach can identify when executed on 38 target programs from three different datasets.

关键词： Codes Instruments Computer bugs Fuzzing data structures Libraries Task analysis

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LIVE-ITS: LSH-based Interactive visualization Explorer for large-Scale Incomplete Time Series

LIVE-ITS: LSH-based Interactive Visualization Explorer for L...

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ieee International Conference on Big data

作者： Hongjie Chen Aaron D. Beachnau Panos Thomas Pranav Maneriker Josh Kimball Ryan A. Rossi Dolby Labs. Atlanta USA Adobe Research San Jose USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Recent advances in time series research have created a significant demand for better time series visualization techniques, especially for large-scale datasets that contain millions of time series or more. In this paper, we consider a common use scenario where analysts aim to identify representative time series from a large collection. These representative time series generalize as many time series as possible and can be used for downstream tasks. Building a visualization system for this scenario involves many challenges, including visualizing, selecting, and highlighting a subset of time series from the overall dataset. Moreover, the potential for time series to be incomplete due to missing records adds an extra layer of difficulty. To address these challenges, we propose a novel visualization system, called the Locality Sensitive Hashing-based Interactive visualization Explorer for large-scale Incomplete Time Series (LIVE-ITS). On the frontend, LIVE-ITS allows analysts to interact with the system and select representative time series in areas of interest. In the backend, LIVE-ITS not only selects an optimal subset that represents as many time series as possible, but also achieves the best possible time complexity. Experiments on both synthetic dataset and real-world datasets show that LIVE-ITS exhibits high partition accuracy and high response efficiency, further validating the effectiveness of our proposed visualization system.

关键词： Accuracy Runtime Scalability Time series analysis Buildings data visualization Big data Labeling Time complexity Synthetic data

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A Decade Survey of Content Based Image Retrieval Using Deep Learning

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ieee TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2022年第5期32卷 2687-2704页

作者： Dubey, Shiv Ram Indian Inst Informat Technol Comp Vis Grp Chittoor 517646 India

The content based image retrieval aims to find the similar images from a large scale dataset against a query image. Generally, the similarity between the representative features of the query image and dataset images is used to rank the images for retrieval. In early days, various hand designed feature descriptors have been investigated based on the visual cues such as color, texture, shape, etc. that represent the images. However, the deep learning has emerged as a dominating alternative of hand-designed feature engineering from a decade. It learns the features automatically from the data. This paper presents a comprehensive survey of deep learning based developments in the past decade for content based image retrieval. The categorization of existing state-of-the-art methods from different perspectives is also performed for greater understanding of the progress. The taxonomy used in this survey covers different supervision, different networks, different descriptor type and different retrieval type. A performance analysis is also performed using the state-of-the-art methods. The insights are also presented for the benefit of the researchers to observe the progress and to make the best choices. The survey presented in this paper will help in further research progress in image retrieval using deep learning.

关键词： Image retrieval Deep learning Measurement Taxonomy Internet visualization Feature extraction Content based image retrieval deep learning CNNs survey supervised and unsupervised learning

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DSNet: A Dual-Stream Framework for Weakly-Supervised Gigapixel Pathology Image analysis

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ieee TRANSACTIONS ON MEDICAL IMAGING 2022年第8期41卷 2180-2190页

作者： Xiang, Tiange Song, Yang Zhang, Chaoyi Liu, Dongnan Chen, Mei Zhang, Fan Huang, Heng O'Donnell, Lauren Cai, Weidong Univ Sydney Sch Comp Sci Sydney NSW 2008 Australia Univ New South Wales Sch Comp Sci & Engn Kensington NSW 2052 Australia Microsoft Corp Redmond WA 98052 USA Harvard Med Sch Brigham & Womens Hosp Boston MA 02115 USA Univ Pittsburgh Dept Elect & Comp Engn Pittsburgh PA 15261 USA

We present a novel weakly-supervised framework for classifying whole slide images (WSIs). WSIs, due to their gigapixel resolution, are commonly processed by patch-wise classification with patch-level labels. However, patch-level labels require precise annotations, which is expensive and usually unavailable on clinical data. With image-level labels only, patch-wise classification would be sub-optimal due to inconsistency between the patch appearance and image-level label. To address this issue, we posit that WSI analysis can be effectively conducted by integrating information at both high magnification (local) and low magnification (regional) levels. We auto-encode the visual signals in each patch into a latent embedding vector representing local information, and down-sample the raw WSI to hardware-acceptable thumbnails representing regional information. The WSI label is then predicted with a Dual-Stream Network (DSNet), which takes the transformed local patch embeddings and multi-scale thumbnail images as inputs and can be trained by the image-level label only. Experiments conducted on three large-scale public datasets demonstrate that our method outperforms all recent state-of-the-art weakly-supervised WSI classification methods.

关键词： visualization Encoding Annotations Redundancy Pathology Metastasis Image analysis Weakly-supervised training image classification whole slide images

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