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检索条件"任意字段=Conference on Visual Data Exploration and Analysis II"

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A gaze-contingent system for foveated multiresolution visualization of vector and volumetric data

A gaze-contingent system for foveated multiresolution visual...

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2020 conference on visualization and data analysis, VDA 2020

作者： Ananpiriyakul, Thanawut Anghel, Joshua Potter, Kristi Joshi, Alark SnapLogic Amazon NREL United States USF Department of Computer Science University of San Francisco San FranciscoCA United States

Computational complexity is a limiting factor for visualizing large-scale scientific data. Most approaches to render large datasets are focused on novel algorithms that leverage cutting-edge graphics hardware to provide users with an interactive experience. In this paper, we alternatively demonstrate foveated imaging which allows interactive exploration using low-cost hardware by tracking the gaze of a participant to drive the rendering quality of an image. Foveated imaging exploits the fact that the spatial resolution of the human visual system decreases dramatically away from the central point of gaze, allowing computational resources to be reserved for areas of importance. We demonstrate this approach using face tracking to identify the gaze point of the participant for both vector and volumetric datasets and evaluate our results by comparing against traditional techniques. In our evaluation, we found a significant increase in computational performance using our foveated imaging approach while maintaining high image quality in regions of visual attention. © 2020, Society for Imaging Science and Technology.

关键词： Large dataset

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DeepFakes Evolution: analysis of Facial Regions and Fake Detection Performance 25th

DeepFakes Evolution: Analysis of Facial Regions and Fake Det...

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25th International conference on Pattern Recognition Workshops, ICPR 2020

作者： Tolosana, Ruben Romero-Tapiador, Sergio Fierrez, Julian Vera-Rodriguez, Ruben Biometrics and Data Pattern Analytics Universidad Autonoma de Madrid Madrid Spain

ISBN: (纸本)9783030688202

Media forensics has attracted a lot of attention in the last years in part due to the increasing concerns around DeepFakes. Since the initial DeepFake databases from the 1 st generation such as UADFV and FaceForensics++ up to the latest databases of the 2 nd generation such as Celeb-DF and DFDC, many visual improvements have been carried out, making fake videos almost indistinguishable to the human eye. This study provides an exhaustive analysis of both 1 st and 2 nd DeepFake generations in terms of facial regions and fake detection performance. Two different methods are considered in our experimental framework: i) the traditional one followed in the literature and based on selecting the entire face as input to the fake detection system, and ii) a novel approach based on the selection of specific facial regions as input to the fake detection system. Among all the findings resulting from our experiments, we highlight the poor fake detection results achieved even by the strongest state-of-the-art fake detectors in the latest DeepFake databases of the 2 nd generation, with Equal Error Rate results ranging from 15% to 30%. These results remark the necessity of further research to develop more sophisticated fake detectors. © 2021, Springer Nature Switzerland AG.

关键词： Fake detection

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Deep Learning on visual and Location data for V2I mmWave Beamforming

Deep Learning on Visual and Location Data for V2I mmWave Bea...

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International conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Guillem Reus-Muns Batool Salehi Debashri Roy Tong Jian Zifeng Wang Jennifer Dy Stratis Ioannidis Kaushik Chowdhury Northeastern University Boston USA

ISBN: (纸本)9781665406697

Accurate beam alignment in the millimeter-wave (mmWave) band introduces considerable overheads involving brute-force exploration of multiple beam-pair combinations and beam retraining due to mobility. This cost becomes often intractable under high mobility scenarios, where fast beamforming algorithms that can quickly adapt the beam configurations are still under development for 5G and beyond. Besides, blockage prediction is a key capability in order to establish mmWave reliable links. In this paper, we propose a data fusion approach that takes inputs from visual edge devices and localization sensors to (i) reduce the beam selection overhead by narrowing down the search to a small set containing the best possible beam-pairs and (ii) detect blockage conditions between transmitters and receivers. We evaluate our approach through joint simulation of multi-modal data from vision and localization sensors and RF data. Additionally, we show how deep learning based fusion of images and Global Positioning System (GPS) data can play a key role in configuring vehicle-to-infrastructure (V2I) mmWave links. We show a 90% top-10 beam selection accuracy and a 92.86% blockage prediction accuracy. Furthermore, the proposed approach achieves a 99.7% reduction on the beam selection time while keeping a 94.86% of the maximum achievable throughput.

关键词： Deep learning Location awareness Wireless communication visualization Array signal processing Vehicle-to-infrastructure data integration

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Educational big-data practice: A model for combining requirements and technologies 2nd

Educational big-data practice: A model for combining require...

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2nd International Cognitive Cities conference, IC3 2019

作者： Hsu, Sheng-Kuei Zhaoqing University Zhaoqing China

ISBN: (纸本)9789811561122

Institutional research (IR) has revealed the need for data processing in a university, and big-data technology can be regarded as a technical approach. The combination of the two methods can help universities introduce data-driven decision-making models. On the demand side of big-data processing on campus, we proposed three groups of university stakeholders—university, student, and society—with student learning outcomes as the main research topic. On the technical side, we proposed a data processing model from the perspective of big-data processing, including data gathering, pre-processing, storage and integration, analysis and exploration, and visual presentation. We applied the proposed learning outcomes and the big-data processing model to Tzu-Chi University, exploring student learning outcomes with big-data technology. In an initial experiment, this application model is feasible. Finally, we proposed several relevant conclusions to promote educational big-data research as a reference for related research. © Springer Nature Singapore Pte Ltd 2020.

关键词： Big data

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Automated image extraction from Instagram for social research: A technical and ethical exploration 8

Automated image extraction from Instagram for social researc...

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8th International conference on Technological Ecosystems for Enhancing Multiculturality, TEEM 2020

作者： Varela-Rodríguez, Miguel Vicente-Mariño, Miguel Department of Sociology and Social Work Faculty of Commerce University of Valladolid Valladolid Spain Department of Sociology and Social Work Faculty of Social Legal and Communication Sciences University of Valladolid Valladolid Spain

ISBN: (纸本)9781450388504

The use of social media data in social research has grown exponentially since the early 2010s, with many social researchers incorporating some degree of social media analysis. Following the 2018 Cambridge Analytica controversy, most social media providers locked access to their users' data, leaving researchers with limited options to study the information in it. Scholars have taken different stands, advocating data policies that allow for critical research, achieving partnerships with data providers, or - sometimes - violating the Terms of Use of the platforms to access the information. Despite the limitations, great progress has been made using text-based data, while image-based methodologies remain limited, partly because of the lockout. This paper proposes a methodology for automated image extraction from Instagram, using Instalooter. It presents the necessary setup and steps to obtain images based on a series of search parameters before discussing the ethical implications of its potential use. © 2020 ACM.

关键词： visual communication

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A compact encoding of security logs for high performance activity detection

A compact encoding of security logs for high performance act...

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Euromicro conference on Parallel, Distributed and Network-Based Processing

作者： Michele Ianni Elio Masciari DIETI Dipartimento di Informatica University of Verona Verona Italy DIETI University Federico II and ICAR CNR Napoli Italy

ISBN: (纸本)9781665414555;9781665447645

The rise of cyber crime observed in recent years calls for more efficient and effective data exploration and analysis tools. In this respect, the need to support advanced analytics on activity logs and real time data is driving data scientist' interest in order to design and implement scalable cyber security solutions. However, when data science algorithms are leveraged for huge amount of data, their fully scalable deployment faces a number of technical challenges that grow with the complexity of the algorithms involved and the task to be tackled. Thus algorithms, that were originally designed for classical scenarios, must often be redesigned in order to be effectively used for cyber security purposes. In this paper, we explore these problems and then propose a solution which has proven to be very effective for compressing suspicious activities in smaller and larger cyber environment in order to make the intelligent analysis and simulation of threats more efficient.

关键词： Analytical models Tools data science Real-time systems Encoding Complexity theory Security

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An Interactive visualization System for Near-Earth Space exploration data 4

An Interactive Visualization System for Near-Earth Space Exp...

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4th International conference on Environmental Science and Material Application (ESMA)

作者： Yu, Haijun Li, Shengyang Zhang, Tao Univ Chinese Acad Sci CAS Technol & Engn Ctr Space Utilizat Key Lab Space Utilizat Beijing Peoples R China Chinese Acad Sci Technol & Engn Ctr Space Utilizat Key Lab Space Utilizat Beijing Peoples R China Chinese Acad Sci Technol & Engn Ctr Space Utilizat Beijing Peoples R China

Aiming at the actual needs of efficient and convenient visual analysis of near-Earth space exploration data, this paper studies the functional realization mode, data flow and interactive method, analyzes the functional framework and data flow of the interactive visualization system of near-Earth space exploration data in detail, designs a multi-level, loosely coupled and easily extensible system architecture, and focuses on solving the logic model design, the fusion rendering of the multivariable data, the shadow calculation, the data clustering analysis, and other key technologies. The application of the system has realized the interactive and visual analysis of the massive and multi-source near-Earth space exploration data, and provided convenient data analysis service for the vast number of scientific research and application users, which helps to better play the potential value of near-Earth space exploration data.

关键词： Space research

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Large-scale Clustering of People Diagnosed with Parkinson's Disease using Acoustic analysis of Sustained Vowels: Findings in the Parkinson's Voice Initiative Study 13

Large-scale Clustering of People Diagnosed with Parkinson's ...

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13th International Joint conference on Biomedical Engineering Systems and Technologies

作者： Tsanas, Athanasios Arora, Siddharth Univ Edinburgh Edinburgh Med Sch Usher Inst Edinburgh Midlothian Scotland Univ Oxford Dept Math Oxford England

ISBN: (纸本)9789897583988

The heterogeneity of symptoms in Parkinson's Disease (PD) has motivated investigating PD subtypes using cluster analysis techniques. Previous studies investigating PD clustering have typically focused on symptoms assessed using standardized clinical evaluations and patient reported outcome measures. Here, we explore PD subtype delineation using speech signals. We used data from the recently concluded Parkinson's Voice Initiative (PVI) study where sustained vowels were solicited and collected under non-controlled acoustic conditions. We acoustically characterized 2097 sustained vowel /a/ recordings from 1138 PD participants using 307 dysphonia measures which had previously been successfully used in applications including differentiating healthy controls from PD participants, and matching speech dysphonia to the standard PD clinical metric quantifying symptom severity. We applied unsupervised feature selection to obtain a concise subset of the originally computed dysphonia measures and explored hierarchical clustering combined with 2D-data projections using t-distributed stochastic neighbor embedding to facilitate visual exploration of PD subgroups. We computed four main clusters which provide tentative insights into different dominating speech-associated pathologies. Collectively, these findings provide new insights into the nature of PD towards exploring speech-PD data-driven subtyping.

关键词： Acoustic analysis Clustering Parkinson's Disease Parkinson's Voice Initiative (PVI)

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Urdu Sentiment Corpus (v1.0): Linguistic exploration and visualization of Labeled dataset for Urdu Sentiment analysis 2

Urdu Sentiment Corpus (v1.0): Linguistic Exploration and Vis...

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2nd International conference on Information Science and Communication Technology, ICISCT 2020

作者： Khan, Muhammad Yaseen Nizami, Muhammad Suffian Mohammad Ali Jinnah University Center for Language Computing Department of Computer Science Karachi Pakistan

ISBN: (纸本)9781728168999

The significance of the labeled dataset is not obscure from artificial intelligence practitioners. We have seen much phenomenal work, in natural language processing, for many languages (like English, Chinese, and Arabic, etc.), due to the reason for the availability of substantial data. For the Urdu language, despite the third largest spoken language in the world, very little research work is shown;hence, it is adjudged as a 'morphologically rich' but 'resource-poor' language. Further, the researchers working on Urdu natural language processing are in a quandary due to the lack of availability of labeled/annotated datasets. This paper shares the data, 'Urdu Sentiment Corpus' (USC), and insights therein, of Urdu tweets for the sentiment analysis and polarity detection. The dataset is consisting of tweets, such that it casts a political shadow and presents a competitive environment between two separate political parties versus the government of Pakistan. Overall, the dataset is comprising over 17, 185 tokens with 52% records as positive, and 48 % records as negative. This paper shares the visual insights (from document-level to word-level) into the textual similarities, manifold-learning, etc. In addition to it, this paper also presents a Part-of-Speech wise analysis and an unpretentious technique for the extraction of sentiment lexicons from the corpus. © 2020 IEEE.

关键词： visualization

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Optimizing Selection Criteria for the CALET Ultra-Heavy Cosmic Ray analysis 38

Optimizing Selection Criteria for the CALET Ultra-Heavy Cosm...

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38th International Cosmic Ray conference, ICRC 2023

作者： Zober, Wolfgang V. Rauch, Brian F. Ficklin, Anthony W. Cannady, Nicholas Adriani, O. Akaike, Y. Asano, K. Asaoka, Y. Berti, E. Bigongiari, G. Binns, W.R. Bongi, M. Brogi, P. Bruno, A. Cannady, N. Castellini, G. Checchia, C. Cherry, M.L. Collazuol, G. de Nolfo, G.A. Ebisawa, K. Ficklin, A.W. Fuke, H. Gonzi, S. Guzik, T.G. Hams, T. Hibino, K. Ichimura, M. Ioka, K. Ishizaki, W. Israel, M.H. Kasahara, K. Kataoka, J. Kataoka, R. Katayose, Y. Kato, C. Kawanaka, N. Kawakubo, Y. Kobayashi, K. Kohri, K. Krawczynski, H.S. Krizmanic, J.F. Maestro, P. Marrocchesi, P.S. Messineo, A.M. Mitchell, J.W. Miyake, S. Moiseev, A.A. Mori, M. Mori, N. Motz, H.M. Munakata, K. Nakahira, S. Nishimura, J. Okuno, S. Ormes, J.F. Ozawa, S. Pacini, L. Papini, P. Rauch, B.F. Ricciarini, S.B. Sakai, K. Sakamoto, T. Sasaki, M. Shimizu, Y. Shiomi, A. Spillantini, P. Stolzi, F. Sugita, S. Sulaj, A. Takita, M. Tamura, T. Terasawa, T. Torii, S. Tsunesada, Y. Uchihori, Y. Vannuccini, E. Wefel, J.P. Yamaoka, K. Yanagita, S. Yoshida, A. Yoshida, K. Zober, W.V. Department of Physics McDonnell Center for the Space Sciences Washington University St. LouisMO63130 United States CRESST-II University of Maryland Baltimore County NASA Goddard Space Flight Center United States Department of Physics University of Florence Via Sansone 1 Sesto Fiorentino50019 Italy INFN Sezione di Firenze Via Sansone 1 Sesto Fiorentino50019 Italy Waseda Research Institute for Science and Engineering Waseda University 17 Kikuicho Shinjuku Tokyo162-0044 Japan JEM Utilization Center Human Spaceflight Technology Directorate Japan Aerospace Exploration Agency 2-1-1 Sengen Ibaraki Tsukuba305-8505 Japan Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa Chiba277-8582 Japan Via Madonna del Piano 10 Sesto Fiorentino50019 Italy Department of Physical Sciences Earth and Environment University of Siena via Roma 56 Siena53100 Italy INFN Sezione di Pisa Polo Fibonacci Largo B. Pontecorvo 3 Pisa56127 Italy Department of Physics McDonnell Center for the Space Sciences Washington University One Brookings Drive St. LouisMO63130-4899 United States Heliospheric Physics Laboratory NASA GSFC GreenbeltMD20771 United States Center for Space Sciences and Technology University of Maryland Baltimore County 1000 Hilltop Circle BaltimoreMD21250 United States Astroparticle Physics Laboratory NASA GSFC GreenbeltMD20771 United States Center for Research and Exploration in Space Sciences and Technology NASA GSFC GreenbeltMD20771 United States Department of Physics and Astronomy Louisiana State University 202 Nicholson Hall Baton RougeLA70803 United States Department of Physics and Astronomy University of Padova Via Marzolo 8 Padova35131 Italy INFN Sezione di Padova Via Marzolo 8 Padova35131 Italy Institute of Space and Astronautical Science Japan Aerospace Exploration Agency 3-1-1 Yoshinodai Chuo Kanagawa Sagamihara252-5210 Japan Kanagawa University 3-27-1 Rokkakubashi Kanagawa Ka

CALET, the Calorimetric Electron Telescope, launched to the International Space Station in August 2015 and in continuous operation since, has gathered over seven years of data so far. CALET is able to measure cosmic-ray (CR) electrons, nuclei, and gamma rays and with its 27 radiation length deep Total Absorption Calorimeter (TASC), measures particle energy, allowing for the determination of spectra and secondary to primary ratios of the more abundant CR nuclei through 28Ni, while the main charge detector (CHD) can measure Ultra-Heavy (UH) CR nuclei through 40Zr. CALET UHCR analyses use a special high duty cycle UH trigger with an expanded geometry that does not require passage through the TASC. To effectively analyze UHCR trigger events, a number of screens and corrections have been developed for the analysis. From time- and position-dependent detector response corrections based on 14Si and 26Fe, to an angle-dependent geomagnetic cutoff rigidity selections and minimum deposited energy screens, a number of methods have been explored to optimize UH statistics to varying effect. In this work, we aim to show how these event selection screens and corrections have been developed, how the rigidity screens shown previously by Rauch et al. [1] compare to the newer TASC methodology shown in Zober et al. (2023) [2], and how TASC selections may be used to influence analysis on the full UH-trigger dataset. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmology

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