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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Suraj P. Kesavan Harsh Bhatia Keshav Dasu Olga Pearce Kwan-Liu Ma University of California Davis Lawrence Livermore National Laboratory

ISBN: (数字)9798350393804

ISBN: (纸本)9798350393811

Whereas rapidly increasing heterogeneous compute capabilities continue to facilitate further scalability, modern applications often instead get limited by suboptimal data movement, as more and more data needs to be shipped across different hardware components (i.e., CPUs, GPUs, and other types of accelerators). We posit that understanding and improving data movement in modern use-cases require a holistic understanding of the underlying Hardware usage as well as the Communication patterns within the overall context of the Application, or as we call it, the HAC domain. Collecting and correlating HAC data currently requires interacting with several profiling tools and libraries, resulting in a tedious workflow that is neither scalable nor portable. Furthermore, existing tools for visualizing data movement profiles also focus on these domains individually, rather than offering a holistic *** present a unified framework for tracking and visualizing data movement trends in large-scale applications deployed on heterogeneous architectures. Our framework has two interoperable components. (1) DMTracker is a lean software layer that provides a simple interface for configurable HAC profiling of GPU-enabled applications and abstracts away the complexity in using several profiling tools, resulting in a streamlined and time-correlated event history across the HAC domains. (2) DMVis is a web-based tool that combines several linked visualizations to offer holistic visual insights into the runtime behavior and resources utilization of applications, proving pivotal in identifying computationally expensive tasks and data transfers across devices. In this paper, we present the design and prototype of our framework, developed in consultation with domain experts and demonstrated on two case studies, including one for a large language model training. Initial impressions from the experts indicate a positive turn in their usual workflow of observing and tuning the performance through imp

关键词： Training visualization Runtime Tracking data visualization Computer architecture Hardware

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WhatsNext: Guidance-enriched Exploratory data analysis with Interactive, Low-Code Notebooks

WhatsNext: Guidance-enriched Exploratory Data Analysis with ...

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ieee symposium on Visual Languages and Human Centric Computing (VL/HCC)

作者： Chen Chen Jane Hoffswell Shunan Guo Ryan Rossi Yeuk-Yin Chan Fan Du Eunyee Koh Zhicheng Liu University of Maryland College Park MD USA Adobe Research Seattle WA USA Adobe Research San Jose CA USA

Computational notebooks such as Jupyter are popular for exploratory data analysis and insight finding. Despite the module-based structure, notebooks visually appear as a single thread of interleaved cells containing text, code, visualizations, and tables, which can be unorganized and obscure users' data analysis workflow. Furthermore, users with limited coding expertise may struggle to quickly engage in the analysis process. In this work, we design and implement an interactive notebook framework, WhatsNext, with the goal of supporting low-code visual data exploration with insight-based user guidance. In particular, we (1) re-design a standard notebook cell to include a recommendation panel that suggests possible next-step exploration questions or analysis actions to take, and (2) create an interactive, dynamic tree visualization that reflects the analytic dependencies between notebook cells to make it easy for users to see the structure of the data exploration threads and trace back to previous steps.

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A large-Scale Comparison of Python Code in Jupyter Notebooks and Scripts

A Large-Scale Comparison of Python Code in Jupyter Notebooks...

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19th International Conference on Mining Software Repositories (MSR)

作者： Grotov, Konstantin Titov, Sergey Sotnikov, Vladimir Golubev, Yaroslav Bryksin, Timofey JetBrains Res Boston MA 02210 USA ITMO Univ St Petersburg Russia

ISBN: (纸本)9781450393034

In recent years, Jupyter notebooks have grown in popularity in several domains of software engineering, such as data science, machine learning, and computer science education. Their popularity has to do with their rich features for presenting and visualizing data, however, recent studies show that notebooks also share a lot of drawbacks: high number of code clones, low reproducibility, etc. In this work, we carry out a comparison between Python code written in Jupyter Notebooks and in traditional Python scripts. We compare the code from two perspectives: structural and stylistic. In the first part of the analysis, we report the difference in the number of lines, the usage of functions, as well as various complexity metrics. In the second part, we show the difference in the number of stylistic issues and provide an extensive overview of the 15 most frequent stylistic issues in the studied mediums. Overall, we demonstrate that notebooks are characterized by the lower code complexity, however, their code could be perceived as more entangled than in the scripts. As for the style, notebooks tend to have 1.4 times more stylistic issues, but at the same time, some of them are caused by specific coding practices in notebooks and should be considered as false positives. With this research, we want to pave the way to studying specific problems of notebooks that should be addressed by the development of notebook-specific tools, and provide various insights that can be useful in this regard.

关键词： Codes data visualization Machine learning data science Software Reproducibility of results Encoding

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SecBox: A Lightweight Container-based Sandbox for Dynamic Malware analysis

SecBox: A Lightweight Container-based Sandbox for Dynamic Ma...

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ieee symposium on Network Operations and Management

作者： Jan von der Assen Alberto Huertas Celdrán Adrian Zermin Raffael Mogicato Gérôme Bovet Burkhard Stiller Department of Informatics Communication Systems Group University of Zurich UZH Zürich Switzerland Cyber-Defence Campus armasuisse Science & Technology Thun Switzerland

Cybersecurity solutions based on machine learning (ML) and behavioral fingerprinting have demonstrated their suitability when detecting heterogeneous malware. However, most solutions are black boxes missing explainable and visual capabilities needed to analyze relevant metrics and malicious behaviors to be collected. In this demonstration, SecBox, a dynamic malware analysis platform with integrated data collection and visualization for malware execution, is presented. To provide a lightweight sandboxing approach, the architecture relies on Linux containers for isolation. The sandboxing and data analysis components of the SecBox architecture are deployed in a test bed to show the analysis of two malware families. In the presented scenario, the Monti ransomware and CoinMiner, a Monero-based cryptojacker are analyzed after obtaining them from a public database.

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Review of the State-of-the-Art of data Gloves 8

Review of the State-of-the-Art of Data Gloves

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8th ieee International symposium on Smart Electronic Systems, iSES 2022

作者： Pan, Mingzhang Tang, Yingzhe Li, Hongqi Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology School of Mechanical Engineering Guangxi University Nanning530004 China Brain-Controlled Intelligent Systems Laboratory School of Software Northwestern Polytechnical University Xi'an710072 China Yangtze River Delta Research Institute of NPU Taicang215400 China

ISBN: (纸本)9798350399226

Human-machine interaction has been the focus of recent research, with data gloves as one of the approaches gaining popularity for their high accuracy and convenience. This paper summarizes the latest research status of data gloves with a focus on the area of glove-based gesture recognition. The core technology of gesture recognition is described in detail from data processing to classification, and a quantitative analysis of the recent research is conducted. We found that though studies on applications and algorithms are the mainstream, the hardware design also accounts for a pretty large proportion of about 30%. Moreover, the number of gestures, subjects, and algorithms for majority of relevant studies has been summarized. © 2022 ieee.

关键词： Gesture recognition

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Causality Testing, Directed Information and Spike Trains

Causality Testing, Directed Information and Spike Trains

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ieee International symposium on Information Theory

作者： A. Theocharous G.G. Gregoriou P. Sapountzis I. Kontoyiannis U. of Cambridge U. of Crete FORTH

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

Directed information has been used in information theory and in statistics as a functional that quantifies causal influences present in signals and empirical data. In this work, the causally conditional directed information (CCDI) rate is identified as a statistic for detecting causal relationships between discrete time series, in the presence of potential confounders. A hypothesis test is introduced for identifying the temporally causal influence of $(x_{n})$ on $(y_{\mathrm{Y}})$ , causally conditioned on a possibly confounding third time series $(z_{n})$ . Under natural assumptions it is shown that the absence of temporally causal influence is equivalent to the CCDI rate being zero. The plug-in estimator for this functional is identified with the log-likelihood ratio test statistic for the desired test. This statistic is shown to be asymptotically normal under the alternative hypothesis and asymptotically $\chi^{2}$ distributed under the null, facilitating the computation of p-values from empirical data. The resulting hypothesis test is employed in the analysis of spike train data recorded from neurons in the V4 and FEF brain regions of behaving animals during a visual attention task. The test results are seen to identify interesting and biologically relevant information.

关键词： visualization Animals Time series analysis Neurons Distributed databases Cause effect analysis Task analysis

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Computational Understanding of Narratives: A Survey

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ieee ACCESS 2022年 10卷 101575-101594页

作者： Ranade, Priyanka Dey, Sanorita Joshi, Anupam Finin, Tim Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Baltimore MD 21250 USA

Storytelling, and the delivery of societal narratives, enable human beings to communicate, connect, and understand one another and the world around them. Narratives can be defined as spoken, visual, or written accounts of interconnected events and actors, generally evolving through some notion of time. Today, information is typically conveyed over online communication mediums, such as social media and blogging websites. Consequently, the act of narrative delivery itself has shifted from simply imparting information through self-contained structures such as books, to more fragmented structures, such as social media websites, where evolving story events are constructed over multiple online sources. Ubiquitous online conversation can manifest into sophisticated narratives that have the potential to influence wide-spread user interpretations of cultural sentiments, attitudes, values, as well as geopolitical events and facts. As a result, narratives are actively being used as strategic tools for shaping local events, promoting collective opinions, and asserting ideologies and propaganda, making them sources of interest for identifying themes, intentions, and goals across multiple communities and potential adversaries. Identifying fragmented narratives, extracting thematic and temporal components that constitute evolving narratives, and locating signs of active rhetoric framing tactics, are difficult to detect and analyze without large-scale automation. This problem can be addressed through the use of natural language understanding technologies. Our goal is to document and discuss methods to efficiently construct, extract, and detect evolving online narratives. The novel contribution of this paper is the formal collation and documentation of such technologies and research areas, as well as extensive discussion on open research challenges and goals in the definition, identification, construction, generation, and representation of online narratives. To our knowledge, there

关键词： Social networking (online) Documentation data mining Blogs Semantics visualization Videos Cultural differences Rhetoric Oral communication Natural languages Global communication Narratives storytelling narrative extraction story evolution narrative generation computational semantic analysis natural language understanding misinformation propaganda journalism

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Understanding and Automating Graphical Annotations on Animated Scatterplots

Understanding and Automating Graphical Annotations on Animat...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Danqing Shi Antti Oulasvirta Tino Weinkauf Nan Cao Aalto University KTH Royal Institute of Technology Tongji University

ISBN: (数字)9798350393804

ISBN: (纸本)9798350393811

Scatterplots are commonly used in various contexts, from scientific publications to infographics for the general public. However, not everyone is able to read them, and even experts may struggle to notice some important information such as overlapping clusters or temporal changes. To address these issues, a computational approach for annotating scatterplots has been developed. This approach involves various forms of annotation, including drawing lines to show correlations, circling areas to show clusters, and indicating movement with arrows. The approach is based on a study that identified common annotation strategies used by people to annotate scatterplots. These strategies are distilled into an automated method for generating graphical annotations on scatterplots. The method involves a problem formulation using a Markov Decision Process and a model for making annotation decisions. The model generates step-by-step graphical annotations by analyzing data insights and observing the chart. The final result conveys a narrative that is easy to understand and allows for the conveyance of temporal changes in the data. The study results suggest that the method can generate understandable and functional annotations that are comparable to those created by human experts. This approach can potentially reduce the time and effort required to read scatterplots, making it a useful tool for data visualization novices.

关键词： Analytical models data analysis Correlation Annotations Markov decision processes data visualization Big data applications

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Design of Patterned Transcendental Metamaterial Cover Structures for Crosstalk Reduction in FBAR Parallel Feed Lines

Design of Patterned Transcendental Metamaterial Cover Struct...

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2024 Photonics and Electromagnetics Research symposium, PIERS 2024

作者： Chu, Tongchuan Cao, Xin Zhou, Longjian Cai, Qiangming Zhu, Yuyu Southwest University of Science and Technology School of Information Engineering Mianyang621010 China South China University of Technology School of Electronic and Information Engineering Guangzhou510640 China

ISBN: (纸本)9798350375909

With the rapid development of modern high-speed circuit technology, electronic devices have the properties of high data transmission rates, steeper edge changing rates, and large-scale integration which can lead to increased crosstalk challenges in high-speed planar circuit designs. Severe crosstalk problems can result in disrupted data transmission and increase bit error rates (BER). Therefore, in order to reduce crosstalk problems and increase signal transmission quality, this paper studies the far-end crosstalk (FEXT) suppression of film bulk acoustic resonator (FBAR) parallel feed lines in high-speed data rate transmission. First, the theory of FBAR crosstalk is analyzed, and the influential factors such as line spacing, trace coupling length on crosstalk in FBAR feedlines are analyzed from the perspectives of frequency domain and time domain. Then, several structures based on geometric fractal substrate covers are proposed. The proposed structures utilize modern mathematical transcendental planar geometry such as modified Hilbert fractal, Minkowski fractal and von Koch fractal metamaterial covers. By adding these structures, the distributed capacitance of the line is increased to neutralize the magnetic coupling between the lines, thus reducing the FEXT both in frequency and time domains. The optimization is both performed through full-wave simulation and circuit simulation. As the results show that, in the frequency domain, the FEXT can be maximumly reduced about 26 dB from 0.1 GHz to 16 GHz. For signal transmitting verification, the lines are fed with DDR5 (the fifth-generation double data rate) signals, and the FEXT can be reduced over 21 mV, which corresponds to over 95% of the FEXT reduction in the time domain. The proposed structure can be applied in modern high speed planar circuits and wireless communication systems. © 2024 ieee.

关键词： Frequency domain analysis

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Software Tools and Evaluation Models for Visual Analytics: Trends, Challenges, and Future Directions

Software Tools and Evaluation Models for Visual Analytics: T...

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2025 International Conference on Visual Analytics and data visualization, ICVADV 2025

作者： Lakumarapu, Srikanth Sudha, P. Ruba Pajila, P.J. Beslin Sasirekha, K. Vinoth, R. Siva Subramanian, R. Cvr College of Engineering Department of Cse Telangana India S.A Engineering College Dept of Cse India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Dept of Cse India R.M.D Engineering College Dept of Csbs India Rmk College of Engg and Tech Department of Cse India Rmk College of Engg and Tech Dept of Cse India

ISBN: (纸本)9798331521394

Visual analytics (VA) is a complex interdisciplinary field which applies data visualization and analysis methods in order to extract knowledge from Big data. This paper aims at presenting an overview of the most common software tools and assessment frameworks for VA with an emphasis on the open source, commercial and specific tools for BI, healthcare and scientific purposes. It discusses the issues that VA has concerning varied data types, their usability and how the system can be expanded to accommodate large data sets. The paper also considers recent trends in AI and machine learning, virtual and augmented reality, and synchronous shared environments. Privacy and data bias issues are also drawn out as ethical concerns. Last, the paper presents the directions of further research to enhance the efficiency and usability of VA tools and make them more ethical in data decision-making. © 2025 ieee.

关键词： Video analysis

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