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2022 ieee symposium on Visual Languages and Human-Centric Computing

作者： Boyd, Alicia E. Jakpor, Jibiana Johnson, Brittany Depaul Univ Chicago IL 60604 USA Mockingbird Preparatory High Sch Dallas CA USA George Mason Univ Fairfax VA 22030 USA

ISBN: (数字)9781665442145

ISBN: (纸本)9781665442152;9781665442145

Both research and practice are increasingly using data to drive innovation. While innovative technology has the potential to better our day to day lives, we too often see examples of innovative technology doing more harm than good. These elevate concerns around ethics and equity, and point to the need for interventions that support intersectional analyses that prevent harm to marginalized communities. Traditional development tools narrowly focus on solving technical challenges, without helping practitioners make explicit connections to the potential impact their contributions may have, which studies have shown is an important factor when changing behavior in software teams. In this showpiece, we introduce QUINTESSENCE, a novel proof-of-concept tool that supports reflexivity, as it pertains to intersectional identities, in data analysis and modeling. In the future, we plan to work with practitioners to improve QUINTESSENCE for practical use, including a deeper dive into effective reflexive support and the addition of more analytic support in the modeling phases.

关键词： data science intersectionality visualization reflexivity software development

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Constructing Hierarchical Continuity in Hilbert & Moore Treemaps

Constructing Hierarchical Continuity in Hilbert & Moore Tree...

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25th Eurographics Conference on visualization, EuroVis 2023

作者： Scheibel, Willy Döllner, Jürgen University of Potsdam Digital Engineering Faculty Hasso Plattner Institute Germany

ISBN: (纸本)9783038682202

The Hilbert and Moore treemap layout algorithms are based on the space-filling Hilbert and Moore curves, respectively, to map tree-structured datasets to a 2D treemap layout. Considering multiple snapshots of a time-variant dataset, one of the design goals for Hilbert and Moore treemaps is layout stability, i.e., low changes in the layout for low changes in the underlying tree-structured data. For this, their underlying space-filling curve is expected to be continuous across all nodes and hierarchy levels, which has to be considered throughout the layouting process. We propose optimizations to subdivision templates, their orientation, and discuss the continuity of the underlying space-filling curve. We show real-world examples of Hilbert and Moore treemaps for small and large datasets with continuous space-filling curves, allowing for improved layout stability. © 2023 The Authors.

关键词： large datasets

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P6: A Declarative Language for Integrating Machine Learning in Visual Analytics

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2021年第2期27卷 380-389页

作者： Li, Jianping Kelvin Ma, Kwan-Liu Univ Calif Davis Davis CA 95616 USA

We present P6, a declarative language for building high performance visual analytics systems through its support for specifying and integrating machine learning and interactive visualization methods. As data analysis methods based on machine learning and artificial intelligence continue to advance, a visual analytics solution can leverage these methods for better exploiting large and complex data. However, integrating machine learning methods with interactive visual analysis is challenging. Existing declarative programming libraries and toolkits for visualization lack support for coupling machine learning methods. By providing a declarative language for visual analytics, P6 can empower more developers to create visual analytics applications that combine machine learning and visualization methods for data analysis and problem solving. Through a variety of example applications, we demonstrate P6's capabilities and show the benefits of using declarative specifications to build visual analytics systems. We also identify and discuss the research opportunities and challenges for declarative visual analytics.

关键词： visual analytics interactive visualization machine learning toolkits declarative specification

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Can We Exploit All datasets? Multimodal Emotion Recognition Using Cross-Modal Translation

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ieee ACCESS 2022年 10卷 64516-64524页

作者： Yoon, Yeo Chan Jeju Natl Univ Dept Artificial Intelligence Jeju 63243 South Korea

The use of sufficiently large datasets is important for most deep learning tasks, and emotion recognition tasks are no exception. Multimodal emotion recognition is the task of considering multiple types of modalities simultaneously to improve accuracy and robustness, typically utilizing three modalities: visual, audio, and text. Similar to other deep learning tasks, large datasets are required. Various heterogeneous datasets exist, including unimodal datasets constructed for traditional unimodal recognition and bimodal or trimodal datasets for multi-modal emotion recognition. A trimodal emotion recognition model shows high performance and robustness by comprehensively considering multiple modalities. However, the use of unimodal or bimodal datasets in this case is problematic. In this study, we propose a novel method to improve the performance of emotion recognition based on a cross-modal translator that can translate between the three modalities. The proposed method can train a multimodal model based on three modalities with different types of heterogeneous datasets, and the dataset does not require alignment between modalities: visual, audio, and text. We achieved a high performance exceeding the baseline in CMU-MOSEI and IEMOCAP, which are representative multimodal datasets, by adding unimodal and bimodal datasets to the trimodal dataset.

关键词： Emotion recognition Transformers Task analysis visualization Training Semantics data models Deep learning emotion recognition generative adversarial networks machine learning multimodal emotion recognition

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Top Research Challenges and Opportunities for Near Real-Time Extreme-Scale visualization of Scientific data

Top Research Challenges and Opportunities for Near Real-Time...

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ieee International Conference on e-Science and Grid Computing

作者： David Pugmire Kenneth Moreland Tushar M. Athawale James Hammer Jian Huang Computer Science & Math Division Oak Ridge National Laboratory Oak Ridge TN USA Electrical Engineering & Computer Science University of Tennessee Knoxville Knoxville TN USA

ISBN: (数字)9798350365610

ISBN: (纸本)9798350365627

The rapid advancement in scientific simulations and experimental facilities has resulted in the generation of vast amounts of data at unprecedented scales. The analysis and visualization of large amounts of data is a challenge in and of itself, but the requirements for timeliness significantly magnify these difficulties. Near real-time visualization is critical to monitor and analyze the data produced by these large facilities, but current production tools are not well-suited to these requirements. In this position paper, we share our perspective on some of the challenges, and thus, opportunities for research that stand in the way of near-real-time visualization of large scientific data.

关键词： data visualization Production Real-time systems data models Monitoring

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A Proposed Multifaceted Approach for IoT data Integration in Education Sector for Dissimilar Network

A Proposed Multifaceted Approach for IoT Data Integration in...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Chawla, Simmi Tomar, Parul Gambhir, Sapna JCBUST Department of Computer Engineering YMCA Faridabad India

ISBN: (纸本)9798350376470

In this heterogeneous scenario, there are multiple sources of multimedia due to the diversity of IoT platforms. The Effective data integration is severely hampered by the growth of multimedia data produced by a wide range of Internet of Things (IoT) devices in heterogeneous contexts. To tackle this problem, a novel approach is designed. This is a complete approach to the smooth integration of multimedia data into the Internet of Things environment by utilizing adaptive processing, semantic data modeling, and effective data fusion. The method is reliable, scalable, and flexible enough to handle a wide range of data kinds, such as sensor readings, photos, audio, and videos [1]. The major challenges are associated with heterogeneous environments in the education sector are: 1) data is completely in unstructured manner 2) Mapping various streaming data sets from various sources to a single, common schema 3) The problem of Real time data collection, representations and visualization of large datasets during data streaming in the IoT Ecosystem[11]. The goal of this proposed multifaceted approach is to maximize resource utilization, improve educational outcomes, and offer individualized learning experiences by utilizing IoT technologies [2]. A Unified schema is designed in context to perform mapping among different heterogeneous entities. By providing a comprehensive framework and critical analysis for integrating IoT data across dissimilar networks, it seeks to unlock new opportunities for personalized learning, operational efficiency, and data-driven decision-making in the education sector. © 2024 ieee.

关键词： data assimilation

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Co-design for Heterogeneous Integration: A Failure analysis Perspective 23

Co-design for Heterogeneous Integration: A Failure Analysis ...

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International symposium on Physical Design (ISPD)

作者： Douglas, Erica Deitz, Julia Ruggles, Timothy Perry, Daniel Cummings, Damion Rodriguez, Mark Valdez, Nichole Boyce, Brad Sandia Natl Labs Mat Phys & Chem Sci Ctr Albuquerque NM 87123 USA

ISBN: (纸本)9781450399784

As scaling for CMOS transistors asymptotically approaches the end of Moore's Law, the need to push into 3D integration schemes to innovate capabilities is gaining significant traction. Further, rapid development of new semiconductor solutions, such as heterogeneous integration, has turned the semiconductor industry's consistent march towards next generation products into new arenas. In 2018, the Department of Energy Office of Science (DOE SC) released their "Basic Research Needs for Microelectronics," communicating a strong push towards "parallel but intimately networked efforts to create radically new capabilities,"1 which they have coined as "co-design." Advanced packaging and heterogeneous integration, particularly with mixed semiconductor materials (e.g., CMOS FPGAs & GaN RF amplifiers) is a realm ripe for applicability towards DOE SC's co-design call to action. In theory, development occurring at all scales across the semiconductor ecosystem, particularly across disciplines that are not traditionally adjacent, should significantly accelerate innovation. In reality, co-design requires a paradigm shift in approach, requiring not only interconnected parallel development. Further, accurate ground truth data during learning cycles is critical in order to effectively and efficiently communicate across disparate disciplines and advise design iterations across the microelectronics ecosystem. [GRAPHICS] This talk will outline three orthogonal facets towards co-design for HI: (1) on-going efforts towards development of materials characterization and failure analysis techniques to enable accurate evaluation of materials and heterogeneously integrated components, (2) development of artificial intelligence & machine learning algorithms for large scale, high throughput process development and characterization, and (3) development of capabilities for rapid communication and visualization of data across disparate disciplines.

关键词： Heterogeneous integration co-design microelectronics materials characterization failure analysis artificial intelligence machine learning

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analysis of Latent Factors Affecting Video Game Performance Using Machine Learning Methods 32

Analysis of Latent Factors Affecting Video Game Performance ...

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32nd ieee International symposium on Industrial Electronics, ISIE 2023

作者： Orlova, Julia Stepanov, Anton Vinogradov, Anton Orlova, Lubov Baldycheva, Anna Somov, Andrey Skolkovo Institute of Science and Technology Russia IATE MEPHI Russia University of Exeter United Kingdom

ISBN: (纸本)9798350399714

E-sports is the state-of-the-art digital sport where players and teams compete against each other in various gaming disciplines. The most significant part of the industry includes tournaments specifically in multiplayer team-based games where outstanding individual performance is not enough and where a team is perceived as a single mechanism. In such cases, the game performance should not be limited to the individual team member contributions and take into account internal players' interactions. It is essential to emphasize that e-sports players are humans, therefore, while evaluating their performance it is necessary to take into account the psychological and emotional points. In this work, we propose an approach to the game performance analysis focusing on the factors insufficiently investigated earlier, such as the team interaction efficiency in terms of auxiliary in-game attack tool usage, as well as the teammates' emotional state described by an arousal-valence model. First, we perform the analysis of a large amount of in-game Counter Strike:Global Offensive discipline tournament data, derive detailed metrics related to the team actions and show their strong correlation with the game outcome characterized by a high level of significance (p-value © 2023 ieee.

关键词： Sports

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Fault Report Generation for ICT Systems by Jointly Learning Time-series and Text data

Fault Report Generation for ICT Systems by Jointly Learning ...

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ieee/IFIP Network Operations and Management symposium

作者： Fukuda, Nobukazu Wu, Chao Horiuchi, Shingo Tayama, Kenichi NTT Corp NTT Access Network Serv Syst Labs Tokyo Japan

ISBN: (纸本)9781665406017

The number of ICT services operated on large-scale systems is increasing, and faults in the target systems need to be detected for the stability of the services. When a fault occurs in the target system, the operators will analyze the monitoring data to identify a root cause and determine recovery actions. However, it is becoming inefficient to analyze them manually or detect faults with handcrafted rules. In this paper, we propose a method of generating human-readable fault reports by leveraging timeseries data and textual metadata of metrics for detecting faults in a target system. It can jointly encode heterogeneous sources of monitoring data and can integrate asynchronous metrics of multiple services. To collect realistic datasets in a production environment, we conducted experiments by injecting faults into an e-commerce service implemented in a Kubernetes cluster. Our evaluation shows that the proposed method can generate fault reports appropriately and outperformed the baseline method in different scenarios.

关键词： Service Management AIOps Root Cause analysis

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Scalable Community Detection for Complex data Graphs via Hyperbolic Network Embedding and Graph databases

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ieee TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2021年第2期8卷 1269-1282页

作者： Tsitseklis, Konstantinos Krommyda, Maria Karyotis, Vasileios Kantere, Verena Papavassiliou, Symeon Natl Tech Univ Athens Sch Elect & Comp Engn Inst Commun & Comp Syst ICCS Zografos 15780 Greece Ionian Univ Dept Informat Corfu 49132 Greece

Community detection and its variations is one of the typically employed approaches for analyzing graph data originating from various diverse fields. In this paper, we focus on a particular approach for community detection capitalizing on hyperbolic network embedding, which is aimed at analyzing large data graphs. In order to enable its scaling to arbitrary sized data sets and respective data graphs, we extend it by incorporating a graph database approach. This allows for handling a larger number of nodes and edges in the data graph. Also, we turn our focus on the discovery and visualization of communities in Resource Description Framework (RDF) data, namely over linked datasets from diverse areas, explaining how our approach can accommodate relevant analysis. We demonstrate the applicability of the new approach over both real-world and artificially generated datasets showing its feasibility in producing correct results, while being able to scale seamlessly in large datasets. The approach can be used for multi-lateral analysis of feature-rich graph data, originating from diverse sources, enabling the discovery of hidden correlations through the hyperbolic network embedding.

关键词： databases Resource description framework data visualization Feature extraction Image edge detection Task analysis Europe Community detection feature-rich information graph data graph databases hyperbolic network embedding scalable clustering

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