Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 58
Filtrar
1.
Artigo em Inglês | MEDLINE | ID: mdl-36048995

RESUMO

Networks are an important means for the representation and analysis of data in a variety of research and application areas. While there are many efficient methods to create layouts for networks to support their visual analysis, approaches for the comparison of networks are still underexplored. Especially when it comes to the comparison of weighted networks, which is an important task in several areas, such as biology and biomedicine, there is a lack of efficient visualization approaches. With the availability of affordable high-quality virtual reality (VR) devices, such as head-mounted displays (HMDs), the research field of immersive analytics emerged and showed great potential for using the new technology for visual data exploration. However, the use of immersive technology for the comparison of networks is still underexplored. With this work, we explore how weighted networks can be visually compared in an immersive VR environment and investigate how visual representations can benefit from the extended 3D design space. For this purpose, we develop different encodings for 3D node-link diagrams supporting the visualization of two networks within a single representation and evaluate them in a pilot user study. We incorporate the results into a more extensive user study comparing node-link representations with matrix representations encoding two networks simultaneously. The data and tasks designed for our experiments are similar to those occurring in real-world scenarios. Our evaluation shows significantly better results for the node-link representations, which is contrary to comparable 2D experiments and indicates a high potential for using VR for the visual comparison of networks.

2.
IEEE Trans Vis Comput Graph ; 28(9): 3307-3323, 2022 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-33439846

RESUMO

Visual analytics enables the coupling of machine learning models and humans in a tightly integrated workflow, addressing various analysis tasks. Each task poses distinct demands to analysts and decision-makers. In this survey, we focus on one canonical technique for rule-based classification, namely decision tree classifiers. We provide an overview of available visualizations for decision trees with a focus on how visualizations differ with respect to 16 tasks. Further, we investigate the types of visual designs employed, and the quality measures presented. We find that (i) interactive visual analytics systems for classifier development offer a variety of visual designs, (ii) utilization tasks are sparsely covered, (iii) beyond classifier development, node-link diagrams are omnipresent, (iv) even systems designed for machine learning experts rarely feature visual representations of quality measures other than accuracy. In conclusion, we see a potential for integrating algorithmic techniques, mathematical quality measures, and tailored interactive visualizations to enable human experts to utilize their knowledge more effectively.


Assuntos
Algoritmos , Gráficos por Computador , Árvores de Decisões , Humanos , Aprendizado de Máquina
3.
IEEE Comput Graph Appl ; 41(6): 7-12, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34890313

RESUMO

The increasing use of artificial intelligence (AI) technologies across application domains has prompted our society to pay closer attention to AI's trustworthiness, fairness, interpretability, and accountability. In order to foster trust in AI, it is important to consider the potential of interactive visualization, and how such visualizations help build trust in AI systems. This manifesto discusses the relevance of interactive visualizations and makes the following four claims: i) trust is not a technical problem, ii) trust is dynamic, iii) visualization cannot address all aspects of trust, and iv) visualization is crucial for human agency in AI.


Assuntos
Inteligência Artificial , Confiança , Humanos , Responsabilidade Social
4.
Artigo em Inglês | MEDLINE | ID: mdl-34898435

RESUMO

Coaches and analysts prepare for upcoming matches by identifying common patterns in the positioning and movement of the competing teams in specific situations. Existing approaches in this domain typically rely on manual video analysis and formation discussion using whiteboards; or expert systems that rely on state-of-the-art video and trajectory visualization techniques and advanced user interaction. We bridge the gap between these approaches by contributing a light-weight, simplified interaction and visualization system, which we conceptualized in an iterative design study with the coaching team of a European first league soccer team. Our approach is walk-up usable by all domain stakeholders, and at the same time, can leverage advanced data retrieval and analysis techniques: a virtual magnetic tactic-board. Users place and move digital magnets on a virtual tactic-board, and these interactions get translated to spatio-temporal queries, used to retrieve relevant situations from massive team movement data. Despite such seemingly imprecise query input, our approach is highly usable, supports quick user exploration, and retrieval of relevant results via query relaxation. Appropriate simplified result visualization supports in-depth analyses to explore team behavior, such as formation detection, movement analysis, and what-if analysis. We evaluated our approach with several experts from European first league soccer clubs. The results show that our approach makes the complex analytical processes needed for the identification of tactical behavior directly accessible to domain experts for the first time, demonstrating our support of coaches in preparation for future encounters.

5.
Artigo em Inglês | MEDLINE | ID: mdl-34478370

RESUMO

Multiscale visualizations are typically used to analyze multiscale processes and data in various application domains, such as the visual exploration of hierarchical genome structures in molecular biology. However, creating such multiscale visualizations remains challenging due to the plethora of existing work and the expression ambiguity in visualization research. Up to today, there has been little work to compare and categorize multiscale visualizations to understand their design practices. In this work, we present a structured literature analysis to provide an overview of common design practices in multiscale visualization research. We systematically reviewed and categorized 122 published journal or conference papers between 1995 and 2020. We organized the reviewed papers in a taxonomy that reveals common design factors. Researchers and practitioners can use our taxonomy to explore existing work to create new multiscale navigation and visualization techniques. Based on the reviewed papers, we examine research trends and highlight open research challenges.

6.
Artigo em Inglês | MEDLINE | ID: mdl-34379592

RESUMO

We present VisInReport, a visual analytics tool that supports the manual analysis of discourse transcripts and generates reports based on user interaction. As an integral part of scholarly work in the social sciences and humanities, discourse analysis involves an aggregation of characteristics identified in the text, which, in turn, involves a prior identification of regions of particular interest. Manual data evaluation requires extensive effort, which can be a barrier to effective analysis. Our system addresses this challenge by augmenting the users' analysis with a set of automatically generated visualization layers. These layers enable the detection and exploration of relevant parts of the discussion supporting several tasks, such as topic modeling or question categorization. The system summarizes the extracted events visually and verbally, generating a content-rich insight into the data and the analysis process. During each analysis session, VisInReport builds a shareable report containing a curated selection of interactions and annotations generated by the analyst. We evaluate our approach on real-world datasets through a qualitative study with domain experts from political science, computer science, and linguistics. The results highlight the benefit of integrating the analysis and reporting processes through a visual analytics system, which supports the communication of results among collaborating researchers.

7.
Int J Mol Sci ; 22(15)2021 Jul 30.
Artigo em Inglês | MEDLINE | ID: mdl-34361005

RESUMO

Although the development of gene delivery systems based on non-viral vectors is advancing, it remains a challenge to deliver plasmid DNA into human blood cells. The current "gold standard", namely linear polyethyleneimine (l-PEI 25 kDa), in particular, is unable to produce transgene expression levels >5% in primary human B lymphocytes. Here, it is demonstrated that a well-defined 24-armed poly(2-dimethylamino) ethyl methacrylate (PDMAEMA, 755 kDa) nano-star is able to reproducibly elicit high transgene expression (40%) at sufficient residual viability (69%) in primary human B cells derived from tonsillar tissue. Moreover, our results indicate that the length of the mitogenic stimulation prior to transfection is an important parameter that must be established during the development of the transfection protocol. In our hands, four days of stimulation with rhCD40L post-thawing led to the best transfection results in terms of TE and cell survival. Most importantly, our data argue for an impact of the B cell subsets on the transfection outcomes, underlining that the complexity and heterogeneity of a given B cell population pre- and post-transfection is a critical parameter to consider in the multiparametric approach required for the implementation of the transfection protocol.


Assuntos
Linfócitos B/metabolismo , Técnicas de Transferência de Genes , Vetores Genéticos/genética , Linfócitos B/citologia , Células Cultivadas , Humanos , Metacrilatos/química , Nylons/química , Transgenes
8.
IEEE Comput Graph Appl ; 41(4): 125-132, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34264822

RESUMO

In recent years, research on immersive environments has experienced a new wave of interest, and immersive analytics has been established as a new research field. Every year, a vast amount of different techniques, applications, and user studies are published that focus on employing immersive environments for visualizing and analyzing data. Nevertheless, immersive analytics is still a relatively unexplored field that needs more basic research in many aspects and is still viewed with skepticism. Rightly so, because in our opinion, many researchers do not fully exploit the possibilities offered by immersive environments and, on the contrary, sometimes even overestimate the power of immersive visualizations. Although a growing body of papers has demonstrated individual advantages of immersive analytics for specific tasks and problems, the general benefit of using immersive environments for effective analytic tasks remains controversial. In this article, we reflect on when and how immersion may be appropriate for the analysis and present four guiding scenarios. We report on our experiences, discuss the landscape of assessment strategies, and point out the directions where we believe immersive visualizations have the greatest potential.

9.
Risk Anal ; 41(11): 2016-2030, 2021 11.
Artigo em Inglês | MEDLINE | ID: mdl-33580509

RESUMO

Infectious diseases pose a serious threat to humans. Therefore, it is crucial to understand how accurately people perceive these risks. However, accuracy can be operationalized differently depending on the standard of comparison. The present study investigated accuracy in risk perceptions for three infectious diseases (avian influenza, seasonal influenza, common cold) using three different standards for accuracy: Social comparison (self vs. others' risk perceptions), general problem level (risk perceptions for diseases with varying threat levels), and dynamic problem level (risk perceptions during epidemics/seasons vs. nonepidemic/off-season times). Four online surveys were conducted using a repeated cross-sectional design. Two surveys were conducted during epidemics/seasons of avian influenza, seasonal influenza, and common cold in 2006 (n = 387) and 2016 (n = 370) and two surveys during nonepidemic/off-season times for the three diseases in 2009 (n = 792) during a swine flu outbreak and in 2018 (n = 422) during no outbreak of zoonotic influenza. While on average participants felt less at risk than others, indicating an optimistic bias, risk perceptions matched the magnitude of risk associated with the three infectious diseases. Importantly, a significant three-way interaction indicated dynamic accuracy in risk perceptions: Participants felt more at risk for seasonal influenza and common cold during influenza and cold seasons, compared with off-season times. However, these dynamic increases were more pronounced in the perceived risk for others than for oneself (optimistic bias). The results emphasize the importance of using multiple approaches to assess accuracy of risk perception as they provided different information on how accurately people gauge their risk when facing infectious diseases.


Assuntos
Influenza Aviária/epidemiologia , Influenza Humana/epidemiologia , Infecções por Orthomyxoviridae/epidemiologia , Animais , Aves , Estudos Transversais , Humanos , Risco , Estações do Ano
10.
IEEE Trans Vis Comput Graph ; 27(3): 2220-2236, 2021 03.
Artigo em Inglês | MEDLINE | ID: mdl-31514139

RESUMO

Visualization has been deemed a useful technique by researchers and practitioners, alike, leaving a trail of arguments behind that reason why visualization works. In addition, examples of misleading usages of visualizations in information communication have occasionally been pointed out. Thus, to contribute to the fundamental understanding of our discipline, we require a comprehensive collection of arguments on "why visualize?" (or "why not?"), untangling the rationale behind positive and negative viewpoints. In this paper, we report a theoretical study to understand the underlying reasons of various arguments; their relationships (e.g., built-on, and conflict); and their respective dependencies on tasks, users, and data. We curated an argumentative network based on a collection of arguments from various fields, including information visualization, cognitive science, psychology, statistics, philosophy, and others. Our work proposes several categorizations for the arguments, and makes their relations explicit. We contribute the first comprehensive and systematic theoretical study of the arguments on visualization. Thereby, we provide a roadmap towards building a foundation for visualization theory and empirical research as well as for practical application in the critique and design of visualizations. In addition, we provide our argumentation network and argument collection online at https://whyvis.dbvis.de, supported by an interactive visualization.

11.
IEEE Trans Vis Comput Graph ; 27(2): 517-527, 2021 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-33048714

RESUMO

The overview-driven visual analysis of large-scale dynamic graphs poses a major challenge. We propose Multiscale Snapshots, a visual analytics approach to analyze temporal summaries of dynamic graphs at multiple temporal scales. First, we recursively generate temporal summaries to abstract overlapping sequences of graphs into compact snapshots. Second, we apply graph embeddings to the snapshots to learn low-dimensional representations of each sequence of graphs to speed up specific analytical tasks (e.g., similarity search). Third, we visualize the evolving data from a coarse to fine-granular snapshots to semi-automatically analyze temporal states, trends, and outliers. The approach enables us to discover similar temporal summaries (e.g., reoccurring states), reduces the temporal data to speed up automatic analysis, and to explore both structural and temporal properties of a dynamic graph. We demonstrate the usefulness of our approach by a quantitative evaluation and the application to a real-world dataset.

12.
IEEE Trans Vis Comput Graph ; 27(2): 550-560, 2021 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-33048721

RESUMO

Many processes, from gene interaction in biology to computer networks to social media, can be modeled more precisely as temporal hypergraphs than by regular graphs. This is because hypergraphs generalize graphs by extending edges to connect any number of vertices, allowing complex relationships to be described more accurately and predict their behavior over time. However, the interactive exploration and seamless refinement of such hypergraph-based prediction models still pose a major challenge. We contribute Hyper-Matrix, a novel visual analytics technique that addresses this challenge through a tight coupling between machine-learning and interactive visualizations. In particular, the technique incorporates a geometric deep learning model as a blueprint for problem-specific models while integrating visualizations for graph-based and category-based data with a novel combination of interactions for an effective user-driven exploration of hypergraph models. To eliminate demanding context switches and ensure scalability, our matrix-based visualization provides drill-down capabilities across multiple levels of semantic zoom, from an overview of model predictions down to the content. We facilitate a focused analysis of relevant connections and groups based on interactive user-steering for filtering and search tasks, a dynamically modifiable partition hierarchy, various matrix reordering techniques, and interactive model feedback. We evaluate our technique in a case study and through formative evaluation with law enforcement experts using real-world internet forum communication data. The results show that our approach surpasses existing solutions in terms of scalability and applicability, enables the incorporation of domain knowledge, and allows for fast search-space traversal. With the proposed technique, we pave the way for the visual analytics of temporal hypergraphs in a wide variety of domains.

13.
IEEE Trans Vis Comput Graph ; 27(2): 1623-1633, 2021 02.
Artigo em Inglês | MEDLINE | ID: mdl-33052856

RESUMO

Segmenting biologging time series of animals on multiple temporal scales is an essential step that requires complex techniques with careful parameterization and possibly cross-domain expertise. Yet, there is a lack of visual-interactive tools that strongly support such multi-scale segmentation. To close this gap, we present our MultiSegVA platform for interactively defining segmentation techniques and parameters on multiple temporal scales. MultiSegVA primarily contributes tailored, visual-interactive means and visual analytics paradigms for segmenting unlabeled time series on multiple scales. Further, to flexibly compose the multi-scale segmentation, the platform contributes a new visual query language that links a variety of segmentation techniques. To illustrate our approach, we present a domain-oriented set of segmentation techniques derived in collaboration with movement ecologists. We demonstrate the applicability and usefulness of MultiSegVA in two real-world use cases from movement ecology, related to behavior analysis after environment-aware segmentation, and after progressive clustering. Expert feedback from movement ecologists shows the effectiveness of tailored visual-interactive means and visual analytics paradigms at segmenting multi-scale data, enabling them to perform semantically meaningful analyses. A third use case demonstrates that MultiSegVA is generalizable to other domains.


Assuntos
Gráficos por Computador , Movimento , Animais , Análise por Conglomerados , Fatores de Tempo
14.
Sensors (Basel) ; 20(18)2020 Sep 22.
Artigo em Inglês | MEDLINE | ID: mdl-32971822

RESUMO

The technical progress in the last decades makes photo and video recording devices omnipresent. This change has a significant impact, among others, on police work. It is no longer unusual that a myriad of digital data accumulates after a criminal act, which must be reviewed by criminal investigators to collect evidence or solve the crime. This paper presents the VICTORIA Interactive 4D Scene Reconstruction and Analysis Framework ("ISRA-4D" 1.0), an approach for the visual consolidation of heterogeneous video and image data in a 3D reconstruction of the corresponding environment. First, by reconstructing the environment in which the materials were created, a shared spatial context of all available materials is established. Second, all footage is spatially and temporally registered within this 3D reconstruction. Third, a visualization of the hereby created 4D reconstruction (3D scene + time) is provided, which can be analyzed interactively. Additional information on video and image content is also extracted and displayed and can be analyzed with supporting visualizations. The presented approach facilitates the process of filtering, annotating, analyzing, and getting an overview of large amounts of multimedia material. The framework is evaluated using four case studies which demonstrate its broad applicability. Furthermore, the framework allows the user to immerse themselves in the analysis by entering the scenario in virtual reality. This feature is qualitatively evaluated by means of interviews of criminal investigators and outlines potential benefits such as improved spatial understanding and the initiation of new fields of application.

15.
Brain Sci ; 10(8)2020 Aug 10.
Artigo em Inglês | MEDLINE | ID: mdl-32784990

RESUMO

The progress of technology has increased research on neuropsychological emotion and attention with virtual reality (VR). However, direct comparisons between conventional two-dimensional (2D) and VR stimulations are lacking. Thus, the present study compared electroencephalography (EEG) correlates of explicit task and implicit emotional attention between 2D and VR stimulation. Participants (n = 16) viewed angry and neutral faces with equal size and distance in both 2D and VR, while they were asked to count one of the two facial expressions. For the main effects of emotion (angry vs. neutral) and task (target vs. nontarget), established event related potentials (ERP), namely the late positive potential (LPP) and the target P300, were replicated. VR stimulation compared to 2D led to overall bigger ERPs but did not interact with emotion or task effects. In the frequency domain, alpha/beta-activity was larger in VR compared to 2D stimulation already in the baseline period. Of note, while alpha/beta event related desynchronization (ERD) for emotion and task conditions were seen in both VR and 2D stimulation, these effects were significantly stronger in VR than in 2D. These results suggest that enhanced immersion with the stimulus materials enabled by VR technology can potentiate induced brain oscillation effects to implicit emotion and explicit task effects.

16.
Eur Radiol Exp ; 4(1): 22, 2020 04 03.
Artigo em Inglês | MEDLINE | ID: mdl-32246291

RESUMO

PRIMAGE is one of the largest and more ambitious research projects dealing with medical imaging, artificial intelligence and cancer treatment in children. It is a 4-year European Commission-financed project that has 16 European partners in the consortium, including the European Society for Paediatric Oncology, two imaging biobanks, and three prominent European paediatric oncology units. The project is constructed as an observational in silico study involving high-quality anonymised datasets (imaging, clinical, molecular, and genetics) for the training and validation of machine learning and multiscale algorithms. The open cloud-based platform will offer precise clinical assistance for phenotyping (diagnosis), treatment allocation (prediction), and patient endpoints (prognosis), based on the use of imaging biomarkers, tumour growth simulation, advanced visualisation of confidence scores, and machine-learning approaches. The decision support prototype will be constructed and validated on two paediatric cancers: neuroblastoma and diffuse intrinsic pontine glioma. External validation will be performed on data recruited from independent collaborative centres. Final results will be available for the scientific community at the end of the project, and ready for translation to other malignant solid tumours.


Assuntos
Inteligência Artificial , Biomarcadores/análise , Neoplasias Encefálicas/diagnóstico por imagem , Neoplasias Encefálicas/terapia , Glioma/diagnóstico por imagem , Glioma/terapia , Neuroblastoma/diagnóstico por imagem , Neuroblastoma/terapia , Criança , Computação em Nuvem , Técnicas de Apoio para a Decisão , Progressão da Doença , Europa (Continente) , Feminino , Humanos , Masculino , Fenótipo , Prognóstico , Carga Tumoral
17.
IEEE Comput Graph Appl ; 40(2): 98-102, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32149615

RESUMO

We share our experiences teaching university students about clustering algorithms using EduClust, an online visualization we developed. EduClust supports professors in preparing teaching material and students in visually and interactively exploring cluster steps and the effects of changing clustering parameters. We used EduClust for two years in our computer science lectures on clustering algorithms and share our experience integrating the online application in a data science curriculum. We also point to opportunities for future development.

18.
Front Psychol ; 11: 567817, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33633620

RESUMO

Cognition is both empowered and limited by representations. The matrix lens model explicates tasks that are based on frequency counts, conditional probabilities, and binary contingencies in a general fashion. Based on a structural analysis of such tasks, the model links several problems and semantic domains and provides a new perspective on representational accounts of cognition that recognizes representational isomorphs as opportunities, rather than as problems. The shared structural construct of a 2 × 2 matrix supports a set of generic tasks and semantic mappings that provide a unifying framework for understanding problems and defining scientific measures. Our model's key explanatory mechanism is the adoption of particular perspectives on a 2 × 2 matrix that categorizes the frequency counts of cases by some condition, treatment, risk, or outcome factor. By the selective steps of filtering, framing, and focusing on specific aspects, the measures used in various semantic domains negotiate distinct trade-offs between abstraction and specialization. As a consequence, the transparent communication of such measures must explicate the perspectives encapsulated in their derivation. To demonstrate the explanatory scope of our model, we use it to clarify theoretical debates on biases and facilitation effects in Bayesian reasoning and to integrate the scientific measures from various semantic domains within a unifying framework. A better understanding of problem structures, representational transparency, and the role of perspectives in the scientific process yields both theoretical insights and practical applications.

19.
IEEE Trans Vis Comput Graph ; 26(1): 1001-1011, 2020 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-31443000

RESUMO

We present a framework that allows users to incorporate the semantics of their domain knowledge for topic model refinement while remaining model-agnostic. Our approach enables users to (1) understand the semantic space of the model, (2) identify regions of potential conflicts and problems, and (3) readjust the semantic relation of concepts based on their understanding, directly influencing the topic modeling. These tasks are supported by an interactive visual analytics workspace that uses word-embedding projections to define concept regions which can then be refined. The user-refined concepts are independent of a particular document collection and can be transferred to related corpora. All user interactions within the concept space directly affect the semantic relations of the underlying vector space model, which, in turn, change the topic modeling. In addition to direct manipulation, our system guides the users' decision-making process through recommended interactions that point out potential improvements. This targeted refinement aims at minimizing the feedback required for an efficient human-in-the-loop process. We confirm the improvements achieved through our approach in two user studies that show topic model quality improvements through our visual knowledge externalization and learning process.

20.
J Sports Sci ; 37(24): 2774-2782, 2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-31402759

RESUMO

To prepare their teams for upcoming matches, analysts in professional soccer watch and manually annotate up to three matches a day. When annotating matches, domain experts try to identify and improve suboptimal movements based on intuition and professional experience. The high amount of matches needing to be analysed manually result in a tedious and time-consuming process, and results may be subjective. We propose an automatic approach for the realisation of effective region-based what-if analyses in soccer. Our system covers the automatic detection of region-based faulty movement behaviour, as well as the automatic suggestion of possible improved alternative movements. As we show, our approach effectively supports analysts and coaches investigating matches by speeding up previously time-consuming work. We enable domain experts to include their domain knowledge in the analysis process by allowing to interactively adjust suggested improved movement, as well as its implications on region control. We demonstrate the usefulness of our proposed approach via an expert study with three invited domain experts, one being head coach from the first Austrian soccer league. As our results show that experts most often agree with the suggested player movement (83%), our proposed approach enhances the analytical capabilities in soccer and supports a more efficient analysis.


Assuntos
Movimento , Reconhecimento Automatizado de Padrão , Futebol , Análise e Desempenho de Tarefas , Humanos
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA
...