| 1. |
- Albrecht, Mario, et al.
(författare)
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On Open Problems in Biological Network Visualization
- 2009
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Ingår i: Graph Drawing. - Berlin Heidelberg New York : Springer. - 9783642118043 ; , s. 256-267
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Bokkapitel (refereegranskat)abstract
- Much of the data generated and analyzed in the life sciences can be interpreted and represented by networks or graphs. Network analysis and visualization methods help in investigating them, and many universal as well as special-purpose tools and libraries are available for this task. However, the two fields of graph drawing and network biology are still largely disconnected. Hence, visualization of biological networks does typically not apply state-of-the-art graph drawing techniques, and graph drawing tools do not respect the drawing conventions of the life science community.In this paper, we analyze some of the major problems arising in biological network visualization.We characterize these problems and formulate a series of open graph drawing problems. These use cases illustrate the need for efficient algorithms to present, explore, evaluate, and compare biological network data. For each use case, problems are discussed and possible solutions suggested.
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| 2. |
- Alfalahi, Alyaa, et al.
(författare)
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Expanding a dictionary of marker words for uncertainty and negation using distributional semantics
- 2015
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Ingår i: EMNLP 2015 - 6th International Workshop on Health Text Mining and Information Analysis, LOUHI 2015 : Proceedings of the Workshop - Proceedings of the Workshop. - : Association for Computational Linguistics. - 9781941643327 ; , s. 90-96
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Konferensbidrag (refereegranskat)abstract
- Approaches to determining the factuality of diagnoses and findings in clinical text tend to rely on dictionaries of marker words for uncertainty and negation. Here, a method for semi-automatically expanding a dictionary of marker words using distributional semantics is presented and evaluated. It is shown that ranking candidates for inclusion according to their proximity to cluster centroids of semantically similar seed words is more successful than ranking them according to proximity to each individual seed word.
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| 3. |
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| 4. |
- Borgo, Rita, et al.
(författare)
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Crowdsourcing for Information Visualization : Promises and Pitfalls
- 2017
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Ingår i: Evaluation in the Crowd. - Cham : Springer. - 9783319664347 - 9783319664354 ; , s. 96-138
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Bokkapitel (refereegranskat)abstract
- Crowdsourcing offers great potential to overcome the limitations of controlled lab studies. To guide future designs of crowdsourcing-based studies for visualization, we review visualization research that has attempted to leverage crowdsourcing for empirical evaluations of visualizations. We discuss six core aspects for successful employment of crowdsourcing in empirical studies for visualization – participants, study design, study procedure, data, tasks, and metrics & measures. We then present four case studies, discussing potential mechanisms to overcome common pitfalls. This chapter will help the visualization community understand how to effectively and efficiently take advantage of the exciting potential crowdsourcing has to offer to support empirical visualization research.
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| 5. |
- Büschel, Wolfgang, et al.
(författare)
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Interaction for Immersive Analytics
- 2018
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Ingår i: Immersive Analytics. - Cham : Springer. - 9783030013875 - 9783030013882 ; , s. 95-138
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- In this chapter, we briefly review the development of natural user interfaces and discuss their role in providing human-computer interaction that is immersive in various ways. Then we examine some opportunities for how these technologies might be used to better support data analysis tasks. Specifically, we review and suggest some interaction design guidelines for immersive analytics. We also review some hardware setups for data visualization that are already archetypal. Finally, we look at some emerging system designs that suggest future directions.
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| 6. |
- Cernea, Daniel, 1983-, et al.
(författare)
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A Study of Emotion-triggered Adaptation Methods for Interactive Visualization
- 2013
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Ingår i: UMAP 2013 Extended Proceedings. - : CEUR-WS.org. ; , s. 9-16
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Konferensbidrag (refereegranskat)abstract
- As the size and complexity of datasets increases, both visual-ization systems and their users are put under more pressure to oer quickand thorough insights about patterns hidden in this ocean of data. Whilenovel visualization techniques are being developed to better cope withthe various data contexts, users nd themselves increasingly often undermental bottlenecks that can induce a variety of emotions. In this paper,we execute a study to investigate the eectiveness of various emotion-triggered adaptation methods for visualization systems. The emotionsconsidered are boredom and frustration, and are measured by means ofbrain-computer interface technology. Our ndings suggest that less intru-sive adaptive methods perform better at supporting users in overcomingemotional states with low valence or arousal, while more intrusive onestend to be misinterpreted or perceived as irritating.
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| 7. |
- Cernea, Daniel, 1983-, et al.
(författare)
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A Survey of Technologies on the Rise for Emotion-Enhanced Interaction
- 2015
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Ingår i: Journal of Visual Languages and Computing. - : Elsevier. - 1045-926X .- 1095-8533. ; 31:A, s. 70-86
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Tidskriftsartikel (refereegranskat)abstract
- Emotions are a major part of the human existence and social interactions. Some might say that emotions are one of the aspects that make us truly human. However, while we express emotions in various life settings, the world of computing seems to struggle with supporting and incorporating the emotional dimension. In the last decades, the concept of affect has gotten a new upswing in research, moving beyond topics like market research and product development, and further exploring the area of emotion-enhanced interaction.In this article, we highlight techniques that have been employed more intensely for emotion measurement in the context of affective interaction. Besides capturing the functional principles behind these approaches and the inherent volatility of human emotions, we present relevant applications and establish a categorization of the roles of emotion detection in interaction. Based on these findings, we also capture the main challenges that emotion measuring technologies will have to overcome in order to enable a truly seamless emotion-driven interaction.
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| 8. |
- Cernea, Daniel, 1983-, et al.
(författare)
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An Interactive Visualization for Tabbed Browsing Behavior Analysis
- 2014
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Ingår i: Computer Vision, Imaging and Computer Graphics. - Berlin, Heidelberg : Springer. - 9783662449103 ; , s. 69-84
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Bokkapitel (refereegranskat)abstract
- Web browsers are at the core of online user experience, enablinga wide range of Web applications, like communication, games, entertainment, development, etc. Additionally, given the variety and complexity of online-supported tasks, users have started parallelizing and organizing their online browser sessions by employing multiple browser windows and tabs. However, there are few solutions that support analysts and casual users in detecting and extracting patterns from these parallel browsing histories. In this paper we introduce WebComets, an interactive visualization for exploring multi-session multi-user parallel browsing logs. After highlighting visual and functional aspects of the system, we introduce a motif-based contextual search for enabling the filtering and comparison of user navigation patterns. We further highlight the functionality of WebComets with a use case. Our investigations suggest that parallel browser history visualization can offer better insight into user tabbed browsing behavior and support the recognition of online navigation patterns.
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| 9. |
- Cernea, Daniel, 1983-, et al.
(författare)
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Controlling In-Vehicle Systems with a Commercial EEG Headset: Performance and Cognitive Load
- 2012
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Ingår i: Visualization of Large and Unstructured Data Sets. - : Schloss Dagstuhl - Leibniz-Zentrum für Informatik.
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Konferensbidrag (refereegranskat)abstract
- Humans have dreamed for centuries to control their surroundings solely by the power of theirminds. These aspirations have been captured by multiple science fiction creations, like theNeuromancer novel by William Gibson or the Brainstorm cinematic movie, to name just a few.Nowadays these dreams are slowly becoming reality due to a variety of brain-computer interfaces(BCI) that detect neural activation patterns and support the control of devices by brain signals.An important field in which BCIs are being successfully integrated is the interaction withvehicular systems. In this paper we evaluate the performance of BCIs, more specifically a commercialelectroencephalographic (EEG) headset, in combination with vehicle dashboard systemsand highlight the advantages and limitations of this approach. Further, we investigate the cognitiveload that drivers experience when interacting with secondary in-vehicle devices via touchcontrols or a BCI headset. As in-vehicle systems are increasingly versatile and complex, it becomesvital to capture the level of distraction and errors that controlling these secondary systemsmight introduce to the primary driving process. Our results suggest that the control with theEEG headset introduces less distraction to the driver, probably as it allows the eyes of the driverto remain focused on the road. Still, the control of the vehicle dashboard by EEG is efficientonly for a limited number of functions, after which increasing the number of in-vehicle controlsamplifies the detection of false commands.
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| 10. |
- Cernea, Daniel, 1983-, et al.
(författare)
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Detecting Insight and Emotion in Visualization Applications with a Commercial EEG Headset
- 2011
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Ingår i: <em>Proceedings of the SIGRAD 2011 Conference on Evaluations of Graphics and Visualization - Efficiency, Usefulness, Accessibility, Usability</em>, KTH, Stockholm, Sweden.. - Linköping : Linköping University Electronic Press. - 9789173930086 ; , s. 53-60
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Konferensbidrag (refereegranskat)abstract
- Insight represents a special element of knowledge building. From the beginning of their lives, humans experience moments of insight in which a certain idea or solution becomes as clear to them as never before. Especially in the field of visual representations, insight has the potential to be at the core of comprehension and pattern recognition. Still, one problem is that this moment of clarity is highly unpredictable and complex in nature, and many scientists have investigated different aspects of its generation process in the hope of capturing the essence of this eureka (Greek, for "I have found") moment. In this paper, we look at insight from the spectrum of information visualization. In particular, we inspect the possible correlation between epiphanies and emotional responses subjects experience when having an insight. In order to check the existence of such a connection, we employ a set of initial tests involving the EPOC mobile electroencephalographic (EEG) headset for detecting emotional responses generated by insights. The insights are generated by open-ended tasks that take the form of visual riddles and visualization applications. Our results suggest that there is a strong connection between insight and emotions like frustration and excitement. Moreover, measuring emotional responses via EEG during an insight-related problem solving results in non-intrusive, nearly automatic detection of the major Aha! moments the user experiences. We argue that this indirect detection of insights opens the door for the objective evaluation and comparison of various visualizations techniques.
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| 11. |
- Cernea, Daniel, 1983-, et al.
(författare)
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EEG-based Measurement of Subjective Parameters in Evaluations
- 2011
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Ingår i: <em> HCI International 2011 Posters' Extended Abstracts</em>. - Berlin Heidelberg : Springer. - 9783642220944 ; , s. 279-283
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Konferensbidrag (refereegranskat)abstract
- Evaluating new approaches, be it new interaction techniques, new applications or even new hardware, is an important task, which has to be done to ensure both usability and user satisfaction. The drawback of evaluating subjective parameters is that this can be relatively time consuming, and the outcome is possibly quite imprecise. Considering the recent release of cost-efficient commercial EEG headsets, we propose the utilization of electro-encephalographic (EEG) devices for evaluation purposes. The goal of our research is to evaluate if a commercial EEG headset can provide cutting-edge support during user studies and evaluations. Our results are encouraging and suggest that wireless EEG technology is a viable alternative for measuring subjectivity in evaluation scenarios.
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| 12. |
- Cernea, Daniel, 1983-, et al.
(författare)
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Emotion-Prints : Interaction-Driven Emotion Visualization on Multi-Touch Interfaces
- 2015
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Ingår i: Proceedings of SPIE 9397: Visualization and Data Analysis 2015, San Francisco, CA, USA, February 8-12, 2015. - : SPIE - International Society for Optical Engineering. - 9781628414875 ; , s. 9397-0A-
-
Konferensbidrag (refereegranskat)abstract
- Emotions are one of the unique aspects of human nature, and sadly at the same time one of the elements that our technological world is failing to capture and consider due to their subtlety and inherent complexity. But with the current dawn of new technologies that enable the interpretation of emotional states based on techniques involving facial expressions, speech and intonation, electrodermal response (EDS) and brain-computer interfaces (BCIs), we are finally able to access real-time user emotions in various system interfaces. In this paper we introduce emotion-prints, an approach for visualizing user emotional valence and arousal in the context of multi-touch systems. Our goal is to offer a standardized technique for representing user affective states in the moment when and at the location where the interaction occurs in order to increase affective self-awareness, support awareness in collaborative and competitive scenarios, and offer a framework for aiding the evaluation of touch applications through emotion visualization. We show that emotion-prints are not only independent of the shape of the graphical objects on the touch display, but also that they can be applied regardless of the acquisition technique used for detecting and interpreting user emotions. Moreover, our representation can encode any affective information that can be decomposed or reduced to Russell’s two-dimensional space of valence and arousal. Our approach is enforced by a BCI-based user study and a follow-up discussion of advantages and limitations.
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| 13. |
- Cernea, Daniel, 1983-, et al.
(författare)
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Emotion Scents : A Method of Representing User Emotions on GUI Widgets
- 2013
-
Ingår i: Proceedings of SPIE 8654. - : SPIE - International Society for Optical Engineering. ; , s. 86540F-
-
Konferensbidrag (refereegranskat)abstract
- The world of desktop interfaces has been dominated for years by the concept of windows and standardized user interface (UI) components. Still, while supporting the interaction and information exchange between the users and the computer system, graphical user interface (GUI) widgets are rather one-sided, neglecting to capture the subjective facets of the user experience. In this paper, we propose a set of design guidelines for visualizing user emotions on standard GUI widgets (e.g., buttons, check boxes, etc.) in order to enrich the interface with a new dimension of subjective information by adding support for emotion awareness as well as post-task analysis and decision making. We highlight the use of an EEG headset for recording the various emotional states of the user while he/she is interacting with the widgets of the interface. We propose a visualization approach, called emotion scents, that allows users to view emotional reactions corresponding to di erent GUI widgets without in uencing the layout or changing the positioning of these widgets. Our approach does not focus on highlighting the emotional experience during the interaction with an entire system, but on representing the emotional perceptions and reactions generated by the interaction with a particular UI component. Our research is motivated by enabling emotional self-awareness and subjectivity analysis through the proposed emotionenhanced UI components for desktop interfaces. These assumptions are further supported by an evaluation of emotion scents.
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| 14. |
- Cernea, Daniel, 1983-, et al.
(författare)
-
Group Affective Tone Awareness and Regulation through Virtual Agents
- 2014
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Ingår i: Proceedings of the Workshop on Affective Agents. ; , s. 9-16
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Konferensbidrag (refereegranskat)abstract
- It happens increasingly often that experts need to collaboratein order to exchange ideas, views and opinions on their path towardsunderstanding. However, every collaboration process is inherently fragileand involves a large set of human subjective aspects, including socialinteraction, personality, and emotions. In this paper we present Pogat,an affective virtual agent designed to support the collaboration processaround displays by increasing user awareness of the group affective tone.A positive group affective tone, where all the participants of a groupexperience emotions of a positive valence, has been linked to fosteringcreativity in groups and supporting the entire collaboration process. Atthe same time, a negative or inexistent group affective tone can suggestnegative emotions in some of the group members, emotions that canlead to an inefficient or even obstructed collaboration. A study of ourapproach suggests that Pogat can increase the awareness of the overallaffective state of the group as well as positively affect the efficiency ofgroups in collaborative scenarios.
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| 15. |
- Cernea, Daniel, 1983-, et al.
(författare)
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Measuring Subjectivity : Supporting Evaluations with the Emotiv EPOC Neuroheadset
- 2012
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Ingår i: Künstliche Intelligenz. - : Springer Berlin/Heidelberg. - 0933-1875 .- 1610-1987. ; 26:2, s. 177-182
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Tidskriftsartikel (refereegranskat)abstract
- Since the dawn of the industrial era, modern devices and interaction methods have undergone rigorous evaluations in order to ensure their functionality and quality, as well as usability. While there are many methods for measuring objective data, capturing and interpreting subjective factors—like the feelings or states of mind of the users—is still an imprecise and usually post-event process. In this paper we propose the utilization of the Emotiv EPOC commercial electroencephalographic (EEG) neuroheadset for real-time support during evaluations and user studies. We show in two evaluation scenarios that the wireless EPOC headsets can be used efficiently for supporting subjectivity measurement. Additionally, we highlight situations that may result in a lower accuracy, as well as explore possible reasons and propose solutions for improving the error rates of the device.
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| 16. |
- Cernea, Daniel, et al.
(författare)
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R3 - Un dispozitiv de intrare configurabil pentru interacţiunea liberă în spaţiu
- 2010
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Ingår i: Romanian Journal of Human-Computer Interaction. - Bucharest : Matrix Rom. - 1843-4460. ; 3, s. 45-50
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Tidskriftsartikel (refereegranskat)abstract
- În ultima perioadă s-a abordat tot mai des problema implementării unor dispozitive de intrare care să sprijine interacţiunea 3D prin oferirea a 6 sau a mai multor grade de libertate (degrees of freedom sau DoF). Cu toate acestea, astfel de dispozitive care să fie disponibile pentru interacţiune liberă în spaţiu - adică fără a fi necesară o suprafaţă ca sistem de referinţă, cum este cazul unui mouse - sunt proiectate doar pentru un tip restrâns de aplicaţii. De asemenea, aparatele de intrare de acest tip sunt rareori intuitive în utilizare şi limitate ca număr. Pentru a combate aceste probleme, în acest articol propunem un dispozitiv de complexitate şi costuri de implementare reduse, care poate fi utilizat în spaţiul liber şi este extrem de configurabil, susţinând nativ o interacţiune intuitivă cu variate medii virtuale. R3 (roll - rostogolire, rotate - rotire, rattle - agitare) oferă acurateţea necesară pentru navigare şi indicare - atât în 2D, cât şi în 3D – în aplicaţii de modelare şi jocuri, dar şi feedback tactil prin prezenţa unui trackball, toate acestea într-o manieră orientată spre utilizator. În plus, dispozitivul poate fi trecut uşor în modul de mouse, oferind astfel oricând suport pentru interacţiunea cu sistemele de operare convenţionale.
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| 17. |
- Cernea, Daniel, 1983-, et al.
(författare)
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Tangible and Wearable User Interfaces for Supporting Collaboration among Emergency Workers
- 2012
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Ingår i: Collaboration and Technology. - Berlin, Heidelberg : Springer. ; , s. 192-199
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Konferensbidrag (refereegranskat)abstract
- Ensuring a constant flow of information is essential for offeringquick help in different types of disasters. In the following, we report on a workin-progress distributed, collaborative and tangible system for supporting crisismanagement. On one hand, field operators need devices that collect information—personal notes and sensor data—without interrupting their work. Onthe other hand, a disaster management system must operate in different scenariosand be available to people with different preferences, backgrounds and roles.Our work addresses these issues by introducing a multi-level collaborative systemthat manages real-time data flow and analysis for various rescue operators.
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| 18. |
- Cernea, Daniel, 1983-
(författare)
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User-Centered Collaborative Visualization
- 2015
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The last couple of years have marked the entire field of information technology with the introduction of a new global resource, called data. Certainly, one can argue that large amounts of information and highly interconnected and complex datasets were available since the dawn of the computer and even centuries before. However, it has been only a few years since digital data has exponentially expended, diversified and interconnected into an overwhelming range of domains, generating an entire universe of zeros and ones. This universe represents a source of information with the potential of advancing a multitude of fields and sparking valuable insights. In order to obtain this information, this data needs to be explored, analyzed and interpreted.While a large set of problems can be addressed through automatic techniques from fields like artificial intelligence, machine learning or computer vision, there are various datasets and domains that still rely on the human intuition and experience in order to parse and discover hidden information. In such instances, the data is usually structured and represented in the form of an interactive visual representation that allows users to efficiently explore the data space and reach valuable insights. However, the experience, knowledge and intuition of a single person also has its limits. To address this, collaborative visualizations allow multiple users to communicate, interact and explore a visual representation by building on the different views and knowledge blocks contributed by each person.In this dissertation, we explore the potential of subjective measurements and user emotional awareness in collaborative scenarios as well as support flexible and user-centered collaboration in information visualization systems running on tabletop displays. We commence by introducing the concept of user-centered collaborative visualization (UCCV) and highlighting the context in which it applies. We continue with a thorough overview of the state-of-the-art in the areas of collaborative information visualization, subjectivity measurement and emotion visualization, combinable tabletop tangibles, as well as browsing history visualizations. Based on a new web browser history visualization for exploring user parallel browsing behavior, we introduce two novel user-centered techniques for supporting collaboration in co-located visualization systems. To begin with, we inspect the particularities of detecting user subjectivity through brain-computer interfaces, and present two emotion visualization techniques for touch and desktop interfaces. These visualizations offer real-time or post-task feedback about the users’ affective states, both in single-user and collaborative settings, thus increasing the emotional self-awareness and the awareness of other users’ emotions. For supporting collaborative interaction, a novel design for tabletop tangibles is described together with a set of specifically developed interactions for supporting tabletop collaboration. These ring-shaped tangibles minimize occlusion, support touch interaction, can act as interaction lenses, and describe logical operations through nesting operations. The visualization and the two UCCV techniques are each evaluated individually capturing a set of advantages and limitations of each approach. Additionally, the collaborative visualization supported by the two UCCV techniques is also collectively evaluated in three user studies that offer insight into the specifics of interpersonal interaction and task transition in collaborative visualization. The results show that the proposed collaboration support techniques do not only improve the efficiency of the visualization, but also help maintain the collaboration process and aid a balanced social interaction.
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| 19. |
- Cernea, Daniel, 1983-, et al.
(författare)
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Visualizing Group Affective Tone in Collaborative Scenarios
- 2014
-
Konferensbidrag (refereegranskat)abstract
- A large set of complex datasets require the use of collaborative visualization solutions in order to harness the knowledge and experience of multiple experts. However, be it co-located or distributed, the collaboration process is inherently fragile, as small mistakes in communication or various human aspects can quickly derail it. In this paper, we introduce a novel visualization technique that highlights the group affective tone (GAT), also known as the presence of homogeneous emotional reactions within a group. The goal of our visualization is to improve users’ awareness of GAT, thus fostering a positive group affective tone that has been proven to increase effectiveness and creativity in collaborative scenarios.
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| 20. |
- Cernea, Daniel, 1983-, et al.
(författare)
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WebComets : A Tab-Oriented Approach for Browser History Visualization
- 2013
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Konferensbidrag (refereegranskat)abstract
- Web browsers are our main gateways to the Internet. With their help we read articles, we learn, we listen to music, we share our thoughts and feelings, we write e-mails, or we chat. Current Web browser histories have mostly no visualization capabilities as well as limited options to filter patterns and information. Furthermore, such histories disregard the existence of parallel navigation in multiple browser windows andtabs. But a good understanding of parallel browsing behavior is of critical importance for the casual user and the behavioural analyst, while at the same time having implications in the design of search engines, Web sites and Web browsers. In this paper we present WebComets, an interactive visualization for extended browser histories. Our visualization employs browser histories that capture—among others—the taboriented, parallel nature of Web page navigation. Results presented in this paper suggest that WebComets better supports the analysis and comparison of parallel browsing and corresponding behavior patterns than common browser histories.
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| 21. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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A survey of surveys on the use of visualization for interpreting machine learning models
- 2020
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Ingår i: Information Visualization. - : Sage Publications. - 1473-8716 .- 1473-8724. ; 19:3, s. 207-233
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Tidskriftsartikel (refereegranskat)abstract
- Research in machine learning has become very popular in recent years, with many types of models proposed to comprehend and predict patterns and trends in data originating from different domains. As these models get more and more complex, it also becomes harder for users to assess and trust their results, since their internal operations are mostly hidden in black boxes. The interpretation of machine learning models is currently a hot topic in the information visualization community, with results showing that insights from machine learning models can lead to better predictions and improve the trustworthiness of the results. Due to this, multiple (and extensive) survey articles have been published recently trying to summarize the high number of original research papers published on the topic. But there is not always a clear definition of what these surveys cover, what is the overlap between them, which types of machine learning models they deal with, or what exactly is the scenario that the readers will find in each of them. In this article, we present a metaanalysis (i.e. a ‘‘survey of surveys’’) of manually collected survey papers that refer to the visual interpretation of machine learning models, including the papers discussed in the selected surveys. The aim of our article is to serve both as a detailed summary and as a guide through this survey ecosystem by acquiring, cataloging, and presenting fundamental knowledge of the state of the art and research opportunities in the area. Our results confirm the increasing trend of interpreting machine learning with visualizations in the past years, and that visualization can assist in, for example, online training processes of deep learning models and enhancing trust into machine learning. However, the question of exactly how this assistance should take place is still considered as an open challenge of the visualization community.
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| 22. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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Analyzing the Evolution of JavaScript Applications
- 2019
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Ingår i: Proceedings of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE. - : SciTePress. - 9789897583759 ; , s. 359-366
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Konferensbidrag (refereegranskat)abstract
- Software evolution analysis can shed light on various aspects of software development and maintenance. Up to date, there is little empirical evidence on the evolution of JavaScript (JS) applications in terms of maintainability and changeability, even though JavaScript is among the most popular scripting languages for front-end web applications. In this study, we investigate JS applications’ quality and changeability trends over time by examining the relevant Laws of Lehman. We analyzed over 7,500 releases of JS applications and reached some interesting conclusions. The results show that JS applications continuously change and grow, there are no clear signs of quality degradation while the complexity remains the same over time, despite the fact that the understandability of the code deteriorates.
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| 23. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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DeforestVis : Behavior Analysis of Machine Learning Models with Surrogate Decision Stumps
- 2024
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Ingår i: Computer graphics forum (Print). - : John Wiley & Sons. - 0167-7055 .- 1467-8659.
-
Tidskriftsartikel (refereegranskat)abstract
- As the complexity of Machine Learning (ML) models increases and their application in different (and critical) domains grows, there is a strong demand for more interpretable and trustworthy ML. A direct, model-agnostic, way to interpret such models is to train surrogate models—such as rule sets and decision trees—that sufficiently approximate the original ones while being simpler and easier-to-explain. Yet, rule sets can become very lengthy, with many if-else statements, and decision tree depth grows rapidly when accurately emulating complex ML models. In such cases, both approaches can fail to meet their core goal—providing users with model interpretability. To tackle this, we propose DeforestVis, a visual analytics tool that offers summarization of the behavior of complex ML models by providing surrogate decision stumps (one-level decision trees) generated with the Adaptive Boosting (AdaBoost) technique. DeforestVis helps users to explore the complexity vs fidelity trade-off by incrementally generating more stumps, creating attribute-based explanations with weighted stumps to justify decision making, and analyzing the impact of rule overriding on training instance allocation between one or more stumps. An independent test set allows users to monitor the effectiveness of manual rule changes and form hypotheses based on case-by-case analyses. We show the applicability and usefulness of DeforestVis with two use cases and expert interviews with data analysts and model developers.
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| 24. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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Empirical Study : Visual Analytics for Comparing Stacking to Blending Ensemble Learning
- 2021
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Ingår i: Proceedings of the 23rd International Conference on Control Systems and Computer Science (CSCS23), 26–28 May 2021, Bucharest, Romania. - : IEEE. - 9781665439404 - 9781665439398 ; , s. 1-8
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Stacked generalization (also called stacking) is an ensemble method in machine learning that uses a metamodel to combine the predictive results of heterogeneous base models arranged in at least one layer. K-fold cross-validation is employed at the various stages of training in this method. Nonetheless, another validation strategy is to try out several splits of data leading to different train and test sets for the base models and then use only the latter to train the metamodel—this is known as blending. In this work, we present a modification of an existing visual analytics system, entitled StackGenVis, that now supports the process of composing robust and diverse ensembles of models with both aforementioned methods. We have built multiple ensembles using our system with the two respective methods, and we tested the performance with six small- to large-sized data sets. The results indicate that stacking is significantly more powerful than blending based on three performance metrics. However, the training times of the base models and the final ensembles are lower and more stable during various train/test splits in blending rather than stacking.
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| 25. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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Evaluating StackGenVis with a Comparative User Study
- 2022
-
Ingår i: Proceedings of the 15th IEEE Pacific Visualization Symposium (PacificVis '22). - : IEEE. - 9781665423359 - 9781665423366 ; , s. 161-165
-
Konferensbidrag (refereegranskat)abstract
- Stacked generalization (also called stacking) is an ensemble method in machine learning that deploys a metamodel to summarize the predictive results of heterogeneous base models organized into one or more layers. Despite being capable of producing high-performance results, building a stack of models can be a trial-and-error procedure. Thus, our previously developed visual analytics system, entitled StackGenVis, was designed to monitor and control the entire stacking process visually. In this work, we present the results of a comparative user study we performed for evaluating the StackGenVis system. We divided the study participants into two groups to test the usability and effectiveness of StackGenVis compared to Orange Visual Stacking (OVS) in an exploratory usage scenario using healthcare data. The results indicate that StackGenVis is significantly more powerful than OVS based on the qualitative feedback provided by the participants. However, the average completion time for all tasks was comparable between both tools.
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| 26. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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FeatureEnVi : Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches
- 2022
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Ingår i: IEEE Transactions on Visualization and Computer Graphics. - : IEEE. - 1077-2626 .- 1941-0506. ; 28:4, s. 1773-1791
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Tidskriftsartikel (refereegranskat)abstract
- The machine learning (ML) life cycle involves a series of iterative steps, from the effective gathering and preparation of the data—including complex feature engineering processes—to the presentation and improvement of results, with various algorithms to choose from in every step. Feature engineering in particular can be very beneficial for ML, leading to numerous improvements such as boosting the predictive results, decreasing computational times, reducing excessive noise, and increasing the transparency behind the decisions taken during the training. Despite that, while several visual analytics tools exist to monitor and control the different stages of the ML life cycle (especially those related to data and algorithms), feature engineering support remains inadequate. In this paper, we present FeatureEnVi, a visual analytics system specifically designed to assist with the feature engineering process. Our proposed system helps users to choose the most important feature, to transform the original features into powerful alternatives, and to experiment with different feature generation combinations. Additionally, data space slicing allows users to explore the impact of features on both local and global scales. FeatureEnVi utilizes multiple automatic feature selection techniques; furthermore, it visually guides users with statistical evidence about the influence of each feature (or subsets of features). The final outcome is the extraction of heavily engineered features, evaluated by multiple validation metrics. The usefulness and applicability of FeatureEnVi are demonstrated with two use cases and a case study. We also report feedback from interviews with two ML experts and a visualization researcher who assessed the effectiveness of our system.
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| 27. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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HardVis : Visual Analytics to Handle Instance Hardness Using Undersampling and Oversampling Techniques
- 2023
-
Ingår i: Computer graphics forum (Print). - : John Wiley & Sons. - 0167-7055 .- 1467-8659. ; 42:1, s. 135-154
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Tidskriftsartikel (refereegranskat)abstract
- Despite the tremendous advances in machine learning (ML), training with imbalanced data still poses challenges in many real-world applications. Among a series of diverse techniques to solve this problem, sampling algorithms are regarded as an efficient solution. However, the problem is more fundamental, with many works emphasizing the importance of instance hardness. This issue refers to the significance of managing unsafe or potentially noisy instances that are more likely to be misclassified and serve as the root cause of poor classification performance. This paper introduces HardVis, a visual analytics system designed to handle instance hardness mainly in imbalanced classification scenarios. Our proposed system assists users in visually comparing different distributions of data types, selecting types of instances based on local characteristics that will later be affected by the active sampling method, and validating which suggestions from undersampling or oversampling techniques are beneficial for the ML model. Additionally, rather than uniformly undersampling/oversampling a specific class, we allow users to find and sample easy and difficult to classify training instances from all classes. Users can explore subsets of data from different perspectives to decide all those parameters, while HardVis keeps track of their steps and evaluates the model’s predictive performance in a test set separately. The end result is a well-balanced data set that boosts the predictive power of the ML model. The efficacy and effectiveness of HardVis are demonstrated with a hypothetical usage scenario and a use case. Finally, we also look at how useful our system is based on feedback we received from ML experts.
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| 28. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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StackGenVis : Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics
- 2021
-
Ingår i: IEEE Transactions on Visualization and Computer Graphics. - : IEEE Computer Society Digital Library. - 1077-2626 .- 1941-0506. ; 27:2, s. 1547-1557
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Tidskriftsartikel (refereegranskat)abstract
- In machine learning (ML), ensemble methods—such as bagging, boosting, and stacking—are widely-established approaches that regularly achieve top-notch predictive performance. Stacking (also called "stacked generalization") is an ensemble method that combines heterogeneous base models, arranged in at least one layer, and then employs another metamodel to summarize the predictions of those models. Although it may be a highly-effective approach for increasing the predictive performance of ML, generating a stack of models from scratch can be a cumbersome trial-and-error process. This challenge stems from the enormous space of available solutions, with different sets of data instances and features that could be used for training, several algorithms to choose from, and instantiations of these algorithms using diverse parameters (i.e., models) that perform differently according to various metrics. In this work, we present a knowledge generation model, which supports ensemble learning with the use of visualization, and a visual analytics system for stacked generalization. Our system, StackGenVis, assists users in dynamically adapting performance metrics, managing data instances, selecting the most important features for a given data set, choosing a set of top-performant and diverse algorithms, and measuring the predictive performance. In consequence, our proposed tool helps users to decide between distinct models and to reduce the complexity of the resulting stack by removing overpromising and underperforming models. The applicability and effectiveness of StackGenVis are demonstrated with two use cases: a real-world healthcare data set and a collection of data related to sentiment/stance detection in texts. Finally, the tool has been evaluated through interviews with three ML experts.
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| 29. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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t-viSNE : A Visual Inspector for the Exploration of t-SNE
- 2018
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Ingår i: Presented at IEEE Information Visualization (VIS '18), Berlin, Germany, 21-26 October, 2018.
-
Konferensbidrag (refereegranskat)abstract
- The use of t-Distributed Stochastic Neighborhood Embedding (t-SNE) for the visualization of multidimensional data has proven to be a popular approach, with applications published in a wide range of domains. Despite their usefulness, t-SNE plots can sometimes be hard to interpret or even misleading, which hurts the trustworthiness of the results. By opening the black box of the algorithm and showing insights into its behavior through visualization, we may learn how to use it in a more effective way. In this work, we present t-viSNE, a visual inspection tool that enables users to explore anomalies and assess the quality of t-SNE results by bringing forward aspects of the algorithm that would normally be lost after the dimensionality reduction process is finished.
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| 30. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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t-viSNE : Interactive Assessment and Interpretation of t-SNE Projections
- 2020
-
Ingår i: IEEE Transactions on Visualization and Computer Graphics. - : IEEE. - 1077-2626 .- 1941-0506. ; 26:8, s. 2696-2714
-
Tidskriftsartikel (refereegranskat)abstract
- t-Distributed Stochastic Neighbor Embedding (t-SNE) for the visualization of multidimensional data has proven to be a popular approach, with successful applications in a wide range of domains. Despite their usefulness, t-SNE projections can be hard to interpret or even misleading, which hurts the trustworthiness of the results. Understanding the details of t-SNE itself and the reasons behind specific patterns in its output may be a daunting task, especially for non-experts in dimensionality reduction. In this work, we present t-viSNE, an interactive tool for the visual exploration of t-SNE projections that enables analysts to inspect different aspects of their accuracy and meaning, such as the effects of hyper-parameters, distance and neighborhood preservation, densities and costs of specific neighborhoods, and the correlations between dimensions and visual patterns. We propose a coherent, accessible, and well-integrated collection of different views for the visualization of t-SNE projections. The applicability and usability of t-viSNE are demonstrated through hypothetical usage scenarios with real data sets. Finally, we present the results of a user study where the tool’s effectiveness was evaluated. By bringing to light information that would normally be lost after running t-SNE, we hope to support analysts in using t-SNE and making its results better understandable.
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| 31. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations
- 2020
-
Ingår i: Computer graphics forum (Print). - : John Wiley & Sons. - 0167-7055 .- 1467-8659. ; 39:3, s. 713-756
-
Tidskriftsartikel (refereegranskat)abstract
- Machine learning (ML) models are nowadays used in complex applications in various domains such as medicine, bioinformatics, and other sciences. Due to their black box nature, however, it may sometimes be hard to understand and trust the results they provide. This has increased the demand for reliable visualization tools related to enhancing trust in ML models, which has become a prominent topic of research in the visualization community over the past decades. To provide an overview and present the frontiers of current research on the topic, we present a State-of-the-Art Report (STAR) on enhancing trust in ML models with the use of interactive visualization. We define and describe the background of the topic, introduce a categorization for visualization techniques that aim to accomplish this goal, and discuss insights and opportunities for future research directions. Among our contributions is a categorization of trust against different facets of interactive ML, expanded and improved from previous research. Our results are investigated from different analytical perspectives: (a) providing a statistical overview, (b) summarizing key findings, (c) performing topic analyses, and (d) exploring the data sets used in the individual papers, all with the support of an interactive web-based survey browser. We intend this survey to be beneficial for visualization researchers whose interests involve making ML models more trustworthy, as well as researchers and practitioners from other disciplines in their search for effective visualization techniques suitable for solving their tasks with confidence and conveying meaning to their data.
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| 32. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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VisEvol : Visual Analytics to Support Hyperparameter Search through Evolutionary Optimization
- 2021
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Ingår i: Computer graphics forum (Print). - : John Wiley & Sons. - 0167-7055 .- 1467-8659. ; 40:3, s. 201-214
-
Tidskriftsartikel (refereegranskat)abstract
- During the training phase of machine learning (ML) models, it is usually necessary to configure several hyperparameters. This process is computationally intensive and requires an extensive search to infer the best hyperparameter set for the given problem. The challenge is exacerbated by the fact that most ML models are complex internally, and training involves trial-and-error processes that could remarkably affect the predictive result. Moreover, each hyperparameter of an ML algorithm is potentially intertwined with the others, and changing it might result in unforeseeable impacts on the remaining hyperparameters. Evolutionary optimization is a promising method to try and address those issues. According to this method, performant models are stored, while the remainder are improved through crossover and mutation processes inspired by genetic algorithms. We present VisEvol, a visual analytics tool that supports interactive exploration of hyperparameters and intervention in this evolutionary procedure. In summary, our proposed tool helps the user to generate new models through evolution and eventually explore powerful hyperparameter combinations in diverse regions of the extensive hyperparameter space. The outcome is a voting ensemble (with equal rights) that boosts the final predictive performance. The utility and applicability of VisEvol are demonstrated with two use cases and interviews with ML experts who evaluated the effectiveness of the tool.
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| 33. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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VisRuler : Visual Analytics for Extracting Decision Rules from Bagged and Boosted Decision Trees
- 2023
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Ingår i: Information Visualization. - : Sage Publications. - 1473-8716 .- 1473-8724. ; 22:2, s. 115-139
-
Tidskriftsartikel (refereegranskat)abstract
- Bagging and boosting are two popular ensemble methods in machine learning (ML) that produce many individual decision trees. Due to the inherent ensemble characteristic of these methods, they typically outperform single decision trees or other ML models in predictive performance. However, numerous decision paths are generated for each decision tree, increasing the overall complexity of the model and hindering its use in domains that require trustworthy and explainable decisions, such as finance, social care, and health care. Thus, the interpretability of bagging and boosting algorithms—such as random forest and adaptive boosting—reduces as the number of decisions rises. In this paper, we propose a visual analytics tool that aims to assist users in extracting decisions from such ML models via a thorough visual inspection workflow that includes selecting a set of robust and diverse models (originating from different ensemble learning algorithms), choosing important features according to their global contribution, and deciding which decisions are essential for global explanation (or locally, for specific cases). The outcome is a final decision based on the class agreement of several models and the explored manual decisions exported by users. We evaluated the applicability and effectiveness of VisRuler via a use case, a usage scenario, and a user study. The evaluation revealed that most users managed to successfully use our system to explore decision rules visually, performing the proposed tasks and answering the given questions in a satisfying way.
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| 34. |
- Chatzimparmpas, Angelos
(författare)
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Visual Analytics for Explainable and Trustworthy Machine Learning
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The deployment of artificial intelligence solutions and machine learning research has exploded in popularity in recent years, with numerous types of models proposed to interpret and predict patterns and trends in data from diverse disciplines. However, as the complexity of these models grows, it becomes increasingly difficult for users to evaluate and rely on the model results, since their inner workings are mostly hidden in black boxes, which are difficult to trust in critical decision-making scenarios. While automated methods can partly handle these problems, recent research findings suggest that their combination with innovative methods developed within information visualization and visual analytics can lead to further insights gained from models and, consequently, improve their predictive ability and enhance trustworthiness in the entire process. Visual analytics is the area of research that studies the analysis of vast and intricate information spaces by combining statistical and machine learning models with interactive visual interfaces. By following this methodology, human experts can better understand such spaces and apply their domain expertise in the process of building and improving the underlying models.The primary goals of this dissertation are twofold, focusing on (1) methodological aspects, by conducting qualitative and quantitative meta-analyses to support the visualization research community in making sense of its literature and to highlight unsolved challenges, as well as (2) technical solutions, by developing visual analytics approaches for various machine learning models, such as dimensionality reduction and ensemble learning methods. Regarding the first goal, we define, categorize, and examine in depth the means for visual coverage of the different trust levels at each stage of a typical machine learning pipeline and establish a design space for novel visualizations in the area. Regarding the second goal, we discuss multiple visual analytics tools and systems implemented by us to facilitate the underlying research on the various stages of the machine learning pipeline, i.e., data processing, feature engineering, hyperparameter tuning, understanding, debugging, refining, and comparing models. Our approaches are data-agnostic, but mainly target tabular data with meaningful attributes in diverse domains, such as health care and finance. The applicability and effectiveness of this work were validated with case studies, usage scenarios, expert interviews, user studies, and critical discussions of limitations and alternative designs. The results of this dissertation provide new avenues for visual analytics research in explainable and trustworthy machine learning.
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| 35. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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Visualization for Trust in Machine Learning Revisited : The State of the Field in 2023
- 2024
-
Ingår i: IEEE Computer Graphics and Applications. - : IEEE. - 0272-1716 .- 1558-1756. ; 44:3, s. 99-113
-
Tidskriftsartikel (refereegranskat)abstract
- Visualization for explainable and trustworthy machine learning remains one of the most important and heavily researched fields within information visualization and visual analytics with various application domains, such as medicine, finance, and bioinformatics. After our 2020 state-of-the-art report comprising 200 techniques, we have persistently collected peer-reviewed articles describing visualization techniques, categorized them based on the previously established categorization schema consisting of 119 categories, and provided the resulting collection of 542 techniques in an online survey browser. In this survey article, we present the updated findings of new analyses of this dataset as of fall 2023 and discuss trends, insights, and eight open challenges for using visualizations in machine learning. Our results corroborate the rapidly growing trend of visualization techniques for increasing trust in machine learning models in the past three years, with visualization found to help improve popular model explainability methods and check new deep learning architectures, for instance.
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| 36. |
- Collins, Christopher, et al.
(författare)
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Visual Text Analytics : Report from Dagstuhl Seminar 22191
- 2022
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Text data is one of the most abundant types of data available, produced every day across all domains of society. Understanding the contents of this data can support important policy decisions, help us understand society and culture, and improve business processes. While machine learning techniques are growing in their power for analyzing text data, there is still a clear role for human analysis and decision-making. This seminar explored the use of visual analytics applied to text data as a means to bridge the complementary strengths of people and computers. The field of visual text analytics applies visualization and interaction approaches which are tightly coupled to natural language processing systems to create analysis processes and systems for examining text and multimedia data. During the seminar, interdisciplinary working groups of experts from visualization, natural language processing, and machine learning examined seven topic areas to reflect on the state of the field, identify gaps in knowledge, and create an agenda for future cross-disciplinary research. This report documents the program and the outcomes of Dagstuhl Seminar 22191 "Visual Text Analytics".
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| 37. |
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| 38. |
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| 39. |
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| 40. |
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| 41. |
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Computer Vision, Imaging and Computer Graphics Theory and Applications : 14th International Joint Conference, VISIGRAPP 2019, Prague, Czech Republic, February 25–27, 2019, Revised Selected Papers
- 2020
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Samlingsverk (redaktörskap) (refereegranskat)abstract
- This book constitutes thoroughly revised and selected papers from the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2019, held in Prague, Czech Republic, in February 2019. The 25 thoroughly revised and extended papers presented in this volume were carefully reviewed and selected from 395 submissions. The papers contribute to the understanding of relevant trends of current research on computer graphics; human computer interaction; information visualization; computer vision.
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| 42. |
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Computer Vision, Imaging and Computer Graphics Theory and Applications : 15th International Joint Conference, VISIGRAPP 2020 Valletta, Malta, February 27–29, 2020, Revised Selected Papers
- 2022. - 1
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Samlingsverk (redaktörskap) (refereegranskat)abstract
- This book constitutes thoroughly revised and selected papers from the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2020, held in Valletta, Malta, in February 2020. The 25 thoroughly revised and extended papers presented in this volume were carefully reviewed and selected from 455 submissions. The papers contribute to the understanding of relevant trends of current research on computer graphics; human computer interaction; information visualization; computer vision.
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| 43. |
- Conroy, Melanie, et al.
(författare)
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Uncertainty in humanities network visualization
- 2024
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Ingår i: Frontiers in Communication. - : Frontiers Media S.A.. - 2297-900X. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- Network visualization is one of the most widely used tools in digital humanities research. The idea of uncertain or “fuzzy” data is also a core notion in digital humanities research. Yet network visualizations in digital humanities do not always prominently represent uncertainty. In this article, we present a mathematical and logical model of uncertainty as a range of values which can be used in network visualizations. We review some of the principles for visualizing uncertainty of different kinds, visual variables that can be used for representing uncertainty, and how these variables have been used to represent different data types in visualizations drawn from a range of non-humanities fields like climate science and bioinformatics. We then provide examples of two diagrams: one in which the variables displaying degrees of uncertainty are integrated into the graph and one in which glyphs are added to represent data certainty and uncertainty. Finally, we discuss how probabilistic data and what-if scenarios could be used to expand the representation of uncertainty in humanities network visualizations.
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| 44. |
- Einsfeld, Katja, et al.
(författare)
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Knowledge Generation Through Human-Centered Information Visualization
- 2009
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Ingår i: Information Visualization. - : Palgrave Macmillan Ltd. - 1473-8716 .- 1473-8724. ; 8:3, s. 180-196
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Tidskriftsartikel (refereegranskat)abstract
- One important intention of human-centered information visualization is to represent huge amounts of abstract data in a visual representation that allows even users from foreign application domains to interact with the visualization, to understand the underlying data, and finally, to gain new, application-related knowledge. The visualization will help experts as well as non-experts to link previously or isolated knowledge-items in their mental map with new insights.Our approach explicitly supports the process of linking knowledge-items with three concepts. At first, the representation of data items in an ontology categorizes and relates them. Secondly, the use of various visualization techniques visually correlates isolated items by graph-structures, layout, attachment, integration, or hyperlink techniques. Thirdly, the intensive use of visual metaphors relates a known source domain to a less known target domain. In order to realize a scenario of these concepts, we developed a visual interface for non-experts to maintain complex wastewater treatment plants. This domain-specific application is used to give our concepts a meaningful background.
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| 45. |
- Espadoto, Mateus, et al.
(författare)
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Toward a Quantitative Survey of Dimension Reduction Techniques
- 2021
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Ingår i: IEEE Transactions on Visualization and Computer Graphics. - : IEEE. - 1077-2626 .- 1941-0506. ; 27:3, s. 2153-2173
-
Tidskriftsartikel (refereegranskat)abstract
- Dimensionality reduction methods, also known as projections, are frequently used in multidimeDimensionality reduction methods, also known as projections, are frequently used in multidimensional data exploration in machine learning, data science, and information visualization. Tens of such techniques have been proposed, aiming to address a wide set of requirements, such as ability to show the high-dimensional data structure, distance or neighborhood preservation, computational scalability, stability to data noise and/or outliers, and practical ease of use. However, it is far from clear for practitioners how to choose the best technique for a given use context. We present a survey of a wide body of projection techniques that helps answering this question. For this, we characterize the input data space, projection techniques, and the quality of projections, by several quantitative metrics. We sample these three spaces according to these metrics, aiming at good coverage with bounded effort. We describe our measurements and outline observed dependencies of the measured variables. Based on these results, we draw several conclusions that help comparing projection techniques, explain their results for different types of data, and ultimately help practitioners when choosing a projection for a given context. Our methodology, datasets, projection implementations, metrics, visualizations, and results are publicly open, so interested stakeholders can examine and/or extend this benchmark.nsional data exploration in machine learning, data science, and information visualization. Tens of such techniques have been proposed, aiming to address a wide set of requirements, such as ability to show the high-dimensional data structure, distance or neighborhood preservation, computational scalability, stability to data noise and/or outliers, and practical ease of use. However, it is far from clear for practitioners how to choose the best technique for a given use context. We present a survey of a wide body of projection techniques that helps answering this question. For this, we characterize the input data space, projection techniques, and the quality of projections, by several quantitative metrics. We sample these three spaces according to these metrics, aiming at good coverage with bounded effort. We describe our measurements and outline observed dependencies of the measured variables. Based on these results, we draw several conclusions that help comparing projection techniques, explain their results for different types of data, and ultimately help practitioners when choosing a projection for a given context. Our methodology, datasets, projection implementations, metrics, visualizations, and results are publicly open, so interested stakeholders can examine and/or extend this benchmark.
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| 46. |
- Feyer, Stefan P., et al.
(författare)
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2D, 2.5D, or 3D? : An Exploratory Study on Multilayer Network Visualisations in Virtual Reality
- 2024
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Ingår i: IEEE Transactions on Visualization and Computer Graphics. - : IEEE. - 1077-2626 .- 1941-0506. ; 30:1, s. 469-479
-
Tidskriftsartikel (refereegranskat)abstract
- Relational information between different types of entities is often modelled by a multilayer network (MLN) - a network with subnetworks represented by layers. The layers of an MLN can be arranged in different ways in a visual representation, however, the impact of the arrangement on the readability of the network is an open question. Therefore, we studied this impact for several commonly occurring tasks related to MLN analysis. Additionally, layer arrangements with a dimensionality beyond 2D, which are common in this scenario, motivate the use of stereoscopic displays. We ran a human subject study utilising a Virtual Reality headset to evaluate 2D, 2.5D, and 3D layer arrangements. The study employs six analysis tasks that cover the spectrum of an MLN task taxonomy, from path finding and pattern identification to comparisons between and across layers. We found no clear overall winner. However, we explore the task-to-arrangement space and derive empirical-based recommendations on the effective use of 2D, 2.5D, and 3D layer arrangements for MLNs.
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| 47. |
- Golub, Koraljka, et al.
(författare)
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Automatic subject classification for improving retrieval in a Swedish repository
- 2017
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Ingår i: ISKO UK Conference 2017: Knowledge Organization: what's the story?, 11 – 12 September 2017, London.
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Konferensbidrag (refereegranskat)abstract
- The recent adoption of the Dewey Decimal Classification (DDC) in Sweden has ignited discussions about automated subject classification especially for digital collections, which generally seem to lack subject indexing from controlled vocabularies. This is particularly problematic in the context of academic resource retrieval tasks, which require an understanding of discipline-specific terminologies and the narratives behind their internal ontologies. The currently available experimental classification software have not been adequately tested and their usefulness is unproven especially for Swedish language resources. We address these issues by investigating a unifying framework of automatic subject indexing for the DDC, including an analysis of suitable interactive visualisation features for supporting these aims. We will address the disciplinary narratives behind the DDC in selected subject areas and the preliminary results will include an analysis of the data collection and a breakdown of the methodology. Major visualisation possibilities in support of the classification process are also outlined. The project will contribute significantly to Swedish information infrastructure by improving the findability of Swedish research resources by subject searching, one of the most common yet the most challenging types of searching.
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| 48. |
-
Graph Drawing and Network Visualization : 26th International Symposium, GD 2018, Barcelona, Spain, September 26-28, 2018, Proceedings
- 2018
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Proceedings (redaktörskap) (refereegranskat)abstract
- This book constitutes the refereed proceedings of the 26th International Symposium on Graph Drawing and Network Visualization, GD 2018, held in Barcelona, Spain, in September 2018. The 41 full papers presented in this volume were carefully reviewed and selected from 85 submissions. They were organized in topical sections named: planarity variants; upward drawings; RAC drawings; orders; crossings; crossing angles; contact representations; specialized graphs and trees; partially fixed drawings, experiments; orthogonal drawings; realizability; and miscellaneous. The book also contains one invited talk in full paper length and the Graph Drawing contest report.
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| 49. |
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| 50. |
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