Iacono Lucas, Veas Eduardo Enrique
2021
AVL RACING and the Knowledge Visualization group of Know-Center GmbH, are evaluating the performance of racing drivers using the latest wearables technologies, data analytics and vehicle dynamics simulation software from AVL. The goal is to measure human factors related with biosensors synchronized with vehicle data at a Driver-in-the-Loop (DiL) and vehicle dynamics simulation software AVL VSM™ RACE
Iacono Lucas, Veas Eduardo Enrique
2021
Know-Center is developing human-centered intelligent systems that detect cognitive, emotional and health related states by action, perception and by means of cognitive and health metrics. Models of human behavior and intention allow to be derived during different activities. The innovative set-up is reflected by linking the human telemetry (HT) system with activity monitors and by synchronizing the data. This article details our system composed of several wearable sensors, such as EEG, eye-tracker, ECG, EMG, and a data-logger, and methodology used to perform our studies
di Sciascio Maria Cecilia, Strohmaier David, Errecalde Marcelo Luis, Veas Eduardo Enrique
2019
Digital libraries and services enable users to access large amounts of data on demand. Yet, quality assessment of information encountered on the Internet remains an elusive open issue. For example, Wikipedia, one of the most visited platforms on the Web, hosts thousands of user-generated articles and undergoes 12 million edits/contributions per month. User-generated content is undoubtedly one of the keys to its success but also a hindrance to good quality. Although Wikipedia has established guidelines for the “perfect article,” authors find it difficult to assert whether their contributions comply with them and reviewers cannot cope with the ever-growing amount of articles pending review. Great efforts have been invested in algorithmic methods for automatic classification of Wikipedia articles (as featured or non-featured) and for quality flaw detection. Instead, our contribution is an interactive tool that combines automatic classification methods and human interaction in a toolkit, whereby experts can experiment with new quality metrics and share them with authors that need to identify weaknesses to improve a particular article. A design study shows that experts are able to effectively create complex quality metrics in a visual analytics environment. In turn, a user study evidences that regular users can identify flaws, as well as high-quality content based on the inspection of automatic quality scores.
di Sciascio Maria Cecilia, Brusilovsky Peter, Trattner Christoph, Veas Eduardo Enrique
2019
Information-seeking tasks with learning or investigative purposes are usually referred to as exploratory search. Exploratory search unfolds as a dynamic process where the user, amidst navigation, trial and error, and on-the-fly selections, gathers and organizes information (resources). A range of innovative interfaces with increased user control has been developed to support the exploratory search process. In this work, we present our attempt to increase the power of exploratory search interfaces by using ideas of social search—for instance, leveraging information left by past users of information systems. Social search technologies are highly popular today, especially for improving ranking. However, current approaches to social ranking do not allow users to decide to what extent social information should be taken into account for result ranking. This article presents an interface that integrates social search functionality into an exploratory search system in a user-controlled way that is consistent with the nature of exploratory search. The interface incorporates control features that allow the user to (i) express information needs by selecting keywords and (ii) to express preferences for incorporating social wisdom based on tag matching and user similarity. The interface promotes search transparency through color-coded stacked bars and rich tooltips. This work presents the full series of evaluations conducted to, first, assess the value of the social models in contexts independent to the user interface, in terms of objective and perceived accuracy. Then, in a study with the full-fledged system, we investigated system accuracy and subjective aspects with a structural model revealing that when users actively interacted with all of its control features, the hybrid system outperformed a baseline content-based–only tool and users were more satisfied.
Jorge Guerra Torres, Carlos Catania, Veas Eduardo Enrique
2019
Modern Network Intrusion Detection systems depend on models trained with up-to-date labeled data. Yet, the process of labeling a network traffic dataset is specially expensive, since expert knowledge is required to perform the annotations. Visual analytics applications exist that claim to considerably reduce the labeling effort, but the expert still needs to ponder several factors before issuing a label. And, most often the effect of bad labels (noise) in the final model is not evaluated. The present article introduces a novel active learning strategy that learns to predict labels in (pseudo) real-time as the user performs the annotation. The system called RiskID, presents several innovations: i) a set of statistical methods summarize the information, which is illustrated in a visual analytics application, ii) that interfaces with the active learning strategy forbuilding a random forest model as the user issues annotations; iii) the (pseudo) real-time predictions of the model are fed back visually to scaffold the traffic annotation task. Finally, iv) an evaluation framework is introduced that represents a complete methodology for evaluating active learning solutions, including resilience against noise.
Barreiros Carla, Pammer-Schindler Viktoria, Veas Eduardo Enrique
2019
We present a visual interface for communicating the internal state of a coffee machine via a tree metaphor. Nature-inspired representations have a positive impact on human well-being. We also hypothesize that representing the coffee machine asa tree stimulates emotional connection to it, which leads to better maintenance performance.The first study assessed the understandability of the tree representation, comparing it with icon-based and chart-based representations. An online survey with 25 participants indicated no significant mean error difference between representations.A two-week field study assessed the maintenance performance of 12 participants, comparing the tree representation with the icon-based representation. Based on 240 interactions with the coffee machine, we concluded that participants understood themachine states significantly better in the tree representation. Their comments and behavior indicated that the tree representation encouraged an emotional engagement with the machine. Moreover, the participants performed significantly more optional maintenance tasks with the tree representation.
Barreiros Carla, Veas Eduardo Enrique, Pammer-Schindler Viktoria
2018
This paper describes a novel visual metaphor to communicate sensor information of a connected device. The Internet of Things aims to extend every device with sensing and computing capabilities. A byproduct is that even domestic machines become increasingly complex, tedious to understand and maintain. This paper presents a prototype instrumenting a coffee machine with sensors. The machine streams the sensor data, which is picked up by an augmented reality application serving a nature metaphor. The nature metaphor, BioAR, represents the status derived from the coffee machine sensors in the features of a 3D virtual tree. The tree is meant to pass for a living proxy of the machine it represents. The metaphor, shown either with AR or a simple holographic display, reacts to the user manipulation of the machine and its workings. A first user study validates that the representation is correctly understood, and that it inspires affect for the machine. A second user study validates that the metaphor scales to a large number of machines.
di Sciascio Maria Cecilia, Sabol Vedran, Veas Eduardo Enrique
2017
Whenever users engage in gathering and organizing new information, searching and browsing activities emerge at the core of the exploration process. As the process unfolds and new knowledge is acquired, interest drifts occur inevitably and need to be accounted for. Despite the advances in retrieval and recommender algorithms, real-world interfaces have remained largely unchanged: results are delivered in a relevance-ranked list. However, it quickly becomes cumbersome to reorganize resources along new interests, as any new search brings new results. We introduce an interactive user-driven tool that aims at supporting users in understanding, refining, and reorganizing documents on the fly as information needs evolve. Decisions regarding visual and interactive design aspects are tightly grounded on a conceptual model for exploratory search. In other words, the different views in the user interface address stages of awareness, exploration, and explanation unfolding along the discovery process, supported by a set of text-mining methods. A formal evaluation showed that gathering items relevant to a particular topic of interest with our tool incurs in a lower cognitive load compared to a traditional ranked list. A second study reports on usage patterns and usability of the various interaction techniques within a free, unsupervised setting.
di Sciascio Maria Cecilia, Sabol Vedran, Veas Eduardo Enrique
2017
Whenever we gather or organize knowledge, the task of search-ing inevitably takes precedence. As exploration unfolds, it be-comes cumbersome to reorganize resources along new interests,as any new search brings new results. Despite huge advances inretrieval and recommender systems from the algorithmic point ofview, many real-world interfaces have remained largely unchanged:results appear in an infinite list ordered by relevance with respect tothe current query. We introduceuRank, a user-driven visual tool forexploration and discovery of textual document recommendations.It includes a view summarizing the content of the recommenda-tion set, combined with interactive methods for understanding, re-fining and reorganizing documents on-the-fly as information needsevolve. We provide a formal experiment showing thatuRankuserscan browse the document collection and efficiently gather items rel-evant to particular topics of interest with significantly lower cogni-tive load compared to traditional list-based representations.
Tatzgern Markus, Grasset Raphael, Veas Eduardo Enrique, Schmalstieg Dieter
2015
Augmented reality (AR) enables users to retrieve additional information about real world objects and locations. Exploring such location-based information in AR requires physical movement to different viewpoints, which may be tiring and even infeasible when viewpoints are out of reach. In this paper, we present object-centric exploration techniques for handheld AR that allow users to access information freely using a virtual copy metaphor. We focus on the design of techniques that allow the exploration of large real world objects. We evaluated our interfaces in a series of studies in controlled conditions and compared them to a 3D map interface, which is a more common method for accessing location-based information. Based on our findings, we put forward design recommendations that should be considered by future generations of location-based AR browsers, 3D tourist guides or situated urban planning.
Mutlu Belgin, Veas Eduardo Enrique, Trattner Christoph
2015
Visualizations have a distinctive advantage when dealing with the information overload problem: since theyare grounded in basic visual cognition, many people understand them. However, creating the appropriaterepresentation requires specific expertise of the domain and underlying data. Our quest in this paper is tostudy methods to suggest appropriate visualizations autonomously. To be appropriate, a visualization hasto follow studied guidelines to find and distinguish patterns visually, and encode data therein. Thus, a visu-alization tells a story of the underlying data; yet, to be appropriate, it has to clearly represent those aspectsof the data the viewer is interested in. Which aspects of a visualization are important to the viewer? Canwe capture and use those aspects to recommend visualizations? This paper investigates strategies to recom-mend visualizations considering different aspects of user preferences. A multi-dimensional scale is used toestimate aspects of quality for charts for collaborative filtering. Alternatively, tag vectors describing chartsare used to recommend potentially interesting charts based on content. Finally, a hybrid approach combinesinformation on what a chart is about (tags) and how good it is (ratings). We present the design principlesbehindVizRec, our visual recommender. We describe its architecture, the data acquisition approach with acrowd sourced study, and the analysis of strategies for visualization recommendation