It is common in mobile augmented reality (AR) applications to use clues or visual markers to indicate the location of points of interest (POIs) that are outside the current field of view of the camera or screen. These clues can take many forms, such as arrows, symbols, or text labels, and are typically overlaid on the camera view or displayed in a separate area of the screen. Visual clues indicating relevance, orientation, and distance to off-screen objects help users better understand their surroundings and explore or navigate more effectively. This paper compares 4 proposed focus+context techniques with visual clues to off-screen POIs' position and distance: a modified 2D arrow and three arc-based representations. Moreover, we apply already known techniques to represent POIs' relevance and to solve the visual cluttering in environments with many POIs. The results of a user study revealed that the participants preferred the 2D arrow technique and could find the most relevant POI faster with it, even with a large number of objects. However, the EqualArc technique reduces error rates for POI selection accuracy.

One of the most popular eSports (electronic sports) game types practiced is the Multiplayer Online Battle Arena (MOBA) genre, represented by one of the most popular competitive games, League of Legends (LoL). As in many traditional sports, to improve player and team performance, players and coaches analyze all the game events, such as, the positions and trajectories of the players, representing their movements, events and actions they performed during the game (spatial and temporal data). This paper presents VisuaLeague, a visualization tool for analysis of LoL matches for single player, teams, professional matches, and multiple games. The tool offers interaction with the visualizations, filtering and aggregation of data, and clustering to solve the common problems presented in analysis with voluminous amount of data, like cluttering and overlapping. We evaluated VisuaLeague through a user study covering the various types of analysis with two professional coaches. Results indicate that the tool was overall intuitive, useful, efficient and innovative and coaches show a particular interest in the analysis of professional training matches and multiple games as those provide visualizations that often lack in common tools, specially, regarding spatio-temporal data.

BACKGROUND: IsomiRs are miRNA variants that vary in length and/or sequence when compared to their canonical forms. These variants display differences in length and/or sequence, including additions or deletions of one or more nucleotides (nts) at the 5' and/or 3' end, internal editings or untemplated 3' end additions. Most available tools for small RNA-seq data analysis do not allow the identification of isomiRs and often require advanced knowledge of bioinformatics. To overcome this, we have developed IsomiR Window, a platform that supports the systematic identification, quantification and functional exploration of isomiR expression in small RNA-seq datasets, accessible to users with no computational skills. METHODS: IsomiR Window enables the discovery of isomiRs and identification of all annotated non-coding RNAs in RNA-seq datasets from animals and plants. It comprises two main components: the IsomiR Window pipeline for data processing; and the IsomiR Window Browser interface. It integrates over ten third-party softwares for the analysis of small-RNA-seq data and holds a new algorithm that allows the detection of all possible types of isomiRs. These include 3' and 5'end isomiRs, 3' end tailings, isomiRs with single nucleotide polymorphisms (SNPs) or potential RNA editings, as well as all possible fuzzy combinations. IsomiR Window includes all required databases for analysis and annotation, and is freely distributed as a Linux virtual machine, including all required software. RESULTS: IsomiR Window processes several datasets in an automated manner, without restrictions of input file size. It generates high quality interactive figures and tables which can be exported into different formats. The performance of isomiR detection and quantification was assessed using simulated small-RNA-seq data. For correctly mapped reads, it identified different types of isomiRs with high confidence and 100% accuracy. The analysis of a small RNA-seq data from Basal Cell Carcinomas (BCCs) using isomiR Window confirmed that miR-183-5p is up-regulated in Nodular BCCs, but revealed that this effect was predominantly due to a novel 5'end variant. This variant displays a different seed region motif and 1756 isoform-exclusive mRNA targets that are significantly associated with disease pathways, underscoring the biological relevance of isomiR-focused analysis. IsomiR Window is available at https://isomir.fc.ul.pt/ .

Augmented Reality (AR) applications for mobile devices are common nowadays, however the information superimposed over the image captured by the device's camera is limited to its field of view. To address this problem in the visualization of points of interest (POI), we have combined the AR view with the display of the POI's location in a map to increase user awareness about nearby POI. It gives insight about the orientation and distance between the POI and the user. We developed 3 solutions: (1) including cues to off-screen POI in a frame at the border of the AR view and providing an alternative map view; (2) combining the AR view with a mini-map superimposed on its top left corner; and (3) combining the AR view with a circular radar view on its top left corner. A usability study performed to compare these approaches revealed that, although all the approaches were positively appreciated, the solution with a mini-map was the preferred one.

The phenomenon of eSports has been increasing over the years, including the interest in online video games, by players, coaches and analysts. One of the most popular genres of these games is the Multiplayer Online Battle Arena (MOBA) and games like League of Legend (LoL) have increased its player base and event viewership. Similarly to other sports, players and coaches analyze the games and all the games events, such as players' movements and spatial, temporal events in which they participate, to understand how they play and to define new strategies to help them progress. This paper presents the VisuaLeague II tool that helps players and coaches to analyze LoL game data using animated maps and other visualizations; and presents a comparative study between VisuaLeague II and other two tools (LoL replay system and OP.GG) to understand if the proposed visualization techniques are adequate for analyzing player's performance. The results suggest that players are capable of visualizing game-related data through the use of animated maps, to solve common analytic tasks and the comparison suggests that participants prefer to visualize spatio-temporal data dynamically, i.e., using animated maps or the replay system.

Over the last years, with the increasing use of telemetry techniques, the extraction of data from video games became a much easier and reliable task. Among many types of video games, MOBAs (Multiplayer Online Battle Arena) are one of the most widely played and, consequently, the analysis of game events has received attention of players, spectators, coaches and analysts to assess the performance of players. This data can range from players' position, to more specific events, such as, the position of a player's death. Our main goal is to get a better understanding of which techniques are more adequate to handle the visualization of this type of spatio-temporal information data, associated to player performance analysis in video games. This paper addresses this problem presenting a user study to evaluate the adequacy of animated maps and the analytical strategies followed by players when using spatio-temporal data to analyse player performance. To support the user study, we developed the VisuaLeague prototype for the visualization of in-game player trajectories and events during a match in the MOBA game League of Legends. The results support the adequacy of using the animated maps technique to convey information to users in this context. Moreover, they also point out towards a high degree of importance given to the spatio-temporal components of the data for player performance analysis.

The integration of sensors in smartphones and tablets is enabling the emergence of augmented reality (AR) applications for these devices. However, the image captured and the superimposed information is limited to the camera's field-of-view and, in the visualization of points of interest (PoI), symbol overlapping may occur because there is no control over their number and location. Thus, we developed an Android prototype application that aims to: provide better environment awareness using clues to off-screen objects and a 2D map with a radar view, reduce symbol overlapping via aggregation visualizations, and guide the attention of the user by featuring different levels of symbol relevance according to user preferences. An empirical study with search tasks revealed participants preferred having all techniques active instead of none or only some of them. This suggests there is value in combining visualization techniques to address mobile AR challenges, and, possibly, that there is still space to integrate more techniques without overloading the users.

Augmented and Virtual Reality techniques are becoming more widespread and Medicine is one of the fields which can benefit from their use. In this paper we describe a smartphone application that applies these two techniques to provide visualizations of a patient's brain. This application was designed with the goal of guiding a physician in a medical procedure called Transcranial Magnetic Stimulation (TMS), however it can be adapted and extended to other uses. The application was submitted to user tests involving two experts in TMS and researchers in Biomedical Engineering, the results were positive and provided useful feedback that will be considered when further developing the application.

This paper presents a Serious Game with the main purpose of inducing attitude changes as a way to prevent bullying. The target audience are young people between 10 and 12 years old. The rationale for prevention is: first, to help victims of these aggressive episodes to improve behavioral competence to avoid or to deal with future real bullying situations; and, second, to promote in the bystanders empathy towards the victims.

We present an AR application for visualizing solar radiation data on facades of buildings, generated from LiDAR data and climatic observations. Data can be visualized using colored surfaces and glyphs. A user study revealed the proposed AR visualizations were easy to use, which can lead to leverage the potential benefits of AR visualizations: to detect errors in the simulated data, to give support to the installation of photovoltaic equipment and to raise public awareness of the use of facades for power production.

The evolution of mobile devices, especially the integration of sensors, is fostering the development of context aware visualization applications. The adaptation to usage contexts is crucial to overcome the diverse limitations that exist in a mobile device, namely the screen size. However, most of the currently develop applications focus on specific contexts. In this paper we propose an adaptive visualization framework that has the goal of enabling the development of applications that use a diverse set of contexts and adaptation methods.

In this paper we present an interactive point of interest query interface, based on the location and orientation of the user. This interface gives clues about the position, relative to the user, of all the points of interest in the vicinity. The interaction provided can also be used to complement the presentation of points of interest on a map, helping identify the association between the icons drawn on a map, and the corresponding real world objects in the neighbourhood of the user.

The increasing popularity of mobile devices has fostered the development of visualization applications for these devices. However, the reduced screen size and different interaction devices, which people are not familiarized, present some challenges to visualization in a mobile environment. This paper describes how, as a proof of concept, a combination of two different techniques can reduce the over cluttering of icons on a mobile device screen. An evaluation of these techniques is also presented.

We are developing a prototype for the visualization of geo-referenced information. The data is organized in several topics. The user interactively selects the geographical region and the topics he/she is interest on. The main features of this prototype are: filtering mechanisms to control the amount of data displayed; representations with different levels of detail selected according to the scale of representation. In order to include semantic criteria to reduce the amount of data to display, we use a degree of interest function.

We describe a platform to build virtual environments inhabited by autonomous virtual humans. We propose an architecture comprising several software components assembled to define a platform. The architecture includes a 3D-modelling software, a rendering engine, a library for simulating rigid body dynamics and an agent development framework.

IVPrototype is an information visualization prototype for the visualization of both geometrical and non-geometrical data. Our goal is to provide a generic system that automatically generates data visualization and dynamically adapts the interface according to the data in order to control the amount of information displayed. The information is organized in one or several information groups, which aggregate semantically related information. Icons and coloured surfaces are the visual mappings provided. Other features of this prototype are: a data independent architecture; several filtering mechanisms; use of degree of interest functions; automatic selection of icons based on scale factor and semantic criteria.

The visualization of large volumes of information requires mechanisms to reduce the amount of information to be displayed. In this paper, we propose a technique to combine the usage of filters (like the degree-of-interest function) with different, scale-dependent representations. This eliminates the need to create new representations or to use graphical attributes to show the degree of interest of each object. This approach is specially useful when the number of variables is large or the information density is too high. The method can be complemented with zoom mechanisms and distortion of the position of the objects in space, taking advantage of the reduction of the area occupied by those objects with less interest to increase the area available for those with a higher degree of interest, normally located near the focus.