How Evolutionary Visual Software Analytics Supports Knowledge Discovery

[EN] Evolutionary visual software analytics is a specialization of visual analytics. It is aimed at supporting software maintenance processes by aiding the understanding and comprehension of software evolution with the active participation of users. Therefore, it deals with the analysis of software projects that have been under development and maintenance for several years and which are usually formed by thousands of software artifacts,which are also associated to logs from communications, defect-tracking and software configuration management systems. Accordingly, evolutionary visual software analytics aims to assist software developers and software project managers by means of an integral approach that takes into account knowledge extraction techniques as well as visual representations that make use of interaction techniques and linked views. Consequently,this paper discusses the implementation of an architecture based on the evolutionary visual software analytics process and how it supports knowledge discovery during software maintenance tasks.[ES] Analítica de software visual evolutivos es una especialización de la analítica visual. Está dirigido a apoyar los procesos de mantenimiento de software, ayudando al entendimiento y la comprensión de la evolución del software, con la participación activa de los usuarios. Por lo tanto, tiene que ver con el análisis de los proyectos de software que han estado bajo desarrollo y mantenimiento por varios años y que por lo general están formados por miles de artefactos de software, que también están asociadas a los registros de las comunicaciones, seguimiento de defectos y sistemas de gestión de configuración de software. En consecuencia, la analítica de software visual evolutivos tiene como objetivo ayudar a los desarrolladores de software y administradores de proyectos de software a través de un enfoque integral que tenga en cuenta las técnicas de extracción de conocimiento, así como representaciones visuales que hacen uso de técnicas de interacción y vistas enlazadas. En consecuencia, en este documento se analiza la implementación de una arquitectura basada en el proceso de analítica de software visual evolutivos y la forma en que apoya el descubrimiento de conocimiento durante las tareas de mantenimiento de softwar

Personal Semantic Timeframe

Human memories are often not grouped around objective times and places but rather guided by subjective perception of these dimensions. Various techniques are used to recall personal information such as remembering names, conferences and numbers, but how different experiences or events or the event that has taken place two years earlier raises a question. Occasionally, having experienced an event, one may be asked about its absolute time in autobiographical memory. It is surprisingly difficult to predict the time when this date needs to be remembered. There is a tendency to use partial temporal information such as birthdays, parties or seasons to remember, rather than a specific date e.g. 21 September 1996. People need appropriate facts or personal semantics of their time to access to their past experiences while remembering. A user study was conducted to explore the use of past personal temporal information and capture this information to be used as personal time search features in an augmented memory system called Digital Parrot. These features aim to make temporal dates more easily accessible while remembering. A proposed design was made according to requirements that are derived from findings of psychology perspective, an exploration of the use of time study, and the visualizing time study. To evaluate how effective these features in locating and recalling past experiences, a user study was conducted with post questionnaires. The result of this study indicated that the most beneficial personal time search features are personal timespans, personal and public landmarks, and personal images. The findings from all studies of the thesis were used to provide recommendations for future work to develop and implement personal time search in Digital Parrot system

Towards automated infographic design: Deep learning-based auto-extraction of extensible timeline

Designers need to consider not only perceptual effectiveness but also visual styles when creating an infographic. This process can be difficult and time consuming for professional designers, not to mention non-expert users, leading to the demand for automated infographics design. As a first step, we focus on timeline infographics, which have been widely used for centuries. We contribute an end-to-end approach that automatically extracts an extensible timeline template from a bitmap image. Our approach adopts a deconstruction and reconstruction paradigm. At the deconstruction stage, we propose a multi-task deep neural network that simultaneously parses two kinds of information from a bitmap timeline: 1) the global information, i.e., the representation, scale, layout, and orientation of the timeline, and 2) the local information, i.e., the location, category, and pixels of each visual element on the timeline. At the reconstruction stage, we propose a pipeline with three techniques, i.e., Non-Maximum Merging, Redundancy Recover, and DL GrabCut, to extract an extensible template from the infographic, by utilizing the deconstruction results. To evaluate the effectiveness of our approach, we synthesize a timeline dataset (4296 images) and collect a real-world timeline dataset (393 images) from the Internet. We first report quantitative evaluation results of our approach over the two datasets. Then, we present examples of automatically extracted templates and timelines automatically generated based on these templates to qualitatively demonstrate the performance. The results confirm that our approach can effectively extract extensible templates from real-world timeline infographics.Comment: 10 pages, Automated Infographic Design, Deep Learning-based Approach, Timeline Infographics, Multi-task Mode

Envisioning the qualitative effects of robot manipulation actions using simulation-based projections

Autonomous robots that are to perform complex everyday tasks such as making pancakes have to understand how the effects of an action depend on the way the action is executed. Within Artificial Intelligence, classical planning reasons about whether actions are executable, but makes the assumption that the actions will succeed (with some probability). In this work, we have designed, implemented, and analyzed a framework that allows us to envision the physical effects of robot manipulation actions. We consider envisioning to be a qualitative reasoning method that reasons about actions and their effects based on simulation-based projections. Thereby it allows a robot to infer what could happen when it performs a task in a certain way. This is achieved by translating a qualitative physics problem into a parameterized simulation problem; performing a detailed physics-based simulation of a robot plan; logging the state evolution into appropriate data structures; and then translating these sub-symbolic data structures into interval-based first-order symbolic, qualitative representations, called timelines. The result of the envisioning is a set of detailed narratives represented by timelines which are then used to infer answers to qualitative reasoning problems. By envisioning the outcome of actions before committing to them, a robot is able to reason about physical phenomena and can therefore prevent itself from ending up in unwanted situations. Using this approach, robots can perform manipulation tasks more efficiently, robustly, and flexibly, and they can even successfully accomplish previously unknown variations of tasks

Exploring Software Engineering Subjects by Using Visual Learning Analytics Techniques

[EN]The application of the Information and Communication Technologies to teaching and learning processes is linked to the development of new tools and services that can help students and teachers. Learning platforms are a clear example of this. They are very popular tools in eLearning contexts and provide different kinds of learning applications and services. In addition, these environments also register most of the interactions between the learning process stakeholders and the system. This information could potentially be used to make decisions but usually it is stored as raw data, which is very difficult to understand. This work presents a system that employs visual learning analytic techniques to facilitate the exploitation of that information. The system presented includes several tools that make possible to explore issues such as: when interaction is carried out, which contents are the most important for users, how they interact with others, etc. The system was tested in the context of a software engineering subject, taking into account the stored logs of five academic years. From this analysis it is possible to see how visual analytics can help decision-making and in this context how it helps to improve educational processes

Quantified Self Analytics Tools for Self-regulated Learning with myPAL

One of the major challenges in higher education is developing self-regulation skills for lifelong learning. We address this challenge within the myPAL project, in medical education context, utilising the vast amount of student assessment and feedback data collected throughout the programme. The underlying principle of myPAL is Quantified Self -- the use of personal data to enable students to become lifelong learners. myPAL is facilitating this with learning analytics combined with interactive nudges. This paper reviews the state of the art in Quantified Self analytics tools to identify what approaches can be adopted in myPAL and what gaps require further research. The paper contributes to awareness and reflection in technology-enhanced learning by: (i) identifying requirements for intelligent personal adaptive learning systems that foster self-regulation (using myPAL as an example); (ii) analysing the state of the art in text analytics and visualisation related to Quantified Self for self-regulated learning; and (iii) identifying open issues and suggesting possible ways to address them

Assessing COVID-19 impact on user opinion towards videogames - Sentiment analysis and structural break detection on steam data

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceAs we live in a world where the videogame industry grows day by day and new media is constantly emerging, user feedback can be widely found online. User reviews are a highly valuable data source when studying player perception of a videogame. They are also apparently volatile to updates released by developers and other external events, which may change user opinion over time. Here we assess whether the COVID-19 pandemic outbreak fell in this category, having or not a noticeable impact on the player view and popularity of videogames. In this research, we build and implement a method to collect active player data and user reviews, identifying the sentiment contained in the expressed opinions. Furthermore, we investigate the existence of structural breaks in the time series we target. For this purpose, we targeted user-reviews and active player data collected of Steam’s twenty most popular Massive Multiplayer Online Role- Playing Games. To collect sentiment polarity values, two Natural Language Processing Python libraries were used, TextBlob and VADER, and structural break detection was put into practice using strucchange R package. The results of this work show us that despite having a great effect on the number of active players, the COVID-19 pandemic did not produce the same impact on Steam user reviews. Nonetheless, we were able to identify one of the platform’s major reviewing related updates as a structural break. We believe this approach can be used for further assessments on public opinion towards a specific product, in the future

A Pattern Approach to Examine the Design Space of Spatiotemporal Visualization

Pattern language has been widely used in the development of visualization systems. This dissertation applies a pattern language approach to explore the design space of spatiotemporal visualization. The study provides a framework for both designers and novices to communicate, develop, evaluate, and share spatiotemporal visualization design on an abstract level. The touchstone of the work is a pattern language consisting of fifteen design patterns and four categories. In order to validate the design patterns, the researcher created two visualization systems with this framework in mind. The first system displayed the daily routine of human beings via a polygon-based visualization. The second system showed the spatiotemporal patterns of co-occurring hashtags with a spiral map, sunburst diagram, and small multiples. The evaluation results demonstrated the effectiveness of the proposed design patterns to guide design thinking and create novel visualization practices