The Twitter has become a powerful medium for people to express their feelings and reactions towards some recent event or happening event. Analyzing and exploring such tweet data gives us in-depth understanding of the underlying event from people perspectives. In this work, we use the power of visualization to explore people’s tweets regarding the COVID-19 pandemic from the time period between April 2020 and March 2021. We use a number of visualizations targeting to show the sentimental polarity of people’s tweets over time and important keywords in people’s tweets and their evolution over time. The resulting visualizations give us an opportunity to explore people’s feelings and reactions during a whole pandemic year and to see how it evolved over time.

Most visual analytics systems assume that all foraging for data happens before the analytics process; once analysis begins, the set of data attributes considered is fixed. Such separation of data construction from analysis precludes iteration that can enable foraging informed by the needs that arise in-situ during the analysis. The separation of the foraging loop from the data analysis tasks can limit the pace and scope of analysis. In this paper, we present CAVA, a system that integrates data curation and data augmentation with the traditional data exploration and analysis tasks, enabling information foraging in-situ during analysis. Identifying attributes to add to the dataset is difficult because it requires human knowledge to determine which available attributes will be helpful for the ensuing analytical tasks. CAVA crawls knowledge graphs to provide users with a a broad set of attributes drawn from external data to choose from. Users can then specify complex operations on knowledge graphs to construct additional attributes. CAVA shows how visual analytics can help users forage for attributes by letting users visually explore the set of available data, and by serving as an interface for query construction. It also provides visualizations of the knowledge graph itself to help users understand complex joins such as multi-hop aggregations. We assess the ability of our system to enable users to perform complex data combinations without programming in a user study over two datasets. We then demonstrate the generalizability of CAVA through two additional usage scenarios. The results of the evaluation confirm that CAVA is effective in helping the user perform data foraging that leads to improved analysis outcomes, and offer evidence in support of integrating data augmentation as a part of the visual analytics pipeline.

In general, humanoid robots mostly use fixed-devices (e.g., camera or sensors) to detect human non-verbal communication, which have limitations in many real-life scenarios. Wearable devices could play an important role in many real-life scenarios. To address this, we propose using Myo armband for human-robot interaction using hand- and arm-based gestures. We present our end-to-end Spagti framework that is used first to train the user gestures using Myo armband and then to interact with a humanoid robot, called ROBIN, in real-time using space-free gestures.

Software engineering is vital for IoT systems to design systems that are secure, interoperable, modifiable, and scalable. However, industry and academia are still working on many crucial questions related to software engineering for IoT systems, for example, regarding the best practices for developing IoT systems, how to select the hardware, communication, and software architectures of IoT systems, which communications protocols are the most suitable for a system, and how to guarantee security and privacy when dealing with consumer products often composing IoT systems.

Abstract Many visual analytics systems allow users to interact with machine learning models towards the goals of data exploration and insight generation on a given dataset. However, in some situations, insights may be less important than the production of an accurate predictive model for future use. In that case, users are more interested in generating of diverse and robust predictive models, verifying their performance on holdout data, and selecting the most suitable model for their usage scenario. In this paper, we consider the concept of Exploratory Model Analysis (EMA), which is defined as the process of discovering and selecting relevant models that can be used to make predictions on a data source. We delineate the differences between EMA and the well‐known term exploratory data analysis in terms of the desired outcome of the analytic process: insights into the data or a set of deployable models. The contributions of this work are a visual analytics system workflow for EMA, a user study, and two use cases validating the effectiveness of the workflow. We found that our system workflow enabled users to generate complex models, to assess them for various qualities, and to select the most relevant model for their task.

One of the main concerns during usage of the current smart mobile devices in public is the vulnerability of password hacking by shoulder-suffering or smudge attack. The traditional user authentication techniques such as PIN code or patterns-based password are easy target of such attacks. In this paper, we propose using simple shapes (e.g., circle, triangle, etc.) to get users' touch behavioral biometrics data. The users are asked to draw over these shapes while the developed system extracts 25 different features (e.g., finger middle stroke and its pressure, velocity, mobile orientation, etc.) for the model training and authentication purpose. The proposed solution is simple for all kinds of users and could solve the problem of password hacking through shoulder-suffering or smudge attacks.

In this paper, we propose an extension to the Chord diagram to show the relations between categorical values in two dimensions. We divide the Chord diagram into two equal slices, where the nodes in each slice represent the categorical variables in one dimension. The chords or relations go from nodes of one slice to the nodes of other slice to show the values of these categorical variables with respect to the categorical values in the other dimension. The proposed solution takes less space compared to using multiple charts for such scenarios.

Analysis and understanding of large software systems requires exploring not only the software structure but also associated software metrics over the development releases. Information visualization helps in this regard greatly through interactive visualizations in comparison to exploring these through the source code or traditional software diagrams like UML diagrams. In this paper, we present our developed visualization tool, called HiMVis, that visualizes software hierarchies and metrics through multi-views visualizations on the same screen. HiMVis visualizes packages and class hierarchies through two space-filling interactive layouts. Further, it shows on demand through multiple views more than fifty software metrics information associated to a particular class or interface over all development releases. We provide a number of interaction and filtering options in the tool to make the exploration of the underlying software system more intuitive. We also conducted a brief user study with 10 participants to determine the usability of the developed tool.

Visualizing large software system structure in compact representations would help software architects and analysts in understanding the overall software structure accurately and efficiently. Space-filling techniques (e.g., Sunburst or Tree-map) are nowadays used for producing compact representations of large hierarchical data. In this paper, we use the Sunburst layout with some enhancements to show the overall software system structure and the inside details in a compact visual form, in order to make it more readable for software architects and analysts. We also report some initial findings of the preliminary conducted evaluation study.

Due to the usage of social media platforms for different purposes in our current digital social life, people are interested to explore and understand their personal networks (called the ego networks) and how their contacts are associated with different social circles on these social media platforms. In this work, we propose an enhanced multi-layered Chord layout to show people' ego networks and the associated social circles in interactive visual forms. This proposed solution enables the users to explore intuitively how their contacts are associated to different social circles through a set of provided interaction and filtering options.

In this paper, we present an algorithm, called CluE (The Cluster Expander of Compound Graphs), that expands cluster nodes in compound graphs. CluE was designed to work with multiple layout algorithms (e.g., orthogonal layout algorithm, Sugyima layout algorithm, etc.). It keeps the unexpanded nodes in their relative layers with slight changes in their original coordination. This helps the users in maintaining their mental map of the underlying graph. We applied the proposed algorithm for navigating through compound graphs representing the failure mechanisms in embedded systems. We also performed a brief user evaluation study in order to measure the users' acceptance level with regard to the application of CluE for compound graphs. Results show a high acceptance ratio from users having different backgrounds, which indicates the intuitiveness and feasibility of our approach.

The current mobile paradigm is in many ways fundamentally different from the conventional desktop paradigm due to many factors such as multi-touch gesture interaction, usage of sensors, single-task focused model, etc. These factors mean several new challenges for the interaction designers with regard to communicating their ideas and thoughts during early design activities, which they might be unable to tackle properly using traditional prototyping techniques. Therefore, we envision that research must address the need for a change in existing prototyping techniques as well as focus on novel prototyping approaches and frameworks that would support not only the interaction design process but also the whole development process of mobile app development. In the footsteps of the first workshop, PID-MAD 2014 provides a platform to the interested communities for discussing issues and will bring together researchers and practitioners for sharing their knowledge and experience in order to tackle the upcoming challenges.

In this work, we present a new technique, called ExpanD, for expanding compound nodes in stereoscopic platforms. In this technique, stereoscopic depth is used to encode the structural relationships in compound graphs using the containment representation, while explicit edges are used to represent the adjacency relations between the graph nodes. Further, stereoscopic depth is used to show the parent-child relations in such a manner that the children level is rendered in a plane closer to the viewer than the parent node. The technique provides a novel interactive operation for expanding or contracting nodes in order to align graph nodes in the 3D space with minimum occlusion. Different visual cues are used together to encode other data aspects, e.g., color to encode node status, shape to encode node type, and stereoscopic depth to encode hierarchical relations between nodes.

Recent changes in the mobile environment; such as the addition of multi-touch gestures, usage of sensors, or single-focused mobile apps; brought several challenges for interaction designers in communicating their ideas and thoughts enduring early design activities. Traditional prototyping techniques may not provide sufficient support due to the lack of current mobile interaction paradigms in them. Therefore, a shift is required in prototyping techniques and approaches in order to support properly the interaction design process of mobile application development for the current mobile environment.

Providing interactive mockups of mobile applications in designing phases introduces new challenges for interaction designers compared to the traditional way of static mockups. In particular, the complexity of this process increases when it comes to enabling the user to actually explore the intended user experience of the mobile environment by enhancing traditional handmade sketches or tool-generated wireframes with concrete mobile interaction elements. We introduce a framework, called i2ME (interactive Mockup-Building for Mobile Environment), for building interactive mockups with concrete mobile interaction elements. The framework enhances the static mockups (handmade or tool-generated) with screen transitions and multi-touch gestures, and enables the deployment of the resulting HTML5+JavaScript based interactive mockups to multiple platforms and device classes.

Evaluating interface usability and system functionality is time-consuming and effort-intense. The short time-span involved in development iterations, such as those in agile development, makes it challenging for software teams to perform ongoing interface usability evaluation and system functionality testing. We propose a way to perform product ongoing evaluation, thus enabling software teams to perform interface usability evaluation alongside automated system functionality testing. We use formal task models, created in our defined TaMoGolog task modeling language, to conduct product evaluation experiments through TaMUlator. TaMUlator is a tool we developed for use at the Integrated Development Environment (IDE) level. Our case study revealed that software teams can easily engage in system evaluation by using TaMUlator on an iterative basis.

One of the challenges in software development is to collect and analyze the end users' feedback in an effective and efficient manner. In this paper we present a tool to manage user evaluation alongside the process of software development. The tool is based on the idea that user evaluation should be managed iteratively from within the integrated development environment (IDE) in order to provide high quality user interface. The main capabilities include creating the experiment object as part of the software project; deriving development tasks from the analysis of evaluation data; and tracing these tasks to and from the code. Further, we provide a library to enable development of Java aspects for creation of automatic measures to increase the body of the evaluation data. Using this tool, development teams can manage user-centered design (UCD) activities at the IDE level, hence developing software products with an adequate level of usability.