Assembly is underutilized as a beginner programming language. Its simple and repetitive syntax allows students to focus on logic and problem-solving. While the low level of abstraction causes Assembly to be challenging to work with, using Assembly gives students better insights into how computers operate and more fundamental skills for procedural programming. This paper introduces Assembly Academy, a programming puzzle game utilizing a virtual robot. We include a use case demonstrating how the game provides feedback to students to keep them engaged and motivated while learning Assembly.

We present GridSet, a novel set visualization for exploring elements, their attributes, intersections, as well as entire sets. In this set visualization, each set representation is composed of glyphs, which represent individual elements and their attributes utilizing different visual encodings. In each set, elements are organized within a grid treemap layout that can provide space-efficient overviews of the elements structured by set intersections across multiple sets. These intersecting elements can be connected among sets through visual links. These visual representations for the individual set, elements, and intersection in GridSet facilitate novel interaction approaches for undertaking analysis tasks by utilizing both macroscopic views of sets, as well as microscopic views of elements and attribute details. In order to perform multiple set operations, GridSet supports a simple and straightforward process for set operations through dragging and dropping set objects. Our use cases involving two large set-typed datasets demonstrate that GridSet facilitates the exploration and identification of meaningful patterns and distributions of elements with respect to attributes and set intersections for solving complex analysis problems in set-typed data.

Due to the scale of data and the complexity of analysis tasks, insight discovery often requires coordinating multiple visualizations (views), with each view displaying different parts of data or the same data from different perspectives. For example, to analyze car sales records, a marketing analyst uses a line chart to visualize the trend of car sales, a scatterplot to inspect the price and horsepower of different cars, and a matrix to compare the transaction amounts in types of deals. To explore related information across multiple views, current visual analysis tools heavily rely on brushing and linking techniques, which may require a significant amount of user effort (e.g., many trial-and-error attempts). There may be other efficient and effective ways of displaying cross-view data relationships to support data analysis with multiple views, but currently there are no guidelines to address this design challenge. In this article, we present systematic design considerations for visualizing cross-view data relationships, which leverages descriptive aspects of relationships and usable visual context of multi-view visualizations. We discuss pros and cons of different designs for showing cross-view data relationships, and provide a set of recommendations for helping practitioners make design decisions.

The detection and prediction of natural catastrophes or man-made disasters before they occur has recently shone the light on several relatively new technologies. Due to the significant development of mobile hardware and software technologies, a smartphone has become an important device for detecting and warning about such disasters. Specifically, disaster-related data can be collected from diverse sources including smartphones' sensors and social networks, and then the collected data are further analyzed to detect disasters and alert people about them. These collective data enable a user to have access to a variety of essential information related to disaster events. Using the example of a communicable disease outbreak, such information helps to identify and detect the ground zero of a disaster, as well as make sense of the means of transmission, progress, and patterns of the disaster. In this paper, we discuss a novel approach for analyzing and interacting with collective sensor data in a visual, real-time, and scalable fashion, offering diverse perspectives and data management components.

Large high-resolution displays (LHRD) enable visualization of extremely large-scale data sets with high resolution, large physical size, scalable rendering performance, advanced interaction methods, and collaboration. Despite the advantages, applications for LHRD can be developed only by a select group of researchers and programmers, since its software implementation requires design and development paradigms different from typical desktop environments. It is critical for developers to understand and take advantage of appropriate software tools and methods for developing their LHRD applications. In this paper, we present a survey of the state-of-the-art software frameworks and applications for cluster-based LHRD, highlighting a three-aspect taxonomy. This survey can aid LHRD application and framework developers in choosing more suitable development techniques and software environments for new LHRD applications, and guide LHRD researchers to open needs in LHRD software frameworks.

Technological devices are being rapidly adopted into schools for education, but we have limited understanding of the value and ways through which the devices can benefit learning. As opposed to research placing the value of these devices in terms of digitality, we make use of theories of embodiment to under-stand how the physicality of the devices can support learning and sensemaking. We conducted a month-long study to collect data on students' strategies, patterns, attitudes and behaviors toward the use of a suite of devices, for the completion of a course assignment. Themes uncovered include the objectification of information, the immediate awareness of possibilities, an expectation of interaction, coherence of interaction and territorialization of technology spaces. We present a model for the role of physicality of devices with regards to educational activities, and argue for the need to construct a digital ecology to provide a cohesive experience of learning.

We discuss how to automatically obtain the metric calibration of an ad hoc network of cameras with no centralized processor. We model the set of uncalibrated cameras as nodes in a communication network, and propose a distributed algorithm in which each camera performs a local, robust bundle adjustment over the camera parameters and scene points of its neighbors in an overlay “vision graph.” We analyze the performance of the algorithm on both simulated and real data, and show that the distributed algorithm results in a fairer allocation of messages per node while achieving comparable calibration accuracy to centralized bundle adjustment.