Evaluation of Mobile Phones for Large Display Interaction

Large displays have become more and more common in the last few years. While interaction with these displays can be conducted using standard methods such as computer mouse and keyboard, this approach causes issues in multi-user environments, where the various conditions for providing multiple keyboards and mice, together with the facilities to employ them, cannot be met. To solve this problem, interaction using mobile phones was proposed by several authors. Previous solutions were specialized interaction metaphors only for certain applications. To gain more insight into general interaction patterns realizable with smart phones, we created a set of general test cases using a well-known taxonomy for interactions. These test cases were then evaluated in a user study, comparing smart phone usage against the traditional keyboard/mouse-combination. Results (time and user satisfaction) show strengths and weaknesses when using the new interaction with the smart phone. With further evaluations we draw conclusions on how to improve large display interaction using smart phones in general

A General Introduction To Graph Visualization Techniques

Generally, a graph is an abstract data type used to represent relations among a given set of data entities. Graphs are used in numerous applications within the field of information visualization, such as VLSI (circuit schematics), state-transition diagrams, and social networks. The size and complexity of graphs easily reach dimensions at which the task of exploring and navigating gets crucial. Moreover, additional requirements have to be met in order to provide proper visualizations. In this context, many techniques already have been introduced. This survey aims to provide an introduction on graph visualization techniques helping the reader to gain a first insight into the most fundamental techniques. Furthermore, a brief introduction about navigation and interaction tools is provided

Visualization and Evolution of Software Architectures

Software systems are an integral component of our everyday life as we find them in tools and embedded in equipment all around us. In order to ensure smooth, predictable, and accurate operation of these systems, it is crucial to produce and maintain systems that are highly reliable. A well-designed and well-maintained architecture goes a long way in achieving this goal. However, due to the intangible and often complex nature of software architecture, this task can be quite complicated. The field of software architecture visualization aims to ease this task by providing tools and techniques to examine the hierarchy, relationship, evolution, and quality of architecture components. In this paper, we present a discourse on the state of the art of software architecture visualization techniques. Further, we highlight the importance of developing solutions tailored to meet the needs and requirements of the stakeholders involved in the analysis process

Controlling In-Vehicle Systems with a Commercial EEG Headset: Performance and Cognitive Load

Humans have dreamed for centuries to control their surroundings solely by the power of their minds. These aspirations have been captured by multiple science fiction creations, such as the Neuromancer novel by William Gibson or the Brainstorm cinematic movie, to name just a few. Nowadays, these dreams are slowly becoming reality due to a variety of brain-computer interfaces (BCI) that detect neural activation patterns and support the control of devices by brain signals. An important field in which BCIs are being successfully integrated is the interaction with vehicular systems. In this paper, we evaluate the performance of BCIs, more specifically a commercial electroencephalographic (EEG) headset in combination with vehicle dashboard systems, and highlight the advantages and limitations of this approach. Further, we investigate the cognitive load that drivers experience when interacting with secondary in-vehicle devices via touch controls or a BCI headset. As in-vehicle systems are increasingly versatile and complex, it becomes vital to capture the level of distraction and errors that controlling these secondary systems might introduce to the primary driving process. Our results suggest that the control with the EEG headset introduces less distraction to the driver, probably as it allows the eyes of the driver to remain focused on the road. Still, the control of the vehicle dashboard by EEG is efficient only for a limited number of functions, after which increasing the number of in-vehicle controls amplifies the detection of false commands

Evaluation of the Driving Performance and User Acceptance of a Predictive Eco-Driving Assistance System for Electric Vehicles

In this work, a predictive eco-driving assistance system (pEDAS) with the goal to assist drivers in improving their driving style and thereby reducing the energy consumption in battery electric vehicles while enhancing the driving safety and comfort is introduced and evaluated. pEDAS in this work is equipped with two model predictive controllers (MPCs), namely reference-tracking MPC and car-following MPC, that use the information from onboard sensors, signal phase and timing (SPaT) messages from traffic light infrastructure, and geographical information of the driving route to compute an energy-optimal driving speed. An optimal speed suggestion and informative advice are indicated to the driver using a visual feedback. pEDAS provides continuous feedback and encourages the drivers to perform energy-efficient car-following while tracking a preceding vehicle, travel at safe speeds at turns and curved roads, drive at energy-optimal speed determined using dynamic programming in freeway scenarios, and travel with a green-wave optimal speed to cross the signalized intersections at a green phase whenever possible. Furthermore, to evaluate the efficacy of the proposed pEDAS, user studies were conducted with 41 participants on a dynamic driving simulator. The objective analysis revealed that the drivers achieved mean energy savings up to 10%, reduced the speed limit violations, and avoided unnecessary stops at signalized intersections by using pEDAS. Finally, the user acceptance of the proposed pEDAS was evaluated using the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB). The results showed an overall positive attitude of users and that the perceived usefulness and perceived behavioral control were found to be the significant factors in influencing the behavioral intention to use pEDAS.Comment: Submitted to Transportation Research Part C: Emerging Technologies Journa

О механизме перехода современного образования в профессиональную деятельность

Глубина и масштабы модернизации системы образования зависят от ответа на следующие вопросы: Каков механизм перехода от образовательной деятельности к практической? Всегда ли этот механизм был одинаков и какова его форма, адекватная вызовам наших дней? Рассматриваются основные институциональные способы, обеспечивающие адекватность содержания образования специфике практической реализации полученных компетенций

Implementing Visual Analytics Pipelines with Simulation Data

Visual analytics has been widely studied in the past decade both in academia and industry to improve data exploration, minimize the overall cost, and improve data analysis. In this chapter, we explore the idea of visual analytics in the context of simulation data. This would then provide us with the capability to not only explore our data visually but also to apply machine learning models in order to answer high-level questions with respect to scheduling, choosing optimal simulation parameters, finding correlations, etc. More specifically, we examine state-of-the-art tools to be able to perform these above-mentioned tasks. Further, to test and validate our methodology we followed the human-centered design process to build a prototype tool called ViDAS (Visual Data Analytics of Simulated Data). Our preliminary evaluation study illustrates the intuitiveness and ease-of-use of our approach with regards to visual analysis of simulated data

Live attenuated virus vaccine protects against SARS-CoV-2 variants of concern B.1.1.7 (Alpha) and B.1.351 (Beta).

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