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This paper sheds light on the role of visualization methods within the design thinking process. It provides a conceptual framework showing illustrative examples of visualization for each phase indicating its functions and benefits. Based on a thorough understanding of the functions and benefits of knowledge visualization in general, this paper seeks to provide a first overview of the use of visualization in design thinking informing and supporting practitioners and researcher for more conscious selections of visualization methods in their design thinking efforts.

International audience; In this digital age of the computer, Internet, and social media and Internet of Things, e-assessments have become an accepted method to determine if students have learned materials presented in a course. With acceptance of this electronic means of assessing students, many questions arise about this method. What should be the format of e-assessment? What amount of time? What kinds of questions should be asked (multiple choice, short answer, etc.)? These are only a few of the many different questions. In addition, educators have always had to contend with the possibility that some students might cheat on an examination. It is widely known that students are often times more technologically savvy than their professors. So how does one prevent students from cheating on an e-assessment? Understandably, given the amount of information available on e-assessments and the variety of formats to choose from, choosing to administer e-assessments over paper-based assessments can lead to confusion on the part of the professor. This paper presents helpful guidance for lecturers who want to introduce e-assessments in their class, and it provides recommendations about the technical infrastructure to implement to avoid students cheating. It is based on literature review, on an international survey that gathers insights and experiences from lecturers who are using e-assessment in their class, and on technological evaluation of e-assessment infrastructure.

Despite efforts to reduce them, traffic accidents continue to increase and bypass reduction targets. The costs of traffic accidents are enormous, killing 1.35 million people every year and costing 3% of most countries’ GDP. Recent research aims to target interventions at high-accident-density locations, called accident hotspots. New methods and technologies can systematically identify hotspots, but it remains unclear whether hotspots contribute to accident costs as well as volume. This paper investigates the monetary and human costs of accident hotspots. We analyze a dataset of all accidents from 2011 - 2017 in Switzerland. We identify hotspots, then analyze their contributions to traffic accident costs. We find that hotspot accidents are not different in monetary costliness or injury rates from non-hotspot accidents, so hotspots drive costs along with accident volume. However, hotspot accidents are less fatal, so hotspot targeting might not be best for fatalities. If hotspots are reduced to normal road conditions, total monetary costs can be reduced by up to 5% per year as a theoretical upper bound. Targeting the top 10% most frequent, costly, injurious, or deadly hotspots yeilds different results for different cost types, with accident number and monetary cost targets creating the highest reductions overall.

Building semantic segmentation is a crucial task for building information modeling (BIM). Current research generally exploits terrestrial image data, which provides only limited view of a building. By contrast, oblique imagery acquired by unmanned aerial vehicle (UAV) can provide richer information of both the building and its surroundings at a larger scale. In this paper, we present a novel pipeline for building semantic segmentation from oblique UAV images using a fully convolutional neural network (FCN). To cope with the lack of UAV image annotations at facade level, we leverage existing ground-view facades databases to simulate various aerial-view images based on estimated homography, yielding abundant synthetic aerial image annotations as training data. The FCN is trained end-to-end and tested on full-tile UAV images. Experiments demonstrate that the incorporation of simulated views can significantly boost the prediction accuracy of the network on UAV images and achieve reasonable segmentation performance.

The phenomenon of crowdsourcing has emerged as a new pattern of digitally mediated collaboration. This novel socio-technical arrangement changes the organization of work as well as its general nature and takes place in information systems (IS) in which humans face many threats to their dignity. For this reason, the importance of ethical issues within this new form of employment arises. Hence, in this paper we focus on the ethical issues in crowd work – a perspective that has been largely neglected by current crowdsourcing research. We analyze recent crowdsourcing literature and extract ethical issues by following the PAPA (privacy, accuracy, property and accessibility of information) concept, a well-established approach in IS. The review focuses on the individual perspective of crowdworkers, which addresses their working conditions and benefits. Although, the literature review exhibits that there are PAPA dimensions in crowdsourcing, only few focus on the crowdworkers as individuals. Our findings contribute to further research in crowdsourcing by introducing an ethical framework and give practical insight into how to design sustainable and ethical crowd work.

Retro-directive antenna systems are of special interest in satellite communications due to their self-tracking capabilities. Since frequency bands are regulated, a key issue is the realization of a large frequency gap between transmitted and received signal. In this paper, we present simulations of a novel phase-locked loop (PLL) based retro-directive antenna system for C-band which can realize arbitrary frequency translation between received and transmitted signal. It is also able to work with different array element spacings and can correct beam pointing errors.We show bistatic radiation patterns which are the response to plane waves impinging from different directions on a system with four element arrays. A frequency translation from 5.8 to 7.0 GHz is shown for different array element spacing. We demonstrate the suitability of the proposed system architecture for retro-directive applications. It can realize large frequency gaps while keeping system complexity on a reasonable level.

In this paper we present our work on enabling Balloon launched high altitude UAV Missions for an autopilot system previously used only at lower levels in visual line of sight conditions. One field of our research in the context of flying robotics is focused on high altitude pseudo satellites (HAPS). To gain operational experience in high altitude flying and for system and payload testing, a balloon launched small UAV (sub 10kg) system was designed including building an optimized airframe. Balloon launching was chosen because it offers fast and clearly regulated access to the desired altitudes. Our autopilot system has proven its capabilities in many years of flight experiments with different platforms (helicopter and fixed wing). The main characteristics are modularity and easy use for scientists. On the hardware level the task was to integrate the existing segmented systems of the research autopilot in a compact form factor, with the possible use in larger platforms in mind. The design was driven by the special thermal requirements resulting from flying in stratospheric conditions. In the autopilot software, several mission specific functions had to be added, which only required moderate effort due to the modular system design. Major changes included adding a flight termination manager. A launch routine was developed allowing a safe transition from free-fall to stable horizontal flight in thin air after being dropped from the balloon. Extensive testing was performed to validate the design. Simulating the mission, including balloon ascend, was used to check the mission software. Thermal and pressure conditions at altitude were replicated in a thermal vacuum chamber with additional sensors applied to identify problems. The simulation and control laws were verified by means of low altitude test flights.

Very high resolution satellite imagery used to be a rare commodity, with infrequent satellite pass-over times over a specific area-of-interest obviating many useful applications. Today, more and more such satellite systems are available, with visual analysis and interpretation of imagery still important to derive relevant features and changes from satellite data. In order to allow efficient, robust and routine image analysis for humanitarian purposes, semi-automated feature extraction is of increasing importance for operational emergency mapping tasks. In the frame of the European Earth Observation program COPERNICUS and related research activities under the European Union's Seventh Framework Program, substantial scientific developments and mapping services are dedicated to satellite based humanitarian mapping and monitoring. In this paper, recent results in methodological research and development of routine services in satellite mapping for humanitarian situational awareness are reviewed and discussed. Ethical aspects of sensitivity and security of humanitarian mapping are deliberated. Furthermore methods for monitoring and analysis of refugee/internally displaced persons camps in humanitarian settings are assessed. Advantages and limitations of object-based image analysis, sample supervised segmentation and feature extraction are presented and discussed.