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Software development is a complex task. Merely focussing on functional requirements is not sufficient any more. Developers are responsible to take many non-functional requirements carefully into account. Security is amongst the most challenging, as getting it wrong will result in a large user-base being potentially at risk. A similar situation exists for administrators. Security defaults have been put into place here to encounter lacking security controls. As first attempts to establish security by default in software development are flourishing, the question on their usability for developers arises.
In this paper we study the effectiveness and efficiency of Content Security Policy (CSP) enforced as security default in a web framework. When deployed correctly, CSP is a valid protection mean in a defence-in-depth strategy against code injection attacks. In this paper we present a first qualitative laboratory study with 30 participants to discover how developers deal with CSP when deployed as security default. Our results emphasize that the deployment as security default has its benefits but requires careful consideration of a comprehensive information flow in order to improve and not weaken security. We provide first insights to inform research about aiding developers in the creation of secure web applications with usable security by default.
Dementia not only affects the cognitive capabilities, especially memory and orientation, but also physical capabilities, which are associated with a decrease of physical activities. Here, ICT can play a major role to improve health, quality of life and wellbeing in older adults suffering from dementia and related stakeholders, such as relatives, professional and informal caregivers. The aim of the presented system is to increase physical and cognitive capabilities of people with dementia and their caregivers to support them in daily life activities, reduce the strain of the caregivers and improve both their wellbeing.
Scientific or statistical research has long been the domain of dedicated programming languages such as R, SPSS or SAS. A few years other competitors entered the arena, among them Python with its powerful SciPy package. The following article introduces SciPy by applying a small subset of its functionality to a well-known dataset.
The formulation of transport network problems is represented as a translation between two domain specific languages: from a network description language, used by network simulation community, to a problem description language, understood by generic non-linear solvers. A universal algorithm for this translation is developed, an estimation of its computational complexity given, and an efficient application of the algorithm demonstrated on a number of realistic examples. Typically, for a large gas transport network with about 10K elements the translation and solution of non-linear system together require less than 1 sec on the common hardware. The translation procedure incorporates several preprocessing filters, in particular, topological cleaning filters, which accelerate the solution procedure by factor 8.
Large, high-resolution displays are highly suitable for creation of digital environments for co-located collaborative task solving. Yet, placing multiple users in a shared environment may increase the risk of interferences, thus causing mental discomfort and decreasing efficiency of the team. To mitigate interferences coordination strategies and techniques were introduced. However, in a mixed-focus collaboration scenarios users switch now and again between loosely and tightly collaboration, therefore different coordination techniques might be required depending on the current collaboration state of team members. For that, systems have to be able to recognize collaboration states as well as transitions between them to ensure a proper adjustment of the coordination strategy. Previous studies on group behavior during collaboration in front of large displays investigated solely collaborative coupling states, not transitions between them though. To address this gap, we conducted a study with 12 participant dyads in front of a tiled display and let them solve two tasks in two different conditions (focus and overview). We looked into group dynamics and categorized transitions by means of changes in proximity, verbal communication, visual attention, visual interface, and gestures. The findings can be valuable for user interface design and development of group behavior models.
Towards explaining deep learning networks to distinguish facial expressions of pain and emotions
(2018)
Deep learning networks are successfully used for object and face recognition in images and videos. In order to be able to apply such networks in practice, for example in hospitals as a pain recognition tool, the current procedures are only suitable to a limited extent. The advantage of deep learning methods is that they can learn complex non-linear relationships between raw data and target classes without limiting themselves to a set of hand-crafted features provided by humans. However, the disadvantage is that due to the complexity of these networks, it is not possible to interpret the knowledge that is stored inside the network. It is a black-box learning procedure. Explainable Artificial Intelligence (AI) approaches mitigate this problem by extracting explanations for decisions and representing them in a human-interpretable form. The aim of this paper is to investigate the explainable AI method Layer-wise Relevance Propagation (LRP) and apply it to explain how a deep learning network distinguishes facial expressions of pain from facial expressions of emotions such as happiness and disgust.
Digitisation has brought a major upheaval to the mobility sector, and in the future, self-driving cars will probably be one of the transport modes. This study extends transport and user acceptance research by analysing in greater depth how the new modes of autonomous private cars, autonomous carsharing and autonomous taxis fit into the existing traffic mix from today's perspective. It focuses on accounting for relative added value. For this purpose, user preference theory was used as a base for an online survey (n=172) on the relative added value of the new autonomous traffic modes. Results show that users see advantages in the autonomous modes for driving comfort and time utilization whereas, in comparison to conventional cars, in many other areas – especially in terms of driving pleasure and control – they see no advantages or even relative disadvantages. Compared to public transport, the autonomous modes offer added values in almost all characteristics. This analysis at the partwor th level provides a more detailed explanation for user acceptance of automated driving.
We present a novel forearm-and-glove tactile interface that can enhance 3D interaction by guiding hand motor planning and coordination. In particular, we aim to improve hand motion and pose actions related to selection and manipulation tasks. Through our user studies, we illustrate how tactile patterns can guide the user, by triggering hand pose and motion changes, for example to grasp (select) and manipulate (move) an object. We discuss the potential and limitations of the interface, and outline future work.
Shared Autonomous Vehicles: Potentials for a Sustainable Mobility and Risks of Unintended Effects
(2018)
Automated and connected cars could significantly reduce congestion and emissions through a more efficient flow of traffic and a reduction in the number of vehicles. An increase in demand for driving with autonomous vehicles is also conceivable due to higher comfort and improved quality of time using driverless cars. So far, empirical evidence supporting this hypothesis is missing. To analyze the influence of autonomous driving on mobility behavior and to uncover user preferences, which serve as an indicator for future travel mode choices, we conducted an online survey with a paired comparison of current and future travel modes with 302 German participants. The results do not confirm the hypothesis that ownership will become an outdated model in the future. Instead they suggest that private cars, whether traditional or fully automated, will remain the preferred travel mode. At the same time, carsharing will benefit from full automation more than private cars. However, findings indicate that the growth of carsharing will mainly be at the expense of public transport, showing that more effort should be placed in making public transportation more attractive if sustainable mobility is to be developed.
Influence of design of extrusion blow molding (EBM) in terms of extrusion direction set-up and draw ratio as well as process conditions (mold temperature) on storage modulus of high density polyethylene EBM containers was analyzed with dynamic mechanical analysis. All three parameters - mold temperature, flow direction and draw ratio - are statistically significant and lead to relative and absolute evaluation of storage modulus. Furthermore, flow induced changes in crystallinity was analyzed by differential scanning calorimetry. Obtained data on deformation properties can be employed for more sophisticated finite element simulations with the aim to reach more sustainable extrusion blow molding production.
Quantifying the spectrum occupancy in an outdoor 5 GHz WiFi network with directional antennas
(2018)
WiFi-based Long Distance networks are seen as a promising alternative for bringing broadband connectivity to rural areas. A key factor for the profitability of these networks is using license free bands. This work quantifies the current spectrum occupancy in our testbed, which covers rural and urban areas alike. The data mining is conducted on the same WiFi card and in parallel with an operational network. The presented evaluations reveal tendencies for various aspects: occupancy compared to population density, occupancy fluctuations, (joint)-vacant channels, the mean channel vacant duration, different approaches to model/forecast occupancy, and correlations among related interfaces.
Text is one of the key sources of information for social sciences and humanities which, with the rise and development of computational technologies, has been mostly available via digital libraries, archives and websites. It enables researchers to increasingly deal with large scale text corpora that require the use of advanced software tools to process them and extract information. Computational linguistics - a discipline that has emerged on the border of computer science, linguistics and statistics - has achieved certain results in automated text analysis and information extraction, e.g., tools for part-of-speech tagging, grammar parsing, semantic role labelling, sentiment analysis and anaphora resolution have been developed and successfully used in many scientific projects. However, there still exists a gap between technology available and the needs of social sciences: named entity recognizers are incapable of identifying actors, sentiment analysis just provides the overall mood of an expression but is not able to identify the evaluation of information by the utterer, topic modeling tools can only assign a topic to a document, but fall short of measuring its frame.
Persons with disabilities have much lower employment rates than the population as a whole and are at a significantly higher risk of living in poverty (OECD, 2011, pp. 50-56 and WHO, 2011, pp. 237-239). However, many of the barriers people with disabilities face, with regards to labor market reintegration, are in fact avoidable. There has for quite some time been evidence that differences in employment and wages, between disabled and non-disabled workers, can only to a limited extent be explained by differences in human capital endowments and productivity (Kidd, Sloane, & Ferko, 2000). Instead, factors such as the absence of access to education and training, and the lack of financial assistance provided are actually significant drivers of labor market exclusion (OECD, 2009, p.15; WHO, 2011, p.239).
Robot deployment in realistic environments is challenging despite the fact that robots can be quite skilled at a large number of isolated tasks. One reason for this is that robots are rarely equipped with powerful introspection capabilities, which means that they cannot always deal with failures in an acceptable manner; in addition, manual diagnosis is often a tedious task that requires technicians to have a considerable set of robotics skills. In this paper, we discuss our ongoing efforts to address some of these problems. In particular, we (i) present our early efforts at developing a robotic black box and consider some factors that complicate its design, (ii) explain our component and system monitoring concept, and (iii) describe the necessity for remote monitoring and experimentation as well as our initial attempts at performing those. Our preliminary work opens a range of promising directions for making robots more usable and reliable in practice.