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SpringFit
(2019)
Joints are crucial to laser cutting as they allow making three-dimensional objects; mounts are crucial because they allow embedding technical components, such as motors. Unfortunately, mounts and joints tend to fail when trying to fabricate a model on a different laser cutter or from a different material. The reason for this lies in the way mounts and joints hold objects in place, which is by forcing them into slightly smaller openings. Such "press fit" mechanisms unfortunately are susceptible to the small changes in diameter that occur when switching to a machine that removes more or less material ("kerf"), as well as to changes in stiffness, as they occur when switching to a different material. We present a software tool called springFit that resolves this problem by replacing the problematic press fit-based mounts and joints with what we call cantilever-based mounts and joints. A cantilever spring is simply a long thin piece of material that pushes against the object to be held. Unlike press fits, cantilever springs are robust against variations in kerf and material; they can even handle very high variations, simply by using longer springs. SpringFit converts models in the form of 2D cutting plans by replacing all contained mounts, notch joints, finger joints, and t-joints. In our technical evaluation, we used springFit to convert 14 models downloaded from the web.
Leveraging spatio-temporal soccer data to define a graphical query language for game recordings
(2019)
For professional soccer clubs, performance and video analysis are an integral part of the preparation and post-processing of games. Coaches, scouts, and video analysts extract information about strengths and weaknesses of their team as well as opponents by manually analyzing video recordings of past games. Since video recordings are an unstructured data source, it is a complex and time-intensive task to find specific game situations and identify similar patterns. In this paper, we present a novel approach to detect patterns and situations (e.g., playmaking and ball passing of midfielders) based on trajectory data. The application uses the metaphor of a tactic board to offer a graphical query language. With this interactive tactic board, the user can model a game situation or mark a specific situation in the video recording for which all matching occurrences in various games are immediately displayed, and the user can directly jump to the corresponding game scene. Through the additional visualization of key performance indicators (e.g.,the physical load of the players), the user can get a better overall assessment of situations. With the capabilities to find specific game situations and complex patterns in video recordings, the interactive tactic board serves as a useful tool to improve the video analysis process of professional sports teams.
Rapid advances in location-acquisition technologies have led to large amounts of trajectory data. This data is the foundation for a broad spectrum of services driven and improved by trajectory data mining. However, for hybrid transactional and analytical workloads, the storing and processing of rapidly accumulated trajectory data is a non-trivial task. In this paper, we present a detailed survey about state-of-the-art trajectory data management systems. To determine the relevant aspects and requirements for such systems, we developed a trajectory data mining framework, which summarizes the different steps in the trajectory data mining process. Based on the derived requirements, we analyze different concepts to store, compress, index, and process spatio-temporal data. There are various trajectory management systems, which are optimized for scalability, data footprint reduction, elasticity, or query performance. To get a comprehensive overview, we describe and compare different exciting systems. Additionally, the observed similarities in the general structure of different systems are consolidated in a general blueprint of trajectory management systems.
The "Bachelor Project"
(2019)
One of the challenges of educating the next generation of computer scientists is to teach them to become team players, that are able to communicate and interact not only with different IT systems, but also with coworkers and customers with a non-it background. The “bachelor project” is a project based on team work and a close collaboration with selected industry partners. The authors hosted some of the teams since spring term 2014/15. In the paper at hand we explain and discuss this concept and evaluate its success based on students' evaluation and reports. Furthermore, the technology-stack that has been used by the teams is evaluated to understand how self-organized students in IT-related projects work. We will show that and why the bachelor is the most successful educational format in the perception of the students and how this positive results can be improved by the mentors.
Tikhonov regularization with oversmoothing penalty for linear statistical inverse learning problems
(2019)
In this paper, we consider the linear ill-posed inverse problem with noisy data in the statistical learning setting. The Tikhonov regularization scheme in Hilbert scales is considered in the reproducing kernel Hilbert space framework to reconstruct the estimator from the random noisy data. We discuss the rates of convergence for the regularized solution under the prior assumptions and link condition. For regression functions with smoothness given in terms of source conditions the error bound can explicitly be established.
Cardiovascular drift response over two different constant-load exercises in healthy non-athletes
(2019)
Cardiovascular drift (CV-d) is a steady increase in heart rate (HR) over time while performing constant load moderate intensity exercise (CME) > 20 min. CV-d presents problems for the prescription of exercise intensity by means of HR, because the work rate (WR) during exercise must be adjusted to maintain target HR, thus disturbing the intended effect of the exercise intervention. It has been shown that the increase in HR during CME is due to changes in WR and not to CV-d.
The target article discusses the question of how educational makerspaces can become places supportive of knowledge construction. This question is too often neglected by people who run makerspaces, as they mostly explain how to use different tools and focus on the creation of a product. In makerspaces, often pupils also engage in physical computing activities and thus in the creation of interactive artifacts containing embedded systems, such as smart shoes or wristbands, plant monitoring systems or drink mixing machines. This offers the opportunity to reflect on teaching physical computing in computer science education, where similarly often the creation of the product is so strongly focused upon that the reflection of the learning process is pushed into the background.
High-dimensional data is particularly useful for data analytics research. In the healthcare domain, for instance, high-dimensional data analytics has been used successfully for drug discovery. Yet, in order to adhere to privacy legislation, data analytics service providers must guarantee anonymity for data owners. In the context of high-dimensional data, ensuring privacy is challenging because increased data dimensionality must be matched by an exponential growth in the size of the data to avoid sparse datasets. Syntactically, anonymising sparse datasets with methods that rely of statistical significance, makes obtaining sound and reliable results, a challenge. As such, strong privacy is only achievable at the cost of high information loss, rendering the data unusable for data analytics. In this paper, we make two contributions to addressing this problem from both the privacy and information loss perspectives. First, we show that by identifying dependencies between attribute subsets we can eliminate privacy violating attributes from the anonymised dataset. Second, to minimise information loss, we employ a greedy search algorithm to determine and eliminate maximal partial unique attribute combinations. Thus, one only needs to find the minimal set of identifying attributes to prevent re-identification. Experiments on a health cloud based on the SAP HANA platform using a semi-synthetic medical history dataset comprised of 109 attributes, demonstrate the effectiveness of our approach.
This is a correction notice for ‘Post-adiabatic supernova remnants in an interstellar magnetic field: oblique shocks and non-uniform environment’ (DOI: https://doi.org/10.1093/mnras/sty1750), which was published in MNRAS 479, 4253–4270 (2018). The publisher regrets to inform that the colour was missing from the colour scales in Figs 8(a)–(d) and Figs 9(a) and (b). This has now been corrected online. The publisher apologizes for this error.
High-throughput RNA sequencing produces large gene expression datasets whose analysis leads to a better understanding of diseases like cancer. The nature of RNA-Seq data poses challenges to its analysis in terms of its high dimensionality, noise, and complexity of the underlying biological processes. Researchers apply traditional machine learning approaches, e. g. hierarchical clustering, to analyze this data. Until it comes to validation of the results, the analysis is based on the provided data only and completely misses the biological context. However, gene expression data follows particular patterns - the underlying biological processes. In our research, we aim to integrate the available biological knowledge earlier in the analysis process. We want to adapt state-of-the-art data mining algorithms to consider the biological context in their computations and deliver meaningful results for researchers.
Predictive coding and its generalization to active inference offer a unified theory of brain function. The underlying predictive processing paradigmhas gained significant attention in artificial intelligence research for its representation learning and predictive capacity. Here, we suggest that it is possible to integrate human and artificial generative models with a predictive coding network that processes sensations simultaneously with the signature of predictive coding found in human neuroimaging data. We propose a recurrent hierarchical predictive coding model that predicts low-dimensional representations of stimuli, electroencephalogram and physiological signals with variational inference. We suggest that in a shared environment, such hybrid predictive coding networks learn to incorporate the human predictive model in order to reduce prediction error. We evaluate the model on a publicly available EEG dataset of subjects watching one-minute long video excerpts. Our initial results indicate that the model can be trained to predict visual properties such as the amount, distance and motion of human subjects in videos.
In the present study, the charge distribution and the charge transport across the thickness of 2- and 3-dimensional polymer nanodielectrics was investigated. Chemically surface-treated polypropylene (PP) films and low-density polyethylene nanocomposite films with 3 wt % of magnesium oxide (LDPE/MgO) served as examples of 2-D and 3-D nanodielectrics, respectively. Surface charges were deposited onto the non-metallized surfaces of the one-side metallized polymer films and found to broaden and to thus enter the bulk of the films upon thermal stimulation at suitable elevated temperatures. The resulting space-charge profiles in the thickness direction were probed by means of Piezoelectrically-generated Pressure Steps (PPSs). It was observed that the chemical surface treatment of PP which led to the formation of nano-structures or the use of bulk nanoparticles from LDPE/MgO nanocomposites enhance charge trapping on or in the respective polymer films and also reduce charge transport inside the respective samples.
Words as social tools
(2019)
Working in iterations and repeatedly improving team workflows based on collected feedback is fundamental to agile software development processes. Scrum, the most popular agile method, provides dedicated retrospective meetings to reflect on the last development iteration and to decide on process improvement actions. However, agile methods do not prescribe how these improvement actions should be identified, managed or tracked in detail. The approaches to detect and remove problems in software development processes are therefore often based on intuition and prior experiences and perceptions of team members. Previous research in this area has focused on approaches to elicit a team's improvement opportunities as well as measurements regarding the work performed in an iteration, e.g. Scrum burn-down charts. Little research deals with the quality and nature of identified problems or how progress towards removing issues is measured. In this research, we investigate how agile development teams in the professional software industry organize their feedback and process improvement approaches. In particular, we focus on the structure and content of improvement and reflection meetings, i.e. retrospectives, and their outcomes. Researching how the vital mechanism of process improvement is implemented in practice in modern software development leads to a more complete picture of agile process improvement.
Feedback in Scrum
(2019)
Improving the way that teams work together by reflecting and improving the executed process is at the heart of agile processes. The idea of iterative process improvement takes various forms in different agile development methodologies, e.g. Scrum Retrospectives. However, these methods do not prescribe how improvement steps should be conducted in detail. In this research we investigate how agile software teams can use their development data, such as commits or tickets, created during regular development activities, to drive and track process improvement steps. Our previous research focused on data-informed process improvement in the context of student teams, where controlled circumstances and deep domain knowledge allowed creation and usage of specific process measures. Encouraged by positive results in this area, we investigate the process improvement approaches employed in industry teams. Researching how the vital mechanism of process improvement is implemented and how development data is already being used in practice in modern software development leads to a more complete picture of agile process improvement. It is the first step in enabling a data-informed feedback and improvement process, tailored to a team's context and based on the development data of individual teams.
Mise-Unseen
(2019)
Creating or arranging objects at runtime is needed in many virtual reality applications, but such changes are noticed when they occur inside the user's field of view. We present Mise-Unseen, a software system that applies such scene changes covertly inside the user's field of view. Mise-Unseen leverages gaze tracking to create models of user attention, intention, and spatial memory to determine if and when to inject a change. We present seven applications of Mise-Unseen to unnoticeably modify the scene within view (i) to hide that task difficulty is adapted to the user, (ii) to adapt the experience to the user's preferences, (iii) to time the use of low fidelity effects, (iv) to detect user choice for passive haptics even when lacking physical props, (v) to sustain physical locomotion despite a lack of physical space, (vi) to reduce motion sickness during virtual locomotion, and (vii) to verify user understanding during story progression. We evaluated Mise-Unseen and our applications in a user study with 15 participants and find that while gaze data indeed supports obfuscating changes inside the field of view, a change is rendered unnoticeably by using gaze in combination with common masking techniques.
Nowadays, structural health monitoring of critical infrastructures is considered as of primal importance especially for managing transport infrastructure however most current SHM methodologies are based on point-sensors that show various limitations relating to their spatial positioning capabilities, cost of development and measurement range. This publication describes the progress in the SENSKIN EC co-funded research project that is developing a dielectric-elastomer sensor, formed from a large highly extensible capacitance sensing membrane and is supported by an advanced micro-electronic circuitry, for monitoring transport infrastructure bridges. The sensor under development provides spatial measurements of strain in excess of 10%, while the sensing system is being designed to be easy to install, require low power in operation concepts, require simple signal processing, and have the ability to self-monitor and report. An appropriate wireless sensor network is also being designed and developed supported by local gateways for the required data collection and exploitation. SENSKIN also develops a Decision-Support-System (DSS) for proactive condition-based structural interventions under normal operating conditions and reactive emergency intervention following an extreme event. The latter is supported by a life-cycle-costing (LCC) and life-cycle-assessment (LCA) module responsible for the total internal and external costs for the identified bridge rehabilitation, analysis of options, yielding figures for the assessment of the economic implications of the bridge rehabilitation work and the environmental impacts of the bridge rehabilitation options and of the associated secondary effects respectively. The overall monitoring system will be evaluated and benchmarked on actual bridges of Egnatia Highway (Greece) and Bosporus Bridge (Turkey).
Based on the notion that time, space, and number are part of a generalized magnitude system, we assume that the dual-systems approach to temporal cognition also applies to numerical cognition. Referring to theoretical models of the development of numerical concepts, we propose that children's early skills in processing numbers can be described analogously to temporal updating and temporal reasoning.
Catholicism
(2019)
Introduction
(2019)
We review the evidence for a putative early 21st-century divergence between global mean surface temperature (GMST) and Coupled Model Intercomparison Project Phase 5 (CMIP5) projections. We provide a systematic comparison between temperatures and projections using historical versions of GMST products and historical versions of model projections that existed at the times when claims about a divergence were made. The comparisons are conducted with a variety of statistical techniques that correct for problems in previous work, including using continuous trends and a Monte Carlo approach to simulate internal variability. The results show that there is no robust statistical evidence for a divergence between models and observations. The impression of a divergence early in the 21st century was caused by various biases in model interpretation and in the observations, and was unsupported by robust statistics.
Diffusion of cosmic rays (CRs) is the key process for understanding their propagation and acceleration. We employ the description of spatial separation of magnetic field lines in magnetohydrodynamic turbulence in Lazarian & Vishniac to quantify the divergence of the magnetic field on scales less than the injection scale of turbulence and show that this divergence induces superdiffusion of CR in the direction perpendicular to the mean magnetic field. The perpendicular displacement squared increases, not as the distance x along the magnetic field, which is the case for a regular diffusion, but as the x 3 for freely streaming CRs. The dependence changes to x 3/2 for the CRs propagating diffusively along the magnetic field. In the latter case, we show that it is important to distinguish the perpendicular displacement with respect to the mean field and to the local magnetic field. We consider how superdiffusion changes the acceleration of CRs in shocks and show how it decreases efficiency of the CRs acceleration in perpendicular shocks. We also demonstrate that in the case when the small-scale magnetic field is generated in the pre-shock region, an efficient acceleration can take place for the CRs streaming without collisions along the magnetic loops.
Already for decades it has been known that the winds of massive stars are inhomogeneous (i.e. clumped). To properly model observed spectra of massive star winds it is necessary to incorporate the 3-D nature of clumping into radiative transfer calculations. In this paper we present our full 3-D Monte Carlo radiative transfer code for inhomogeneous expanding stellar winds. We use a set of parameters to describe dense as well as the rarefied wind components. At the same time, we account for non-monotonic velocity fields. We show how the 3-D density and velocity wind inhomogeneities strongly affect the resonance line formation. We also show how wind clumping can solve the discrepancy between P v and H alpha mass-loss rate diagnostics.
Data analytics are moving beyond the limits of a single data processing platform. A cross-platform query optimizer is necessary to enable applications to run their tasks over multiple platforms efficiently and in a platform-agnostic manner. For the optimizer to be effective, it must consider data movement costs across different data processing platforms. In this paper, we present the graph-based data movement strategy used by RHEEM, our open-source cross-platform system. In particular, we (i) model the data movement problem as a new graph problem, which we prove to be NP-hard, and (ii) propose a novel graph exploration algorithm, which allows RHEEM to discover multiple hidden opportunities for cross-platform data processing.
Leben in der ehemaligen DDR
(2019)
TrussFormer
(2019)
We present TrussFormer, an integrated end-to-end system that allows users to 3D print large-scale kinetic structures, i.e., structures that involve motion and deal with dynamic forces. TrussFormer builds on TrussFab, from which it inherits the ability to create static large-scale truss structures from 3D printed connectors and PET bottles. TrussFormer adds movement to these structures by placing linear actuators into them: either manually, wrapped in reusable components called assets, or by demonstrating the intended movement. TrussFormer verifies that the resulting structure is mechanically sound and will withstand the dynamic forces resulting from the motion. To fabricate the design, TrussFormer generates the underlying hinge system that can be printed on standard desktop 3D printers. We demonstrate TrussFormer with several example objects, including a 6-legged walking robot and a 4m-tall animatronics dinosaur with 5 degrees of freedom.
Editorial
(2019)
The new year starts and many of us have right away been burdened with conference datelines, grant proposal datelines, teaching obligations, paper revisions and many other things. While being more or less successful in fulfilling To‐Do lists and ticking of urgent (and sometimes even important) things, we often feel that our ability to be truly creative or innovative is rather restrained by this (external pressure). With this, we are not alone. Many studies have shown that stress does influence overall work performance and satisfaction. Furthermore, more and more students and entry‐levels look for work‐life balance and search for employers that offer a surrounding and organization considering these needs. High‐Tech and start‐up companies praise themselves for their “Feel‐Good managers” or Yoga programs. But is this really helpful? Is there indeed a relationship between stress, adverse work environment and creativity or innovation? What are the supporting factors in a work environment that lets employees be more creative? What kind of leadership do we need for innovative behaviour and to what extent can an organization create support structures that reduce the stress we feel? The first issue of Creativity and Innovation Management in 2019 gives some first answers to these questions and hopefully some food for thought.
The first paper written by Dirk De Clercq, and Imanol Belausteguigoitia starts with the question which impact work overload has on creative behaviour. The authors look at how employees' perceptions of work overload reduces their creative behaviour. While they find empirical proof for this relationship, they can also show that the effect is weaker with higher levels of passion for work, emotion sharing, and organizational commitment. The buffering effects of emotion sharing and organizational commitment are particularly strong when they are combined with high levels of passion for work. Their findings give first empirical proof that organizations can and should take an active role in helping their employees reducing the effects of adverse work conditions in order to become or stay creative. However, not only work overload is harming creative behaviour, also the fear of losing one's job has detrimental effects on innovative work behaviour. Anahi van Hootegem, Wendy Niesen and Hans de Witte verify that stress and adverse environmental conditions shape our perception of work. Using threat rigidity theory and an empirical study of 394 employees, they show that the threat of job loss impairs employees' innovativeness through increased irritation and decreased concentration. Organizations can help their employees coping better with this insecurity by communicating more openly and providing different support structures. Support often comes from leadership and the support of the supervisor can clearly shape an employee's motivation to show creative behaviour. Wenjing Cai, Evgenia Lysova, Bart A. G. Bossink, Svetlana N. Khapova and Weidong Wang report empirical findings from a large‐scale survey in China where they find that supervisor support for creativity and job characteristics effectively activate individual psychological capital associated with employee creativity.
On a slight different notion, Gisela Bäcklander looks at agile practices in a very well‐known High Tech firm. In “Doing Complexity Leadership Theory: How agile coaches at Spotify practice enabling leadership”, she researches the role of agile coaches and how they practice enabling leadership, a key balancing force in complexity leadership. She finds that the active involvement of coaches in observing group dynamics, surfacing conflict and facilitating and encouraging constructive dialogue leads to a positive working environment and the well‐being of employees. Quotes from the interviews suggest that the flexible structure provided by the coaches may prove a fruitful way to navigate and balance autonomy and alignment in organizations.
The fifth paper of Frederik Anseel, Michael Vandamme, Wouter Duyck and Eric Rietzchel goes a little further down this road and researches how groups can be motivated better to select truly creative ideas. We know from former studies that groups often perform rather poorly when it comes to selecting creative ideas for implementation. The authors find in an extensive field experiment that under conditions of high epistemic motivation, proself motivated groups select significantly more creative and original ideas than prosocial groups. They conclude however, that more research is needed to understand better why these differences occur. The prosocial behaviour of groups is also the theme of Karin Moser, Jeremy F. Dawson and Michael A. West's paper on “Antecedents of team innovation in health care teams”. They look at team‐level motivation and how a prosocial team environment, indicated by the level of helping behaviour and information‐sharing, may foster innovation. Their results support the hypotheses of both information‐sharing and helping behaviour on team innovation. They suggest that both factors may actually act as buffer against constraints in team work, such as large team size or high occupational diversity in cross‐functional health care teams, and potentially turn these into resources supporting team innovation rather than acting as barriers.
Away from teams and onto designing favourable work environments, the seventh paper of Ferney Osorio, Laurent Dupont, Mauricio Camargo, Pedro Palominos, Jose Ismael Pena and Miguel Alfaro looks into innovation laboratories. Although several studies have tackled the problem of design, development and sustainability of these spaces for innovation, there is still a gap in understanding how the capabilities and performance of these environments are affected by the strategic intentions at the early stages of their design and functioning. The authors analyse and compare eight existing frameworks from literature and propose a new framework for researchers and practitioners aiming to assess or to adapt innovation laboratories. They test their framework in an exploratory study with fifteen laboratories from five different countries and give recommendations for the future design of these laboratories. From design to design thinking goes our last paper from Rama Krishna Reddy Kummitha on “Design Thinking in Social Organisations: Understanding the role of user engagement” where she studies how users persuade social organisations to adopt design thinking. Looking at four social organisations in India during 2008 to 2013, she finds that the designer roles are blurred when social organisations adopt design thinking, while users in the form of interconnecting agencies reduce the gap between designers and communities.
The last two articles were developed from papers presented at the 17th International CINet conference organized in Turin in 2016 by Paolo Neirotti and his colleagues. In the first article, Fábio Gama, Johan Frishammar and Vinit Parida focus on ideation and open innovation in small‐ and medium‐sized enterprises. They investigate the relationship between systematic idea generation and performance and the moderating role of market‐based partnerships. Based on a survey among manufacturing SMEs, they conclude that higher levels of performance are reached and that collaboration with customers and suppliers pays off most when idea generation is done in a highly systematic way. The second article, by Anna Holmquist, Mats Magnusson and Mona Livholts, resonates the theme of the CINet conference ‘Innovation and Tradition; combining the old and the new’. They explore how tradition is used in craft‐based design practices to create new meaning. Applying a narrative ‘research through design’ approach they uncover important design elements, and tensions between them.
Please enjoy this first issue of CIM in 2019 and we wish you creativity and innovation without too much stress in the months to come.
Foreword
(2019)
Introduction
(2019)
Over the past decades, it has become more and more obvious that ongoing globalisation processes have substantial impacts on the natural environment. Studies reveal that intensified global economic relations have caused or accelerated dramatic changes in the Earth system, defined as the sum of our planet’s interacting physical, chemical, biological and human processes (Schellnhuber et al. 2004). Climate change, biodiversity loss, disrupted biogeochemical cycles, and land degradation are often cited as emblematic problems of global environmental change (Rockström et al. 2009; Steffen et al. 2015). In this context, the term Anthropocene has lately received widespread attention and gained some prominence in the academic literature
Industry 4.0 and the Internet of Things are recent developments that have lead to the creation of new kinds of manufacturing data. Linking this new kind of sensor data to traditional business information is crucial for enterprises to take advantage of the data’s full potential. In this paper, we present a demo which allows experiencing this data integration, both vertically between technical and business contexts and horizontally along the value chain. The tool simulates a manufacturing company, continuously producing both business and sensor data, and supports issuing ad-hoc queries that answer specific questions related to the business. In order to adapt to different environments, users can configure sensor characteristics to their needs.
Bridging the Gap
(2019)
The recent restructuring of the electricity grid (i.e., smart grid) introduces a number of challenges for today's large-scale computing systems. To operate reliable and efficient, computing systems must adhere not only to technical limits (i.e., thermal constraints) but they must also reduce operating costs, for example, by increasing their energy efficiency. Efforts to improve the energy efficiency, however, are often hampered by inflexible software components that hardly adapt to underlying hardware characteristics. In this paper, we propose an approach to bridge the gap between inflexible software and heterogeneous hardware architectures. Our proposal introduces adaptive software components that dynamically adapt to heterogeneous processing units (i.e., accelerators) during runtime to improve the energy efficiency of computing systems.
Mobile sensing technology allows us to investigate human behaviour on a daily basis. In the study, we examined temporal orientation, which refers to the capacity of thinking or talking about personal events in the past and future. We utilise the mksense platform that allows us to use the experience-sampling method. Individual's thoughts and their relationship with smartphone's Bluetooth data is analysed to understand in which contexts people are influenced by social environments, such as the people they spend the most time with. As an exploratory study, we analyse social condition influence through a collection of Bluetooth data and survey information from participant's smartphones. Preliminary results show that people are likely to focus on past events when interacting with close-related people, and focus on future planning when interacting with strangers. Similarly, people experience present temporal orientation when accompanied by known people. We believe that these findings are linked to emotions since, in its most basic state, emotion is a state of physiological arousal combined with an appropriated cognition. In this contribution, we envision a smartphone application for automatically inferring human emotions based on user's temporal orientation by using Bluetooth sensors, we briefly elaborate on the influential factor of temporal orientation episodes and conclude with a discussion and lessons learned.
The impact of social identity and social dominance on the regulation of human growth: A viewpoint
(2019)
alt'ai is an agent-based simulation inspired by aesthetics, culture and environmental conditions of the Altai mountain region on the borders between Russia, Kazakhstan, China and Mongolia. It is set into a scenario of a remote automated landscape populated by sentient machines, where biological species, machines and environments autonomously interact to produce unforeseeable visual outputs. It poses a question of designing future machine-to-machine authentication protocols that are based on the use of images encoding agent behavior. Also, the simulation provides rich visual perspective on this challenge. The project pleads for a heavily aestheticized approach to design practice and highlights the importance of productively inefficient and information redundant systems.
Process models are an important means to capture information on organizational operations and often represent the starting point for process analysis and improvement. Since the manual elicitation and creation of process models is a time-intensive endeavor, a variety of techniques have been developed that automatically derive process models from textual process descriptions. However, these techniques, so far, only focus on the extraction of traditional, imperative process models. The extraction of declarative process models, which allow to effectively capture complex process behavior in a compact fashion, has not been addressed. In this paper we close this gap by presenting the first automated approach for the extraction of declarative process models from natural language. To achieve this, we developed tailored Natural Language Processing techniques that identify activities and their inter-relations from textual constraint descriptions. A quantitative evaluation shows that our approach is able to generate constraints that closely resemble those established by humans. Therefore, our approach provides automated support for an otherwise tedious and complex manual endeavor.
Workload-Driven Fragment Allocation for Partially Replicated Databases Using Linear Programming
(2019)
In replication schemes, replica nodes can process read-only queries on snapshots of the master node without violating transactional consistency. By analyzing the workload, we can identify query access patterns and replicate data depending to its access frequency. In this paper, we define a linear programming (LP) model to calculate the set of partial replicas with the lowest overall memory capacity while evenly balancing the query load. Furthermore, we propose a scalable decomposition heuristic to calculate solutions for larger problem sizes. While guaranteeing the same performance as state-of-the-art heuristics, our decomposition approach calculates allocations with up to 23% lower memory footprint for the TPC-H benchmark.
Increasing demand for analytical processing capabilities can be managed by replication approaches. However, to evenly balance the replicas' workload shares while at the same time minimizing the data replication factor is a highly challenging allocation problem. As optimal solutions are only applicable for small problem instances, effective heuristics are indispensable. In this paper, we test and compare state-of-the-art allocation algorithms for partial replication. By visualizing and exploring their (heuristic) solutions for different benchmark workloads, we are able to derive structural insights and to detect an algorithm's strengths as well as its potential for improvement. Further, our application enables end-to-end evaluations of different allocations to verify their theoretical performance.
Preface
(2019)
A Landscape for Case Models
(2019)
Case Management is a paradigm to support knowledge-intensive processes. The different approaches developed for modeling these types of processes tend to result in scattered models due to the low abstraction level at which the inherently complex processes are therein represented. Thus, readability and understandability is more challenging than that of traditional process models. By reviewing existing proposals in the field of process overviews and case models, this paper extends a case modeling language - the fragment-based Case Management (fCM) language - with the goal of modeling knowledge-intensive processes from a higher abstraction level - to generate a so-called fCM landscape. This proposal is empirically evaluated via an online experiment. Results indicate that interpreting an fCM landscape might be more effective and efficient than interpreting an informationally equivalent case model.
Currently, a transformation of our technical world into a networked technical world where besides the embedded systems with their interaction with the physical world the interconnection of these nodes in the cyber world becomes a reality can be observed. In parallel nowadays there is a strong trend to employ artificial intelligence techniques and in particular machine learning to make software behave smart. Often cyber-physical systems must be self-adaptive at the level of the individual systems to operate as elements in open, dynamic, and deviating overall structures and to adapt to open and dynamic contexts while being developed, operated, evolved, and governed independently.
In this presentation, we will first discuss the envisioned future scenarios for cyber-physical systems with an emphasis on the synergies networking can offer and then characterize which challenges for the design, production, and operation of these systems result. We will then discuss to what extent our current capabilities, in particular concerning software engineering match these challenges and where substantial improvements for the software engineering are crucial. In today's software engineering for embedded systems models are used to plan systems upfront to maximize envisioned properties on the one hand and minimize cost on the other hand. When applying the same ideas to software for smart cyber-physical systems, it soon turned out that for these systems often somehow more subtle links between the involved models and the requirements, users, and environment exist. Self-adaptation and runtime models have been advocated as concepts to covers the demands that result from these subtler links. Lately, both trends have been brought together more thoroughly by the notion of self-aware computing systems. We will review the underlying causes, discuss some our work in this direction, and outline related open challenges and potential for future approaches to software engineering for smart cyber-physical systems.
Evaluating the performance of self-adaptive systems (SAS) is challenging due to their complexity and interaction with the often highly dynamic environment. In the context of self-healing systems (SHS), employing simulators has been shown to be the most dominant means for performance evaluation. Simulating a SHS also requires realistic fault injection scenarios. We study the state of the practice for evaluating the performance of SHS by means of a systematic literature review. We present the current practice and point out that a more thorough and careful treatment in evaluating the performance of SHS is required.
In Memoriam Siegfried Bauer
(2019)
Siegfried Bauer, an internationally renowned, very creative applied physicist, who also was a prolific materials scientist and engineer, died on December 30, 2018, in Linz, Austria, after a one-year battle with cancer. He was full professor of soft-matter physics at the Johannes Kepler University Linz, Austria, and a scientific leader and innovator across the fields but mainly in the areas of electro-active materials (including electrets) and stretchable and imperceptible electronics.
Dielectric materials for electro-active (electret) and/or electro-passive (insulation) applications
(2019)
Dielectric materials for electret applications usually have to contain a quasi-permanent space charge or dipole polarization that is stable over large temperature ranges and time periods. For electrical-insulation applications, on the other hand, a quasi-permanent space charge or dipole polarization is usually considered detrimental. In recent years, however, with the advent of high-voltage direct-current (HVDC) transmission and high-voltage capacitors for energy storage, new possibilities are being explored in the area of high-voltage dielectrics. Stable charge trapping (as e.g. found in nano-dielectrics) or large dipole polarizations (as e.g. found in relaxor ferroelectrics and high-permittivity dielectrics) are no longer considered to be necessarily detrimental in electrical-insulation materials. On the other hand, recent developments in electro-electrets (dielectric elastomers), i.e. very soft dielectrics with large actuation strains and high breakdown fields, and in ferroelectrets, i.e. polymers with electrically charged cavities, have resulted in new electret materials that may also be useful for HVDC insulation systems. Furthermore, 2-dimensional (nano-particles on surfaces or interfaces) and 3-dimensional (nano-particles in the bulk) nano-dielectrics have been found to provide very good charge-trapping properties that may not only be used for more stable electrets and ferroelectrets, but also for better HVDC electrical-insulation materials with the possibility to optimize charge-transport and field-gradient behavior. In view of these and other recent developments, a first attempt will be made to review a small selection of electro-active (i.e. electret) and electro-passive (i.e. insulation) dielectrics in direct comparison. Such a comparative approach may lead to synergies in materials concepts and research methods that will benefit both areas. Furthermore, electrets may be very useful for sensing and monitoring applications in electrical-insulation systems, while high-voltage technology is essential for more efficient charging and poling of electret materials.
Editorial
(2019)
An essential, respected, and critical aspect of the modern practice of science and scientific publishing is peer review. The process of peer review facilitates best practices in scientific conduct and communication, ensuring that manuscripts published are as accurate, valuable, and clearly communicated. The over 216 papers published in Tectonics in 2018 benefit from the time, effort, and expertise of our reviewers who have provided thoughtfully considered advice on each manuscript. This role is critical to advancing our understanding of the evolution of the continents and their margins, as these reviews lead to even clearer and higher-quality papers. In 2018, the over 443 papers submitted to Tectonics were the beneficiaries of more than 1,010 reviews provided by 668 members of the tectonics community and related disciplines. To everyone who has volunteered their time and intellect to peer reviewing, thank you for helping Tectonics and all other AGU Publications provide the best science possible.
Network science is driven by the question which properties large real-world networks have and how we can exploit them algorithmically. In the past few years, hyperbolic graphs have emerged as a very promising model for scale-free networks. The connection between hyperbolic geometry and complex networks gives insights in both directions: (1) Hyperbolic geometry forms the basis of a natural and explanatory model for real-world networks. Hyperbolic random graphs are obtained by choosing random points in the hyperbolic plane and connecting pairs of points that are geometrically close. The resulting networks share many structural properties for example with online social networks like Facebook or Twitter. They are thus well suited for algorithmic analyses in a more realistic setting. (2) Starting with a real-world network, hyperbolic geometry is well-suited for metric embeddings. The vertices of a network can be mapped to points in this geometry, such that geometric distances are similar to graph distances. Such embeddings have a variety of algorithmic applications ranging from approximations based on efficient geometric algorithms to greedy routing solely using hyperbolic coordinates for navigation decisions.
The availability of detailed virtual 3D building models including representations of indoor elements, allows for a wide number of applications requiring effective exploration and navigation functionality. Depending on the application context, users should be enabled to focus on specific Objects-of-Interests (OOIs) or important building elements. This requires approaches to filtering building parts as well as techniques to visualize important building objects and their relations. For it, this paper explores the application and combination of interactive rendering techniques as well as their semanticallydriven configuration in the context of 3D indoor models.
Short period double degenerate white dwarf (WD) binaries with periods of less than similar to 1 day are considered to be one of the likely progenitors of type Ia supernovae. These binaries have undergone a period of common envelope evolution. If the core ignites helium before the envelope is ejected, then a hot subdwarf remains prior to contracting into a WD. Here we present a comparison of two very rare systems that contain two hot subdwarfs in short period orbits. We provide a quantitative spectroscopic analysis of the systems using synthetic spectra from state-of-the-art non-LTE models to constrain the atmospheric parameters of the stars. We also use these models to determine the radial velocities, and thus calculate dynamical masses for the stars in each system.
In this paper, we consider counting and projected model counting of extensions in abstract argumentation for various semantics. When asking for projected counts we are interested in counting the number of extensions of a given argumentation framework while multiple extensions that are identical when restricted to the projected arguments count as only one projected extension. We establish classical complexity results and parameterized complexity results when the problems are parameterized by treewidth of the undirected argumentation graph. To obtain upper bounds for counting projected extensions, we introduce novel algorithms that exploit small treewidth of the undirected argumentation graph of the input instance by dynamic programming (DP). Our algorithms run in time double or triple exponential in the treewidth depending on the considered semantics. Finally, we take the exponential time hypothesis (ETH) into account and establish lower bounds of bounded treewidth algorithms for counting extensions and projected extension.
BIOMEX (BIOlogy and Mars EXperiment) is an ESA/Roscosmos space exposure experiment housed within the exposure facility EXPOSE-R2 outside the Zvezda module on the International Space Station (ISS). The design of the multiuser facility supports-among others-the BIOMEX investigations into the stability and level of degradation of space-exposed biosignatures such as pigments, secondary metabolites, and cell surfaces in contact with a terrestrial and Mars analog mineral environment. In parallel, analysis on the viability of the investigated organisms has provided relevant data for evaluation of the habitability of Mars, for the limits of life, and for the likelihood of an interplanetary transfer of life (theory of lithopanspermia). In this project, lichens, archaea, bacteria, cyanobacteria, snow/permafrost algae, meristematic black fungi, and bryophytes from alpine and polar habitats were embedded, grown, and cultured on a mixture of martian and lunar regolith analogs or other terrestrial minerals. The organisms and regolith analogs and terrestrial mineral mixtures were then exposed to space and to simulated Mars-like conditions by way of the EXPOSE-R2 facility. In this special issue, we present the first set of data obtained in reference to our investigation into the habitability of Mars and limits of life. This project was initiated and implemented by the BIOMEX group, an international and interdisciplinary consortium of 30 institutes in 12 countries on 3 continents. Preflight tests for sample selection, results from ground-based simulation experiments, and the space experiments themselves are presented and include a complete overview of the scientific processes required for this space experiment and postflight analysis. The presented BIOMEX concept could be scaled up to future exposure experiments on the Moon and will serve as a pretest in low Earth orbit.
Mobile operating systems, such as Google's Android, have become a fixed part of our daily lives and are entrusted with a plethora of private information. Congruously, their data protection mechanisms have been improved steadily over the last decade and, in particular, for Android, the research community has explored various enhancements and extensions to the access control model. However, the vast majority of those solutions has been concerned with controlling the access to data, but equally important is the question of how to control the flow of data once released. Ignoring control over the dissemination of data between applications or between components of the same app, opens the door for attacks, such as permission re-delegation or privacy-violating third-party libraries. Controlling information flows is a long-standing problem, and one of the most recent and practical-oriented approaches to information flow control is secure multi-execution.
In this paper, we present Ariel, the design and implementation of an IFC architecture for Android based on the secure multi-execution of apps. Ariel demonstrably extends Android's system with support for executing multiple instances of apps, and it is equipped with a policy lattice derived from the protection levels of Android's permissions as well as an I/O scheduler to achieve control over data flows between application instances. We demonstrate how secure multi-execution with Ariel can help to mitigate two prominent attacks on Android, permission re-delegations and malicious advertisement libraries.
A distinguishing feature of Answer Set Programming is that all atoms belonging to a stable model must be founded. That is, an atom must not only be true but provably true. This can be made precise by means of the constructive logic of Here-and-There, whose equilibrium models correspond to stable models. One way of looking at foundedness is to regard Boolean truth values as ordered by letting true be greater than false. Then, each Boolean variable takes the smallest truth value that can be proven for it. This idea was generalized by Aziz to ordered domains and applied to constraint satisfaction problems. As before, the idea is that a, say integer, variable gets only assigned to the smallest integer that can be justified. In this paper, we present a logical reconstruction of Aziz’ idea in the setting of the logic of Here-and-There. More precisely, we start by defining the logic of Here-and-There with lower bound founded variables along with its equilibrium models and elaborate upon its formal properties. Finally, we compare our approach with related ones and sketch future work.
For a singularly perturbed parabolic - ODE system we construct the asymptotic expansion in the small parameter in the case, when the degenerate equation has a double root. Such systems, which are called partly dissipative reaction-diffusion systems, are used to model various natural processes, including the signal transmission along axons, solid combustion and the kinetics of some chemical reactions. It turns out that the algorithm of the construction of the boundary layer functions and the behavior of the solution in the boundary layers essentially differ from that ones in case of a simple root. The multizonal initial and boundary layers behaviour was stated.
We investigate how the technology acceptance and learning experience of the digital education platform HPI Schul-Cloud (HPI School Cloud) for German secondary school teachers can be improved by proposing a user-centered research and development framework. We highlight the importance of developing digital learning technologies in a user-centered way to take differences in the requirements of educators and students into account. We suggest applying qualitative and quantitative methods to build a solid understanding of a learning platform's users, their needs, requirements, and their context of use. After concept development and idea generation of features and areas of opportunity based on the user research, we emphasize on the application of a multi-attribute utility analysis decision-making framework to prioritize ideas rationally, taking results of user research into account. Afterward, we recommend applying the principle build-learn-iterate to build prototypes in different resolutions while learning from user tests and improving the selected opportunities. Last but not least, we propose an approach for continuous short- and long-term user experience controlling and monitoring, extending existing web- and learning analytics metrics.
New Public Governance (NPG) as a paradigm for collaborative forms of public service delivery and Blockchain governance are trending topics for researchers and practitioners alike. Thus far, each topic has, on the whole, been discussed separately. This paper presents the preliminary results of ongoing research which aims to shed light on the more concrete benefits of Blockchain for the purpose of NPG. For the first time, a conceptual analysis is conducted on process level to spot benefits and limitations of Blockchain-based governance. Per process element, Blockchain key characteristics are mapped to functional aspects of NPG from a governance perspective. The preliminary results show that Blockchain offers valuable support for governments seeking methods to effectively coordinate co-producing networks. However, the extent of benefits of Blockchain varies across the process elements. It becomes evident that there is a need for off-chain processes. It is, therefore, argued in favour of intensifying research on off-chain governance processes to better understand the implications for and influences on on-chain governance.
Monte-Carlo calculations are carried out to simulate the light transport in dense materials. Focus lies on the calculation of diffuse light transmission through films of scattering and absorbing media considering additionally the effect of dependent scattering. Different influences like interaction type between particles, particle size, composition etc. can be studied by this program. Simulations in this study show major influences on the diffuse transmission. Further simulations are carried out to model a sunscreen film and study best compositions of this film and will be presented.
Peace orders of modern times
(2019)
Monitoring is a key prerequisite for self-adaptive software and many other forms of operating software. Monitoring relevant lower level phenomena like the occurrences of exceptions and diagnosis data requires to carefully examine which detailed information is really necessary and feasible to monitor. Adaptive monitoring permits observing a greater variety of details with less overhead, if most of the time the MAPE-K loop can operate using only a small subset of all those details. However, engineering such an adaptive monitoring is a major engineering effort on its own that further complicates the development of self-adaptive software. The proposed approach overcomes the outlined problems by providing generic adaptive monitoring via runtime models. It reduces the effort to introduce and apply adaptive monitoring by avoiding additional development effort for controlling the monitoring adaptation. Although the generic approach is independent from the monitoring purpose, it still allows for substantial savings regarding the monitoring resource consumption as demonstrated by an example.
Monitoring is a key functionality for automated decision making as it is performed by self-adaptive systems, too. Effective monitoring provides the relevant information on time. This can be achieved with exhaustive monitoring causing a high overhead consumption of economical and ecological resources. In contrast, our generic adaptive monitoring approach supports effectiveness with increased efficiency. Also, it adapts to changes regarding the information demand and the monitored system without additional configuration and software implementation effort. The approach observes the executions of runtime model queries and processes change events to determine the currently required monitoring configuration. In this paper we explicate different possibilities to use the approach and evaluate their characteristics regarding the phenomenon detection time and the monitoring effort. Our approach allows balancing between those two characteristics. This makes it an interesting option for the monitoring function of self-adaptive systems because for them usually very short-lived phenomena are not relevant.