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In the past years, algorithms for 3D shape tracking using radial functions in spherical coordinates represented with different methods have been proposed. However, we have seen that mainly measurements from the lateral surface of the target can be expected in a lot of dynamic scenarios and only few measurements from the top and bottom parts leading to an error-prone shape estimate in the top and bottom regions when using a representation in spherical coordinates. We, therefore, propose to represent the shape of the target using a radial function in cylindrical coordinates, as these only represent regions of the lateral surface, and no information from the top or bottom parts is needed. In this paper, we use a Fourier-Chebyshev double series for 3D shape representation since a mixture of Fourier and Chebyshev series is a suitable basis for expanding a radial function in cylindrical coordinates. We investigate the method in a simulated and real-world maritime scenario with a CAD model of the target boat as a reference. We have found that shape representation in cylindrical coordinates has decisive advantages compared to a shape representation in spherical coordinates and should preferably be used if no prior knowledge of the measurement distribution on the surface of the target is available.
Random matrices are used to filter the center of gravity (CoG) and the covariance matrix of measurements. However, these quantities do not always correspond directly to the position and the extent of the object, e.g. when a lidar sensor is used.In this paper, we propose a Gaussian processes regression model (GPRM) to predict the position and extension of the object from the filtered CoG and covariance matrix of the measurements. Training data for the GPRM are generated by a sampling method and a virtual measurement model (VMM). The VMM is a function that generates artificial measurements using ray tracing and allows us to obtain the CoG and covariance matrix that any object would cause. This enables the GPRM to be trained without real data but still be applied to real data due to the precise modeling in the VMM. The results show an accurate extension estimation as long as the reality behaves like the modeling and e.g. lidar measurements only occur on the side facing the sensor.
Die digitale Transformation von Geschäftsprozessen und die stärkere Integration von IT-Systemen führen zu Chancen und Risiken für kleine und mittlere Unternehmen (KMU). Risiken, die zu fehlender IT-Governance, Risk und Compliance (GRC) führen können. Ziel dieses Beitrags ist es, die Design- und Evaluierungsphase der Erstellung eines Artefakts darzustellen. Dabei wird der Design Science Research Ansatz nach Hevner verwendet. Das Artefakt wird für die Auswahl von Standards entwickelt, indem KMU-relevante Ausprägungen und bestehende Rahmenwerke auf die definierten Kriterien angepasst werden.
This paper aims to apply the basics of the Service-Dominant Logic, especially the concept of creating benefits through serving, to the stationary retail industry. In the industrial context, the shift from a product-driven point of view to a service-driven perspective has been discussed widely. However, there are only few connections to how this can be applied to the retail sector on a B2C-level and how retailers can use smart services in order to enable customer engagement, loyalty and retention. The expectations of customers towards future stationary retail develop significantly as consumers got used to the comfort of online shopping. Especially the younger generation—the Generation Z—seems to have changed their priorities from the bare purchase of products to an experience- and service-driven approach when shopping over-the-counter. To stay successful long-term, companies from this sector need to adapt to the expectations of their future main customer group. Therefore, this paper will analyse the specific needs of Generation Z, explain how smart services contribute to creating benefit for this customer group and how this affects the economic sustainability of these firms.
As one of the most important branches of the industry in Germany and
the European Union, the mechanical and plant engineering sector is confronted with fundamental changes due to ever shorter innovation cycles and increased competitive pressure. This makes it even more important to increase the level of service components in business models with a low service level, which are still frequently found in SMEs. This paper is dedicated to the changes that the individual components of a business model have experienced and will experience. Special attention is paid to economic sustainability, since service business models can also positively influence the long-term nature of a business. Seven interviews conducted with relevant companies serve as the empirical basis of this paper. The analysed effects of smart services and active customer integration are structured and summarized within the three pillars of every business model (value proposition, the value creation architecture and the revenue mechanic).
Motion estimation is an essential element for autonomous vessels. It is used e.g. for lidar motion compensation as well as mapping and detection tasks in a maritime environment. Because the use of gyroscopes is not reliable and a high performance inertial measurement unit is quite expensive, we present an approach for visual pitch and roll estimation that utilizes a convolutional neural network for water segmentation, a stereo system for reconstruction and simple geometry to estimate pitch and roll. The algorithm is validated on a novel, publicly available dataset recorded at Lake Constance. Our experiments show that the pitch and roll estimator provides accurate results in comparison to an Xsens IMU sensor. We can further improve the pitch and roll estimation by sensor fusion with a gyroscope. The algorithm is available in its implementation as a ROS node.
Durch eine Aufweitung des Kristallgitters mittels Niedertemperatur-Eindiffusion von Kohlenstoff und/oder Stickstoffatomen können in der Randzone von nichtrostenden Stählen eine hohe Härte und eine hohe Verschleißbeständigkeit erzeugt werden, ohne dass zusätzliche Legierungselemente verwendet werden müssen. Die metallkundlichen Hintergründe für die Härtung, die Wirkung auf Verschleißvorgänge und mögliche Anwendungsbereiche werden geschildert. Anhand von Reibwerten wird gezeigt, in welcher Weise das Reibungsverhalten bei Schraubverbindungen durch die Behandlung verändert wird. Über Migrationsversuche wird nachgewiesen, dass die Ionenabgabe durch die Oberflächenhärtung nicht erhöht, sondern sogar abgesenkt wird. Neben dem besseren Verschleißschutz und einer höheren Dauerfestigkeit sichert diese Oberflächenbehandlung am nichtrostenden Stahl den Schutz gegen die Kontamination von Pharmaprodukten durch Metallabrieb/-ionen. Tests an oberflächengehärteten Edelstahlproben ergaben weiterhin, dass durch die Oberflächenhärtung die Biokompatibilität des nichtrostenden Edelstahls nicht beeinträchtigt wird.
Die Beständigkeit von hochlegierten korrosions- und säurebeständigen Stählen wird primär durch den Chromgehalt bestimmt. Allerdings gibt es entlang der Wertschöpfungskette von der Stahlerschmelzung bis zum fertigen Produkt eine Vielzahl weiterer Einflussfaktoren. Dem Schleifen kommt hier eine besondere Bedeutung zu, da es je nach Wahl der Prozessparameter sowohl zu einer signifikanten Verschlechterung als auch zu einer Verbesserung der Korrosionsbeständigkeit führen kann. Im vorliegenden Beitrag wird aufgezeigt, dass die erzeugte Rauheit nur eine begrenzte Aussagekraft bietet. Vielmehr erhöhen lokale Mikrodefekte die Anfälligkeit gegen Lochfraß – je nach Ausprägung und Anzahl. Die Automatisierung für die Innenbearbeitung von Behältern im pharmazeutischen Apparatebau kann dabei zu einer signifikanten Verbesserung der Oberfläche und einem homogeneren Erscheinungsbild führen.
Uzbekistan is an emerging tourism destination that has experienced a strong increase in tourists since 2017. However, little research on tourism development in Uzbekistan exists to date. This study therefore analyzes possible research topics and proposes a tourism research agenda for Uzbekistan. A mix of methods was used consisting of participant observation, semi-structured qualitative expert interviews and qualitative content anal- ysis. The results revealed a variety of research deficits in different areas, which could be synthesized into a total of ten research fields, which were clustered into three overarching areas, namely market research, management, and culture & environment. The subordi- nate research fields identified are Demand, Statistics, Potentials, Governance, Products, Infrastructure & Development, Marketing, Heritage & Nation-building, Sustainability as well as Peace & Conflict Prevention. A strategic research plan based on this tourism research agenda could help to foster a purposeful scientific debate. Tourism research in these fields has both the potential to investigate and compare theoretical issues in an unique context and to produce applied research results that can make a relevant contri- bution to tourism development in Uzbekistan.
The Black Forest offers renewable energy as a specific tourist destination in the form of bioenergy villages (BEV). Particularly expert tourists tend to visit them. The results of two quantitative surveys on the supply and demand side show that there is, up to now, an untapped potential among experienceoriented
tourists for this type of niche tourism.
The Global Sanctions Data Base (GSDB): an update that includes the years of the Trump presidency
(2021)
As organizations struggle to cope with digital transformation in
an innovation environment, partnerships between startups and established
companies have become increasingly important. Building upon years of
practical experience and empirical research, we present advantages,
obstacles, and the keys to successful corporate-startup collaboration.
Die digitale Transformation von Geschäftsprozessen und die stärkere Einbindung von IT-Systemen erzeugen bei kleinen und mittelständischen Unternehmen (KMU) Chancen und Risiken zugleich. Risiken, die insbesondere in einer fehlenden IT-Compliance resultieren können. Wie Studien zeigen, sind KMU in Bezug auf IT-Compliance-Maßnahmen im Vergleich zu kapitalmarktorientierten Unternehmen jedoch im Rückstand [1]. Im Beitrag wird mithilfe von Experteninterviews und einer qualitativen Datenanalyse der Frage nachgegangen, welcher Status quo an Maßnahmen aktuell implementiert und wie der empfundene Compliance-Reifegrad ist. Weiterhin werden die Gründe und Motive erörtert, die zu diesem Zustand geführt haben. Letztlich sind Treiber identifiziert worden, die zu einem höheren Bewusstsein in der Zukunft führen können. Die Arbeit zeigt interessante Erkenntnisse aus der Praxis, da die Experteninterviews Einblicke in den aktuellen Status quo in Bezug auf IT-Compliance liefern.
Targetless Lidar-camera registration is a repeating task in many computer vision and robotics applications and requires computing the extrinsic pose of a point cloud with respect to a camera or vice-versa. Existing methods based on learning or optimization lack either generalization capabilities or accuracy. Here, we propose a combination of pre-training and optimization using a neural network-based mutual information estimation technique (MINE [1]). This construction allows back-propagating the gradient to the calibration parameters and enables stochastic gradient descent. To ensure orthogonality constraints with respect to the rotation matrix we incorporate Lie-group techniques. Furthermore, instead of optimizing on entire images, we operate on local patches that are extracted from the temporally synchronized projected Lidar points and camera frames. Our experiments show that this technique not only improves over existing techniques in terms of accuracy, but also shows considerable generalization capabilities towards new Lidar-camera configurations.
We are interested in computing a mini-batch-capable end-to-end algorithm to identify statistically independent components (ICA) in large scale and high-dimensional datasets. Current algorithms typically rely on pre-whitened data and do not integrate the two procedures of whitening and ICA estimation. Our online approach estimates a whitening and a rotation matrix with stochastic gradient descent on centered or uncentered data. We show that this can be done efficiently by combining Batch Karhunen-Löwe-Transformation [1] with Lie group techniques. Our algorithm is recursion-free and can be organized as feed-forward neural network which makes the use of GPU acceleration straight-forward. Because of the very fast convergence of Batch KLT, the gradient descent in the Lie group of orthogonal matrices stabilizes quickly. The optimization is further enhanced by integrating ADAM [2], an improved stochastic gradient descent (SGD) technique from the field of deep learning. We test the scaling capabilities by computing the independent components of the well-known ImageNet challenge (144 GB). Due to its robustness with respect to batch and step size, our approach can be used as a drop-in replacement for standard ICA algorithms where memory is a limiting factor.
The detection of anomalous or novel images given a training dataset of only clean reference data (inliers) is an important task in computer vision. We propose a new shallow approach that represents both inlier and outlier images as ensembles of patches, which allows us to effectively detect novelties as mean shifts between reference data and outliers with the Hotelling T2 test. Since mean-shift can only be detected when the outlier ensemble is sufficiently separate from the typical set of the inlier distribution, this typical set acts as a blind spot for novelty detection. We therefore minimize its estimated size as our selection rule for critical hyperparameters, such as, e.g., the size of the patches is crucial. To showcase the capabilities of our approach, we compare results with classical and deep learning methods on the popular datasets MNIST and CIFAR-10, and demonstrate its real-world applicability in a large-scale industrial inspection scenario.
Purpose
The goal of this research survey was to propose an entrepreneurship education model for students in higher education institutions.
Methodology
A questionnaire was distributed to 246 randomly sampled students at the Universitas Negeri Jakarta. The data was analyzed through Structural Equation Modeling to study the variables of entrepreneurship education for higher education students and examine whether it can be predicted by the university leadership as a facilitator of entrepreneurial culture, university departments as promoters of entrepreneurial skills, and university research as an incubator of local business
development.
Findings
The results show that university leadership as a facilitator of entrepreneurial culture is supported by the university leadership’s fostering a culture of entrepreneurial thinking. It was also evident that the university placed sufficient emphasis on entrepreneurial education, and it successfully motivated lecturers to embrace entrepreneurship education, and students to embrace entrepreneurship education. The results also indicated that university departments acted as promoters of entrepreneurial skills and stimulated students to attain sufficient entrepreneurial skills during their university education. Lastly, the university research also proved as an incubator of local business development and was found influenced by the university conducting research projects with local
private sector businesses and supporting graduates planning to launch start-ups.
Implications to Research and Practice
The survey results will provide valuable policy insights to improve entrepreneurship education. The university faculty and students would have opportunities to gain practical experience in local private sector businesses. The model of entrepreneurship education proposed herein can be applied for higher education students.
This research project has been awarded as part of the research competition organized by Connect2Recover, which is a global initiative by the International Telecommunication Union (ITU) with the priority of reinforcing and strengthening the digital infrastructure and ecosystems of developing countries. Carried out by an international and transdisciplinary research consortium, the project sets out to analyze the prospects of digital federation and data sharing within the context of Botswana. Considering the country’s stage of economic and digital development, the project team identified Botswana’s smallholder agricultural sector as the most important area of digital transformation given the development need of the country’s primary sector.
Derived from semi-structured interviews, a focus group, as well as secondary research, the project team developed a digital transformation roadmap based on three development stages: (a) crowdfarming pilot, (b) crowdfarming marketplace, and (c) digital ecosystem for smallholder agriculture. Based on a detailed review of Botswana’s smallholder agriculture and the government’s digitalization strategy, the report envisions each phase, especially the pilot project, in terms of a minimal viable product. This is to consider the low level of digital penetration of Botswana’s primary sector, while providing an incentive to connect smallholders with consumers, traders, and retailers.
The project team has been successful in receiving commitment from actual smallholder farmers, the farmer association and government, as well as support for the idea of developing a crowdfarming marketplace as a novel production model and, eventually, a digital agriculture ecosystem for smallholder farmers, livestock producers, and agricultural technology companies and start-ups. The report is a proposal for a phase-one pilot project with the objective to advance smallholder agribusiness in Botswana.
Systematic Generation of XSS and SQLi Vulnerabilities in PHP as Test Cases for Static Code Analysis
(2022)
Synthetic static code analysis test suites are important to test the basic functionality of tools. We present a framework that uses different source code patterns to generate Cross Site Scripting and SQL injection test cases. A decision tree is used to determine if the test cases are vulnerable. The test cases are split into two test suites. The first test suite contains 258,432 test cases that have influence on the decision trees. The second test suite contains 20 vulnerable test cases with different data flow patterns. The test cases are scanned with two commercial static code analysis tools to show that they can be used to benchmark and identify problems of static code analysis tools. Expert interviews confirm that the decision tree is a solid way to determine the vulnerable test cases and that the test suites are relevant.
This policy brief presents the possibilities of using big data analytics for safe, decarbonised and climate-resilient infrastructure. The policy brief focuses on current constraints and limitations to applying big data analytics to the infrastructure ecosystem and presents several examples and best practices for different infrastructure sectors and at different policy levels (national, municipal) to highlight recommendations and policy requirements needed for deep digital transformation and sustainable solutions in infrastructure planning and delivery.
The use of deep learning models with medical data is becoming more widespread. However, although numerous models have shown high accuracy in medical-related tasks, such as medical image recognition (e.g. radiographs), there are still many problems with seeing these models operating in a real healthcare environment. This article presents a series of basic requirements that must be taken into account when developing deep learning models for biomedical time series classification tasks, with the aim of facilitating the subsequent production of the models in healthcare. These requirements range from the correct collection of data, to the existing techniques for a correct explanation of the results obtained by the models. This is due to the fact that one of the main reasons why the use of deep learning models is not more widespread in healthcare settings is their lack of clarity when it comes to explaining decision making.
Nowadays, the importance of early active patient mobilization in the recovery and rehabilitation phase has increased significantly. One way to involve patients in the treatment is a gamification-like approach, which is one of the methods of motivation in various life processes. This article shows a system prototype for patients who require physical activity because of active early mobilization after medical interventions or during illness. Bedridden patients and people with a sedentary lifestyle (predominantly lying in bed) are also potential users. The main idea for the concept was non-contact system implementation for the patients making them feel effortless during its usage. The system consists of three related parts: hardware, software, and game application. To test the relevance and coherence of the system, it was used by 35 people. The participants were asked to play a video game requiring them to make body movements while lying down. Then they were asked to take part in a small survey to evaluate the system's usability. As a result, we offer a prototype consisting of hardware and software parts that can increase and diversify physical activity during active early mobilization of patients and prevent the occurrence of possible health problems due to predominantly low activity. The proposed design can be possibly implemented in hospitals, rehabilitation centers, and even at home.
Home health applications have evolved over the last few decades. Assistive systems such as a data platform in connection with health devices can allow for health-related data to be automatically transmitted to a database. However, there remain significant challenges concerning intermodular communication. Central among them is the challenge of achieving interoperability, the ability of devices to communicate and share data with each other. A major goal of this project was to extend an existing data platform (COMES®) and establish working interoperability by connecting assistive devices with differing approaches. We describe this process for a sleep monitoring and a physical exercise device. Furthermore, we aimed to test this setup and the implementation with a data platform in both a laboratory and an in-home setting with 11 elderly participants. The platform modification was realized, and the relevant changes were made so that the incoming data could be processed by the data platform, as well as visually displayed in real-time. Data was recorded by the respective device and transmitted into the data server with minor disruptions. Our observations affirmed that difficulties and data loss are far more likely to occur with increasing technical complexity, in the event of instable internet connection, or when the device setup requires (elderly) subjects to take specific steps for proper functioning. We emphasize the importance for tests and evaluations of home health technologies in real-life circumstances.
Healthy sleep is required for sufficient restoration of the human body and brain. Therefore, in the case of sleep disorders, appropriate therapy should be applied timely, which requires a prompt diagnosis. Traditionally, a sleep diary is a part of diagnosis and therapy monitoring for some sleep disorders, such as cognitive behaviour therapy for insomnia. To automatise sleep monitoring and make it more comfortable for users, substituting a sleep diary with a smartwatch measurement could be considered. With the aim of providing accurate results, a study with a total of 30 night recordings was conducted. Objective sleep measurement with a Samsung Galaxy Watch 4 was compared with a subjective approach (sleep diary), evaluating the four relevant sleep characteristics: time of getting asleep, wake up time, sleep efficiency (SE), and total sleep time (TST). The performed analysis has demonstrated that the median difference between both measurement approaches was equal to 7 and 3 minutes for a time of getting asleep and wake up time correspondingly, which allows substituting a subjective measurement with a smartwatch. The SE was determined with a median difference between the two measurement methods of 5.22%. This result also implicates a possibility of substitution. Some single recordings have indicated a higher variance between the two approaches. Therefore, the conclusion can be made that a substitution provides reliable results primarily in the case of long-term monitoring. The results of the evaluation of the TST measurement do not allow to recommend substitution of the measurement method.
In order to support entrepreneurs in the Business Model Innovation (BMI) process, practice-oriented frameworks such as the St. Gallen Business Model NavigatorTM (BMN) are considered to be a powerful tool. The aim of this paper is to identify strengths and limitations of the BMN when applied to start-ups in their early stages and to contribute to the optimization of the BMN in terms of applicability for start-ups. Furthermore, the paper aims to emphasize the importance of BMI for start-ups and the relevance of a supporting framework as well as formulating a unified catalogue of requirements of BMI frameworks for start-ups.
The citizen-centered health platform project is intended to provide a platform that can be used in EU cross-border regions, where social and economic exchange occurs across national borders. The overriding challenges are: (a) social: improving citizen-centered health and care provision; (b) technical: providing a digital platform for networking citizens, service providers, and municipal actors; (c) economic: developing long-term successful (sustainable) business models/value chains. The platform should strengthen and expand existing networks and establish new regional networks. Each network addresses particular challenges and apply them in a region-specific manner. Here, the national boundary conditions and the interregional needs play an essential role. These objectives require sufficient participation of civil society representatives. Furthermore, the platform will establish an overarching, sustainable, and knowledge-based network of health experts. The platform is to be jointly developed and implemented in the regions and follow an open-access approach. Therefore, synergies will be shared more quickly, strengthening competencies and competitiveness. In addition to practice partners, scientific and municipal institutions and SMEs are involved. The actors thus contribute to scientific performance, innovative strength, and resilience.
Low-Code Development Platforms (LCDPs) enable non-information technology (IT) personnel to develop applications and workflows independently of the IT department. Consequently, these digital platforms help to overcome the growing need for software development. However, science and practice warn of several barriers that slow down or hinder the usage of LCDPs. This publication scientifically identifies, analyzes, and discusses challenges during implementation and application of LCDPs from both perspectives in a holistic manner. Therefore, we conduct an exploratory study (data from scientific literature, expert interviews, and practical studies) and assign the challenges to the socio-technical system model. The results show that the scientific and practical communities recognize common challenges (especially knowledge transfer) but also perceive differences related to technological (science) and social (practice) aspects. This paper proposes future research directions for academia, such as governance, culture change, and value evaluation of LCDPs. Additionally, practitioners can prepare for possible challenges when using LCPDs.
This paper presents the current state of development and selected technological challenges in the application of ecologically and economically sustainable nets for aquaculture based on ongoing development projects. These aim at the development of a new material system of high-strength stainless steel wires as net material with environmentally compatible antifouling properties for nearshore and offshore aquacultures. Current plastic netting materials will be replaced with high-strength stainless steel to provide a more environmentally friendly system that can withstand more severe mechanical stresses (waves, storms, tides and predators). A new antifouling strategy is expected to solve current challenges, such as ecological damage (e.g., due to pollution from copper-containing antifouling substances or microplastics), high maintenance costs (e.g., cleaning and repairs), and shorter service life. Approaches for the next development steps are presented based on previous experience as well as calculation models based on this experience.
Cultural Mapping 4.0
(2021)
Cultural mapping aims to capture and visualize tangible and intangible cultural assets. This extend abstract proposes the consequent extension of analogue forms of cultural mapping using digital technologies, and its contribution is two-fold. First, the necessary theoretical basis is provided by a literature review of the still-young field of cultural mapping and the complementary disciplines of participatory mapping and digital story-mapping. Second, we propose a digitally enhanced Cultural Mapping 4.0 vision based on a case study from an ongoing research project in the Lake Constance region. Digital participatory mapping approaches are applied to capture data, and to validate and disseminate the results, story-mapping - a spatial form of digital storytelling - is used.
This paper examines the interdependencies of tourism, Buddhism and sustainability combining in-depth-interviews with Buddhism experts and non-participant observation in a mixed-method approach. The area under investigation is the Alpine region of Austria, Germany and Switzerland, since it is home to Asian and Western forms of Buddhism tourism alike. Results show that Buddhism tourism as a value-based activity on the one hand is not commercial, but since demand is rising, on the other hand tendencies towards more commercial forms can be observed. As a modest form of activity Buddhism tourism does not shape the landscape of the Alpine area and by its nature it incorporates sustainability.
Beim data-driven learning (DDL) werden Lernerinnen und Lerner angeleitet, sprachliche Muster mit Hilfe von Korpuswerkzeugen zu entdecken und eigene Korpusabfragen durchzuführen. Am Beispiel einer Unterrichtseinheit für den Wirtschaftsdeutsch-Unterricht wird der Einsatz von DDL erläutert. Es wird deutlich, welche Chancen korpuslinguistische Verfahren bieten, aber auch, welche Probleme beim DDL auftreten können. Vor allem für die Planung des Fachsprachenunter-richts können korpuslinguistische Analysen hilfreich sein: Zu nennen sind die Bedarfsermittlung, die Auswahl von Materialien, die Identifizierung von typischem Wortschatz und häufigen Mustern sowie die Erstellung von Übungsmaterialien. Das Praxisbeispiel, das auf andere Kontexte übertragen werden kann, illustriert, wie sich korpuslinguistische Verfahren und DDL auf die Unterrichtsplanung und -durchführung auswirken: Sprache wird als Datenmenge betrachtet; der Fokus liegt auf sprachlichen Mustern; Fragen nach der Korrektheit bzw. der Angemessenheit werden thematisiert.
Deep transformation models
(2021)
We present a deep transformation model for probabilistic regression. Deep learning is known for outstandingly accurate predictions on complex data but in regression tasks it is predominantly used to just predict a single number. This ignores the non-deterministic character of most tasks. Especially if crucial decisions are based on the predictions, like in medical applications, it is essential to quantify the prediction uncertainty. The presented deep learning transformation model estimates the whole conditional probability distribution, which is the most thorough way to capture uncertainty about the outcome. We combine ideas from a statistical transformation model (most likely transformation) with recent transformation models from deep learning (normalizing flows) to predict complex outcome distributions. The core of the method is a parameterized transformation function which can be trained with the usual maximum likelihood framework using gradient descent. The method can be combined with existing deep learning architectures. For small machine learning benchmark datasets, we report state of the art performance for most dataset and partly even outperform it. Our method works for complex input data, which we demonstrate by employing a CNN architecture on image data.
In this article, the collection of classes of matrices presented in [J. Garloff, M. Adm, ad J. Titi, A survey of classes of matrices possessing the interval property and related properties, Reliab. Comput. 22:1-14, 2016] is continued. That is, given an interval of matrices with respect to a certain partial order, it is desired to know whether a special property of the entire matrix interval can be inferred from some of its element matrices lying on the vertices of the matrix interval. The interval property of some matrix classes found in the literature is presented, and the interval property of further matrix classes including the ultrametric, the conditionally positive semidefinite, and the infinitely divisible matrices is given for the first time. For the inverse M-matrices the cardinality of the required set of vertex matrices known so far is significantly reduced.
Probabilistic Short-Term Low-Voltage Load Forecasting using Bernstein-Polynomial Normalizing Flows
(2021)
The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level. However, high fluctuations and increasing electrification cause huge forecast errors with traditional point estimates. Probabilistic load forecasts take future uncertainties into account and thus enables various applications in low-carbon energy systems. We propose an approach for flexible conditional density forecasting of short-term load based on Bernstein-Polynomial Normalizing Flows where a neural network controls the parameters of the flow. In an empirical study with 363 smart meter customers, our density predictions compare favorably against Gaussian and Gaussian mixture densities and also outperform a non-parametric approach based on the pinball loss for 24h-ahead load forecasting for two different neural network architectures.
The State of Custom
(2021)
In our article, we engage with the anthropologist Gerd Spittler’s pathbreaking
article “Dispute settlement in the shadow of Leviathan” (1980) in which
he strives to integrate the existence of state courts (the eponymous Leviathan’s
shadow) in (post-)colonial Africa into the analysis on non-state court legal practices.
According to Spittler, it is because of undesirable characteristics inherent
in state courts that the disputing parties tended to rather involve mediators than
pursue a state court judgment. The less people liked state courts, the more likely
they were to (re-)turn to dispute settlement procedures. Now how has this situation
changed in the last four decades since its publication date? We relate his findings
to contemporary debates in legal anthropology that investigate the relationship
between disputing, law and the state. We also show through our own work in
Africa and Asia, particularly in Southern Ethiopia and Kyrgyzstan, in what ways
Spittler’s by now classical contribution to the field of legal anthropology in 1980
can be made fruitful for a contemporary anthropology of the state at a time when
not only (legal) anthropology has changed, but especially the way states deal with
putatively “customary” forms of dispute settlement.
Bittamo
(2021)
The Ethiopian state increasingly seeks to enlist putative ‘traditional authorities’ to lend legitimacy to policies and interventions in the southwestern peripheries of the country. The underlying assumptions do not accord with the perceptions of the local populations: among the Kara in the South Omo region, legitimacy is predicated upon duty and accountability, and higher degrees of public legitimacy are disconnected from authority and direct command over other people’s conduct. The office of the Kara bitti, the highest spiritual leader, thus proves intractable to such attempts at enlistment and has been little affected by the radical transformation of the Kara’s lives through increasing integration into the Ethiopian state over recent decades. But even as the office has changed little, the lives of those expected to assume the role of bitti has, and the duties of a bitti strongly constrain the office holder and limit their personal ambitions and participation in politics at the local, regional and national level.
Preliminary results of homomorphic deconvolution application to surface EMG signals during walking
(2021)
Homomorphic deconvolution is applied to sEMG signals recorded during walking. Gastrocnemius lateralis and tibialis anterior signals were acquired according to SENIAM recommendation. MUAP parameters like amplitude and scale were estimated, whilst the MUAP shape parameter was fixed. This features a useful time-frequency representation of sEMG signal. Estimation of scale MUAP parameter was verified extracting the mean frequency of filtered EMG signal, extracted from the scale parameter estimated with two different MUAP shape values.
Normal breathing during sleep is essential for people’s health and well-being. Therefore, it is crucial to diagnose apnoea events at an early stage and apply appropriate therapy. Detection of sleep apnoea is a central goal of the system design described in this article. To develop a correctly functioning system, it is first necessary to define the requirements outlined in this manuscript clearly. Furthermore, the selection of appropriate technology for the measurement of respiration is of great importance. Therefore, after performing initial literature research, we have analysed in detail three different methods and made a selection of a proper one according to determined requirements. After considering all the advantages and disadvantages of the three approaches, we decided to use the impedance measurement-based one. As a next step, an initial conceptual design of the algorithm for detecting apnoea events was created. As a result, we developed an activity diagram on which the main system components and data flows are visually represented.
Respiratory diseases are leading causes of death and disability in the world. The recent COVID-19 pandemic is also affecting the respiratory system. Detecting and diagnosing respiratory diseases requires both medical professionals and the clinical environment. Most of the techniques used up to date were also invasive or expensive.
Some research groups are developing hardware devices and techniques to make possible a non-invasive or even remote respiratory sound acquisition. These sounds are then processed and analysed for clinical, scientific, or educational purposes.
We present the literature review of non-invasive sound acquisition devices and techniques.
The results are about a huge number of digital tools, like microphones, wearables, or Internet of Thing devices, that can be used in this scope.
Some interesting applications have been found. Some devices make easier the sound acquisition in a clinic environment, but others make possible daily monitoring outside that ambient. We aim to use some of these devices and include the non-invasive recorded respiratory sounds in a Digital Twin system for personalized health.
The present work proposes the use of modern ICT technologies such as smartphones, NFCs, internet, and web technologies, to help patients in carrying out their therapies. The implemented system provides a calendar with a reminder of the assumptions, ensures the drug identification through NFC, allows remote assistance from healthcare staff and family members to check and manage the therapy in real-time. The system also provides centralized information on the patient's therapeutic situation, helpful in choosing new compatible therapies.
Location-aware mobile devices are becoming increasingly popular and GPS sensors are built into nearly every portable unit with computational capabilities. At the same time, the emergence of location-aware virtual services and ideas calls for new efficient spatial real-time queries. Communication latency in mobile environments interacting with high decentralization and the need of scalability in high-density systems with immense client counts leads to major challenges. In this paper we describe a decentralized architecture for continuous range queries in settings in which both, the requested and the requesting clients, are mobile. While prior works commonly use a request-response approach we provide a stream-based adaptive grid solution dealing with arbitrary high client counts and improving communication latency that meets given hard real-time constraints.
Uncertainty about the future requires companies to create discontinuous innovations. Established companies, however, struggle to do so; whereas independent startups seem to better cope with this. Consequently, established companies set up entrepreneurial initiatives to make use of startups' benefits. Consequently, this led-amongst others-to great interest in socalled corporate entrepreneurship (CE) programs and to the development and characterization of several different forms. Their processes to achieve certain objectives, yet, are still rather ineffective. Thus, considerations of the actions performed in preparation for and during CE programs could be one approach to improve this but are still absent today. Furthermore, the increasing use of several CE programs in parallel seems to bear the potential for synergies and, thus, more efficient use of resources. Aiming to provide insights to both issues, this study analyzes actions of CE programs, by looking at interviews with managers of seven corporate incubators and accelerator programs of five established German tech-companies.
In today's volatile world, established companies must be capable of optimizing their core business with incremental innovations while simultaneously developing discontinuous innovations to maintain their long-term competitiveness. Balancing both is a major challenge for companies, since different types of innovation require different organizational structures, operational modes and management styles. Established companies tend to excel in improving their current business through incremental innovations which are closely related to their current knowledge base and competencies. However, this often goes hand in hand with challenges in the exploration of knowledge that is new to the company and that is essential for the development of discontinuous innovations. In this respect, the concept of corporate entrepreneurship is recognized as a way to strengthen the exploration of new knowledge and to support the development of discontinuous innovation. For managing corporate entrepreneurship more effectively, it is crucial to understand which types of knowledge can be created through corporate entrepreneurship and which organizational designs are more suited to gain certain types of knowledge. To answer these questions, this study analyzed 23 semi-structured interviews conducted with established companies that are running such entrepreneurial activities. The results show (1) that three general types of knowledge can be explored through corporate entrepreneurship and (2) that some organizational designs are more suited to explore certain knowledge types than others are.
We compared vulnerable and fixed versions of the source code of 50 different PHP open source projects based on CVE reports for SQL injection vulnerabilities. We scanned the source code with commercial and open source tools for static code analysis. Our results show that five current state-of-the-art tools have issues correctly marking vulnerable and safe code. We identify 25 code patterns that are not detected as a vulnerability by at least one of the tools and 6 code patterns that are mistakenly reported as a vulnerability that cannot be confirmed by manual code inspection. Knowledge of the patterns could help vendors of static code analysis tools, and software developers could be instructed to avoid patterns that confuse automated tools.
Durch die zunehmende Vernetzung und den Anstieg von eingesetzter Hard- und Software hat sich die Komplexität der Unternehmensarchitektur von Unternehmen über die Jahre stetig erhöht. Das Aufkommen nutzerfreundlicher Informationstechnologie (IT)-Lösungen befähigt außerdem Fachbereiche, IT innovativ einzusetzen. Dies erhöht die Heterogenität und damit nochmals die Komplexität der Unternehmensarchitektur. Darüber hinaus treibt dieser IT-Einsatz die Digitalisierung in den Unternehmen maßgeblich voran. Dies wirft die Frage auf, ob Unternehmen überhaupt noch eine Relevanz in der Reduktion der Komplexität durch IT-Integration sehen oder ob dies vor dem Hintergrund der Digitalisierung schon ein alter Hut ist. Experteninterviews und eine qualitative Datenanalyse zeigen, dass IT-Integration und Digitalisierung keine disjunkten Phänomene sind, sondern sich gegenseitig beeinflussen. Die Ergebnisse betonen, wie unterschiedlich der Begriff aufgefasst werden kann und dass die einheitliche Nutzung damit essenziell ist. Darüber hinaus zeigen sie, dass Digitalisierung einerseits Treiber der IT-Integration ist, andererseits aber auch die Möglichkeiten zur Umsetzung verändert. Dabei ist die Integrationsentscheidung durch die Vielzahl an Vor- und Nachteile komplex. Fachbereichs-IT ist selten explizites Ziel von IT-Integrationsprojekten. Der Beitrag zeigt den wissenschaftlichen Forschungsbedarf in neuen technologischen Möglichkeiten zur IT-Integration und in der Balance von Flexibilität und IT-Integration in der Unternehmensarchitektur. Er beleuchtet, dass eine gemeinsame Sprache die Basis für IT-Integrationsprojekte ist und dass eine Kultur, in der Fachbereiche aktiv an IT-Integrationsentscheidungen teilhaben, das Ziel eines jeden Unternehmens sein sollte. Insgesamt zeigen die Analysen, dass IT-Integration noch lange kein alter Hut, sondern, im Gegenteil, brandaktuell ist.
Kleine und mittelständische Unternehmen (KMU) sind bekannt für ihre Innovationskraft und bilden das Rückgrat der deutschen Wirtschaft. Wie Studien zeigen sind sie in Bezug auf Compliance-Maßnahmen im Vergleich zu
kapitalmarktorientierten Unternehmen jedoch im Rückstand. Eine gesonderte Betrachtung der IT-Compliance erfolgt dabei in den Studien in der Regel nicht. Auch wenn zu den Gründen und Motiven fehlender IT-Compliance-Strukturen in KMU kaum Forschungsergebnisse vorliegen, zeigen doch die vielen Publikationen, die sich mit Teilaspekten von Compliance und KMU beschäftigen, dass Handlungsbedarf besteht. Insbesondere die aktuellen Veränderungen unter dem Stichwort Digitalisierung deuten auf eine gesteigerte Bedeutung von IT-Compliance-Maßnahmen vor allem in mittelständischen Unternehmen. In dieser Arbeit sollen daher mithilfe einer Literaturrecherche die aktuell behandelten Themen in Bezug auf IT-Compliance und KMU analysiert sowie aktuelle Themenschwerpunkte herausgearbeitet werden.
We propose and apply a requirements engineering approach that focuses on security and privacy properties and takes into account various stakeholder interests. The proposed methodology facilitates the integration of security and privacy by design into the requirements engineering process. Thus, specific, detailed security and privacy requirements can be implemented from the very beginning of a software project. The method is applied to an exemplary application scenario in the logistics industry. The approach includes the application of threat and risk rating methodologies, a technique to derive technical requirements from legal texts, as well as a matching process to avoid duplication and accumulate all essential requirements.