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Gamification has been increasingly applied to software engineering education in the past. The approaches vary from applying game elements on a conceptual phase in the course to using specific tools to engage the students more and support their learning goals. However, existing tools usually have game elements, such as quizzes or challenges, but do not provide a more computer game-like experience. Therefore, we try to raise the level of gamified learning experience to another level by proposing Gamify-IT. Gamify-IT is a Unity- and web-based game platform intended to help students learn software engineering. It follows an immersive role-play game characteristic where the students explore a world, find and solve minigames and clear dungeons with SE tasks. Lecturers can configure the worlds, e.g., to add content hints. Furthermore, they can add and configure minigames and dungeons to include exercises in a fully gamified way. Thereby, they customize their course in Gamify-IT to adapt the world very precisely to other materials such as lectures or exercises. Results of an evaluation of our initial prototype show that (i) students like to engage with the platform, (ii) students are motivated to learn when using Gamify-IT, and (iii) the minigames support students in understanding the learning objectives.
This research evaluates current measurement scales for ambidexterity and proposes a new approach for the measurement of this important construct. We argue that current measurement approaches may be unsuitable to capture the concept of ambidexterity. Through a systematic scale development process, we derive a measurement scale with dual items that simultaneously refer to both dimensions, exploitation and exploration, thus reflecting the true nature of ambidexterity. An extensive pre-test with 39 executives suggests that our scale is suitable for capturing ambidexterity. Our measurement model enhances conceptual clarity of ambidexterity and can serve as a base for future investigations of the concept.
Human pose estimation (HPE) is integral to scene understanding in numerous safety-critical domains involving human-machine interaction, such as autonomous driving or semi-automated work environments. Avoiding costly mistakes is synonymous with anticipating failure in model predictions, which necessitates meta-judgments on the accuracy of the applied models. Here, we propose a straightforward human pose regression framework to examine the behavior of two established methods for simultaneous aleatoric and epistemic uncertainty estimation: maximum a-posteriori (MAP) estimation with Monte-Carlo variational inference and deep evidential regression (DER). First, we evaluate both approaches on the quality of their predicted variances and whether these truly capture the expected model error. The initial assessment indicates that both methods exhibit the overconfidence issue common in deep probabilistic models. This observation motivates our implementation of an additional recalibration step to extract reliable confidence intervals. We then take a closer look at deep evidential regression, which, to our knowledge, is applied comprehensively for the first time to the HPE problem. Experimental results indicate that DER behaves as expected in challenging and adverse conditions commonly occurring in HPE and that the predicted uncertainties match their purported aleatoric and epistemic sources. Notably, DER achieves smooth uncertainty estimates without the need for a costly sampling step, making it an attractive candidate for uncertainty estimation on resource-limited platforms.
The benefits of urban data cannot be realized without a political and strategic view of data use. A core concept within this view is data governance, which aligns strategy in data-relevant structures and entities with data processes, actors, architectures, and overall data management. Data governance is not a new concept and has long been addressed by scientists and practitioners from an enterprise perspective. In the urban context, however, data governance has only recently attracted increased attention, despite the unprecedented relevance of data in the advent of smart cities. Urban data governance can create semantic compatibility between heterogeneous technologies and data silos and connect stakeholders by standardizing data models, processes, and policies. This research provides a foundation for developing a reference model for urban data governance, identifies challenges in dealing with data in cities, and defines factors for the successful implementation of urban data governance. To obtain the best possible insights, the study carries out qualitative research following the design science research paradigm, conducting semi-structured expert interviews with 27 municipalities from Austria, Germany, Denmark, Finland, Sweden, and the Netherlands. The subsequent data analysis based on cognitive maps provides valuable insights into urban data governance. The interview transcripts were transferred and synthesized into comprehensive urban data governance maps to analyze entities and complex relationships with respect to the current state, challenges, and success factors of urban data governance. The findings show that each municipal department defines data governance separately, with no uniform approach. Given cultural factors, siloed data architectures have emerged in cities, leading to interoperability and integrability issues. A city-wide data governance entity in a cross-cutting function can be instrumental in breaking down silos in cities and creating a unified view of the city’s data landscape. The further identified concepts and their mutual interaction offer a powerful tool for developing a reference model for urban data governance and for the strategic orientation of cities on their way to data-driven organizations.
Measuring cardiorespiratory parameters in sleep, using non-contact sensors and the Ballistocardiography technique has received much attention due to the low-cost, unobtrusive, and non-invasive method. Designing a user-friendly, simple-to-use, and easy-to-deployment preserving less error-prone remains open and challenging due to the complex morphology of the signal. In this work, using four forcesensitive resistor sensors, we conducted a study by designing four distributions of sensors, in order to simplify the complexity of the system by identifying the region of interest for heartbeat and respiration measurement. The sensors are deployed under the mattress and attached to the bed frame without any interference with the subjects. The four distributions are combined in two linear horizontal, one linear vertical, and one square, covering the influencing region in cardiorespiratory activities. We recruited 4 subjects and acquired data in four regular sleeping positions, each for a duration of 80 seconds. The signal processing was performed using discrete wavelet transform bior 3.9 and smooth level of 4 as well as bandpass filtering. The results indicate that we have achieved the mean absolute error of 2.35 and 4.34 for respiration and heartbeat, respectively. The results recommend the efficiency of a triangleshaped structure of three sensors for measuring heartbeat and respiration parameters in all four regular sleeping positions.
Large critical systems, such as those created in the space domain, are usually developed by a large number of organizations and, furthermore, they have to comply with standards. Yet, the different stakeholders often do not have a common understanding of the needed quality of requirements specifications. Achieving such a common understanding is a laborious process that is currently not sufficiently supported. Moreover, such a common understanding must be aligned with the standards. In this paper, we present an approach that can be used to align the different stakeholder perceptions regarding the quality of requirements specifications. Existing quality models for requirements specifications are analyzed for equivalences, and transferred into a common representation, the so-called Aligned Quality Map (AQM). Furthermore, a process is defined that supports the alignment of different stakeholder perspectives with regard to the quality of requirements specifications using AQM, which is validated in a case study in the context of European space projects. AQM has been created and populated with an initial set of quality models. It is designed in such way that it can be extended to include further quality models. The case study has shown that an alignment of different stakeholder perspectives and the quality model of the European Cooperation for Space Standardization using AQM is feasible. The approach allows for aligning different stakeholder perspectives for a common understanding of the quality of requirements specifications in the context of standards. Furthermore, AQM supports the assessment of requirements specifications.
Software development teams have to face stress caused by deadlines, staff turnover, or individual differences in commitment, expertise, and time zones. While students are typically taught the theory of software project management, their exposure to such stress factors is usually limited. However, preparing students for the stress they will have to endure once they work in project teams is important for their own sake, as well as for the sake of team performance in the face of stress. Team performance has been linked to the diversity of software development teams, but little is known about how diversity influences the stress experienced in teams. In order to shed light on this aspect, we provided students with the opportunity to self-experience the basics of project management in self-organizing teams, and studied the impact of six diversity dimensions on team performance, coping with stressors, and positive perceived learning effects. Three controlled experiments at two universities with a total of 65 participants suggest that the social background impacts the perceived stressors the most, while age and work experience have the highest impact on perceived learnings. Most diversity dimensions have a medium correlation with the quality of work, yet no significant relation to the team performance. This lays the foundation to improve students’ training for software engineering teamwork based on their diversity-related needs and to create diversity-sensitive awareness among educators, employers and researchers.
OpenAPI, WADL, RAML, and API Blueprint are popular formats for documenting Web APIs. Although these formats are in general both human and machine-readable, only the part of the format describing the syntax of a Web API is machine-understandable. Descriptions, which explain the meaning and purpose of Web API elements, are embedded as natural language text snippets into documents and target human readers but not machines. To enable machines to read and process these state-of-practice Web API documentation, we propose a Transformer model that solves the generic task of identifying a Web API element within a syntax structure that matches a natural language query. For our first prototype, we focus on the Web API integration task of matching output with input parameters and fined-tuned a pre-trained CodeBERT model to the downstream task of question answering with samples from 2,321 OpenAPI documentation. We formulate the original question answering problem as a multiple choice task: given a semantic natural language description of an output parameter (question) and the syntax of the input schema (paragraph), the model chooses the input parameter (answer) in the schema that best matches the description. The paper describes the data preparation, tokenization, and fine-tuning process as well as discusses possible applications of our model as part of a recommender system. Furthermore, we evaluate the generalizability and the robustness of our fine-tuned model, with the result that it achieves an accuracy of 81.46% correctly chosen parameters.
Non-fungible tokens (NFTs) are unique digital assets that have recently gained significant popularity, particularly in the digital art sector. The success of NFTs and other blockchain-based innovations depends on their ac-acceptance and use by consumers. This study aims to understand the impact of moral values on the acceptance of NFTs. Based on a quantitative survey with over 800 complete responses, the analysis shows that moral aspects of NFTs are indeed important for potential users. However, there is an attitude-behavior gap, as the positive impact of moral values on the intention to use NFTs is not reflected in the actual current usage of NFTs by the respondents. This study contributes to knowledge by providing new empirical data on the acceptance of NFTs and highlighting the role of moral values on the acceptance decision.
Providing a digital infrastructure, platform technologies foster interfirm collaboration between loosely coupled companies, enabling the formation of ecosystems and building the organizational structure for value co-creation. Despite the known potential, the development of platform ecosystems creates new sources of complexity and uncertainty due to the involvement of various independent actors. For a platform ecosystem to succeed, it is essential that the platform ecosystem participants are aligned, coordinated, and given a common direction. Traditionally, product roadmaps have served these purposes during product development. A systematic mapping study was conducted to better understand how product roadmapping could be used in the dynamic environment of platform ecosystems. One result of the study is that there are hardly any concrete approaches for product roadmapping in platform ecosystems so far. However, many challenges on the topic are described in the literature from different perspectives. Based on the results of the systematic mapping study, a research agenda for product roadmapping in platform ecosystems is derived and presented.
The respiratory rate is a vital sign indicating breathing illness. It is necessary to analyze the mechanical oscillations of the patient's body arising from chest movements. An inappropriate holder on which the sensor is mounted, or an inappropriate sensor position is some of the external factors which should be minimized during signal registration. This paper considers using a non-invasive device placed under the bed mattress and evaluates the respiratory rate. The aim of the work is the development of an accelerometer sensor holder for this system. The normal and deep breathing signals were analyzed, corresponding to the relaxed state and when taking deep breaths. The evaluation criterion for the holder's model is its influence on the patient's respiratory signal amplitude for each state. As a result, we offer a non-invasive system of respiratory rate detection, including the mechanical component providing the most accurate values of mentioned respiratory rate.
Multi-versioning and MVCC are the foundations of many modern DBMSs. Under mixed workloads and large datasets, the creation of the transactional snapshot can become very expensive, as long-running analytical transactions may request old versions, residing on cold storage, for reasons of transactional consistency. Furthermore, analytical queries operate on cold data, stored on slow persistent storage. Due to the poor data locality, snapshot creation may cause massive data transfers and thus lower performance. Given the current trend towards computational storage and near-data processing, it has become viable to perform such operations in-storage to reduce data transfers and improve scalability. neoDBMS is a DBMS designed for near-data processing and computational storage. In this paper, we demonstrate how neoDBMS performs snapshot computation in-situ. We showcase different interactive scenarios, where neoDBMS outperforms PostgreSQL 12 by up to 5×.
For a long time, most discrete accelerators have been attached to host systems using various generations of the PCI Express interface. However, with its lack of support for coherency between accelerator and host caches, fine-grained interactions require frequent cache-flushes, or even the use of inefficient uncached memory regions. The Cache Coherent Interconnect for Accelerators (CCIX) was the first multi-vendor standard for enabling cache-coherent host-accelerator attachments, and already is indicative of the capabilities of upcoming standards such as Compute Express Link (CXL). In our work, we compare and contrast the use of CCIX with PCIe when interfacing an ARM-based host with two generations of CCIX-enabled FPGAs. We provide both low-level throughput and latency measurements for accesses and address translation, as well as examine an application-level use-case of using CCIX for fine-grained synchronization in an FPGA-accelerated database system. We can show that especially smaller reads from the FPGA to the host can benefit from CCIX by having roughly 33% shorter latency than PCIe. Small writes to the host have a latency roughly 32% higher than PCIe, though, since they carry a higher coherency overhead. For the database use-case, the use of CCIX allowed to maintain a constant synchronization latency even with heavy host-FPGA parallelism.
We present a multitask network that supports various deep neural network based pedestrian detection functions. Besides 2D and 3D human pose, it also supports body and head orientation estimation based on full body bounding box input. This eliminates the need for explicit face recognition. We show that the performance of 3D human pose estimation and orientation estimation is comparable to the state-of-the-art. Since very few data sets exist for 3D human pose and in particular body and head orientation estimation based on full body data, we further show the benefit of particular simulation data to train the network. The network architecture is relatively simple, yet powerful, and easily adaptable for further research and applications.
The scoring of sleep stages is an essential part of sleep studies. The main objective of this research is to provide an algorithm for the automatic classification of sleep stages using signals that may be obtained in a non-obtrusive way. After reviewing the relevant research, the authors selected a multinomial logistic regression as the basis for their approach. Several parameters were derived from movement and breathing signals, and their combinations were investigated to develop an accurate and stable algorithm. The algorithm was implemented to produce successful results: the accuracy of the recognition of Wake/NREM/REM stages is equal to 73%, with Cohen's kappa of 0.44 for the analyzed 19324 sleep epochs of 30 seconds each. This approach has the advantage of using the only movement and breathing signals, which can be recorded with less effort than heart or brainwave signals, and requiring only four derived parameters for the calculations. Therefore, the new system is a significant improvement for non-obtrusive sleep stage identification compared to existing approaches.
Together with many success stories, promises such as the increase in production speed and the improvement in stakeholders' collaboration have contributed to making agile a transformation in the software industry in which many companies want to take part. However, driven either by a natural and expected evolution or by contextual factors that challenge the adoption of agile methods as prescribed by their creator(s), software processes in practice mutate into hybrids over time. Are these still agile In this article, we investigate the question: what makes a software development method agile We present an empirical study grounded in a large-scale international survey that aims to identify software development methods and practices that improve or tame agility. Based on 556 data points, we analyze the perceived degree of agility in the implementation of standard project disciplines and its relation to used development methods and practices. Our findings suggest that only a small number of participants operate their projects in a purely traditional or agile manner (under 15%). That said, most project disciplines and most practices show a clear trend towards increasing degrees of agility. Compared to the methods used to develop software, the selection of practices has a stronger effect on the degree of agility of a given discipline. Finally, there are no methods or practices that explicitly guarantee or prevent agility. We conclude that agility cannot be defined solely at the process level. Additional factors need to be taken into account when trying to implement or improve agility in a software company. Finally, we discuss the field of software process-related research in the light of our findings and present a roadmap for future research.
Context: The manufacturing industry is facing a transformation with regard to Industry 4.0 (I4). A transformation towards full automation of production including a multitude of innovations is necessary. Startups and entrepreneurial processes can support such a transformation as has been shown in other industries. However, I4 has some specifics, so it is unclear how entrepreneurship can be adapted in I4. Understanding these specifics is important to develop suitable training programs for I4 startups and to accelerate the transformation.
Objective: This study identifies and outlines the essential characteristics and constraints of entrepreneurial processes in I4.
Method: 14 semi-structured interviews were conducted with experts in the field of I4 entrepreneurship. The interviews were analysed and categorized by qualitative analyses.
Results: The interviews revealed several characteristics of I4 that have a significant impact on the various phases of the entrepreneurial process. Examples of such specifics include the difficult access to customers, the necessary deep understanding of the customer and the domain, the difficulty of testing risky assumptions, and the complex development and productization of solutions. The complexity of hardware and software components, cost structures, and necessary customer-specific customizations affect the scalability of I4 startups. These essential characteristics also require specialised skills and resources from I4 startups.
Context: The software-intensive business is characterized by increasing market dynamics, rapid technological changes, and fast-changing customer behaviors. Organizations face the challenge of moving away from traditional roadmap formats to an outcome-oriented approach that focuses on delivering value to the customer and the business. An important starting point and a prerequisite for creating such outcome-oriented roadmaps is the development of a product vision to which internal and external stakeholders can be aligned. However, the process of creating a product vision is little researched and understood.
Objective: The goal of this paper is to identify lessons-learned from product vision workshops, which were conducted to develop outcome-oriented product roadmaps.
Method: We conducted a multiple-case study consisting of two different product vision workshops in two different corporate contexts.
Results: Our results show that conducting product vision workshops helps to create a common understanding among all stakeholders about the future direction of the products. In addition, we identified key organizational aspects that contribute to the success of product vision workshops, including the participation of employees from functionally different departments.
Product roadmaps in the new mobility domain: state of the practice and industrial experiences
(2021)
Context: The New Mobility industry is a young market that includes high market dynamics and is therefore associated with a high degree of uncertainty. Traditional product roadmapping approaches such a detailed planning of features over a long-time horizon typically fail in such environments. For this reason, companies that are active in the field of New Mobility are faced with the challenge of keeping their product roadmaps reliable for stakeholders while at the same time being able to react flexibly to changing market requirements.
Objective: The goal of this paper is to identify the state of practice regarding product roadmapping of New Mobility companies. In addition, the related challenges within the product roadmapping process as well as the success factors to overcome these challenges will be highlighted.
Method: We conducted semi-structured expert interviews with 8 experts (7 German company and one Finnish company) from the field of New Mobility and performed a content analysis.
Results: Overall the results of the study showed that the participating companies are aware of the requirements that the New Mobility sector entails. Therefore, they exhibit a high level of maturity in terms of product roadmapping. Nevertheless, some aspects were revealed that pose specific challenges for the participating companies. One major challenge, for example, is that New Mobility in terms of public clients is often a tender business with non-negotiable product requirements. Thus, the product roadmap can be significantly influenced from the outside. As factors for a successful product roadmapping mainly soft factors such as trust between all people involved in the product development process and transparency throughout the entire roadmapping process were mentioned.
How to prioritize your product roadmap when everything feels important: a grey literature review
(2021)
Context: A key factor in achieving product success is to identify what and in which order outputs must be launched in order to deliver the most value to the customer and the business. Therefore, a well-established process to discover and prioritize the content of the product roadmap in the right way is crucial for the success of a company. However, most companies prioritize their product roadmap items based on opinions of experts or the management. Additionally, increasing market dynamics, rapidly evolving technologies and fast changing customer behavior complicate the conduction of the prioritization process. Therefore, many companies are struggling to finding and establishing suitable techniques for prioritizing their product roadmap.
Objective: In order to gain a better understanding of the prioritization process in a dynamic and uncertain market environment, this paper aims to identify suitable techniques for the prioritization in such environments.
Method: We conducted a Grey Literature Review according to the guidelines of Garousi et al.
Results: 18 techniques for the prioritization of the product roadmap could be identified. 15 techniques are primarily used to prioritize outputs by considering factors such as the expected impact or effort. Two technique are most suitable for prioritizing risky assumptions that need to be validated and one technique focuses on the prioritization of outcomes. All techniques have in common that they should be conducted as cross-functional team activity in order to include different perspectives in the prioritization process.
Near-Data Processing is a promising approach to overcome the limitations of slow I/O interfaces in the quest to analyze the ever-growing amount of data stored in database systems. Next to CPUs, FPGAs will play an important role for the realization of functional units operating close to data stored in non-volatile memories such as Flash.It is essential that the NDP-device understands formats and layouts of the persistent data, to perform operations in-situ. To this end, carefully optimized format parsers and layout accessors are needed. However, designing such FPGA-based Near-Data Processing accelerators requires significant effort and expertise. To make FPGA-based Near-Data Processing accessible to non-FPGA experts, we will present a framework for the automatic generation of FPGA-based accelerators capable of data filtering and transformation for key-value stores based on simple data-format specifications.The evaluation shows that our framework is able to generate accelerators that are almost identical in performance compared to the manually optimized designs of prior work, while requiring little to no FPGA-specific knowledge and additionally providing improved flexibility and more powerful functionality.
Enterprises and information societies confront crucial challenges currently, while Industry 4.0 becomes important in the global manufacturing industry and Society 5.0 should contribute to a supersmart society, especially for healthcare. Physical activity monitoring digital platforms are architected to improve the healthcare status of patients with diabetes and other lifestyle-related diseases. Furthermore, digital platforms are expected to generate profits for health technology companies and help control costs in the healthcare ecosystem. However, current digital enterprise architecture approaches are not well-established, and the potentials have not yet been realized. Design thinking approach and agile software development methodologies can overcome these limitations, beginning with proof of concept and pilot projects and then scaling to the production environment. In this paper, we describe how that the adaptive integrated digital architecture framework (AIDAF) for Design Thinking approach is proposed and verified in a case of a university hospital in the Americas. In addition, challenges and future activities for this area are discussed that cover the directions for Society 5.0.
This paper presents a generic method to enhance performance and incorporate temporal information for cardiorespiratory-based sleep stage classification with a limited feature set and limited data. The classification algorithm relies on random forests and a feature set extracted from long-time home monitoring for sleep analysis. Employing temporal feature stacking, the system could be significantly improved in terms of Cohen’s κ and accuracy. The detection performance could be improved for three classes of sleep stages (Wake, REM, Non-REM sleep), four classes (Wake, Non-REM-Light sleep, Non-REM Deep sleep, REM sleep), and five classes (Wake, N1, N2, N3/4, REM sleep) from a κ of 0.44 to 0.58, 0.33 to 0.51, and 0.28 to 0.44 respectively by stacking features before and after the epoch to be classified. Further analysis was done for the optimal length and combination method for this stacking approach. Overall, three methods and a variable duration between 30 s and 30 min have been analyzed. Overnight recordings of 36 healthy subjects from the Interdisciplinary Center for Sleep Medicine at Charité-Universitätsmedizin Berlin and Leave-One-Out-Cross-Validation on a patient-level have been used to validate the method.
Accurate and safe neurosurgical intervention can be affected by intra-operative tissue deformation, known as brain-shift. In this study, we propose an automatic, fast, and accurate deformable method, called iRegNet, for registering pre-operative magnetic resonance images to intra-operative ultrasound volumes to compensate for brain-shift. iRegNet is a robust end-to-end deep learning approach for the non-linear registration of MRI-iUS images in the context of image-guided neurosurgery. Pre-operative MRI (as moving image) and iUS (as fixed image) are first appended to our convolutional neural network, after which a non-rigid transformation field is estimated. The MRI image is then transformed using the output displacement field to the iUS coordinate system. Extensive experiments have been conducted on two multi-location databases, which are the BITE and the RESECT. Quantitatively, iRegNet reduced the mean landmark errors from pre-registration value of (4.18 ± 1.84 and 5.35 ± 4.19 mm) to the lowest value of (1.47 ± 0.61 and 0.84 ± 0.16 mm) for the BITE and RESECT datasets, respectively. Additional qualitative validation of this study was conducted by two expert neurosurgeons through overlaying MRI-iUS pairs before and after the deformable registration. Experimental findings show that our proposed iRegNet is fast and achieves state-of-the-art accuracies outperforming state-of-the-art approaches. Furthermore, the proposed iRegNet can deliver competitive results, even in the case of non-trained images as proof of its generality and can therefore be valuable in intra-operative neurosurgical guidance.
Nowadays companies are facing increasing market dynamics, rapidly evolving technologies and shifting user expectations. Together with the adoption of lean and agile practices this situation makes it increasingly difficult to plan and predict upfront which products, services or features should be developed in the future. Consequently, many organizations are struggling with their ability to provide reliable and stable product roadmaps by applying traditional approaches. This paper aims at identifying and getting a better understanding of which measures companies have taken to transform their current product roadmapping practices to the requirements of a dynamic and uncertain market environment. This also includes challenges and success factors within this transformation process as well as measures that companies have planned for the future. We conducted 18 semi-structured expert interviews with practitioners of different companies and performed a thematic data analysis. The study shows that the participating companies are aware that the transformation of traditional product roadmapping practices to fulfill the requirements of a dynamic and uncertain market environment is necessary. The most important measures that the participating companies have taken are 1) adequate item planning concerning the timeline, 2) the replacement of a fixed time-based chart by a more flexible structure, 3) the use of outcomes to determine the items (such as features) on the a roadmap, 4) the creation of a central roadmap which allows deriving different representation for each stakeholder and department.
In recent years companies have faced challenges by high market dynamics, rapidly evolving technologies and shifting user expectations. Together with the adaption of lean and agile practices, it is increasingly difficult to predict upfront which products, features or services will satisfy the needs of the customers and the organization. Currently, many new products fail to produce a significant financial return. One reason is that companies are not doing enough product discovery activities. Product discovery aims at tackling the various risks before the implementation of a product starts. The academic literature only provides little guidance for conducting product discovery in practice. Objective: In order to gain a better understanding of product discovery activities in practice, this paper aims at identifying motivations, approaches, challenges, risks, and pitfalls of product discovery reported in the grey literature. Method: We performed a grey literature review (GLR) according to the guidelines to Garousi et al. Results: The study shows that the main motivation for conducting product discovery activities is to reduce the uncertainty to a level that makes it possible to start building a solution that provides value for the customers and the business. Several product discovery approaches are reported in the grey literature which include different phases such as alignment, problem exploration, ideation, and validation. Main challenges are, among others, the lack of clarity of the problem to be solved, the prescription of concrete solutions through management or experts, and the lack of cross-functional collaboration.
AI technologies such as deep learning provide promising advances in many areas. Using these technologies, enterprises and organizations implement new business models and capabilities. In the beginning, AI-technologies have been deployed in an experimental environment. AI-based applications have been created in an ad-hoc manner and without methodological guidance or engineering approach. Due to the increasing importance of AI-technologies, however, a more structured approach is necessary that enable the methodological engineering of AI-based applications. Therefore, we develop in this paper first steps towards methodological engineering of AI-based applications. First, we identify some important differences between the technological foundations of AI- technologies, in particular deep learning, and traditional information technologies. Then we create a framework that enables to engineer AI-applications using four steps: identification of an AI-application type, sub-type identification, lifecycle phase, and definition of details. The introduced framework considers that AI-applications use an inductive approach to infer knowledge from huge collections and streams of data. It not only enables the rapid development of AI-application but also the efficient sharing of knowledge on AI-applications.
Methods based exclusively on heart rate hardly allow to differentiate between physical activity, stress, relaxation, and rest, that is why an additional sensor like activity/movement sensor added for detection and classification. The response of the heart to physical activity, stress, relaxation, and no activity can be very similar. In this study, we can observe the influence of induced stress and analyze which metrics could be considered for its detection. The changes in the Root Mean Square of the Successive Differences provide us with information about physiological changes. A set of measurements collecting the RR intervals was taken. The intervals are used as a parameter to distinguish four different stages. Parameters like skin conductivity or skin temperature were not used because the main aim is to maintain a minimum number of sensors and devices and thereby to increase the wearability in the future.
This document presents a new complete standalone system for a recognition of sleep apnea using signals from the pressure sensors placed under the mattress. The developed hardware part of the system is tuned to filter and to amplify the signal. Its software part performs more accurate signal filtering and identification of apnea events. The overall achieved accuracy of the recognition of apnea occurrence is 91%, with the average measured recognition delay of about 15 seconds, which confirms the suitability of the proposed method for future employment. The main aim of the presented approach is the support of the healthcare system with the cost-efficient tool for recognition of sleep apnea in the home environment.
The ballistocardiography is a technique that measures the heart rate from the mechanical vibrations of the body due to the heart movement. In this work a novel noninvasive device placed under the mattress of a bed estimates the heart rate using the ballistocardiography. Different algorithms for heart rate estimation have been developed.
Product roadmaps are an important tool in product development. They provide direction, enable consistent development in relation to a product vision and support communication with relevant stakeholders. There are many different formats for product roadmaps, but they are often based on the assumption that the future is highly predictable. However, especially software-intensive businesses are faced with increasing market dynamics, rapidly evolving technologies and changing user expectations. As a result, many organizations are wondering what roadmap format is appropriate for them and what components it should have to deal with an unpredictable future. Objectives: To gain a better understanding of the formats of product roadmaps and their components, this paper aims to identify suitable formats for the development and handling of product roadmaps in dynamic and uncertain markets. Method: We performed a grey literature review (GLR) according to the guidelines from Garousi. Results: A Google search identified 426 articles, 25 of which were included in this study. First, various components of the roadmap were identified, especially the product vision, themes, goals, outcomes and outputs. In addition, various product roadmap formats were discovered, such as feature-based, goal-oriented, outcome-driven and a theme-based roadmap. The roadmap components were then assigned to the various product roadmap formats. This overview aims at providing initial decision support for companies to select a suitable product roadmap format and adapt it to their own needs.
Massive data transfers in modern data intensive systems resulting from low data-locality and data-to-code system design hurt their performance and scalability. Near-data processing (NDP) and a shift to code-to-data designs may represent a viable solution as packaging combinations of storage and compute elements on the same device has become viable.
The shift towards NDP system architectures calls for revision of established principles. Abstractions such as data formats and layouts typically spread multiple layers in traditional DBMS, the way they are processed is encapsulated within these layers of abstraction. The NDP-style processing requires an explicit definition of cross-layer data formats and accessors to ensure in-situ executions optimally utilizing the properties of the underlying NDP storage and compute elements. In this paper, we make the case for such data format definitions and investigate the performance benefits under NoFTL-KV and the COSMOS hardware platform.
In networked operating room environments, there is an emerging trend towards standardized non-proprietary communication protocols which allow to build new integration solutions and flexible human-machine interaction concepts. The most prominent endeavor is the IEEE 11073 SDC protocol. For some uses cases, it would be helpful if not just medical devices could be controlled based on SDC, but also building automation systems like light, shutters, air condition, etc. For those systems, the KNX protocol is widely used. We build an SDC-to-KNX gateway which allows to use the SDC protocol for sending commands to connected KNX devices. The first prototype system was successfully implemented at the demonstration operating room at Reutlingen University. This is a first step toward the integration of a broader variety of KNX devices.
Additive manufacturing (AM) is a promising manufacturing method for many industrial sectors. For this application, industrial requirements such as high production volumes and coordinated implementation must be taken into account. These tasks of the internal handling of production facilities are carried out by the Production Planning and Control (PPC) information system. A key factor in the planning and scheduling is the exact calculation of manufacturing times. For this purpose we investigate the use of Machine Learning (ML) for the prediction of manufacturing times of AM facilities.
Serverless computing is an emerging cloud computing paradigm with the goal of freeing developers from resource management issues. As of today, serverless computing platforms are mainly used to process computations triggered by events or user requests that can be executed independently of each other. These workloads benefit from on-demand and elastic compute resources as well as per-function billing. However, it is still an open research question to which extent parallel applications, which comprise most often complex coordination and communication patterns, can benefit from serverless computing.
In this paper, we introduce serverless skeletons for parallel cloud programming to free developers from both parallelism and resource management issues. In particular, we investigate on the well known and widely used farm skeleton, which supports the implementation of a wide range of applications. To evaluate our concepts, we present a prototypical development and runtime framework and implement two applications based on our framework: Numerical integration and hyperparameter optimization - a commonly applied technique in machine learning. We report on performance measurements for both applications and discuss
the usefulness of our approach.
Continuous refactoring is necessary to maintain source code quality and to cope with technical debt. Since manual refactoring is inefficient and error prone, various solutions for automated refactoring have been proposed in the past. However, empirical studies have shown that these solutions are not widely accepted by software developers and most refactorings are still performed manually. For example, developers reported that refactoring tools should support functionality for reviewing changes. They also criticized that introducing such tools would require substantial effort for configuration and integration into the current development environment.
In this paper, we present our work towards the Refactoring-Bot, an autonomous bot that integrates into the team like a human developer via the existing version control platform. The bot automatically performs refactorings to resolve code smells and presents the changes to a developer for asynchronous review via pull requests. This way, developers are not interrupted in their workflow and can review the changes at any time with familiar tools. Proposed refactorings can then be integrated into the code base via the push of a button. We elaborate on our vision, discuss design decisions, describe the current state of development, and give an outlook on planned development and research activities.
To remain competitive in a fast changing environment, many companies started to migrate their legacy applications towards a Microservices architecture. Such extensive migration processes require careful planning and consideration of implications and challenges likewise. In this regard, hands-on experiences from industry practice are still rare. To fill this gap in scientific literature, we contribute a qualitative study on intentions, strategies, and challenges in the context of migrations to Microservices. We investigated the migration process of 14 systems across different domains and sizes by conducting 16 in-depth interviews with software professionals from 10 companies. Along with a summary of the most important findings, we present a separate discussion of each case. As primary migration drivers, maintainability and scalability were identified. Due to the high complexity of their legacy systems, most companies preferred a rewrite using current technologies over splitting up existing code bases. This was often caused by the absence of a suitable decomposition approach. As such, finding the right service cut was a major technical challenge, next to building the necessary expertise with new technologies. Organizational challenges were especially related to large, traditional companies that simultaneously established agile processes. Initiating a mindset change and ensuring smooth collaboration between teams were crucial for them. Future research on the evolution of software systems can in particular profit from the individual cases presented.
While Microservices promise several beneficial characteristics for sustainable long-term software evolution, little empirical research covers what concrete activities industry applies for the evolvability assurance of Microservices and how technical debt is handled in such systems. Since insights into the current state of practice are very important for researchers, we performed a qualitative interview study to explore applied evolvability assurance processes, the usage of tools, metrics, and patterns, as well as participants’ reflections on the topic. In 17 semi-structured interviews, we discussed 14 different Microservice-based systems with software professionals from 10 companies and how the sustainable evolution of these systems was ensured. Interview transcripts were analyzed with a detailed coding system and the constant comparison method.
We found that especially systems for external customers relied on central governance for the assurance. Participants saw guidelines like architectural principles as important to ensure a base consistency for evolvability. Interviewees also valued manual activities like code review, even though automation and tool support was described as very important. Source code quality was the primary target for the usage of tools and metrics. Despite most reported issues being related to Architectural Technical Debt (ATD), our participants did not apply any architectural or service-oriented tools and metrics. While participants generally saw their Microservices as evolvable, service cutting and finding an appropriate service granularity with low coupling and high cohesion were reported as challenging. Future Microservices research in the areas of evolution and technical debt should take these findings and industry sentiments into account.
In this paper, an approach is introduced how reinforcement learning can be used to achieve interoperability between heterogeneous Internet of Things (IoT) components. More specifically, we model an HTTP REST service as a Markov Decision Process and adapt Q-Learning to the properties of REST so that an agent in the role of an HTTP REST client can learn the semantics of the service and, especially an optimal sequence of service calls to achieve an application specific goal. With our approach, we want to open up and facilitate a discussion in the community, as we see the key for achieving interoperability in IoT by the utilization of artificial intelligence techniques.
Among the multitude of software development processes available, hardly any is used by the book. Regardless of company size or industry sector, a majority of project teams and companies use customized processes that combine different development methods— so-called hybrid development methods. Even though such hybrid development methods are highly individualized, a common understanding of how to systematically construct synergetic practices is missing. In this paper, we make a first step towards devising such guidelines. Grounded in 1,467 data points from a large-scale online survey among practitioners, we study the current state of practice in process use to answer the question: What are hybrid development methods made of? Our findings reveal that only eight methods and few practices build the core of modern software development. This small set allows for statistically constructing hybrid development methods. Using an 85% agreement level in the participants’ selections, we provide two examples illustrating how hybrid development methods are characterized by the practices they are made of. Our evidence-based analysis approach lays the foundation for devising hybrid development methods.
Context: Companies in highly dynamic markets increasingly struggle with their ability to plan product development and to create reliable roadmaps. A main reason is the decreasing lack of predictability of markets, technologies, and customer behaviors. New approaches for product roadmapping seem to be necessary in order to cope with today's highly dynamic conditions. Little research is available with respect to such new approaches. Objective: In order to better understand the state of the art and to identify research gaps, this article presents a review of the scientific literature with respect to product roadmapping. Method: We performed a systematic literature review (SLR) with respect to identify papers in the field of computer science. Results: After filtering, the search resulted in a set of 23 relevant papers. The identified papers focus on different aspects such as roadmap types, processes for creating and updating roadmaps, problems and challenges with roadmapping, approaches to visualize roadmaps, generic frameworks and specific aspects such as the combination of roadmaps with business modeling. Overall, the scientific literature covers many important aspects of roadmapping but does provide only little knowledge on how to create product roadmaps under highly dynamic conditions. Research gaps address, for instance, the inclusion of goals or outcomes into product roadmaps, the alignment of a roadmap with a product vision, and the inclusion of product discovery activities in product roadmaps. In addition, the transformation from traditional roadmapping processes to new ways of roadmapping is not sufficiently addressed in the scientific literature.
Software process improvement (SPI) is around for decades, but it is a critically discussed topic. In several waves, different aspects of SPI have been discussed in the past, e.g., large scale company-level SPI programs, maturity models, success factors, and in-project SPI. It is hard to find new streams or a consensus in the community, but there is a trend coming along with agile and lean software development. Apparently, practitioners reject extensive and prescriptive maturity models and move towards smaller, faster and continuous project-integrated SPI. Based on data from two survey studies conducted in Germany (2012) and Europe (2016), we analyze the process customization for projects and practices for implementing SPI in the participating companies. Our findings indicate that, even in regulated industry sectors, companies increasingly adopt in-project SPI activities, primarily with the goal to continuously optimize specific processes. Therefore, with this paper, we want to stimulate a discussion on how to evolve traditional SPI towards a continuous learning environment.
Recognizing human actions is a core challenge for autonomous systems as they directly share the same space with humans. Systems must be able to recognize and assess human actions in real-time. To train the corresponding data-driven algorithms, a significant amount of annotated training data is required. We demonstrate a pipeline to detect humans, estimate their pose, track them over time and recognize their actions in real-time with standard monocular camera sensors. For action recognition, we transform noisy human pose estimates in an image like format we call Encoded Human Pose Image (EHPI). This encoded information can further be classified using standard methods from the computer vision community. With this simple procedure, we achieve competitive state-of-the-art performance in pose based action detection and can ensure real-time performance. In addition, we show a use case in the context of autonomous driving to demonstrate how such a system can be trained to recognize human actions using simulation data.
RoPose-Real: real world dataset acquisition for data-driven industrial robot arm pose estimation
(2019)
It is necessary to employ smart sensory systems in dynamic and mobile workspaces where industrial robots are mounted on mobile platforms. Such systems should be aware of flexible and non-stationary workspaces and able to react autonomously to changing situations. Building upon our previously presented RoPose-system, which employs a convolutional neural network architecture that has been trained on pure synthetic data to estimate the kinematic chain of an industrial robot arm system, we now present RoPose-Real. RoPose-Real extends the prior system with a comfortable and targetless extrinsic calibration tool, to allow for the production of automatically annotated datasets for real robot systems. Furthermore, we use the novel datasets to train the estimation network with real world data. The extracted pose information is used to automatically estimate the observing sensor pose relative to the robot system. Finally we evaluate the performance of the presented subsystems in a real world robotic scenario.
Learning to translate between real world and simulated 3D sensors while transferring task models
(2019)
Learning-based vision tasks are usually specialized on the sensor technology for which data has been labeled. The knowledge of a learned model is simply useless when it comes to data which differs from the data on which the model has been initially trained or if the model should be applied to a totally different imaging or sensor source. New labeled data has to be acquired on which a new model can be trained. Depending on the sensor, this can even get more complicated when the sensor data becomes more abstract and hard to be interpreted and labeled by humans. To enable reuse of models trained for a specific task across different sensors minimizes the data acquisition effort. Therefore, this work focuses on learning sensor models and translating between them, thus aiming for sensor interoperability. We show that even for the complex task of human pose estimation from 3D depth data recorded with different sensors, i.e. a simulated and a Kinect 2TM depth sensor, human pose estimation can greatly improve by translating between sensor models without modifying the original task model. This process especially benefits sensors and applications for which labels and models are difficult if at all possible to retrieve from raw sensor data.
This document presents an algorithm for a nonobtrusive recognition of Sleep/Wake states using signals derived from ECG, respiration, and body movement captured while lying in a bed. As a core mathematical base of system data analytics, multinomial logistic regression techniques were chosen. Derived parameters of the three signals are used as the input for the proposed method. The overall achieved accuracy rate is 84% for Wake/Sleep stages, with Cohen’s kappa value 0.46. The presented algorithm should support experts in analyzing sleep quality in more detail. The results confirm the potential of this method and disclose several ways for its improvement.
Microservices are a topic driven mainly by practitioners and academia is only starting to investigate them. Hence, there is no clear picture of the usage of Microservices in practice. In this paper, we contribute a qualitative study with insights into industry adoption and implementation of Microservices. Contrary to existing quantitative studies, we conducted interviews to gain a more in-depth understanding of the current state of practice. During 17 interviews with software professionals from 10 companies, we analyzed 14 service-based systems. The interviews focused on applied technologies, Microservices characteristics, and the perceived influence on software quality. We found that companies generally rely on well established technologies for service implementation, communication, and deployment. Most systems, however, did not exhibit a high degree of technological diversity as commonly expected with Microservices. Decentralization and product character were different for systems built for external customers. Applied DevOps practices and automation were still on a mediocre level and only very few companies strictly followed the you build it, you run it principle. The impact of Microservices on software quality was mainly rated as positive. While maintainability received the most positive mentions, some major issues were associated with security. We present a description of each case and summarize the most important findings of companies across different domains and sizes. Researchers may build upon our findings and take them into account when designing industry-focused methods.
While the concepts of object-oriented antipatterns and code smells are prevalent in scientific literature and have been popularized by tools like SonarQube, the research field for service-based antipatterns and bad smells is not as cohesive and organized. The description of these antipatterns is distributed across several publications with no holistic schema or taxonomy. Furthermore, there is currently little synergy between documented antipatterns for the architectural styles SOA and Microservices, even though several antipatterns may hold value for both. We therefore conducted a Systematic Literature Review (SLR) that identified 14 primary studies. 36 service-based antipatterns were extracted from these studies and documented with a holistic data model. We also categorized the antipatterns with a taxonomy and implemented relationships between them. Lastly, we developed a web application for convenient browsing and implemented a GitHub-based repository and workflow for the collaborative evolution of the collection. Researchers and practitioners can use the repository as a reference, for training and education, or for quality assurance.
In this paper we describe an interactive web-based visual analysis tool for Formula one races. It first provides an overview about all races on a yearly basis in a calendar-like representation. From this starting point, races can be selected and visually inspected in detail. We support a dynamic race position diagram as well as a more detailed lap times line plot for showing the drivers’ lap times in comparison. Many interaction techniques are supported like selections, filtering, highlighting, color coding, or details-on demand. We illustrate the usefulness of our visualization tool by applying it to a Formula one dataset while we describe the different dynamic visual racing patterns for a number of selected races and drivers.
Due to frequently changing requirements, the internal structure of cloud services is highly dynamic. To ensure flexibility, adaptability, and maintainability for dynamically evolving services, modular software development has become the dominating paradigm. By following this approach, services can be rapidly constructed by composing existing, newly developed and publicly available third-party modules. However, newly added modules might be unstable, resource-intensive, or untrustworthy. Thus, satisfying non-functional requirements such as reliability, efficiency, and security while ensuring rapid release cycles is a challenging task. In this paper, we discuss how to tackle these issues by employing container virtualization to isolate modules from each other according to a specification of isolation constraints. We satisfy non-functional requirements for cloud services by automatically transforming the modules comprised into a container-based system. To deal with the increased overhead that is caused by isolating modules from each other, we calculate the minimum set of containers required to satisfy the isolation constraints specified. Moreover, we present and report on a prototypical transformation pipeline that automatically transforms cloud services developed based on the Java Platform Module System into container-based systems.