The German engineering sector is one of the most prominent industries in Germany in terms of revenues, the number of employees, and reputation for the “Made in Germany” brand. In this industry, digital transformation (DT) has become a significant trend. DT is more than optimizing internal processes by digital means. It entails the offer of digital services and products and the enhancement of customer experience. Complex barriers need to be overcome to drive this transformation forward. Therefore, our study analysis the organizational barriers to DT within the German engineering industry. We follow a quantitative approach to gain insight on organizational barriers by a comparison of digitalized and less digitalized enterprises and their DTs. Our research demonstrates that digitalized enterprises perceive lower degrees of certain barriers in leadership, culture, employees, and skills, which are essential parts in a socio-technical view. However, there are still barriers that digitalized enterprises are struggling with.

With the rise of modern technologies, digital transformation (DT) has become an issue in nearly all industries. In enterprises, DT means to digitalize internal processes, offer digital services and products, and enhance the customer experience. However, complex barriers hinder the successful transformation. Our study follows an explorative mixed-method design. Through a qualitative data collection with 46 participants and a quantitative data collection with 824 participants, we sequentially identify the barriers to DT and transfer them into a testable research model. This paper offers a proposal for an instrument to measure barriers to DT. The identified predictors explain 50% of the alteration problems of the transformation process. The results offer further research an existing instrument to build upon. Also, the instrument is helpful for managers to measure hinders in DT processes and to develop counter-measurements. Our results indicate that many barriers depend on organizational issues rather than the individual level. 

Digital Transformation is one of the megatrends to re-shape industries, non-profit sectors, higher education, and even societies. Digital technologies merge with physical production processes provoking changes in value creation processes. These changes may lead to positive effects such as more effectiveness, revised business models, and closer customer contact. However, there are also negative effects hindering the digital transformation journey. As many firms still struggle, it is essential to understand the barriers which slow down or even stop a digital transformation. Based on qualitative data from 525 respondents working in different sectors, we reevaluate and extend an existing model by using a triangulation approach. The result is a holistic socio-technical model of the barriers to digital transformation consisting of seven distinct dimensions. The model can serve as input for future research instruments. Firms can use it to reflect barriers in their digital transformation.

Digital Transformation (DT) impacts industries, non-profit sectors, higher education, and even societies. As connectivity technologies blend with physical assets, modifications in value creation processes are provoked. These modifications may have positive impacts such as higher effectivity, enhanced business models, and improved customer connection. However, realizing a DT is a complex endeavor. Specific properties, so-called barriers, hinder the DT journey. Thus, it is essential to grasp the barriers and indicate ways to overcome them. We develop a decision matrix for overcoming barriers using qualitative data from participants working in different sectors. This work builds upon a pre-study developing a barrier classification and enhances it with specific recommendations such as “define clear DT responsibilities”. Thus, our work takes the development of plain barrier classifications further. From a theoretical perspective, this work contributes to developing hypothetical models of the effects of recommendations to overcome barriers in the future. From a practical perspective, companies can use the recommendations to plan actions to take.

With the rise of digital technologies, digital transformation (DT) has become an issue in nearly all industries. In enterprises, DT means to digitalize internal processes, offer digital services as well as products, and enhance the customer experience. As the transformation is complex, barriers hinder the successful transformation. However, an instrument for the measurement of barriers and their effects on the DT of an organization is missing. Our research questions are therefore: What are the barriers to DT in industry and industry-related areas? How can they be described and measured?

Technology-based business improvements characterize the so-called digital transformation (DT). For non-profit organizations (NPOs), with their unique value creation structure and specific preconditions in terms of staff and resources, keeping up with the DT is challenging. Still, opportunities for the DT are unknown. Thus far, there are no comprehensive guidelines for DT strategy development in NPOs. Both digital value creation and digitally supported communication with customers can lead to competitive advantages. Therefore, NPO decision-makers must understand the opportunities and the challenges of DT. In our research approach, we aim to answer the research question: What are the fields of action for the digital transformation in NPOs? Following a grounded theory approach, we have developed a theoretical framework including fields of action and guidance for the strategic advancement of DT in NPOs. The results show that NPOs need to be aware of their digital communications channels with volunteer staff, customers and donors. A clear DT vision and new roles help NPOs meet this challenge.

The digitalization of higher education institutions is progressing significantly. Though the use of digital assets enhances the students’ learning experience and offers new opportunities for administration, there are no uniform standards for the use of digital media in teaching and student services. As educational service providers, universities are dependent on students being able to cope with the structures offered. Thus it is essential to ascertain students’ attitudes of the technologies used. We asked students from three blended learning courses about their perceptions. We further asked the students what should be done and by whom. Our results show that students see structural changes occurring not only in themselves but also at the level of the university management. Our research contributes to the actual discussion about the digitalization of higher education by offeringsuggestions for development from a students’ view. The results are valuable for lecturers and faculty managers who want to advance the digitalization of services and learning.

This work presents an extension of Thompson Sampling bandit policy for orchestrating the collection of base recommendation algorithms for e-commerce. We focus on the problem of item-to-item recommendations, for which multiple behavioral and attribute-based predictors are provided to an ensemble learner. In addition, we detail the construction of a personalized predictor based on k-Nearest Neighbors (kNN), with temporal decay capabilities and event weighting. We show how to adapt Thompson Sampling to realistic situations when neither action availability nor reward stationarity is guaranteed. Furthermore, we investigate the effects of priming the sampler with pre-set parameters of reward probability distributions by utilizing the product catalog and/or event history, when such information is available. We report our experimental results based on the analysis of three real-world e-commerce datasets.

This work presents an extension of Thompson Sampling bandit policy for orchestrating the collection of base recommendation algorithms for e-commerce. We focus on the problem of item-to-item recommendations, for which multiple behavioral and attribute-based predictors are provided to an ensemble learner. We show how to adapt Thompson Sampling to realistic situations when neither action availability nor reward stationarity is guaranteed. Furthermore, we investigate the effects of priming the sampler with pre-set parameters of reward probability distributions by utilizing the product catalog and/or event history, when such information is available. We report our experimental results based on the analysis of three real-world e-commerce datasets.

Commercial wearable devices and fitness trackers are commonly sold as black boxes of which little is known about their accuracy. This poses serious issues especially in health-related contexts such as clinical research, where transparency about accuracy and reliability are paramount.

We present a validated algorithm for computing step counting that is optimised for use in constrained computing environments. Released as open source, the algorithm is based on the windowed peak detection approach, which has previously shown high accuracy on smartphones. The algorithm is optimised to run on a programmable smartwatch (Pine Time) and tested on 10 subjects in 8 scenarios, with varying varying positions of the wearable and walking paces.

Our approach achieves a 89% average accuracy, with the highest average accuracy when walking outdoor (98%) and the lowest in a slow-walk scenario (77%). This result can be compared with the built-in step counter of the smartwatch (Bosch BMA421), which yielded a 94% average accuracy for the same use cases. Our work thus shows that an open-source approach for extracting physical activity data from wearable devices is possible and achieves an accuracy comparable to the one produced by proprietary embedded algorithms.

The growth and presence of heterogeneous sensor-equipped Internet-connected devices inside the home can increase efficiency and quality of life for the residents. Simultaneously, these devices continuously collect, process, and transmit data about the residents and their daily lifestyle activities to unknown parties outside the home. Such data can be sensitive and personal, leading to increasingly intimate insights into private lives. This data allows for the implementation of services, personalization support, and benefits offered by smart home technologies. Alas, there has been a surge of cyberattacks on connected home devices that essentially compromise privacy and security of the residents.

Providing privacy and security is a critical issue in smart connected homes. Many residents are concerned about unauthorized access into their homes and about the privacy of their data. However, it is typically challenging to implement privacy and security in a smart connected home because of its heterogeneity of devices, the dynamic nature of the home network, and the fact that it is always connected to the Internet, amongst other things. As the numbers and types of smart home devices are increasing rapidly, so are the risks with these devices. Concurrently, it is also becoming increasingly challenging to gain a deeper understand- ing of the smart home. Such understanding is necessary to build a more privacy-preserving and secure smart connected home. Likewise, it is needed as a precursor to perform a comprehensive privacy and security analysis of the smart home.

In this dissertation, we render a comprehensive description and account of the smart connected home that can be used for conducting risk analysis. In doing so, we organize the underlying smart home devices ac- cording to their functionality, identify their data-collecting capabilities, and survey the data types being collected by them. Such is done using the technical specification of commercial devices, including their privacy policies. This description is then leveraged for identifying threats and for analyzing risks present in smart connected homes. Such is done by analyzing both scholarly literature and examples from the industry, and leveraging formal modeling. Additionally, we identify malicious threat agents and mitigations that are relevant to smart connected homes. This is performed without limiting the research and results to a particular configuration and type of smart home.

This research led to three main findings. First, the majority of the surveyed commercial devices are collecting instances of sensitive and personal data but are prone to critical vulnerabilities. Second, there is a shortage of scientific models that capture the complexity and heterogeneity of real-world smart home deployments, especially those intended for privacy risk analysis. Finally, despite the increasing regulations and attention to privacy and security, there is a lack of proactive and integrative approaches intended to safeguard privacy and security of the residents. We contributed to addressing these three findings by developing a framework and models that enable early identification of threats, better planning for risk management scenarios, and mitigation of potential impacts caused by attacks before they reach the homes and compromise the lives of the residents.

Overall, the scientific contributions presented in this dissertation help deepen the understanding and reasoning about privacy and security concerns affecting smart connected homes, and contributes to advancing the research in the area of risk analysis as applied to such systems.

The growth and presence of heterogeneous connected devices inside the home have the potential to provide increased efficiency and quality of life to the residents. Simultaneously, these devices tend to be Internet-connected and continuously monitor, collect, and transmit data about the residents and their daily lifestyle activities. Such data can be of a sensitive nature, such as camera feeds, voice commands, physiological data, and more. This data allows for the implementation of services, personalization support, and benefits offered by smart home technologies. Alas, there has been a rift of security and privacy attacks on connected home devices that compromise the security, safety, and privacy of the occupants. In this thesis, we provide a comprehensive description of the smart connected home ecosystem in terms of its assets, architecture, functionality, and capabilities. Especially, we focus on the data being collected by smart home devices. Such description and organization are necessary as a precursor to perform a rigorous security and privacy analysis of the smart home. Additionally, we seek to identify threat agents, risks, challenges, and propose some mitigation approaches suitable for home environments. Identifying these is core to characterize what is at stake, and to gain insights into what is required to build more robust, resilient, secure, and privacy-preserving smart home systems. Overall, we propose new concepts, models, and methods serving as a foundation for conducting deeper research work in particular linked to smart connected homes. In particular, we propose a taxonomy of devices; classification of data collected by smart connected homes; threat agent model for the smart connected home; and identify challenges, risks, and propose some mitigation approaches.

The home environment is rapidly becoming more complex with the introduction of numerous and heterogeneous Internet of Things devices. This development into smart connected homes brings with it challenges when it comes to gaining a deeper understanding of the home environment as a socio-technical system. A better understanding of the home is essential to build robust, resilient, and secure smart home systems. In this regard, we developed a novel method for classifying smart home devices in a logical and coherent manner according to their functionality. Unlike other approaches, we build the categorization empirically by mining the technical specifications of 1,193 commercial devices. Moreover, we identify twelve capabilities that can be used to characterize home devices. Alongside the classification, we also quantitatively analyze the entire spectrum of commercial smart home devices in accordance to their functionality and capabilities. Overall, the categorization and analysis provide a foundation for identifying opportunities of generalizations and common solutions for the smart home.

Many living spaces, such as homes, are becoming smarter and connected by using Internet of Things (IoT) technologies. Such systems should ideally be privacy-centered by design given the sensitive and personal data they commonly deal with. Nonetheless, few systematic methodologies exist that deal with privacy threats affecting IoT-based systems. In this paper, we capture the generic function of an IoT system to model privacy so that threats affecting such contexts can be identified and categorized at system design stage. In effect, we integrate an extension to so called Data Flow Diagrams (DFD) in the model, which provides the means to handle the privacy-specific threats in IoT systems. To demonstrate the usefulness of the model, we apply it to the design of a realistic use-case involving Facebook Portal. We use that as a means to elicit the privacy threats and mitigations that can be adopted therein. Overall, we believe that the proposed extension and categorization of privacy threats provide a useful addition to IoT practitioners and researchers in support for the adoption of sound privacy-centered principles in the early stages of the smart living design process.

Smart connected homes are integrated with heterogeneous Internet-connected devices interacting with the physical environment and human users. While they have become an established research area, there is no common understanding of what composes such a pervasive environment making it challenging to perform a scientific analysis of the domain. This is especially evident when it comes to discourse about privacy threats. Recognizing this, we aim to describe a generic smart connected home, including the data it deals with in a novel privacy-centered system model. Such is done using concepts borrowed from the theory of Contextual Integrity. Furthermore, we represent privacy threats formally using the proposed model. To illustrate the usage of the model, we apply it to the design of an ambient-assisted living use-case and demonstrate how it can be used for identifying and analyzing the privacy threats directed to smart connected homes.

The increasing presence of heterogeneous Internet of Things devices inside the home brings with it added convenience and value to the householders. At the same time, these devices tend to be Internet-connected and continuously monitor and collect data about the residents and their daily lifestyle activities. Such data can be of a sensitive nature, given that the house is the place where privacy is naturally expected. To gain insight into this state of affairs, we empirically investigate the privacy policies of 87 different categories of commercial smart home devices in terms of data being collected. This is done using a combination of manual and data mining techniques. The overall contribution of this work is a model that identifies and categorizes smart connected home data in terms of its collection mode, collection method, and collection phase. Our findings bring up several implications for smart connected home privacy, which include the need for better security controls to safeguard the privacy of the householders.

Smart connected home systems bring different privacy challenges to residents. The contribution of this paper is a novel privacy grounded classification of smart connected home systems that is focused on personal data exposure. This classification is built empirically through k-means cluster analysis from the technical specification of 81 commercial Internet of Things (IoT) systems as featured in PrivacyNotIncluded – an online database of consumer IoT systems. The attained classification helps us better understand the privacy implications and what is at stake with different smart connected home systems. Furthermore, we survey the entire spectrum of analyzed systems for their data collection capabilities. Systems were classified into four tiers: app-based accessors, watchers, location harvesters, and listeners, based on the sensing data the systems collect. Our findings indicate that being surveilled inside your home is a realistic threat, particularly, as the majority of the surveyed in-home IoT systems are installed with cameras, microphones, and location trackers. Finally, we identify research directions and suggest some best practices to mitigate the threat of in-house surveillance.

Smart homes promise to improve the quality of life of residents. However, they collect vasts amounts of personal and sensitive data, making privacy protection critically important. We propose a framework, called PRASH, for modeling and analyzing the privacy risks of smart homes. It is composed of three modules: a system model, a threat model, and a set of privacy metrics, which together are used for calculating the privacy risk exposure of a smart home system. By representing a smart home through a formal specification, PRASH allows for early identification of threats, better planning for risk management scenarios, and mitigation of potential impacts caused by attacks before they compromise the lives of residents. To demonstrate the capabilities of PRASH, an executable version of the smart home system configuration was generated using the proposed formal specification, which was then analyzed to find potential attack paths while also mitigating the impacts of those attacks. Thereby, we add important contributions to the body of knowledge on the mitigations of threat agents violating the privacy of users in their homes. Overall, the use of PRASH will help residents to preserve their right to privacy in the face of the emerging challenges affecting smart homes.

With the interconnectedness of heterogeneous IoT devices being deployed in smart living spaces, it is imperative to assure that connected devices are resilient against Denial-of-Service (DoS) attacks. DoS attacks may cause economic damage but may also jeopardize the life of individuals, e.g., in a smart home healthcare environment since there might be situations (e.g., heart attacks), when urgent and timely actions are crucial. To achieve a better understanding of the DoS attack scenario in the ever so private home environment, we conduct a vulnerability assessment of five commercial-off-the-shelf IoT devices: a gaming console, media player, lighting system, connected TV, and IP camera, that are typically found in a smart living space. This study was conducted using an automated vulnerability scanner – Open Vulnerability Assessment System (OpenVAS) – and focuses on semantic DoS attacks. The results of the conducted experiment indicate that the majority of the tested devices are prone to DoS attacks, in particular those caused by a failure to manage exceptional conditions, leading to a total compromise of their availability. To understand the root causes for successful attacks, we analyze the payload code, identify the weaknesses exploited, and propose some mitigations that can be adopted by smart living developers and consumers.

The Internet of Things is enabling innovative ser-vices promising added convenience and value in various domains such as the smart home. Increasingly, households, office envi-ronments and cities, are being fitted with smart camera systems aimed to enhance the security of citizens. At the same time, sev-eral systems being deployed suffer from weak security implemen-tations. Recognizing this, and to understand the extent of this situation, in this study we perform a global vulnerability assess-ment using the Shodan search engine and the Common Vulnera-bilities and Exposures database. This is done to detect smart con-nected cameras exposed on the Internet alongside their sensitive, potentially private, data being broadcasted. Furthermore, we discuss whether the discovered data can be used to compromise the safety and privacy of individuals, and identify some mitiga-tions that can be adopted. The results indicate that a significant number of smart cameras are indeed prone to diverse security and privacy vulnerabilities.

A major concern in the use of Internet of Things (IoT) technologies in general is their reliability in the presence of security threats and cyberattacks. Particularly, there is a growing recognition that IoT environments featuring virtual sensing and interactive machine learning may be subject to additional vulnerabilities when compared to traditional networks and classical batch learning settings. Partly, this is as adversaries could more easily manipulate the user feedback channel with malicious content. To this end, we propose a data-centric anomaly-based detection system, based on machine learning, that facilitates the process of identifying anomalies, particularly those related to poisoning integrity attacks targeting the user feedback channel of interactive machine learning setups. We demonstrate the capabilities of the proposed system in a case study involving a smart campus setup consisting of different smart devices, namely, a smart camera, a climate sensmitter, smart lighting, a smart phone, and a user feedback channel over which users could furnish labels to improve detection of correct system states, namely, activity types happening inside a room. Our results indicate that anomalies targeting the user feedback channel can be accurately detected at 98% using the Random Forest classifier.

The Internet of Things (IoT) market is growing rapidly, allowing continuous evolution of new technologies. Alongside this development, most IoT devices are easy to compromise, as security is often not a prioritized characteristic. This paper proposes a novel IoT Security Model (IoTSM) that can be used by organizations to formulate and implement a strategy for developing end-to-end IoT security. IoTSM is grounded by the Software Assurance Maturity Model (SAMM) framework, however it expands it with new security practices and empirical data gathered from IoT practitioners. Moreover, we generalize the model into a conceptual framework. This approach allows the formal analysis for security in general and evaluates an organization’s security practices. Overall, our proposed approach can help researchers, practitioners, and IoT organizations, to discourse about IoT security from an end-to-end perspective.

We conducted an empirical study of 57 children using a printed Booklet and a digital Tablet instruction for LEGO construction while they wore a head-mounted gaze tracker. Booklets caused a particularly strong pupil dilation when encountered as the first media. Subjective responses confirmed the booklet to be more difficult to use. The children who were least productive and asked for assistance more often had a significantly different pupil pattern than the rest. Our findings suggest that it is possible to collect pupil size data in unconstrained work scenarios, providing insight to task effort and difficulties.

In a wide range of social networks, people’s behavior is influencedby social contagion: we do what our network does.Networks often feature particularly influential individuals,commonly called “influencers’.’ Existing work suggests thatin-game social networks in online games are similar to reallifesocial networks in many respects. However, we do notknow whether there are in-game equivalents to influencers.We therefore applied standard social network features usedto identify influencers to the online multiplayer shooter TomClancy’s The Division. Results show that network featuredefinedinfluencers had indeed an outsized impact on playtimeand social play of players joining their in-game network.

Tracking a person’s activities is relevant in a variety of contexts, from health and group-specific assessments, such as elderly care, to fitness tracking and human–computer interaction. In a clinical context, sensor-based activity tracking could help monitor patients’ progress or deterioration during their hospitalization time. However, during routine hospital care, devices could face displacements in their position and orientation caused by incorrect device application, patients’ physical peculiarities, or patients’ day-to-day free movement. These aspects can significantly reduce algorithms’ performances. In this work, we investigated how shifts in orientation could impact Human Activity Recognition (HAR) classification. To reach this purpose, we propose an HAR model based on a single three-axis accelerometer that can be located anywhere on the participant’s trunk, capable of recognizing activities from multiple movement patterns, and, thanks to data augmentation, can deal with device displacement. Developed models were trained and validated using acceleration measurements acquired in fifteen participants, and tested on twenty-four participants, of which twenty were from a different study protocol for external validation. The obtained results highlight the impact of changes in device orientation on a HAR algorithm and the potential of simple wearable sensor data augmentation for tackling this challenge. When applying small rotations (<20 degrees), the error of the baseline non-augmented model steeply increased. On the contrary, even when considering rotations ranging from 0 to 180 along the frontal axis, our model reached a f1-score of 0.85±0.110.85±0.11 against a baseline model f1-score equal to 0.49±0.120.49±0.12.

We present Open Trumps, a version of the popular card game Top Trumps with decks that are procedurally gen- erated based on open data. The game is played among multiple players through drawing cards and selecting the feature that is most likely to trump the same feature on the other players’ cards. Players can generate their own decks through choosing a suitable dataset and setting cer- tain attributes; the generator then generates a balanced and playable deck using evolutionary computation. In the exam- ple dataset, each card represents a country and the features represent such entities as GDP per capita, mortality rate or tomato production, but in principle any dataset organ- ised as instances with numerical features could be used. We also report the results of an evaluation intended to investi- gate both player experience and the hypothesis that players learn about the data underlying the deck they play with, since understanding the data is key to playing well. The results show that players enjoy playing the game, are enthu- siastic about its potential and answer questions related to decks they have played significantly better than questions related to decks they have not played.

AbstractIn Sweden as well as internationally the teaching and research nexus has been described as the defining charac-teristics of higher education promoting generic skills such as information analysis and critical reflection. Vertically Integrated Projects has been proposed as one educational strategy where research and teaching are linked by in-viting students to take active part in actual research projects. The strategy is well aligned to Scholarship of teaching and learning enabling the transition from a teacher-centred accepted knowledge to a student-centred perspective where students are invited as producers of knowledge. The aim of the current study was to explore students’ experiences of participation in a research-based learning activity with academia and industrial partners, designed as a qualitative explorative study using focus group interviews. Findings describe not only factors students find motivating for learning, but also their experience of being part of professional life with its benefits and challenges.

Pervasive technologies permeating our immediate surroundings provide a wide variety of means for sensing and actuating in our environment, having a great potential to impact the way we live, but also how we work. In this paper, we address the problem of activity recognition in office environments, as a means for inferring contextual information in order to automatically and proactively assists people in their daily activities. To this end we employ state-of-the-art image processing techniques and evaluate their capabilities in a real-world setup. Traditional machine learning is characterized by instances where both the training and test data share the same distribution. When this is not the case, the performance of the learned model is deteriorated. However, often times, the data is expensive or difficult to collect and label. It is therefore important to develop techniques that are able to make the best possible use of existing data sets from related domains, relative to the target domain. To this end, we further investigate in this work transfer learning techniques in deep learning architectures for the task of activity recognition in office settings. We provide herein a solution model that attains a 94% accuracy under the right conditions.

Water-filled mining goaves are extremely prone to water inrush accidents in coal mines, and the transient electromagnetic method (TEM) is a good geophysical method for detecting water-rich areas. Considering that conventional TEM was mainly carried out on the ground, to increase the detection resolution, the underground TEM was used to detect the water-filled goaves in this study. Based on the whole-space model, the data-processing method of the underground TEM was studied. The whole-space geoelectric model was established based on actual coal-measure strata data, and the whole-space TEM response of the water-filled goaves was modeled using the finite-difference time-domain method. The results showed that the low-resistance areas of the apparent resistivity contours can accurately reflect the water abundance of the mining goaves. The underground TEM was used to detect the water abundance of the mining goaf in a mine environment and its detection results were consistent with the actual results.

Mine water inrush stays as one of the major disasters in coalmine production and construction. As one of the principal methods for detecting hidden water-rich areas in coal mines, underground transient electromagnetic method (TEM) adopts the small loop of a magnetic source which generates a kind of whole-space transient electromagnetic field. To study the diffusion of whole-space transient electromagnetic field, a 3-D finite-difference time-domain (FDTD) is employed in simulating the diffusion pattern of whole-space transient electromagnetic field created by the magnetic source in any direction and the whole-space transient electromagnetic response of the 3-D low-resistance body. The simulation results indicate that the diffusion of whole-space transient electromagnetic field is different from ground half-space and that it does not conform to the "smoke ring effect'' of half-space transient electromagnetic field, for the radius of the electric field's contour ring in whole space keeps expanding without moving upward or downward. The low-resistance body can significantly affect the diffusion of transient electromagnetic field. When the excitation direction is consistent with the bearing of the low-resistance body, the coupling between the transient electromagnetic field and the low-resistance body is optimal, and the abnormal response is most obvious. The bearing of the low-resistance body can be distinguished by comparing the response information of different excitation directions. Based on the results above, multi-directional sector detection technology is adapted to detect the water-rich areas, which can not only detect the target ahead of the roadway but also distinguish the bearing of the target. Both numerical simulation and practical application in underground indicate that the mining TEM can accurately reflect the location of water-rich areas.

Better home care and home care technologies are no longer requested solely by nonimmigrant older adults but also by members of the fast-growing older adult immigrant population. However, limited attention has been given to this issue, or to the use of technology in meeting the needs of aging populations. The objective of this review is to map existing knowledge of older adult immigrants' use of information and communication technologies for home care service published in scientific literature from 2014 to 2020. Twelve studies met the established eligibility criteria in a systematic literature search. The results showed older adult immigrants faced similar barriers, which were independent of their ethnic backgrounds but related to their backgrounds as immigrants including lower socioeconomic status, low language proficiency, and comparatively lower levels of social inclusion. Technology use could be facilitated if older adult immigrants received culturally-tailored products and support from family members and from society. The results imply that the included studies do not address or integrate cultural preferences in the development of information and communication technology for home care services. Caregivers might provide an opportunity to bridge gaps between older immigrants' cultural preferences and technology design. This specific research field would also benefit from greater interest in the development of novel methodologies.

The discussion of the digital divide and digital inclusion has extended to older adults. Although knowledge on the digital divide and digital inclusion among native older adults has increased substantially, little is known about the situations of older immigrants in relation to the digital divide. This paper employed the scoping review approach to map the situations and research methods of the digital divide among older immigrants from recent empirical studies. The initial search identified 997 articles, of which 13 articles were selected for this review. The results showed that socioeconomic status, language proficiency, degree of acculturation, level of education, and digital literacy are the most common factors leading to the disparities between native older adults and older immigrants. Although the results showed a narrowing gap as concerns access to the Internet, interventions are needed to reduce the divide among individuals of different ethnicities due to disparities in digital skills and knowledge. The included studies applied quantitative, qualitative, and mixed-method approaches. The homogeneity of the findings of some included studies implied the need to develop more methods and models to study the digital divide among older immigrants. This review suggested that future research incorporate ethnic characteristics in the research design to provide in-depth knowledge about the ethnic group. This knowledge could potentially be utilized for future interventions aimed at narrowing the remaining gap of the digital divide.

In-air gesture interaction enables a natural communication between a man and a machine with its clear semantics and humane mode of operation. In this paper, we propose a real-time recognition system on multiple gestures in the air. It uses the commodity off-the-shelf (COTS) reader with three antennas to detect the radio frequency (RF) signals of the passive radio frequency identification (RFID) Tags attached to the fingers. The idea derives from the crucial insight that the sequential phase profile of the backscatter RF signals is a reliable and well-regulated indicator insinuating space-time situation of the tagged object, which presents a close interdependency with tag's movements and positions. The KL divergence is utilized to extract the dynamic gesture segment by confirming the endpoints of the data flow. To achieve the template matching and classification, we bring in the dynamic time warping (DTW) and k-nearest neighbors (KNN) for similarity scores calculation and appropriate gesture recognition. The experiment results show that the recognition rates for static and dynamic gestures can reach 85% and 90%, respectively. Moreover, it can maintain satisfying performance under different situations, such as diverse antenna-to-user distances and being hidden from view by nonconducting obstacles.

Clique clustering is the problem of partitioning the vertices of a graph into disjoint clusters, where each cluster forms a clique in the graph, while optimizing some objective function. In online clustering, the input graph is given one vertex at a time, and any vertices that have previously been clustered together are not allowed to be separated. The goal is to maintain a clustering with an objective value close to the optimal solution. For the variant where we want to maximize the number of edges in the clusters, we propose an online algorithm based on the doubling technique. It has an asymptotic competitive ratio at most 15.646 and a strict competitive ratio at most 22.641. We also show that no deterministic algorithm can have an asymptotic competitive ratio better than 6. For the variant where we want to minimize the number of edges between clusters, we show that the deterministic competitive ratio of the problem is n−ω(1), where n is the number of vertices in the graph.

The Internet of Things (IoT) unleashes great opportunities to improve our way of living and working through a seamless and highly dynamic cooperation among heterogeneous things including both computer-based systems and physical objects. However, properly dealing with the design, development, deployment and runtime management of IoT applications means to provide solutions for a multitude of challenges related to intelligent distributed systems within the IoT. In this paper we propose Model-Driven Engineering (MDE) as a key enabler for applications running on intelligent distributed loT systems. MDE helps in tackling challenges and supporting the lifecycle of such systems. Specifically, we introduce MDE4IoT, an MDE approach enabling the modelling of things and supporting intelligence as self-adaptation of Emergent Configurations in the IoT. Moreover, we show how MDE, and in particular MDE4IoT, can help in tackling several challenges by providing the Smart Street Lights concrete case.

In this study, we describe the results of our research to model collaborative problem-solving (CPS) competence based on analytics generated from video data. We have collected similar to 500 mins video data from 15 groups of 3 students working to solve design problems collaboratively. Initially, with the help of OpenPose, we automatically generated frequency metrics such as the number of the face-in-the-screen; and distance metrics such as the distance between bodies. Based on these metrics, we built decision trees to predict students' listening, watching, making, and speaking behaviours as well as predicting the students' CPS competence. Our results provide useful decision rules mined from analytics of video data which can be used to inform teacher dashboards. Although, the accuracy and recall values of the models built are inferior to previous machine learning work that utilizes multimodal data, the transparent nature of the decision trees provides opportunities for explainable analytics for teachers and learners. This can lead to more agency of teachers and learners, therefore can lead to easier adoption. We conclude the paper with a discussion on the value and limitations of our approach.

Traffic flow estimates play a key role in traffic network management and planning of transportation networks. Commonly it is the average daily traffic (ADT) flow for different road segments that constitute the data. This paper shows how an advanced and detailed analysis based on hourly flow measurements over the day can contribute to a deeper understanding of how hourly flows together reflect the vehicles’ routes. The proposed method identifies the shortest travel time paths between all possible origins and destinations in a transportation network, and thereafter it identifies the most significant routes in the network by performing statistical tests. For this purpose, the paper presents a mathematical model, a vehicle simulator based on this model, and a statistical framework that is able to find the most probable underlying routes. The paper contains a real test scenario based on 24-hour traffic flows (hour by hour) to demonstrate the applicability of the method.

It is widely known that the time to treatment is vital for patients who suffer from a stroke. Therefore, in different places in the world, the value of acquiring mobile stroke units has been identified. A mobile stroke unit is a specially developed ambulance for stroke treatment, which has special equipment and trained personnel who can perform intravenous stroke treatment (thrombolysis). It is clear that the involvement of mobile stroke units will aid at least a certain proportion of the stroke patients; however, mobile stroke units are often purchased and placed without any developed method or analysis of where and how it should be placed. In the current paper, we use expected value optimization in order to identify, depending on what perspective is of interest, efficiency or equality, the optimal placement of a mobile stroke unit. In an experimental study, considering the Skåne County in Sweden, we show that the placement of a mobile stroke unit may vary considerably depending on which of the two perspectives is adopted.

Continuous deployment has become a widely used practice in web-based software applications. Deploying a new software version to production is a seamless automated process executed thousands of times per day. Continuous deployment reduces the time between a code commit and that commit is active in production. While continuous deployment promises many advantages to software development organizations, the adoption of continuous deployment in the software-intensive embedded systems industry is limited. Several empirical studies have highlighted the challenges associated with software-intensive embedded systems. However, very few studies, if any at all, have attempted to provide a practical approach to realize continuous deployment to these systems. This paper proposes a Controlled Continuous Deployment (CCD) approach, which considers the constraints software-intensive embedded systems have, such as high reliability and availability requirements, limited possibility for rollback after deployment, and the high volume of in-service systems in the market. We derived the approach by conducting a case study at Ericsson AB, focusing on three Radio Access Networks (RAN) technologies embedded software used in 3G, 4G, and 5G mobile networks.  

Modern release engineering practices such as continuous integration and delivery have allowed software development companies to transition from a long release cycle to a shorter one. The shorter release cycle has led to more software releases available to customers. At the same time, companies developing high-volume software-intensive embedded systems often deliver patch releases and maintenance releases on top of major and minor releases to customers who pick and choose what releases apply to them and decide when to upgrade the system, if to upgrade at all. While release engineering has been studied before in web-based, desktop-based, and embedded software, the focus has been on pre-release activities. Few studies have investigated what happens after the release, particularly the role of tracing software from release to deployment in high-volume software-intensive embedded systems. To address this gap, we conducted a qualitative case study at a multi-national telecommunications systems provider focusing on Radio Access Network (RAN) software. RAN software is a complex and large-scale embedded software used in mobile networks Base Stations (BS), providing software functionality for RAN mobile technologies ranging from 2G to 5G. Our study shed light on post-release software traceability and how it is used in the release engineering process.

Continuous deployment has been practiced for many years by companies developing web- and cloud-based applications. To succeed with continuous deployment, these companies have a strong collaboration culture between the operations and development teams. In addition, these companies use AI, analytics, and big data to assist with time-consuming postdeployment activities such as continuous monitoring and fault identification. Thus, the term AIOps has evolved to highlight the importance and difficulty of maintaining highly available applications in a complex and dynamic environment. In contrast, software-intensive embedded systems often provide customer product-related services, such as maintenance, optimization, and support. These services are critical for these companies as they provide significant revenue and increase customer satisfaction. Therefore, the objective of our study is to gain an in-depth understanding of the impact of continuous deployment on product-related services provided by software-intensive embedded systems companies. In addition, we aim to understand how AIOps can support continuous deployment in the context of software-intensive embedded systems. To address this objective, we conducted a case study at a large and multinational telecommunications systems provider focusing on the radio access network (RAN) systems for 4G and 5G networks. The company provides RAN products and three complementing services: rollout, optimization, and customer support. The results from the case study show that the boundaries between product-related services become blurry with continuous deployment. In addition, product-related services, which were conducted in sequence by independent projects, converge with continuous deployment and become part of the same project. Further, AIOps platforms play an important role in reducing costs and increasing postdeployment activities' efficiency and speed. These results show that continuous deployment has a profound impact on the software-intensive system's provider service organization. The service organization becomes the connection between the R&D organization and the customer. In order to cope with the increased speed of releases, deployment and postdeployment activities need to be largely automated. AIOps platforms are seen as a critical enabler in managing the increasing complexity without increasing human involvement.

Supporting customers after they acquire the product is essential for companies producing and selling software-intensive embedded systems products. Generally, customer support is the first interaction point between the product users and the product vendor. Customer support is often engaged with answering customers' questions, troubleshooting, fault identification, and fixing product faults. While continuous deployment advocates for closer cooperation between the ones operating the software and the ones developing it, the means of such collaboration in general and the role of customer support, in particular, has not been addressed in the context of software-intensive embedded systems. Therefore, to better understand the impact that continuous deployment has on customer support and the role customer support should play in this context, we conducted a case study at a multinational company developing and selling telecommunications networks infrastructure. We focused on the 4th and 5th Generation (4G and 5G) Radio Access Networks (RAN) products, which can be considered a high volume product as they cover more than 80% of the world's population. Our study reveals that customer support needs to transition from a transaction-based and passive function triggered by customer support requests, to take an active role characterized by being proactive and preemptive to cope with the shorter operational time of a software version introduced by continuous deployment. In addition, customer support plays an essential role in making the feedback actionable by aggregating and consolidating feedback data to the R&D organization.

Data collected from in-service products play an important role in enabling software-intensive embedded systems suppliers to embrace data-driven practices. Data can be used in many different ways such as to continuously learn and improve the product, enhance post-deployment services, reduce operational cost or create a better user experience. While there is no shortage of possible use cases leveraging data from in-service products, software-intensive embedded systems companies struggle to continuously collect data from their in-service products. Often, data collection is done in an ad-hoc way and targeting specific use cases or needs. Besides, few studies have investigated data collection challenges in relation to the data dimensions, which are the minimum set of quantifiable data aspects that can define software-intensive embedded product data from a collection point of view. To help address data collection challenges, and to provide companies with guidance on how to improve this process, we conducted a case study at a large multinational telecommunications supplier focusing on data characteristics and collection challenges from the Radio Access Networks (RAN) products. We further investigated the relations of these challenges to the data dimensions to increase our understanding of how data dominions contribute to the challenges.

The introduction of cloud technology has reduced the feedback time in software development for many companies. However, companies require significant resources to run applications in an Infrastructure as a Service. The study aims at developing an innovation platform which enables faster deployment of web-based applications. The innovation platform is an add-on for a cloud service, its purpose is to allow developers to set up and use multiple cameras in a cloud environment. Furthermore, the study investigates the drivers and value created by the platform for Axis. We developed a proof of concept built on the innovation platform, using the design science methodology and evaluated it in a focus group. The prototype was a mock version of a grocery store’s internal website, used to show the potential of the innovation platform. Moreover, we conducted semi-structured interviews to investigate the drivers for implementing the platform and the value it created. Results showed that the implementation of the prototype may make the deployment of web-based applications easier. A majority of the interviewees and the focus group participants agreed that the development of the innovation platform should continue and be tested to adopt the platform as a complement to AWS. However, the degree to which the platform should be open source needs a more clear management strategy. The prototype is seen as an innovation platform which may be used to quickly experiment with new ideas. The key drivers for implementing a prototype entails reduced cost, faster time to market and reduced complexity of web-based deployments.

The recent years have witnessed an enormous growth of mobile services for energy management in buildings. However, these solutions are often proprietary, non-interoperable, and handle only a limited function, such as lighting, ventilation, or heating. To address these issues, we have developed an open platform that is an integrated energy management solution for buildings. It includes an ecosystem of mobile services and open APIs as well as protocols for the development of new services and products. Moreover, it has an adapter layer that enables the platform to interoperate with any building management system (BMS) or individual device. Thus, the platform makes it possible for third-party developers to produce mobile energy efficiency applications that will work independently of which BMS and devices are used in the building. To validate the platform, a number of services have been implemented and evaluated in existing buildings. This has been done in cooperation with energy companies and property owners, together with the residents and other users of the buildings. The platform, which we call Elis, has been made available as open source software under an MIT license. View Full-Text

Understanding and managing complex systems has become one of the biggest challenges for research, policy and industry. Modeling and simulation of complex systems promises to enable us to understand how a human nervous system and brain not just maintain the activities of a metabolism, but enable the production of intelligent behavior, how huge ecosystems adapt to changes, or what actually influences climatic changes. Also man-made systems are getting more complex and difficult, or even impossible, to grasp. Therefore we need methods and tools that can help us in, for example, estimating how different infrastructure investments will affect the transport system and understanding the behavior of large Internet-based systems in different situations. This type of system is becoming the focus of research and sustainable management as there are now techniques, tools and the computational resources available. This chapter discusses modeling and simulation of such complex systems. We will start by discussing what characterizes complex systems.

The Internet of Things has become a central and exciting research area encompassing many fields in information and communication technologies and adjacent domains. IoT systems involve interactions with heterogeneous, distributed, and intelligent things, both from the digital and physical worlds including the human in the loop. Thanks to the increasingly wide spectrum of applications and cheap availability of both network connectivity and devices, a number of different stakeholders from industry, academia, society and government are part of the IoT ecosystem.