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With the advent of ‘heavy Artificial Intelligence’ - big data, deep learning, and ubiquitous use of the internet, ethical considerations are widely dealt with in public discussions and governmental bodies. Within Computational Paralinguistics with its manifold topics and possible applications (modelling of long-term, medium-term, and short-term traits and states such as personality, emotion, or speech pathology), we have not yet seen that many contributions. In this article, we try to set the scene by (1) giving a short overview of ethics and privacy, (2) describing the field of Computational Paralinguistics, its history and exemplary use cases, as well as (de-)anonymisation and peculiarities of speech and text data, and (3) proposing rules for good practice in the field, such as choosing the right performance measure, and accounting for representativity and interpretability.

Hundreds of imaging-based artificial intelligence (AI) models have been developed in response to the COVID-19 pandemic. AI systems that incorporate imaging have shown promise in primary detection, severity grading, and prognostication of outcomes in COVID-19, and have enabled integration of imaging with a broad range of additional clinical and epidemiologic data. However, systematic reviews of AI models applied to COVID-19 medical imaging have highlighted problems in the field, including methodologic issues and problems in real-world deployment. Clinical use of such models should be informed by both the promise and potential pitfalls of implementation. How does a practicing radiologist make sense of this complex topic, and what factors should be considered in the implementation of AI tools for imaging of COVID-19? This critical review aims to help the radiologist understand the nuances that impact the clinical deployment of AI for imaging of COVID-19. We review imaging use cases for AI models in COVID-19 (e.g., diagnosis, severity assessment, and prognostication) and explore considerations for AI model development and testing, deployment infrastructure, clinical user interfaces, quality control, and institutional review board and regulatory approvals, with a practical focus on what a radiologist should consider when implementing an AI tool for COVID-19.

Although the number of aspect-oriented software development techniques and tools proposed by the scientific literature have been increasing since the late 80′s, the evidence about the benefits that the aspect-oriented paradigm have reached in real-world settings is scarce. Our objective is to identify and classify the aspect-oriented software development methodologies used to reduce the effort and costs of moving from traditional approaches to the aspect-oriented approach in real-world settings. We conducted a systematic mapping study (SMS). Our search strategies retrieved a set of 3212 papers out of which 115 were selected as relevant studies. We defined eight categories to classify these studies: aspect-oriented methodologies proposed for early aspects; development early phases covered; notations; modeling techniques; supporting tools; aspect-oriented methodologies used in real-world settings; benefits reported and unsolved issues. As a result, 39 named methodologies were reported; they cover the business modeling (14 papers), requirements (93 papers), test cases (1 paper) and design (41 papers) phases of the software development life cycle (SDLC); we found 36 different notations, with UML as the most mentioned (66 papers); 22 modeling techniques were found, where the use cases were the ones that appeared the most, on 43 occasions; 43 support tools of which none was repeated in more than 3 articles; 15 applications in real-world settings; 17 benefits, modularization being the most mentioned with 4 occurrences; and 15 pending improvement opportunities. Finally, we have obtained conclusions: the literature analyzed demonstrates that there are no prevailing standards on methodologies and notations, beyond that the most employed are those belonging to the OMG; we have not found any methodology that includes all phases of the SDLC; the diagrams corresponding to OMG’s standards account for 78% of the results in the facet of modeling techniques; there are no tools that have the greatest predilection and the most used is mentioned only three times; in addition, 41% of the articles do not mention any tool; the evidence of aspect-oriented methodologies application in real-world settings reached just the 10%, although the declared benefits are coincident with those promised in the aspect-orientated literature. The mentioned pending issues can guide new studies (RQ8).

The amount of available information in the digital world contains massive amounts of data, far more than people can consume. Beekeeper AG provides a GDPR-compliant platform for frontline employees, who typically do not have permanent access to digital information. Finding relevant information to perform their job requires efficient filtering principles to reduce the time spent on searching, thus saving work hours. However, with GDPR, it is not always possible to observe user identification and content. Therefore, this paper proposes link prediction in a graph structure as an alternative to presenting the information based on GDPR data. In this study, the research of user interaction data in a graph database was compared with graph machine learning algorithms for extracting and predicting network patterns among the users. The results showed that although the accuracy of the models was below expectations, the know-how developed during the process could generate valuable technical and business insights for Beekeeper AG.

A key element in solving real-life data science problems is selecting the types of models to use. Tree ensemble models (such as XGBoost) are usually recommended for classification and regression problems with tabular data. However, several deep learning models for tabular data have recently been proposed, claiming to outperform XGBoost for some use cases. This paper explores whether these deep models should be a recommended option for tabular data by rigorously comparing the new deep models to XGBoost on various datasets. In addition to systematically comparing their performance, we consider the tuning and computation they require. Our study shows that XGBoost outperforms these deep models across the datasets, including the datasets used in the papers that proposed the deep models. We also demonstrate that XGBoost requires much less tuning. On the positive side, we show that an ensemble of deep models and XGBoost performs better on these datasets than XGBoost alone.

Federated Learning (FL) is a recent development in Artificial Intelligence (AI) which is based on the concept of decentralized data. Cyber-attacks are frequently happening in various applications that are deployed in real-time scenarios due to which most of the industrialists hesitate to move forward in adopting the technology of Internet of Everything (IoE). This paper aims to provide an extensive study on how FL could be utilized for providing better cybersecurity and prevent various cyber-attacks in real-time. We present an extensive survey of the various FL models currently developed by researchers for providing authentication, privacy, trust management and attack detection. We also discuss few real-time use cases that are deployed recently and how FL is adopted in them for providing privacy of data. Based on the study, we conclude the paper with some prominent challenges and future directions on which the researchers can focus for adopting FL in real-time scenarios.

As interest in autonomous driving increases, efforts are being made to meet requirements for the high-level automation of vehicles. In this context, the functionality inside the vehicle cabin plays a key role in ensuring a safe and pleasant journey for driver and passenger alike. At the same time, recent advances in the field of artificial intelligence (AI) have enabled a whole range of new applications and assistance systems to solve automated problems in the vehicle cabin. This paper presents a thorough survey on existing work that utilizes AI methods for use-cases inside the driving cabin, focusing, in particular, on application scenarios related to (1) driving safety and (2) driving comfort. Results from the surveyed works show that AI technology has a promising future in tackling in-cabin tasks within the autonomous driving aspect., Comment: 11 pages, 3 figures, 3 tables

In this paper, we survey state-of-the-art research outcomes in the burgeoning field of reconfigurable intelligent surface (RIS) in view of its potential for significant performance enhancement for next generation wireless communication networks by means of adapting the propagation environment. Emphasis has been placed on several aspects gating the commercially viability of a future network deployment. Comprehensive summaries are provided for practical hardware design considerations and broad implications of artificial intelligence techniques, so are in-depth outlooks on salient aspects of system models, use cases, and physical layer optimization techniques., 22 pages, 18 figures

The emerging popularity of telemedicine solutions brought an alarming problem due to the lack of proper access control solutions. With the inclusion of multi-tiered, heterogeneous infrastructures containing Internet of things and edge computing elements, the severity and complexity of the problem became even more alarming, calling for an established access control framework and methodology. The goal of the research is to define a possible solution with a focus on native cloud integration, possible deployment at multiple points along the path of the healthcare data, and adaptation of the fast healthcare interoperability resources standard. In this paper, the importance of this issue in offline use cases is presented and the effectiveness of the proposed solution is evaluated.

Highly and fully automated driving has been under development for the past two decades in order to increase comfort, efficiency, and traffic safety. Particularly in the latter domain, experts agree on automated driving, especially in case of automated vehicles (AV) with SAE level 4 or higher, having the most promising effects. Automated driving is expected to decrease the number of seriously injured or even killed road users to zero (Vision Zero). However, automated driving is still in an early stage of development and many AV tend to drive very carefully to avoid crashes. So, the goal is to make driving more efficient while maintaining the highest level of safety. In the project “Digitaler Knoten 4.0” cooperative automated driving was assessed regarding efficiency and safety aspects. One of the use cases investigated was turning left with oncoming traffic at an urban intersection as this situation represents one of the most complex situations in urban areas yielding to crashes with—in many cases—serious consequences for the involved road users. At the Application Platform Intelligent Mobility (AIM) Research Intersection in Braunschweig, Germany, an SAE level 3 AV was turning left interacting with oncoming manually driven vehicles (MV). The performance of the AV was compared to MV executing the same manoeuvre. The recorded video-based trajectories of the respective AV as well as MV were analysed regarding the influence of situational factors (e.g. position of the vehicle in the queue and gap acceptance) and kinematic factors (e.g. speed and acceleration) on traffic safety. The similarities and differences between this specific AV and MV were identified yielding insight for further developing algorithms for more efficient driving while maintaining the same traffic safety level. For instance, it appears that the AV shows a very conservative left turning behaviour leading to very safe PET distributions in comparison to left turning MV.

The Domain Name System ( DNS ) is known to be one of the most widely abused protocols by threat actors to use in unconventional ways to hide under normal traffic. Apart from threat actors, DNS is being actively used or rather misused by many other service providers, vendors, and so on, to provide the intended services. An in-depth examination of the DNS logs collected over a long period revealed some very interesting legitimate use cases of the DNS protocol by the industry and other players, apart from its normal name resolution service function. We coin the term “Off-label use of DNS” to represent those use cases. Legitimate here simply means using DNS for non-malicious purposes other than what it was traditionally designed for, which is for providing domain resolution; a dictionary service mapping domain names to corresponding IP addresses. One of the main reasons DNS is used, or possibly misused, for these off-label use cases is the speed of data transfer and reduced overhead in terms of bandwidth. These off-label use cases of DNS can often leak important information about the clients and software they are running and can be leveraged in a variety of ways by the network security defenders/analysts to improve their detection on the network. This research will detail some of those legitimate off-label use cases and how they can be leveraged by the analysts to detect malware trends in the network and much more just by analyzing an enterprise's DNS logs.

Noisy gate-model quantum processing units (QPUs) are currently available from vendors over the cloud, and digital quantum programming approaches exist to run low-depth circuits on physical hardware. These digital representations are ultimately lowered to pulse-level instructions by vendor quantum control systems to affect unitary evolution representative of the submitted digital circuit. Vendors are beginning to open this pulse-level control system to the public via specified interfaces. Robust programming methodologies, software frameworks, and backend simulation technologies for this analog model of quantum computation will prove critical to advancing pulse-level control research and development. Prototypical use cases for this include error mitigation, optimal pulse control, and physics-inspired pulse construction. Here we present an extension to the XACC quantum-classical software framework that enables pulse-level programming for superconducting, gate-model quantum computers, and a novel, general, and extensible pulse-level simulation backend for XACC that scales on classical compute clusters via MPI. Our work enables custom backend Hamiltonian definitions and gate-level compilation to available pulses with a focus on performance and scalability. We end with a demonstration of this capability, and show how to use XACC for pertinent pulse-level programming tasks.

The emergence of CubeSats as a viable means for realizing satellite networks at low costs has given rise to ubiquitous cyber-physical systems spanning air, ground, and space, in what is being recognized as the Internet of Space Things (IoST). IoST is expected to serve a wide variety of applications ranging from monitoring and reconnaissance to in-space backhauling, i.e., the network architecture must serve a plethora of application scenarios with differing service-level agreement (SLA) requirements over the same physical infrastructure in an end-to-end manner. At the same time, since the different use cases might belong to a variety of different stakeholders, IoST must support multi-tenancy and functional isolation of services. Consequently, a network slicing framework is vital to the success of IoST. To this end, an automatic network slicing framework for space-ground integrated networks is presented in this paper. The proposed framework has been designed to address the dual objectives of route computation and resource allocation with minimal SLA violations. Different from the existing state-of-the-art, the framework presented herein is purpose-built for ultra-dense CubeSat networks, and is fully automated. In other words, the framework is purely SLA-based, and does not require prior information concerning the resource requirements associated with a slice. Other key innovations introduced through this framework include a robust SLA model for slice customization, a novel topology construction mechanism, and a unique segment routing-based online admission control solution. Furthermore, the flexibility and efficacy of the proposed framework have been evaluated through a comprehensive use case driven evaluation scenario.

1. Introduction Spatial planning deals with the problem of distributing the limited resource "space" among different uses and users. It can be highly challenging to find a balanced land-use pattern, for example in urban agglomerations. Different interest groups such as residents, industry, and ecologists will claim different desirable land-uses for a given area. Spatial planning is also about locating unwanted land-use such as waste facilities. In this case, interest groups (e.g. city councils, neighbourhood organizations) and individuals will fight nearby locations. This situation is known as the NIMBY problem: “Not In My BackYard!” In democratic societies, decisions such as those in spatial planning are made by political representatives in cooperation with public administration and residents. The final decision will usually be based on a number of consecutive prior decisions, or choices, which are made by different groups of stakeholders. At any of these decision levels, there are two important methods to reach a conclusion: consensus finding, or voting. Both will be preceded by more or less intensive discussions and argumentation. The ultimate goal of discussions is to achieve sustainable development by integrating the objectives of diverse stakeholders. Thus, we argue that discussions are a crucial element of spatial planning procedures and are to be integrated with planning and decision support techniques. Discussions will have diverse formats in different planning projects. For example, the number of participants may vary from only two to hundreds and more; participants may get together or stay separated in space and/or time; discussion may be un-moderated, or moderated and structured. Nevertheless, discussion contributions (statements, messages, arguments, articles) in spatial planning will commonly contain a spatial reference. This does allow to link discussion support to spatially enabled decision support techniques as argued in this chapter. In section 2, we will review general theories on argumentation and introduce major concepts of computer-supported cooperative work. Next, geographically referenced discourse will be analysed in more detail leading to the argumentation map model (section 3). Section 4 develops use cases for GIS-based discussion support, and section 5 presents some existing applications. Finally, we will speculate about future developments in computer support for discussions in spatial planning (section 6).

The present document (D1.1) is the first report of WP1 of UNLOCK-CEI project covering the demand landscape analysis. UNLOCK-CEI’s ambition is to UNLOCK the potential for accelerating the deployment of the Cloud-to-Edge-IoT (CEI) computing continuum in Europe. The focus is on demand-side drivers and challenges that can help identify technology-driven innovation and business opportunities that will stimulate demand value chains. To this end, UNLOCK-CEI deals with several technological concepts surrounding cloud, edge and IoT. This report provides a comprehensive overview of the main technologies that are the core of UNLOCK-CEI, analysing these from a demand market perspective and providing insights on their evolution and maturity, their current and future uptake, the key use-cases, and the main drivers and barriers for the adoption. This report is generated leveraging IDC research on the CEI market and other major public sources., This document contains information in the form of the UNLOCK-CEI project findings, work and products and its use is strictly regulated by the UNLOCK-CEI Consortium Agreement and by Contract no. 101070571. The contents of this document are the sole responsibility of the UNLOCK-CEI consortium and can in no way be taken to reflect the views of the European Commission.

In the last decades, video streaming has been developing significantly. Among current technologies, HTTP Adaptive Streaming (HAS) is considered the de-facto approach in multimedia transmission over the internet. In HAS, the video is split into temporal segments with the same duration (e.g., 4 s), each of which is then encoded into different quality versions and stored at servers. The end user sends requests to the server to retrieve segments with specific quality versions determined by an Adaptive Bitrate (ABR) algorithm for the purpose of adapting the throughput fluctuation. Though the majority of HAS-based media services function well even under throughput restrictions and variations, there are still significant challenges for multimedia systems, especially the tradeoff among the increasing content complexity, various time-related requirements, and Quality of Experience (QoE). Content complexity encompasses the increased demands for data, such as high-resolution videos and high frame rates, as well as novel content formats, such as virtual reality (VR) and augmented reality (AR). Time-related requirements include - but are not limited to - start-up delay and end-to-end latency. QoE can be defined as the level of satisfaction or frustration experienced by the user of an application or service. Optimizing for one aspect usually negatively impacts at least one of the other two aspects. This thesis tackles critical open research questions in the context of HAS that significantly impact the QoE at the client side. The main contributions of this thesis are four-fold:1) This thesis demonstrates that HTTP/3’s features can be utilized to considerably enhance the QoE of the end user by improving the video quality. In a streaming session, the end user would have to download low-quality segments, which impair the QoE, due to the throughput fluctuation. We propose Days of Future Past Plus (DoFP+) approach that leverages HTTP/3’s features to upgrade low-quality segments while downloading others. The experimental results reveal a boost in QoE by as much as 33%. In addition, DoFP+ saves an average of 16% of the downloaded data for all test videos. The findings indicate that the sequential download of segments is more advantageous for retransmissions compared to concurrent downloads, and upgrading lower-quality segments first will result in a more remarkable improvement in QoE.2) This thesis proposes a weighted sum model, namely WISH, to provide a high QoE of the video and allow end users to express their preferences among different parameters, including data usage, stall events, and video quality. WISH takes into account three distinct cost elements, namely throughput cost, buffer cost, and quality cost, for each quality version and integrates them into a weighted sum as the overall cost. The results of the experiments indicate that WISH enhances the QoE by up to 17.6% while at the same time reducing data usage by 36.4% in comparison to state-of-the-art approaches. It also offers a dynamic adaptation to meet the demands of end users.3) To improve segment qualities on high-end mobile devices, this thesis introduces an ABR scheme called WISH-SR that integrates a lightweight Convolutional Neural Network (CNN) to enhance low-resolution/low-quality videos at the client side. WISH-SR extends WISH by utilizing CNN-based Super Resolution (SR) models deployed in mobile devices to improve video quality while remarkably reducing the volume of data transmitted. WISH-SR has the capacity to reduce up to 43% of the total downloaded data and enhance the visual quality compared to WISH.4) Finally, this thesis presents an approach to determine Common Media Client Data (CMCD) parameters from the client and process them at the server for the purpose of generating a suitable bitrate ladder for each client. The bitrate ladder is created based on clients’ device types and network conditions. Our approach is able to reduce the downloaded data while improving the QoE as much as 2.6 times., Minh Nguyen, Dissertation Universität Klagenfurt 2023

In recent years we have seen the emergence of a new type of collaboration software, the so-called “Enterprise Social Software”. The “social features” of this software type have stimulated a renewed interest in Enterprise Collaboration Systems (ECS). In this article we present findings from a longitudinal research project on the introduction and use of ECS in companies. We argue that ERP Systems and ECS are inherently different and that the process-paradigm that is common to ERP cannot be applied identically to ECS. To address this issue, we suggest the two concepts use case and collaboration scenario for the analysis and description of collaboration activity in companies. From the literature and 26 case studies we identified typical use cases and collaboration scenarios that can serve as blueprints for ECS introduction projects. The longitudinal objective of our research is to assist companies with their ECS initiatives and to provide them with a catalog of existing use cases and collaboration scenarios from various industry settings.

Recently, along with several technological advancements in cyber-physical systems, the revolution of Industry 4.0 has brought in an emerging concept named digital twin (DT), which shows its potential to break the barrier between the physical and cyber space in smart manufacturing. However, it is still difficult to analyze and estimate the real-time structural and environmental parameters in terms of their dynamic changes in digital twinning, especially when facing detection tasks of multiple small objects from a large-scale scene with complex contexts in modern manufacturing environments. In this article, we focus on a small object detection model for DT, aiming to realize the dynamic synchronization between a physical manufacturing system and its virtual representation. Three significant elements, including equipment, product, and operator, are considered as the basic environmental parameters to represent and estimate the dynamic characteristics and real-time changes in building a generic DT system of smart manufacturing workshop. A hybrid deep neural network model, based on the integration of MobileNetv2, YOLOv4, and Openpose, is constructed to identify the real-time status from physical manufacturing environment to virtual space. A learning algorithm is then developed to realize the efficient multitype small object detection based on the feature integration and fusion from both shallow and deep layers, in order to facilitate the modeling, monitoring, and optimizing of the whole manufacturing process in the DT system. Experiments and evaluations conducted in three different use cases demonstrate the effectiveness and usefulness of our proposed method, which can achieve a higher detection accuracy for DT in smart manufacturing.

Voltage and current waveforms contain the most authentic and granular information on the behaviors of power systems. In recent years, it has become possible to synchronize waveform data measured from different locations. Thus large-scale coordinated analyses of multiple waveforms over a wide area are within our reach. This development could unleash a set of new concepts, strategies, and tools for monitoring, protecting, and controlling power systems and apparatuses. This paper presents an in-depth review and analysis of the advancements in synchronized waveform data, including measurement devices, data characteristics, use cases, and comparisons with synchrophasor data. Based on the findings, five strategies are proposed to discover and develop synchronized waveform based applications over multiple application areas. The paper also presents three complementary measurement platforms and two data screening algorithms for application implementation. It further discusses committee activities and standard developments useful to explore the full potential of the data.

The SDGs are a universal agenda to address the world’s most pressing societal, environmental and economic challenges. The supply of timely, relevant and reliable data is essential in guiding policies and decisions for successful implementation of the SDGs. Yet official statistics cannot provide all of the data needed to populate the SDG indicator framework. Citizen science offers a novel solution and an untapped opportunity to complement traditional sources of data, such as household surveys, for monitoring progress towards the SDGs, while at the same time mobilizing action and raising awareness for their achievement. This paper presents the potential offered by one specific citizen science tool, Picture Pile, to complement and enhance official statistics to monitor several SDGs and targets. Designed to be a generic and flexible tool, Picture Pile is a web-based and mobile application for ingesting imagery from satellites, orthophotos, unmanned aerial vehicles or geotagged photographs that can then be rapidly classified by volunteers. The results show that Picture Pile could contribute to the monitoring of fifteen SDG indicators under goals 1, 2, 11, 13, 14 and 15 based on the Picture Pile campaigns undertaken to date. Picture Pile could also be modified to support other SDGs and indicators in the areas of ecosystem health, eutrophication and built-up areas, among others. In order to leverage this particular tool for SDG monitoring, its potential must be showcased through the development of use cases in collaboration with governments, NSOs and relevant custodian agencies. Additionally, mutual trust needs to be built among key stakeholders to agree on common goals that would facilitate the use of Picture Pile or other citizen science tools and data for SDG monitoring and impact.

Users are interested in entity information and may use paradigms like Publish/Subscribe systems in Internet of Things (IoT), where entity-centric data comes from multiple sources. The IoT application demands contributed to the data integration and semantic interoperability need while maintaining high usability and low processing system resources usage. Existing approaches dealt with semantic interoperability by using/extending ontologies with strict schemata/semantics and difficult to be updated. Other approaches tackled high usability by providing sensor data abstractions that infer high-level knowledge, but they are either complex for real-time processing or have semantic/syntactic coupling making them non-flexible. Therefore, our key question is: Can a dynamic entity summarisation system that provides high-level abstractions while ensuring semantic/syntactic decoupling, and low use of processing system resources, be defined? In this paper, we address this question by proposing the Abstractive Publish/Subscribe Summarisation System that provides abstractive summaries of numerical IoT data for user-friendly subscriptions by using IoTSAX, an enhanced SAX approximation methodology for dynamic IoT environments, and approximate rule-based reasoning by using embedding models. Our results for two use cases, Medicine and Smart Cities, show that although abstraction can be 2 to 3 times slower in latency, it achieves up to 98% reduction in the notifications’ number. IoTSAX outperforms SAX in approximation error up to 2 to 3 times more and in compression space-saving percentage when data redundancy occurs, while it has a similar or better latency and throughput performance. Finally, concept hierarchy based embedding models can achieve F-scores of up to 0.87 for approximate rule-based reasoning.

Internet of Things (IoT) refers to a wide variety of embedded devices connected to the Internet, enabling them to transmit and share information in smart environments with each other. The regular monitoring of IoT network traffic generated from IoT devices is important for their proper functioning and detection of malicious activities. One such crucial activity is classification of IoT devices in the network traffic. It enables the administrator to monitor the activities of IoT devices which can be useful for proper implementation of Quality of Service, detect malicious IoT devices etc. In literature, various methods are proposed for IoT traffic classification using various machine learning algorithms. However, accuracy of these machine learning algorithms depends on the data generated from various IoT devices, features extracted from network traffic, site at which IoT is deployed etc. Moreover selection of features and machine learning algorithms are manual operations which are prone to error. Therefore it is important to study the network traffic characteristics as well as suitable machine learning algorithms for accurate and optimized IoT traffic classification. In this paper, we perform an in depth comparative analysis of various popular machine learning algorithms using different effective features extracted from IoT network traffic. We utilize a public dataset having 20 days of network traces generated from 20 popular IoT devices. Network traces are first processed to extract the significant features. We then selected state of the art machine learning algorithms based on the recent survey papers for the IoT traffic classification. We then comparatively evaluated the performance of those machine learning algorithms on the basis of classification accuracy, speed, training time etc. Finally, we provided a few suggestions for selecting the machine learning algorithm for different use cases based on the obtained results.

The research objective is to build a web-based library application. The research method consists of data collection, while the application implementation uses string matching, exact matching, and string matching algorithms. Entity-relationship diagrams are also used in building relationships in the database. Use cases are used as a description of the scope of a system that is built. The conclusion of this study is that; 1) This information facility system is very effective in influencing library admins to look for borrowed books, 2) Providing integration that produces non-digital information in one application, and 3) Librarians can take advantage of web-based automation and manage the circulation of book loans and book returns.

The work environment influences workers’ well-being and contributes to the growth of personal experiences. In fact, working in an unhealthy workplace can cause stress, frustration, and anxiety. Therefore, companies have to deal with the workers’ well-being in the work environment, making the management of human factors a crucial aspect. In this context, the introduction of Industry 4.0 technologies can support workplace monitoring and improvement. Some researchers propose structured methods that consider several ergonomic domains together; however, it is necessary to create platforms that support data collection, elaboration, and correlation in an integrated way. Accordingly, this paper presents a tool that supports the monitoring of operators’ activities, the data analysis, and the implementation of corrective actions to make the workplace socially sustainable. Preliminary tests were conducted to assess the functionality of the tool architecture and two use cases are presented. They focus on posture analysis and stress detection by inertial sensors and unsupervised machine learning algorithms, respectively.

Previous research has revealed that newcomer women are disproportionately affected by gender-biased barriers in open source software (OSS) projects. However, this research has focused mainly on social/cultural factors, neglecting the software tools and infrastructure. To shed light on how OSS tools and infrastructure might factor into OSS barriers to entry, we conducted two studies: (1) a field study with five teams of software professionals, who worked through five use cases to analyze the tools and infrastructure used in their OSS projects; and (2) a diary study with 22 newcomers (9 women and 13 men) to investigate whether the barriers matched the ones identified by the software professionals. The field study produced a bleak result: software professionals found gender biases in 73% of all the newcomer barriers they identified. Further, the diary study confirmed these results: Women newcomers encountered gender biases in 63% of barriers they faced. Fortunately, many the kinds of barriers and biases revealed in these studies could potentially be ameliorated through changes to the OSS software environments and tool.

Industrial cyber-physical systems rely increasingly on data from IoT devices and other systems as continuously emerging use cases implement new intelligent features. Edge computing can be seen as an extension of the cloud in close physical proximity, in which some of the typical cloud computing loads are beneficial to run. This paper studies data analytics application development for integration of industrial IoT data and composition of application services executed on edge and cloud. A solution is designed to support heterogeneous hardware and run-time platforms, and focuses on the service layer that enables flexible orchestration of data flows and dynamic service compositions. The unified model and system architecture implemented, using the open Arrowhead Framework model, is verified through two representative industrial use cases. publishedVersion

International audience; Industry 4.0 is leading to rethink how operational decisions are made within companies. In particular, it raises the question of the evolution of employee involvement and autonomy in operational decision-making in a Lean 4.0 context. Dealing with such issues within companies presents high stakes but also involves many risks and difficulties. Therefore, it is necessary to test these new Industry 4.0 autonomy models within our Evolutive Learning Factories by developing a suitable experimental protocol. This article proposes a typology of scenarios and case studies that will serve as a basis for future experiments to study these issues in a standardized work context. This first study framework confirmed that the decisions induced by all the problems and opportunities encountered at the operational level are numerous and varied. This research work is a first step and opens up much broader research perspectives on the contribution of Industry 4.0 technologies in implementing new models of autonomy at work.; L'industrie 4.0 amène à repenser la manière dont les décisions opérationnelles sont prises au sein des entreprises. Se pose notamment la question de l'évolution de l'implication et de l'autonomie des employés dans les prises de décision opération-nelles dans un contexte Lean 4.0. Traiter de telles problématiques au sein des entre-prises présente de forts enjeux mais comporte aussi de nombreux risques et diffi-cultés. Il convient donc de pouvoir tester ces nouveaux modèles d'autonomie 4.0 au sein d'Evolutive Learning Factories en développant un modèle expérimental adapté. Cet article propose une typologie de scénario d'études qui servira de base aux futures expérimentations permettant d'étudier ces problématiques dans un contexte de travail standardisé. Ce premier cadre d'étude a permis de confirmer que les déci-sions induites par l'ensemble des problèmes et opportunités rencontrés au niveau opérationnel sont nombreuses et variées. Ce travail de recherche constitue donc une première étape et ouvre des perspectives de recherche beaucoup plus larges sur l'ap-port des technologies de l'Industrie 4.0 dans la mise en oeuvre de nouveaux modèles d'autonomie au travail.

Harnessing flexibility from distributed energy resources (DER) to participate in various markets while accounting for relevant technical and commercial constraints is essential for the development of low-carbon grids. However, there is no clear definition or even description of the salient features of aggregated DER flexibility, including its steady-state and dynamic features and how these are impacted by network constraints and market requirements. This paper proposes a comprehensive DER flexibility modelling and characterisation framework that is based on the concept of nodal operating envelope (NOE). In particular, capacity, ramp, duration and cost are identified as key flexibility metrics and associated with different but consistent NOEs describing capability, feasibility, ramp, duration, economic, technical and commercial flexibility features. These NOEs, which conceptually arise from a Venn diagram, can be built via optimal power flow (OPF) analysis, visualised in the active-reactive power space, and used by different stakeholders. Results from a canonical test system and a real distribution system illustrate the value and applicability of the proposed framework to model and characterise provision of flexibility and market services from DER for different use cases.

The sixth generation (6G) mobile networks will reshape the world by offering instant, efficient, and intelligent hyper-connectivity, as envisioned by the previously proposed Ubiquitous-X 6G networks. Such hyper-massive and global connectivity will introduce tremendous challenges into the operation and management of 6G networks, calling for revolutionary theories and technological innovations. To this end, we propose a new route to boost network capabilities toward a wisdom-evolutionary and primitive-concise network (WePCN) vision for the Ubiquitous-X 6G network. In particular, we aim to concretize the evolution path toward the WePCN by first conceiving a new semantic representation framework, namely semantic base, and then establishing an intelligent and efficient semantic communication (IE-SC) network architecture. In the IE-SC architecture, a semantic intelligence plane is employed to interconnect the semantic-empowered physical-bearing layer, network protocol layer, and application-intent layer via semantic information flow. The proposed architecture integrates artificial intelligence and network technologies to enable intelligent interactions among various communication objects in 6G. It features a lower bandwidth requirement, less redundancy, and more accurate intent identification. We also present a brief review of recent advances in semantic communications and highlight potential use cases, complemented by a range of open challenges for 6G.

The design of production plants in the form of a Cyber Physical Production System (CPPS) promises rapid adaptability to changing market requirements, high flexibility during production, robust behavior in the event of failure and offers the possibility of integrating customers into the production process. In contradiction to these advantages there are both a low penetration and a still low Technology Readiness Level (TRL) of operational CPPS. This article therefore presents a production system for customized beverage bottling, consisting of industrial resources from well-known machine builders and designed as a CPPS through the use of a Multi-Agent System (MAS). The premise that all products are to be manufactured in a saleable form should clarify the claim of the CPPS to be classified as an industrial plant. Its functionality is tested through an evaluation based on two use cases with different requirements. This conceptual proof should enable the future, practice-oriented investigation of urgent questions from research and industry. As a further result, a complete test method for the development and commissioning of an CPPS, starting with the design phase and ending with a valid, reliable production system, is shown.

To combat the spread of the COVID-19 virus, e-health has taken a sudden leap forward. Already use cases were studied to see if by means of advanced IT tools, particularly e-health applications (apps), patients could be monitored from their homes so they did not need to visit a hospital for frequent checks. The corona pandemic gave rise to the rapid development of tracing (and/or tracking) e-health apps, which allow quickly finding the source of an infection as well as others who might have been infected because they were in the close vicinity of someone who became ill. The development and widespread use of these apps makes it even more urgent than it already was to answer questions regarding to whom the data gathered through such apps belong and what belonging means. Can the owner of the mobile device be considered the “owner” of the data, what does “ownership” then mean, do other stakeholders (such as health care providers, public health authorities) also have a claim to “ownership”?