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Customer privacy is increasingly important to marketers. High-profile breaches of databases containing sensitive customer information, and the growing need to build the infrastructure required to support analysis of big data, present nontrivial obstacles to researchers seeking individual-level customer data from firms. In this paper, we show that recent developments in machine learning may enable firms to transfer a generative model, instead of data, thus potentially obviating the process of anonymizing and sampling customer data for release, for use in a variety of analytic use cases. We demonstrate the efficacy of a specific deep learning model, Generative Adversarial Networks (GANs), in preserving desired characteristics of original data. In real-world settings, we find that GANs can double the accuracy as compared to the best benchmark methods. We also demonstrate that GANs can be used to solve marketing problems of price markups for optimal profits and customer targeting, and that a single GAN can tackle multiple marketing problems. Furthermore, GANs have volume and velocity advantages, as the size of informational transfer grows according to model complexity, and it can readily handle real-time data streams.

Objectives With the rapid evolution and technology advancement, the healthcare sector is evolving day by day. It is taking advantage of different technologies such as Internet of things and Blockchain. Several applications related to daily healthcare activities are adopting the use of these technologies. In this paper, we present a review in which we group different healthcare applications that integrate the Internet of things and Blockchain in their systems. Material and methods A review study about the integration of IoT and Blockchain in healthcare systems was conducted. We searched the databases ScienceDirect, IEEE Xplore, Google Scholar and ACM Digital Library. Results This review focuses on categorizing the use cases of IoT and Blockchain in the healthcare sector. The study listed 6 applications in medical services, namely, remote patient monitoring, electronic medical records management, disease prediction, patient tracking, drug traceability and fighting infectious disease especially COVID-19. The paper also investigates the challenges associated with the adoption of the Blockchain technology in healthcare IoT-based systems and some of the existing solutions. It also introduces some future research directions. Conclusion The survey of the use cases of IoT and Blockchain in the healthcare sector will serve as a state of the art for future researches. In addition, the paper gives some directions to new possible researches that could help to revolutionize the healthcare sector by using other technologies such as artificial intelligence, big data, fog and cloud computing.

Blockchain is a chain of blocks where each block contains a set of transactions that are digitally signed by its verifier and stored across the distributed network so that all the legitimate stakeholders can access/verify them. Due to the attributes of Blockchain such as decentralization, immutability, auditability, transparency, and cryptographic security, it offers various benefits to different domains such as cryptocurrency, financial sectors, private/public segments, insurance, healthcare, supply chain management, Internet of Things, etc. However, the technology is in its early stage and still, there is a range of concerns that are yet to be addressed before its wide adoption. Through this paper, we intend to cover extensive study on the Blockchain that includes taxonomy, application/use-cases, consensus mechanisms, prospective research, future directions, and related technologies. This paper also aims to discuss the opportunities, benefits, and challenges of Blockchain technology and to assist the research community in understanding the same.

The Internet of Things (IoT) and artificial intelligence (AI) are two of the fastest-growing technologies in the world. With more people moving to cities, the concept of a smart city is not foreign. The idea of a smart city is based on transforming the healthcare sector by increasing its efficiency, lowering costs, and putting the focus back on a better patient care system. Implementing IoT and AI for remote healthcare monitoring (RHM) systems requires a deep understanding of different frameworks in smart cities. These frameworks occur in the form of underlying technologies, devices, systems, models, designs, use cases, and applications. The IoT-based RHM system mainly employs both AI and machine learning (ML) by gathering different records and datasets. On the other hand, ML methods are broadly used to create analytic representations and are incorporated into clinical decision support systems and diverse healthcare service forms. After carefully examining each factor in clinical decision support systems, a unique treatment, lifestyle advice, and care strategy are proposed to patients. The technology used helps to support healthcare applications and analyze activities, body temperature, heart rate, blood glucose, etcetera. Keeping this in mind, this paper provides a survey that focuses on the identification of the most relevant health Internet of things (H-IoT) applications supported by smart city infrastructure. This study also evaluates related technologies and systems for RHM services by understanding the most pertinent monitoring applications based on several models with different corresponding IoT-based sensors. Finally, this research contributes to scientific knowledge by highlighting the main limitations of the topic and recommending possible opportunities in this research area.

Following recent advancements in Terahertz (THz)technology, THz communications are currently being celebrated as key enablers for various applications in future generations of communication networks. While typical communication use cases are over medium-range air interfaces, the inherently small beamwidths and transceiver footprints at THz frequencies support nano-communication paradigms. In particular, the use of the THz band for in-body and on-body communications has been gaining attention recently. By exploiting the accurate THz sensing and imaging capabilities, body-centric THz biomedical applications can transcend the limitations of molecular, acoustic, and radio-frequency solutions. In this paper, we study the use of the THz band for body-centric networks, by surveying works on THz device technologies, channel and noise modeling, modulation schemes, and networking topologies. We also promote THz sensing and imaging applications in the healthcare sector, especially for detecting zootonic viruses such as Coronavirus. Wepresent several open research problems for body-centric THz networks.

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.

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).

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.

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.

Identity and access management (I&AM) is the umbrella term for managing users and their permissions. It is required for users to access different services. These services can either be provided from their home organization, like a company or university, or from external service providers, e.g., cooperation partners. I&AM provides the management of identifiers with the attributes, credentials, roles, and permissions the user has. Today, the requirements have evolved from simply accessing individual web services in the internet or at a company to the majority of all IT services from different service providers with various accounts. Several identity management models have been created with different approaches within. In order to adjust to heterogeneous environments, use cases, and the evolution of identity management, this paper extends known requirements for identity management. Existing models and approaches for identity management are mapped to the derived requirements. Based on the mapping, advantages, disadvantages, and gaps are identified. Current approaches suffer, as an example, from trustworthiness and liability issues. Interoperability issues are even more inherent as the approaches partly develop apart, forming an heterogeneous environment. The results from this analysis emphasize the need for one holistic identity management framework., The 15th International Conference on Availability, Reliability and Security (ARES 2020), August 25--28, 2020, Virtual Event, Ireland

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”?

Emerging automotive applications such as in-vehicle Augmented Reality (AR) and fully automated parking require a comprehensive understanding of the vehicle's three-dimensional surrounding represented as an object-level environmental model. In this model, not only 3D poses (positions and orientations) and 3D sizes of detected objects are registered, but 3D shapes (geometries) need to be reconstructed precisely. A combination of 3D object detection and 3D surface reconstruction techniques, referred to as object-level 3D reconstruction, is fundamental to building such environmental models. However, the possibilities to incorporate object-level 3D reconstruction in a car have not been sufficiently explored either in academic research or in the industry. This primarily stems from the cost and resource constraints associated with the automotive domain. In this paper, we address these constraints by proposing implementations of in-vehicle object-level 3D reconstruction in two specific use cases: (i) augmented reality and (ii) automated parking. For augmented reality, we propose a cost-efficient solution called monocular 3D Shaping that requires only a single frame from a monocular camera as input. For automated parking, we propose a resource-efficient alternative that generates more precise 3D reconstruction results by taking advantage of additional 3D sensors (such as Lidars). The crux of our proposed approaches lies in the use of a Latent Shape Space, where various 3D shapes are represented using only two parameters. As a result, highly complex 3D shapes can now be transmitted using a low- to medium-bandwidth in-vehicle communication infrastructure in a cost-effective manner.

An increasing number of studies employ virtual reality (VR) to evaluate interactions between autonomous vehicles (AVs) and pedestrians. VR simulators are valued for their cost-effectiveness, flexibility in developing various traffic scenarios, safe conduct of user studies, and acceptable ecological validity. Reviewing the literature between 2010 and 2020, we found 31 empirical studies using VR as a testing apparatus for both implicit and explicit communication. By performing a systematic analysis, we identified current coverage of critical use cases, obtained a comprehensive account of factors influencing pedestrian behavior in simulated traffic scenarios, and assessed evaluation measures. Based on the findings, we present a set of recommendations for implementing VR pedestrian simulators and propose directions for future research.

Schematic drawings showing railway tracks and equipment are commonly used to visualize railway operations and to communicate system specifications and construction blueprints. Recent advances in on-line collaboration and modeling tools have raised the expectations for quickly making changes to models, resulting in frequent changes to layouts, text, and/or symbols in schematic drawings. Automating the creation of high-quality schematic views from geographical and topological models can help engineers produce and update drawings efficiently. This paper introduces four methods for automatically producing schematic railway drawings with increasing level of quality and control over the result. The final method, implemented in the open-source tool that we have developed, can use any combination of the following optimization criteria, which can have different priorities in different use cases: width and height of the drawing, the diagonal line lengths, and the number of bends. We show how to encode schematic railway drawings as an optimization problem over Boolean and numerical domains, using combinations of unary number encoding, lazy difference constraints, and numerical optimization into an incremental SAT formulation. We compare drawings resulting from each of the four methods, applied to models of real-world engineering projects and existing railway infrastructure. We also show how to add symbols and labels to the track plan, which is important for the usefulness of the final outputs. Since the proposed tool is customizable and efficiently produces high-quality drawings from railML 2.x models, it can be used (as it is or extended) both as an integrated module in an industrial design tool like RailCOMPLETE, or by researchers for visualization purposes.

Common use cases in the Industrial Internet of Things (IIoT) deploy massive amounts of sensors and actuators that communicate with each other or to a remote cloud. While they form too large and too volatile networks to run on ultrareliable, time-synchronized low-latency channels, participants still require reliability and latency guaranties. We elaborate this for safety-critical use cases. This paper focuses on the effects of networking protocols for industrial communication services. It analyzes and compares the traditional Message Queuing Telemetry Transport for Sensor Networks (MQTT-SN) with the Constrained Application Protocol (CoAP) as a current IETF recommendation, and also with emerging Information-centric Networking (ICN) approaches, which are ready for deployment. Our findings indicate a rather diverse picture with a large dependence on deployment: Publish-subscribe protocols are more versatile, whereas ICN protocols are more robust in multihop environments. MQTT-SN competitively claims resources on congested links, while CoAP politely coexists on the price of its performance.

Statistical phylogenetic methods are the foundation for a wide range of evolutionary and epidemiological studies. However, as these methods grow increasingly complex, users often encounter significant challenges with summarizing, visualizing, and communicating their key results.We presentRevGadgets, anRpackage for creating publication-quality figures from the results of a large variety of phylogenetic analyses performed inRevBayes(and other phylogenetic software packages).We demonstrate how to useRevGadgetsthrough a set of vignettes that cover the most common use cases that researchers will encounter.RevGadgetsis an open-source, extensible package that will continue to evolve in parallel withRevBayes, helping researchers to make sense of and communicate the results of a diverse array of analyses.[Bayesian phylogenetics, data visualization, R, RevBayes]

Radio frequency identification (RFID) is a widespread technology used in several different industries. One of its common use cases in logistics is the automation of goods receipt. RFID gates are often deployed, to automatically detect tagged items or load carriers during their passage through the goods receipt gate. At present, however, the design of RFID gates is often based on estimations, and their commissioning is mostly approached via trial and error. Even if the RFID network planning problem is known in the literature, existing algorithms cannot be applied to the design of RFID gates due to some limitations. In this paper, we propose a new evolutionary RFID network planning algorithm to design RFID gates optimally. The objective of our algorithm is to minimise the number of antennas and to adjust their mounting heights and angles. The algorithm ensures a tag coverage of at least 99%, prevents reflections on the ground, and can be used in the future as a standard for planning and commissioning RFID-enabled goods receipt gates. To demonstrate the applicability of our algorithm, we deployed it in a case study involving logistics of the automotive sector. The results of the deployment confirm the quality of our approach, as the RFID gate optimised by the algorithm deployed 4 antennas, with a vertical coverage rate of 99.96%, an horizontal coverage rate of 89.66%, and very interesting values of other evaluation functions, namely load balance and overlapping rate.

Open Source Intelligence (OSINT) has gained importance in more fields of application than just in intelligence agencies. This paper provides an overview of the fundamental methods used to conduct OSINT investigations and presents different use cases where OSINT techniques are applied. Different models of the information cycle applied to OSINT are addressed. Additionally, the terms data, information, and intelligence are explained and correlated with the intelligence cycle. A classification system for entities during OSINT investigations is introduced. By presenting the capabilities of modern search engines, techniques for research within social networks and for penetration tests, the fundamental methods used for information gathering are explained. Furthermore, possible countermeasures to protect one’s privacy against the misuse of openly available information as well as the legal environment in Germany, and the ethical perspective are discussed.

A comparison of using proprietary cloud platform Google Cloud Functions and open source platform Apache OpenWhisk for writing serverless applications based on the Google Cloud Platform was made. The following criteria were chosen for comparison: supported programming languages, convenience and speed of development and deployment of functions, possible ways to pass parameters to functions, options for monitoring the status of functions, speed of the cold start. As a result, main advantages and disadvantages of each platform were identified. Recommendations for use cases are given based on the features of the platforms. The results of the study can be used for further studies in the field of FaaS technologies, as there are a lot of solutions in this area that also need to be compared.

Micro gears represent key components in an increasing number of industry, applications. To satisfy the increasing quality requirements in terms of lifetime and noise emissions, new function oriented measurement strategies need to be implemented, that allow the deployment of Inline Quality Control Cycles. Therefore, a Design of Experiments (DoE) is conducted and the measurement uncertainty systematically evaluated in accordance to Guide to the Expression of Uncertainty of Measurement (GUM). Furthermore, this paper describes a method to optimize the tradeoff between measurement uncertainty and measurement time in optical metrology with a use case in micro gear measurement using focus variation. This optimization method uses a meta-model and hyperparameters to allow the adjustment to specific operating conditions and use cases.

With the rise of network strategies, various businesses using the Internet as a platform have been vigorously developed, among which the scale of e-commerce transactions has increased on a large scale. In order to deeply explore the role and advantages of data fusion and multiobjective optimization technology in consumer online reviews, this paper uses the new and old evaluation model comparison method, algorithm design method, and multiobject research method to collect samples, analyze the technical model, and streamline the algorithm. And it will create an analysis algorithm model that can improve and optimize the consumer’s current online reviews. First, we choose the electricity supplier on the platform of a total of four mobile phones grabbed 32,145 comments. Based on this research on the number of online comment fields of consumers, the results show that 78% of the comments are less than 55 words, indicating that most of the online comments left by consumers are short comments; at the same time, a small number of consumers have left detailed comments. Description, the longest of is reached 612 words. On this basis, further study the efficiency and function analysis of the algorithm proposed in this paper, and we can see that DCDG-MOMA is used in 14-7 and 28-7 use cases as 1 and 2, respectively, which is the least, and at 40-7 and 50-7, the time used is 15 and 20 which is close to PBI, but it is also much less time than the MOMAD algorithm. This further shows that the algorithm really plays an effective role in the actual decision-making process. It has basically realized a more efficient algorithm for consumer online reviews under the background of applying data fusion and multiobjective optimization technology.

The massive number of sensors deployed in the Internet of Things (IoT) produce gigantic amounts of data for facilitating a wide range of applications. Deep Learning (DL) would undoubtedly play a role in generating valuable inferences from this massive volume of data and hence will assist in creating smarter IoT. In this regard, exploring the potential of DL for IoT data analytics becomes highly crucial. This paper begins with a concise discussion on the Deep Neural Network (DNN) and its different architectures. The potential benefits that DL will bring to the IoT are also discussed. Then, a detailed review of DL-driven IoT use-cases is presented. Moreover, this paper formulates a DL-based model for Human Activity Recognition (HAR). It carries out a performance comparison of the proposed model with other machine learning techniques to delineate the superiority of the DL model over other techniques. Apart from enlightening the potential of DL in IoT applications, this paper will serve as an impetus to encourage advanced research in the realm of DL-driven IoT applications.

Very High Resolution satellite and aerial imagery are used to monitor and conduct large scale surveys of ecological systems. Convolutional Neural Networks have successfully been employed to analyze such imagery to detect large animals and salient features. As the datasets increase in volume and number of images, utilizing High Performance Computing resources becomes necessary. In this paper, we investigate three task-parallel, data-driven workflow designs to support imagery analysis pipelines with heterogeneous tasks on high performance computing platforms. We analyze the capabilities of each design when processing 3097 and 1575 images for two distinct use cases, for a total of 4,672 satellite and aerial images and 8.35 TB of data. We experimentally model the execution time of the tasks of the image processing pipelines. We perform experiments to characterize resource utilization, total time to completion and overheads of each design. Our analysis shows which design is best suited to scientific pipelines with similar characteristics.

The ready availability of advanced visualization tools on picture archiving and communication systems workstations or even standard laptops through server-based or cloud-based solutions has enabled greater adoption of these techniques. We describe how radiologists can tailor imaging techniques for optimal 3D reconstructions provide a brief overview of the standard and newer "on-screen" techniques. We describe the process of creating 3D printed models for surgical simulation and education, with examples from the authors' institution and the existing literature. Finally, the review highlights current uses and potential future use cases for virtual reality and augmented reality applications in a pediatric neuroimaging setting.

In healthcare, artificial intelligence (AI) technologies have the potential to create significant value by improving time-sensitive outcomes while lowering error rates for each patient. Diagnostic images, clinical notes, and reports are increasingly generated and stored in electronic medical records. This heterogeneous data presenting us with challenges in data analytics and reusability that is by nature has high complexity, thereby necessitating novel ways to store, manage and process, and reuse big data. This presents an urgent need to develop new, scalable, and expandable AI infrastructure and analytical methods that can enable healthcare providers to access knowledge for individual patients, yielding better decisions and outcomes. In this review article, we briefly discuss the nature of data in breast cancer study and the role of AI for generating “smart data” which offer actionable information that supports the better decision for personalized medicine for individual patients. In our view, the biggest challenge is to create a system that makes data robust and smart for healthcare providers and patients that can lead to more effective clinical decision-making, improved health outcomes, and ultimately, managing the healthcare outcomes and costs. We highlight some of the challenges in using breast cancer data and propose the need for an AI-driven environment to address them. We illustrate our vision with practical use cases and discuss a path for empowering the study of breast cancer databases with the application of AI and future directions.

With smart sensors and embedded drivers, today’s automotive industry has taken a giant leap in emerging technologies like Machine learning, Artificial intelligence, and the Internet of things and started to build data-driven decision-making strategies to compete in global smart manufacturing. This paper proposes a novel design framework that uses Federated learning-Artificial intelligence (FAI) for decision-making and Smart Contract (SC) policies for process execution and control in a completely automated smart automobile manufacturing industry. The proposed design introduces a novel element called Trust Threshold Limit (TTL) that helps moderate the excess usage of embedded equipment, tools, energy, and cost functions, limiting wastages in the manufacturing processes. This research highlights the use cases of AI in decentralised Blockchain with smart contracts, the company’s trading policies, and its advantages for effectively handling market risk assessments during socio-economic crisis. The developed model supported by real-time cases incorporated cost functions, delivery time and energy evaluations. Results spotlight the use of FAI in decision accuracy for the developed smart contract-based Automobile Assembly Model (AAM), thereby qualitatively limiting the threshold level of cost, energy and other control functions in procurement assembly and manufacturing. Customisation and graphical user interface with cloud integration are some challenges of this model.