An Evidence Theory Based Embedding Model for the Management of Smart Water Environments.

Having access to safe water and using it properly is crucial for human well-being, sustainable development, and environmental conservation. Nonetheless, the increasing disparity between human demands and natural freshwater resources is causing water scarcity, negatively impacting agricultural and industrial efficiency, and giving rise to numerous social and economic issues. Understanding and managing the causes of water scarcity and water quality degradation are essential steps toward more sustainable water management and use. In this context, continuous Internet of Things (IoT)-based water measurements are becoming increasingly crucial in environmental monitoring. However, these measurements are plagued by uncertainty issues that, if not handled correctly, can introduce bias and inaccuracy into our analysis, decision-making processes, and results. To cope with uncertainty issues related to sensed water data, we propose combining network representation learning with uncertainty handling methods to ensure rigorous and efficient modeling management of water resources. The proposed approach involves accounting for uncertainties in the water information system by leveraging probabilistic techniques and network representation learning. It creates a probabilistic embedding of the network, enabling the classification of uncertain representations of water information entities, and applies evidence theory to enable decision making that is aware of uncertainties, ultimately choosing appropriate management strategies for affected water areas.

IoT Technologies in Chemical Analysis Systems: Application to Potassium Monitoring in Water.

The in-line determination of chemical parameters in water is of capital importance for environmental reasons. It must be carried out frequently and at a multitude of points; thus, the ideal method is to utilize automated monitoring systems, which use sensors based on many transducers, such as Ion Selective Electrodes (ISE). These devices have multiple advantages, but their management via traditional methods (i.e., manual sampling and measurements) is rather complex. Wireless Sensor Networks have been used in these environments, but there is no standard way to take advantage of the benefits of new Internet of Things (IoT) environments. To deal with this, an IoT-based generic architecture for chemical parameter monitoring systems is proposed and applied to the development of an intelligent potassium sensing system, and this is described in detail in this paper. This sensing system provides fast and simple deployment, interference rejection, increased reliability, and easy application development. Therefore, in this paper, we propose a method that takes advantage of Cloud services by applying them to the development of a potassium smart sensing system, which is integrated into an IoT environment for use in water monitoring applications. The results obtained are in good agreement (correlation coefficient = 0.9942) with those of reference methods.

InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users' Feedback.

Cloud Computing (CC) provides a combination of technologies that allows the user to use the most resources in the least amount of time and with the least amount of money. CC semantics play a critical role in ranking heterogeneous data by using the properties of different cloud services and then achieving the optimal cloud service. Regardless of the efforts made to enable simple access to this CC innovation, in the presence of various organizations delivering comparative services at varying cost and execution levels, it is far more difficult to identify the ideal cloud service based on the user's requirements. In this research, we propose a Cloud-Services-Ranking Agent (CSRA) for analyzing cloud services using end-users' feedback, including Platform as a Service (PaaS), Infrastructure as a Service (IaaS), and Software as a Service (SaaS), based on ontology mapping and selecting the optimal service. The proposed CSRA possesses Machine-Learning (ML) techniques for ranking cloud services using parameters such as availability, security, reliability, and cost. Here, the Quality of Web Service (QWS) dataset is used, which has seven major cloud services categories, ranked from 0-6, to extract the required persuasive features through Sequential Minimal Optimization Regression (SMOreg). The classification outcomes through SMOreg are capable and demonstrate a general accuracy of around 98.71% in identifying optimum cloud services through the identified parameters. The main advantage of SMOreg is that the amount of memory required for SMO is linear. The findings show that our improved model in terms of precision outperforms prevailing techniques such as Multilayer Perceptron (MLP) and Linear Regression (LR).

Distributed Architecture for Unmanned Vehicle Services.

The demand for online services is increasing. Services that would require a long time to understand, use and master are becoming as transparent as possible to the users, that tend to focus only on the final goals. Combined with the advantages of the unmanned vehicles (UV), from the unmanned factor to the reduced size and costs, we found an opportunity to bring to users a wide variety of services supported by UV, through the Internet of Unmanned Vehicles (IoUV). Current solutions were analyzed and we discussed scalability and genericity as the principal concerns. Then, we proposed a solution that combines several services and UVs, available from anywhere at any time, from a cloud platform. The solution considers a cloud distributed architecture, composed by users, services, vehicles and a platform, interconnected through the Internet. Each vehicle provides to the platform an abstract and generic interface for the essential commands. Therefore, this modular design makes easier the creation of new services and the reuse of the different vehicles. To confirm the feasibility of the solution we implemented a prototype considering a cloud-hosted platform and the integration of custom-built small-sized cars, a custom-built quadcopter, and a commercial Vertical Take-Off and Landing (VTOL) aircraft. To validate the prototype and the vehicles' remote control, we created several services accessible via a web browser and controlled through a computer keyboard. We tested the solution in a local network, remote networks and mobile networks (i.e., 3G and Long-Term Evolution (LTE)) and proved the benefits of decentralizing the communications into multiple point-to-point links for the remote control. Consequently, the solution can provide scalable UV-based services, with low technical effort, for anyone at anytime and anywhere.

USE OF MODERN CLOUD SERVICES IN RADIOLOGICAL DIAGNOSTICS TRAINING.

OBJECTIVE: The aim: Is to present and substantiate approaches to the organization of radiation diagnostics training using cloud services. PATIENTS AND METHODS: Materials and methods: The experimental research was carried out at on 306 students of the Bogomolets National Medical University. To perform the set tasks, some theoretical and empirical methods of scientific research were used, namely: system analysis method, bibliosemantic method, statistical method, modeling method. RESULTS: Results: The expediency of building a hybrid digital environment, which combines the capabilities of the corporate and public cloud service and allows one to create an information system of personalized access to electronic educational resources was justified. This environment is implemented within the cloud service Nextcloud. The basic components of radiological diagnostics training by means of network technologies are considered and characterized. An experimental test of the effectiveness of the cloud services use is conducted in the process of training radiological diagnostics. CONCLUSION: Conclusions: The expediency of creating a modern digital educational environment based on the Nextcloud service for training radiological diagnostics has been justified. Moreover, it is demonstrated that pedagogically balanced and reasoned introduction of cloud services in the educational process promotes increased efficiency of educational process of radiological diagnostics.

Human Interaction Smart Subsystem-Extending Speech-Based Human-Robot Interaction Systems with an Implementation of External Smart Sensors.

This paper presents a more detailed concept of Human-Robot Interaction systems architecture. One of the main differences between the proposed architecture and other ones is the methodology of information acquisition regarding the robot's interlocutor. In order to obtain as much information as possible before the actual interaction took place, a custom Internet-of-Things-based sensor subsystems connected to Smart Infrastructure was designed and implemented, in order to support the interlocutor identification and acquisition of initial interaction parameters. The Artificial Intelligence interaction framework of the developed robotic system (including humanoid Pepper with its sensors and actuators, additional local, remote and cloud computing services) is being extended with the use of custom external subsystems for additional knowledge acquisition: device-based human identification, visual identification and audio-based interlocutor localization subsystems. These subsystems were deeply introduced and evaluated in this paper, presenting the benefits of integrating them into the robotic interaction system. In this paper a more detailed analysis of one of the external subsystems-Bluetooth Human Identification Smart Subsystem-was also included. The idea, use case, and a prototype, integration of elements of Smart Infrastructure systems and the prototype implementation were performed in a small front office of the Weegree company as a decent test-bed application area.

Design of a 3D printed smartphone microscopic system with enhanced imaging ability for biomedical applications.

Portable, low-cost smartphone platform microscopic systems have emerged as a potential tool for imaging of various micron and submicron scale particles in recent years (Ozcan; Pirnstill and Coté; Breslauer et al.; Zhu et al.). In most of the reported works, it involves either the use of sophisticated optical set-ups along with a high-end computational tool for postprocessing of the captured images, or it requires a high-end configured smartphone to obtain enhanced imaging of the sample. Present work reports the working of a low-cost, field-portable 520× optical microscope using a smartphone. The proposed smartphone microscopic system has been designed by attaching a 3D printed compact optical set-up to the rear camera of a regular smartphone. By using cloud-based services, an image processing algorithm has been developed which can be accessed anytime through a mobile broadband network. Using this facility, the quality of the captured images can be further enhanced, thus obviating the need for dedicated computational tools for postprocessing of the images. With the designed microscopic system, an optical resolution ∼2 µm has been obtained. Upon postprocessing, the resolution of the captured images can be improved further. It is envisioned that with properly designed optical set-up in 3D printer and by developing an image processing application in the cloud, it is possible to obtain a low-cost, user-friendly, field-portable optical microscope on a regular smartphone that performs at par with that of a laboratory-grade microscope. LAY DESCRIPTION: With the ever-improving features both in hardware and software part, smartphone becomes ubiquitous in the modern civilised society with approximately 8.1 billion cell phone users across the world, and ∼40% of them can be considered as smartphones. This technology is undoubtedly the leading technology of the 21st century. Very recently, various researchers across the globe have utilised different sensing components embedded in the smartphone to convert it into a field-portable low-cost and user-friendly tool which can be used for different sensing and imaging purposes. By using simple optical components such as lens, pinhole, diffuser etc. and the camera of the smartphone, various groups have converted the phone into a microscopic imaging system. Again, by removing the camera lenses of the phone, holography images of microscopic particles by directly casting its shadows on the CMOS sensor on the phone has been demonstrated. The holographic images have subsequently been processed using the dedicated computational tool, and the original photos of the samples can be obtained. All the reported smartphone-based microscopic systems either suffer from relatively low field-of-view (FOV), resolution or it needs a high computational platform. Present work, demonstrate an alternative approach by which a reasonably good resolution (<2 µm) along with high optical magnification (520×) and a large FOV (150 µm) has been obtained on a regular smartphone. For postprocessing of the captured images an image processing algorithm has been developed in the cloud and the same can be accessed by the smartphone application, obviating the need of dedicated computational tool and a high-end configured smartphone for the proposed microscope. For the development of the proposed microscopic system, a simple optical set-up has been fabricated in a 3D printer. The set-up houses all the required optical components and the sample specimen with the 3D-printed XY stage, and it can be attached easily to the rear camera of the smartphone. Using the proposed microscopic system, enhanced imaging of USAF target and red blood cells have been successfully demonstrated. With the readily available optical components and a regular smartphone, the net cost involvement is significantly low (less than $250, including the smartphone). We envisioned that the designed system could be utilised for point-of-care diagnosis in resource-poor settings where access to the laboratory facilities is very limited.

An IoT Based Architecture for Enhancing the Effectiveness of Prototype Medical Instruments Applied to Neurodegenerative Disease Diagnosis.

Human errors are probably the most critical cause of the large amount of medical accidents. Medical cyber-physical systems (MCPS) have been suggested as a possible approach for detecting and limiting the impact of errors and wrong procedures. However, during the initial development phase of medical instruments, regular MCPS systems are not a viable approach, because of the high costs of repeating complex validation procedures, due to modifications of the prototype instrument. In this work, a communication architecture, inspired by recent Internet of Things (IoT) advances, is proposed for connecting prototype instruments to the cloud, to allow direct and real-time interaction between developers and instrument operators. Without loss of generality, a real-world use case is addressed, dealing with the use of transcranial magnetic stimulation (TMS) for neurodegenerative disease diagnosis. The proposed infrastructure leverages on a message-oriented middleware, complemented by historical database for further data processing. Two of the most diffused protocols for cloud data exchange (MQTT and AMQP) have been investigated. The experimental setup has been focused on the real-time performance, which are the most challenging requirements. Time-related metrics confirm the feasibility of the proposed approach, resulting in an end-to-end delay on the order of few tens of milliseconds for local networks and up to few hundreds of milliseconds for geographical scale networks.

A New Application of Internet of Things and Cloud Services in Analytical Chemistry: Determination of Bicarbonate in Water.

In a constantly evolving world, new technologies such as Internet of Things (IoT) and cloud-based services offer great opportunities in many fields. In this paper we propose a new approach to the development of smart sensors using IoT and cloud computing, which open new interesting possibilities in analytical chemistry. According to IoT philosophy, these new sensors are able to integrate the generated data on the existing IoT platforms, so that information may be used whenever needed. Furthermore, the utilization of these technologies permits one to obtain sensors with significantly enhanced features using the information available in the cloud. To validate our new approach, a bicarbonate IoT-based smart sensor has been developed. A classical CO2 ion selective electrode (ISE) utilizes the pH information retrieved from the cloud and then provides an indirect measurement of bicarbonate concentration, which is offered to the cloud. The experimental data obtained are compared to those yielded by three other classical ISEs, with satisfactory results being achieved in most instances. Additionally, this methodology leads to lower-consumption, low-cost bicarbonate sensors capable of being employed within an IoT application, for instance in the continuous monitoring of HCO3- in rivers. Most importantly, this innovative application field of IoT and cloud approaches can be clearly perceived as an indicator for future developments over the short-term.

Smart Management Consumption in Renewable Energy Fed Ecosystems.

Advances in embedded electronic systems, the development of new communication protocols, and the application of artificial intelligence paradigms have enabled the improvement of current automation systems of energy management. Embedded devices integrate different sensors with connectivity, computing resources, and reduced cost. Communication and cloud services increase their performance; however, there are limitations in the implementation of these technologies. If the cloud is used as the main source of services and resources, overload problems will occur. There are no models that facilitate the complete integration and interoperability in the facilities already created. This article proposes a model for the integration of smart energy management systems in new and already created facilities, using local embedded devices, Internet of Things communication protocols and services based on artificial intelligence paradigms. All services are distributed in the new smart grid network using edge and fog computing techniques. The model proposes an architecture both to be used as support for the development of smart services and for energy management control systems adapted to the installation: a group of buildings and/or houses that shares energy management and energy generation. Machine learning to predict consumption and energy generation, electric load classification, energy distribution control, and predictive maintenance are the main utilities integrated. As an experimental case, a facility that incorporates wind and solar generation is used for development and testing. Smart grid facilities, designed with artificial intelligence algorithms, implemented with Internet of Things protocols, and embedded control devices facilitate the development, cost reduction, and the integration of new services. In this work, a method to design, develop, and install smart services in self-consumption facilities is proposed. New smart services with reduced costs are installed and tested, confirming the advantages of the proposed model.

Human robot interaction as a service for combatting COVID-19: an experimental case study.

COVID-19 pandemic has changed today's routines in a variety of fields such as society, economics, health, etc. It is surely known that the most powerful weapon to fight against the disease is the social distancing. Hence, it is strongly recommended by the authorities to decrease human to human interaction (HHI) in order to stop the spread. However, daily routine of people must continue somehow, because of the fact that it is not known when the pandemic will end permanently. Thus, new approaches should be adapted in social environments for COVID-19 prevention. Human robot interaction (HRI) can be seen as a vital mechanism to provide risk free routines in the society. For this purpose, we offer a human robot interaction as a service (HRIaaS) for eatery locations such as restaurants, cafes, etc. where customers should interact with the staff. The proposed service aims to utilize personal smartphones and decrease the number of HHIs for such environments in which strange people involved. Moreover, an experimental case study is conducted to obtain an evaluation with a real world scenario when the proposed service is used versus a contemporary routine with HHIs. The evaluation results show that an average reduction of 41% is achieved per customer in the number of HHIs between customers and serving staff.

GDPRValidator: a tool to enable companies using cloud services to be GDPR compliant.

This article presents a tool called GDPRValidator that aims to assist small and medium-sized enterprises (SMEs) that have migrated their services, or a part of them, to the cloud to be General Data Protection Regulation (GDPR) compliant when they manage and store employees' or customers' data in the cloud. As these companies have a limited budget to hire legal experts to guide them in complying with GDPR, the main objective of this tool is to help SMEs to be more competitive by saving a considerable amount of money. By using GDPRValidator, these companies can learn and begin the GDPR compliance process by themselves and decide whether it will be necessary to hire GDPR legal experts in the end. GDPRValidator implements a process that aids companies in compliance analysis and validation and generates a series of documents with recommendations. These documents do not guarantee full GDPR compliance, but they can help the company better understand the regulation and improve its data management strategies. In order to validate the efficiency and efficacy of the tool, two SMEs have used it and provided feedback about its perceived ease of use and its perceived usefulness for understanding and complying with GDPR. The results of the validation showed that, for both companies, the degree of perceived usefulness and ease of use of GDPRValidator is quite good. All the scores expressed agreement.

Fully Integrated Data Communication Framework by Using Visualization Augmented Reality for Internet of Things Networks.

The new and integrated area called Internet of Things (IoT) has gained popularity due to its smart, objects, services and affordability. These networks are based on data communication, augmented reality (AR), and wired and wireless infrastructures. The basic objective of these network is data communication, environment monitoring, tracking, and sensing by using smart devices and sensor nodes. The dAR is one of the attractive and advanced areas that is integrated in IoT networks in smart homes and smart industries to convert the objects into 3D to visualize information and provide interactive reality-based control. With attraction, this idea has suffered with complex and heavy processes, computation complexities, network communication degradation, and network delay. This article presents a detailed overview of these technologies and proposes a more convenient and fast data communication model by using edge computing and Fifth-Generation platforms. The article also introduces a Visualization Augmented Reality framework for IoT (VAR-IoT) networks fully integrated by communication, sensing, and actuating features with a better interface to control the objects. The proposed network model is evaluated in simulation in terms of applications response time and network delay and it observes the better performance of the proposed framework.

Genetic Information Insecurity as State of the Art.

Genetic information is being generated at an increasingly rapid pace, offering advances in science and medicine that are paralleled only by the threats and risk present within the responsible systems. Human genetic information is identifiable and contains sensitive information, but genetic information security is only recently gaining attention. Genetic data is generated in an evolving and distributed cyber-physical system, with multiple subsystems that handle information and multiple partners that rely and influence the whole ecosystem. This paper characterizes a general genetic information system from the point of biological material collection through long-term data sharing, storage and application in the security context. While all biotechnology stakeholders and ecosystems are valuable assets to the bioeconomy, genetic information systems are particularly vulnerable with great potential for harm and misuse. The security of post-analysis phases of data dissemination and storage have been focused on by others, but the security of wet and dry laboratories is also challenging due to distributed devices and systems that are not designed nor implemented with security in mind. Consequently, industry standards and best operational practices threaten the security of genetic information systems. Extensive development of laboratory security will be required to realize the potential of this emerging field while protecting the bioeconomy and all of its stakeholders.

Integration of the Rosetta suite with the python software stack via reproducible packaging and core programming interfaces for distributed simulation.

The Rosetta software suite for macromolecular modeling is a powerful computational toolbox for protein design, structure prediction, and protein structure analysis. The development of novel Rosetta-based scientific tools requires two orthogonal skill sets: deep domain-specific expertise in protein biochemistry and technical expertise in development, deployment, and analysis of molecular simulations. Furthermore, the computational demands of molecular simulation necessitate large scale cluster-based or distributed solutions for nearly all scientifically relevant tasks. To reduce the technical barriers to entry for new development, we integrated Rosetta with modern, widely adopted computational infrastructure. This allows simplified deployment in large-scale cluster and cloud computing environments, and effective reuse of common libraries for simulation execution and data analysis. To achieve this, we integrated Rosetta with the Conda package manager; this simplifies installation into existing computational environments and packaging as docker images for cloud deployment. Then, we developed programming interfaces to integrate Rosetta with the PyData stack for analysis and distributed computing, including the popular tools Jupyter, Pandas, and Dask. We demonstrate the utility of these components by generating a library of a thousand de novo disulfide-rich miniproteins in a hybrid simulation that included cluster-based design and interactive notebook-based analyses. Our new tools enable users, who would otherwise not have access to the necessary computational infrastructure, to perform state-of-the-art molecular simulation and design with Rosetta.

Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data.

Managing, processing and understanding big healthcare data is challenging, costly and demanding. Without a robust fundamental theory for representation, analysis and inference, a roadmap for uniform handling and analyzing of such complex data remains elusive. In this article, we outline various big data challenges, opportunities, modeling methods and software techniques for blending complex healthcare data, advanced analytic tools, and distributed scientific computing. Using imaging, genetic and healthcare data we provide examples of processing heterogeneous datasets using distributed cloud services, automated and semi-automated classification techniques, and open-science protocols. Despite substantial advances, new innovative technologies need to be developed that enhance, scale and optimize the management and processing of large, complex and heterogeneous data. Stakeholder investments in data acquisition, research and development, computational infrastructure and education will be critical to realize the huge potential of big data, to reap the expected information benefits and to build lasting knowledge assets. Multi-faceted proprietary, open-source, and community developments will be essential to enable broad, reliable, sustainable and efficient data-driven discovery and analytics. Big data will affect every sector of the economy and their hallmark will be 'team science'.

NeuronDepot: keeping your colleagues in sync by combining modern cloud storage services, the local file system, and simple web applications.

Neuroscience today deals with a "data deluge" derived from the availability of high-throughput sensors of brain structure and brain activity, and increased computational resources for detailed simulations with complex output. We report here (1) a novel approach to data sharing between collaborating scientists that brings together file system tools and cloud technologies, (2) a service implementing this approach, called NeuronDepot, and (3) an example application of the service to a complex use case in the neurosciences. The main drivers for our approach are to facilitate collaborations with a transparent, automated data flow that shields scientists from having to learn new tools or data structuring paradigms. Using NeuronDepot is simple: one-time data assignment from the originator and cloud based syncing-thus making experimental and modeling data available across the collaboration with minimum overhead. Since data sharing is cloud based, our approach opens up the possibility of using new software developments and hardware scalabitliy which are associated with elastic cloud computing. We provide an implementation that relies on existing synchronization services and is usable from all devices via a reactive web interface. We are motivating our solution by solving the practical problems of the GinJang project, a collaboration of three universities across eight time zones with a complex workflow encompassing data from electrophysiological recordings, imaging, morphological reconstructions, and simulations.