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The health human resources are the most important and most expensive in the health system, and their management is considered a basic component of the health institutions success. The integration of semantic web technologies in knowledge management and e-learning structure, assure the semantic interoperability. This paper proposes an environment of e-learning based on new semantic technologies able to manage the process of building personalized educational content for training university hospital managers. The necessity of development of such a system results from the competence requirements for the specialists of human resources management (HRM) in the medical system. These goals are achieved using ontologies in implementing the personalized process of learning and in modelling the learning flow. Ontologies are used to model student profile, educational domain and learning process. These models are implemented into an intelligent learning Web platform. This ontology-based platform offers the specific tools to implement a new mechanism to obtain the relevant information from Internet. The implementation of personalization in the e-learning system is achieved starting from the student model to determine the knowledge level and to identify their preferences and interests. The students receive the learning material according to their profile, learning style, initial knowledge and education needs. Learning style and student's profile are the most important parameters in determining individual differences and define the adaptive learning environments. The e-learning prototype system contributes to increase the performance, skills and capacity to assess the health managers, proposes an automatized search method for the desired and needed information in the identified specific professional domain.

A Newton-type algorithm and line search strategies for solving generalized discrete-time algebraic Riccati equations are dealt with. The conceptual algorithm is presented, and its main computational steps are discussed. Evaluation of residuals and closed-loop matrices at each iteration, determination of the step size, and the use of line search with backtracking, are addressed in detail. Algorithmic and implementation issues taken into account in the developed solver are described. An extensive performance investigation on a large collection of examples has been performed, and the results are summarized. Both usual line search and line search with backtracking, and either identity or diagonal performance index matrices are considered. Difficult examples are included. The results often show significantly improved accuracy, measured in terms of normalized and relative residuals, in comparison with the state-of-the-art MATLAB function. The new solver is strongly recommended for improving the solutions computed by other solvers.

Given an n-vertex m-edge graph G with non-negative edge-weights, a shortest cycle of G is one minimizing the sum of the weights on its edges. The girth of G is the weight of such a shortest cycle. We obtain several new approximation algorithms for computing the girth of weighted graphs: - For any graph G with polynomially bounded integer weights, we present a deterministic algorithm that computes, in O~(n^{5/3}+m)-time, a cycle of weight at most twice the girth of G. This matches both the approximation factor and - almost - the running time of the best known subquadratic-time approximation algorithm for the girth of unweighted graphs. - Then, we turn our algorithm into a deterministic (2+epsilon)-approximation for graphs with arbitrary non-negative edge-weights, at the price of a slightly worse running-time in O~(n^{5/3}polylog(1/epsilon)+m). For that, we introduce a generic method in order to obtain a polynomial-factor approximation of the girth in subquadratic time, that may be of independent interest. - Finally, if we assume that the adjacency lists are sorted then we can get rid off the dependency in the number m of edges. Namely, we can transform our algorithms into an O~(n^{5/3})-time randomized 4-approximation for graphs with non-negative edge-weights. This can be derandomized, thereby leading to an O~(n^{5/3})-time deterministic 4-approximation for graphs with polynomially bounded integer weights, and an O~(n^{5/3}polylog(1/epsilon))-time deterministic (4+epsilon)-approximation for graphs with non-negative edge-weights. To the best of our knowledge, these are the first known subquadratic-time approximation algorithms for computing the girth of weighted graphs.

Nowadays cloud computing technologies are the most widely used tools due to their great flexibility and also to their lower maintenance costs. Many vendors of cloud computing have appeared on the market for each type of cloud. These solutions still pose certain vulnerabilities and work to improve the security of cloud computing technologies. We analyze the main cloud computing solutions, analyze the vulnerabilities identified for these solutions, and also calculate the impact of these vulnerabilities based on the NVD scores. We average the scores for each solution for each cloud computing model. This way, we can see the impact of the vulnerabilities identified so far for each cloud computing model. Also, we analyze the number of identified vulnerabilities for during the 2007-2019. This analysis presents the period when the cloud computing solutions has a big interest to the users and to people who wants to hack these solutions.

Digital technology landscape is continuously improving, dragging along both the transformation of public services and new demands of citizens. Emerging new technologies like Artificial Intelligence, Machine Learning, Deep Learning or Internet of Things provide tremendous means to implement intelligent solutions for reshaping digital public services. This paper aims to disclose the most important features of several intelligent technologies and of these types of public services that can be integrated for providing new capabilities. An AI-based architecture for supporting digital public services in the smart transportation sector is presented in order to demonstrate the highlighted ideas and concepts.

We present a new application, developed mostly from scratch, serving as a fast and efficient web crawler, with added network visualization and content analysis tools. It can be used to perform experimental research in a number of fields, including web graph analysis, basic text comparison or even testing out sociological theories, depending on the user’s inclination. A use case is provided, where the application is applied to Romanian news websites, from which several interesting observations can be drawn. The application itself and its code are released under a GPL license, and can be used by other researchers as-is (for use cases similar to our own), or expanded upon by interested developers.

As Cloud computing is a very well developed domain, many companies tend to move their entire activity in Cloud. At the same time, there is a tendency to move some of the data processing from Cloud to Edge as close as possible to the end devices. One main advantage of this approach is minimizing the latency in communication between the end devices and Cloud. Better usage of on premise devices is also a good achievement of the Edge offload. In this paper, we propose an architecture for applications that are connected to ROBIN-Cloud or to a general Cloud. We present how we have encapsulated Python-based microservices in Docker containers. We provide an implementation for the My Cloudy Time Machine application - GIGEL (Guided Intelligent GEared Legend), a nearby autonomous assistive robot. We use this prototype to evaluate the scalability of the proposed architecture. We also present results that show how to gain high performance by tuning a container-based embedded system.

The periodic QZ algorithm involved in the structure-preserving skew-Hamiltonian/Hamiltonian algorithm is investigated. These are key algorithms for many applications in diverse theoretical and practical domains such as periodic systems, (robust) optimal control, and characterization of dynamical systems. Although in use for several years, few examples of skew-Hamiltonian/Hamiltonian eigenproblems have been discovered for which the periodic QZ algorithm either did not converge or required too many iterations to reach the solution. This paper investigates this rare bad convergence behavior and proposes some modifications of the periodic QZ and skew-Hamiltonian/Hamiltonian solvers to avoid nonconvergence failures and improve the convergence speed. The results obtained on a generated set of one million skew-Hamiltonian/Hamiltonian eigenproblems of order 80 show no failures and a significant reduction (sometimes of over 240 times) of the number of iterations.

We consider a community formation problem in social networks, where the users are either friends or enemies. The users are partitioned into conflict-free groups (i.e., independent sets in the conflict graph G−=(V,E) that represents the enmities between users). The dynamics goes on as long as there exists any set of at most k users, k being any fixed parameter, that can change their current groups in the partition simultaneously, in such a way that they all strictly increase their utilities (number of friends i.e., the cardinality of their respective groups minus one). Previously, the best-known upper-bounds on the maximum time of convergence were O(|V|α(G−)) for k≤2 and O(|V|3) for k=3, with α(G−) being the independence number of G−. Our first contribution in this paper consists in reinterpreting the initial problem as the study of a dominance ordering over the vectors of integer partitions. With this approach, we obtain for k≤2 the tight upper-bound O(|V|min{α(G−),|V|−−−√}) and, when G− is the empty graph, the exact value of order (2|V|)3/23. The time of convergence, for any fixed k≥4, was conjectured to be polynomial. In this paper we disprove this. Specifically, we prove that for any k≥4, the maximum time of convergence is in Ω(|V|Θ(log|V|)).

In this paper, we have considered Data Centres (DCs) as computing facilities functioning at the crossroad of electrical, thermal and data networks and have defined optimisation techniques to exploit their energy flexibility. Our methods are leveraging on non-electrical cooling devices such as thermal storage and heat pumps for waste heat reuse and IT workload execution time shifting and spatial relocation in federated DCs. To trade energy flexibility we have defined an Energy Marketplace which allows DCs to act as active energy players integrated into the smart grid, contributing to smart city-level efficiency goals. Reinforcing this vision, we have proposed four innovative business scenarios that enable next generation smart Net-zero Energy DCs acting as energy prosumers at the interface with smart energy grids within smart city environments. Simulation experiments are conducted to determine the DCs potential electrical and thermal energy flexibility in meeting various network level goals and to assess the financial viability of the defined business scenarios. The results show that DCs have a significant amount of energy flexibility which may be shifted and traded to interested stakeholders thus allowing them to gain new revenue streams not foreseen before.

The paper gives an overview of time series modeling and forecasting, using multiplicative SARIM Amodels, with application in assessing and forecasting of epidemiological data. After general view of the mainmodels and the methodological issues used in Box-Jenkins approach, the paper presents a case study having assubject the modeling and forecasting of a time series representing the measles infections, in Great Britain, 1971-1994, quarterly recorded, and an example of intervention analysis, using as exogenous data the measles infections,and as endogenous variable the number of vaccinated persons, in the same time period. The intervention analysisproved to be a useful approach to model interrupted time series, when the time series is affected by the effect ofpopulation vaccination.

The paper gives an overview of time series modeling and forecasting, using multiplicative SARIM Amodels, with application in assessing and forecasting of epidemiological data. After general view of the mainmodels and the methodological issues used in Box-Jenkins approach, the paper presents a case study having assubject the modeling and forecasting of a time series representing the measles infections, in Great Britain, 1971-1994, quarterly recorded, and an example of intervention analysis, using as exogenous data the measles infections,and as endogenous variable the number of vaccinated persons, in the same time period. The intervention analysisproved to be a useful approach to model interrupted time series, when the time series is affected by the effect ofpopulation vaccination.

In these days the scientific community is times bigger comparing with the previous centuries. The need of powerful tools to aggregate and analyse the information about articles, books and other publications becomes greater with each published paper. In this paper we describe a solution for understanding and leveraging this data. The platform integrates the following requirements: data aggregation, data analysis (visualizations, dashboards, graphs), complex and simple searches, and support for data export. The platform brings value to its users due to various reasons such as quick identification of relevant data and in depth analysis on the provided input. Another key feature is the granularity, application being easily configurable for rigorous use cases: just one author, a group of authors or an entire scientific field.

Cloud computing can enable the unraveling of new scientific breakthroughs. We will eventually arrive to compute overwhelmingly large sizes of information, larger than we ever thought about it. Better scheduling algorithms are the key to process Big Data. This paper presents a load balancing scheduling algorithm for Many Task Computing using the computational resources from Cloud, in order to process a huge number of tasks with a finite number of resources. As such, the algorithm can be also used for Big Data, because it scales easily for big applications if we put a load balancing algorithm on top of virtual machines. We impose an upper bound of one for the maximum nodes that can carry an arbitrary job without executing it and we show that this statement holds by simulating the algorithm in MTS2 (Many Task Scheduling Simulator). We also show that the algorithm’s overlay performs even better when there are multiple nodes and we discuss about choosing the best local scheduling policy for the working nodes.

The necessity of achieving high streaming quality requires to combine the benefits of cloud computing with the verticals of a content delivery network into a robust, reliable, flexible and fault-tolerant system. This paper presents a solution for data acquisition, processing and Internet-enabled distribution of multimedia content. The objective is to propose an elastic content gathering network which handles media files delivered, for instance, by unmanned aerial vehicles, being further served on-demand to end users scattered over the globe. We have implemented the framework architecture, the system components with their attached responsibilities and capabilities, evaluating the performance based on the extensive simulations. The designed framework has been validated in terms of ensured correctness. Experimental results have proven the proper behavior of the built system by handling both types of requests, as for the storage of massive incoming data sets and for distributing content through multiple employed servers strategically placed in the proximity of the initiated requests’ locations. Furthermore, once an autonomous and scalable network has been successfully designed, the number of required surrogate servers dynamically adjusts to consume the multimedia services in a cost-efficient manner.

A way of opening new perspectives that has attracted the interest of researchers in counseling and psychotherapy at the beginning of the 1980s is Neuro-linguistic programming (international acronym: NLP). Ϲreated in the 70ꞌs by Βandler R.W. and Grinder J.Т. for the purpose of discovering human excellence, NLP is still considered today one of the “roads” to success, providing the necessary tools to achieve goals. Although the mid-80s’ surveys lowered the importance of the NLP basic ideas, decreasing the interest of Psychology Specialists in the field of Psychology to further research this area, a group of well-known colleagues and students in that period - among which we mention Ϲameron-Βandler, L., DeLozier, J., Dilts, R.Β., Gordon, D., Pucelik, F., Βуron, Α.L., Eicher, J., Муers-Αnderson, М., Gilligan, S.G., Αndreas, S. and Αndreas, Ϲ., Epstein, Т.Α., Hallbom, Т., Smith, S., Reese, E.J., and Reese, М., James, Т., Woodsmall, W., Jacobson S., Lankton, S.R., or Epstein, Т. (and the list may continue) – have made a significant contribution to the development and expansion of NLP since then until the present day.