Miracle-dz/UniFAR-FTUs huggingface dataset:
Prestazioni The Cloud Computing Paradigm : Characteristics , Opportunities and Research Issues

The Cloud Computing paradigm has become one of the most interesting trends in the arena of distributed computing owing to its numerous merits: Cloud frameworks allow the managers of server farms to adapt and optimize resource usage; the “start-up” of small and medium enterprises is becoming easier and less onerous, thanks to the possibility of cutting fixed costs; Clouds can support industrial and academic research efficiently, because they allow complex tasks to be executed in short time with the use of resources allocated on demand. After a short summary of Cloud Computing origins and history, this document aims to illustrate the main ideas underlying the Cloud paradigm, and describe the different kinds of Cloud frameworks, trying to shed light over the confusion about the Cloud Computing concept. This document also examines the peculiarities and similarities of Cloud Computing with respect to the other paradigms that have recently emerged in the field of parallel/distributed computing, such as Peerto-Peer, Grid Computing and Utility Computing. All these paradigms have several common objectives, therefore there are notable benefits that can derive from the use of Cloud solutions in existing and future computing systems: these benefits, and the way to accomplish them, are discussed. Finally, the most important Cloud frameworks that are currently available are illustrated and compared.
Design of a Greenhouse Humiture Monitoring System Based on ZigBee Wireless Sensor Networks

Temperature and humidity are two important factors to the crops growth and product quality in greenhouse. A study on the space-time distribution of humiture in greenhouse is required. The green house humiture monitoring system based on ZigBee wireless sensor networks (ZWSN) is the best solution. The objectives of this study are: (1) to develop ZWSN nodes for measuring the temperature and humidity in greenhouse; (2) to program suitable software making the nodes sleepy without work for energy saving; and (3) to optimize network performance by setting a time delay for each nodes. The designed system had been used to monitor the humiture inside a greenhouse. Experiments showed that this system operates stably, and the energy consumption was 22.4mA at work, 4.7mA in sleep. Its success rate of data packets reception was 97.1% with a delay. The ZWSN system could meet the need of humiture monitoring in greenhouse.
Composition of Resource-Service Chain for Cloud Manufacturing

In distributed manufacturing systems, manufacturing resource composition is one of the most important problems. This is because efficiency of resource selection and resource utilization can all be improved if it is tackled well. However, most of the existing methods neglect temporal relationship between resources. This leads to an inefficient use of resources, because all resources have to be kept available before a business process is started. A temporal composition of resources is more suitable, as it expresses the scheduling and the flow of servicing to a business process. Therefore, resource services invoked in sequential order are called the resource-service chain (RSC), in view that distributed resources are encapsulated into cloud services in a cloud manufacturing (CMfg) environment. We propose an approach, called RSC composition algorithm (RSCCA) that can better cope with the temporal relationship between the resource services in a business process. Specifically, a two-stage composition method based on the degrees of dependency between resource services in workflow is proposed. To begin, in the build-time stage algorithm, RSCCA resolves initial compositions based on task relatedness and temporal dependencies between resource services, and then calculates the usage frequencies of ICs by mining workflow log at workflow runtime stage. Based on this, RSCCA can compose individual resource services as more than sets, especially as chains, allowing flow directions and dynamics to be considered. RSCCA has been tested with different data sets and the results show that it can be very promising.
Scalable ellipsoidal classification for bipartite quantum states

The Separability Problem is approached from the perspective of Ellipsoidal Classification. A Density Operator of dimension N can be represented as a vector in a real vector space of dimension $N^{2}- 1$, whose components are the projections of the matrix onto some selected basis. We suggest a method to test separability, based on successive optimization programs. First, we find the Minimum Volume Covering Ellipsoid that encloses a particular set of properly vectorized bipartite separable states, and then we compute the Euclidean distance of an arbitrary vectorized bipartite Density Operator to this ellipsoid. If the vectorized Density Operator falls inside the ellipsoid, it is regarded as separable, otherwise it will be taken as entangled. Our method is scalable and can be implemented straightforwardly in any desired dimension. Moreover, we show that it allows for detection of Bound Entangled States
Multilinguality , singing synthesis , acoustic emoticons , and other extensions of the Slovak speech synthesizer for SMS reading

After the successful implementation of our speech synthesizer Kempelen in the SMS to voice service of Eurotel, one of the biggest mobile telephone operators in Slovakia, we have found out that several extensions need to be done to make the service even more important for users. In the paper we describe the Slovak speech synthesizer Kempelen, its Hungarian version, we discuss the need of male/female/children voices. We describe a program enabling developers to create a new voice for testing purposes in 10 minutes. We introduce a new feature – speech singing synthesis in SMS to Voice service. Some basic acoustic and signal processing problems of the singing voice synthesis using speech synthesizer are discussed. Last, but not least we introduce a classification of Acousticons (pre-recorded illustrative sounds) which offer much more possibilities, than only acoustical representation of graphical emoticons. The acoustic, aesthetic, psychological, sociological and commercial aspects were taken into account in the classification scheme design.
Publishing Linked Data from relational databases using traditional views

Many achieves have focused on how to publish Linked Data from the relational databases. However, with current RDB2RDF tools, they showed an unsatisfactory performance because of their inherent gaps between RDF Schema (RDFS) and RDB schema. In this paper, we give a light-weight Linked Data publication mechanism based on the traditional views, which make users retrieve the data from the relational databases as RDF datasets and pose SPARQL queries on the traditional web server. We also give a method which can transform the SPARQL queries into the SQL queries based on Jena ARQ parser. The experiment shows that our approach performs efficiently compared to other RDB2RDF tools.
Stepwise Enabling of AUGUSTUS for MediGRID

In the past years researchers from many domains have discovered Grid technology as it opens up new possibilities to solve more complex problems than with traditional cluster computing. In this report we examine the process to enable the life science application AUGUSTUS for execution inMediGRID. The gridification process starts with providing the security requirements and running the application manually using Grid middleware. Afterwards, the application is described as workflow of subsequent program executions, which is automatically distributed on appropriate Grid resources by a workflow engine. Finally, we show how a graphical user interface for end users is created by means of a portal framework.
Establishing Identity Equivalence in Multi-Relational Domains

Identity Equivalence or Alias Detection is an important topic in Intelligence Analysis. Often, terrorists will use multiple different identities to avoid detection. We apply machine learning to the task of determining Identity Equivalence. Two challenges exist in this domain. First, data can be spread across multiple tables. Second, we need to limit the number of false positives. We present a two step approach to combat these issues. First, we use Inductive Logic Programming to find a set of rules that are predictive of aliases. In the second step, we treat each learned rule as a random variable in a Bayesian Network. We use the Bayesian Network to assign a probability that two identities are aliases. We evaluate our technique on several data sets and find that layering Bayesian Network over the rules significantly increases the precision of our system.
A microscopic view on community detection in complex networks

The community structure is a basic characteristic of complex networks. A strong community structure has high modularity. It has been proven an NP-Complete problem to identify the community structure with the highest modularity. Many approximate algorithms have been proposed to alleviate the problem. However, they suffer from inefficiency or low quality. In this paper, we propose a two-step method. The first step of our method analyze the vertex similarity of the network, which is a microscopic view. If a pair of vertices are similar enough, they will be put into the same community. The second step of our method focuses on the increment of modularity of the similarity-based communities generated by the first step. If the number of edges between two communities is greater than the expected number based on random choice, the two communities will be merged. The second step is implemented by the CNM algorithm or its improvement CNM+HE'. The similarity-based community remedies the defect on microscope introduced by CNM or CNM+HE'. Our method runs efficiently and finds meaningful communities effectively. We tested the method on more than twenty datasets. The modularity of community structure found by the method is higher than the state-of-the-art algorithm.
Relation of Exercise Capacity With Lung Volumes Before and After 6-Minute Walk Test in Subjects With COPD

INTRODUCTION: There is growing evidence that exercise-induced variation in lung volumes is an important source of ventilatory limitation and is linked to exercise intolerance in COPD. The aim of this study was to compare the correlations of walk distance and lung volumes measured before and after a 6-min walk test (6MWT) in subjects with COPD. METHODS: Forty-five subjects with stable COPD (mean pre-bronchodilator FEV1: 47 ± 18% predicted) underwent a 6MWT. Body plethysmography was performed immediately pre- and post-6MWT. RESULTS: Correlations were generally stronger between 6-min walk distance and post-6MWT lung volumes than between 6-min walk distance and pre-6MWT lung volumes, except for FEV1. These differences in Pearson correlation coefficients were significant for residual volume expressed as percent of total lung capacity (−0.67 vs −0.58, P = .043), percent of predicted residual volume expressed as percent of total lung capacity (−0.68 vs −0.59, P = .026), inspiratory vital capacity (0.65 vs 0.54, P = .019), percent of predicted inspiratory vital capacity (0.49 vs 0.38, P = .037), and percent of predicted functional residual capacity (−0.62 vs −0.47, P = .023). CONCLUSIONS: In subjects with stable COPD, lung volumes measured immediately after 6MWT are more closely related to exercise limitation than baseline lung volumes measured before 6MWT, except for FEV1. Therefore, pulmonary function testing immediately after exercise should be included in future studies on COPD for the assessment of exercise-induced ventilatory constraints to physical performance that cannot be adequately assessed from baseline pulmonary function testing at rest.
Analyzing Training Information From Random Forests for Improved Image Segmentation

Labeled training data are used for challenging medical image segmentation problems to learn different characteristics of the relevant domain. In this paper, we examine random forest (RF) classifiers, their learned knowledge during training and ways to exploit it for improved image segmentation. Apart from learning discriminative features, RFs also quantify their importance in classification. Feature importance is used to design a feature selection strategy critical for high segmentation and classification accuracy, and also to design a smoothness cost in a second-order MRF framework for graph cut segmentation. The cost function combines the contribution of different image features like intensity, texture, and curvature information. Experimental results on medical images show that this strategy leads to better segmentation accuracy than conventional graph cut algorithms that use only intensity information in the smoothness cost.
Kinetic multi-layer model of gas-particle interactions in aerosols and clouds (KM-GAP): linking condensation, evaporation and chemical reactions of organics, oxidants and water

Abstract. We present a novel kinetic multi-layer model for gas-particle interactions in aerosols and clouds (KM-GAP) that treats explicitly all steps of mass transport and chemical reaction of semi-volatile species partitioning between gas phase, particle surface and particle bulk. KM-GAP is based on the PRA model framework (Poschl-Rudich-Ammann, 2007), and it includes gas phase diffusion, reversible adsorption, surface reactions, bulk diffusion and reaction, as well as condensation, evaporation and heat transfer. The size change of atmospheric particles and the temporal evolution and spatial profile of the concentration of individual chemical species can be modeled along with gas uptake and accommodation coefficients. Depending on the complexity of the investigated system and the computational constraints, unlimited numbers of semi-volatile species, chemical reactions, and physical processes can be treated, and the model shall help to bridge gaps in the understanding and quantification of multiphase chemistry and microphysics in atmospheric aerosols and clouds. In this study we demonstrate how KM-GAP can be used to analyze, interpret and design experimental investigations of changes in particle size and chemical composition in response to condensation, evaporation, and chemical reaction. For the condensational growth of water droplets, our kinetic model results provide a direct link between laboratory observations and molecular dynamic simulations, confirming that the accommodation coefficient of water at ~270 K is close to unity (Winkler et al., 2006). Literature data on the evaporation of dioctyl phthalate as a function of particle size and time can be reproduced, and the model results suggest that changes in the experimental conditions like aerosol particle concentration and chamber geometry may influence the evaporation kinetics and can be optimized for efficient probing of specific physical effects and parameters. With regard to oxidative aging of organic aerosol particles, we illustrate how the formation and evaporation of volatile reaction products like nonanal can cause a decrease in the size of oleic acid particles exposed to ozone.
Application-aware cross-layer framework: Video content distribution in wireless multihop networks

Scalable video coding (SVC) can overcome the user heterogeneity issue, e.g., different screen resolutions or different connectivities, in video-streaming. In wireless multihop networks, the performance of SVC is not adequate, because SVC cannot adapt the lower layers. In order to adapt to changing environmental conditions, e.g., network topology, available resources or channel conditions, a cross-layer framework is required. We propose a new application-aware cross-layer framework which utilizes SVC, network structures and communication types at APP, NET, DLL and PHY layers together. Further, our application-aware cross-layer framework performs transitions at different layers to find the best combination of mechanisms. This is achieved by the following steps. First, we apply a graph-based approach to integrate all mechanisms on the different layers in a single graph. Secondly, video layers are modeled in the graph as virtual sources. Thirdly, we perform an optimal mapping from video layer data rates to physical layer rates. Fourthly, we formulate a multi-source sum rate optimization problem which chooses the best video layer distribution among users and the best combination of mechanisms at all layers. Finally, we demonstrate that our application-aware cross-layer framework outperforms current approaches.
Molecular dynamics with coupling to an external bath

In molecular dynamics (MD) simulations the need often arises to maintain such parameters as temperature or pressure rather than energy and volume, or to impose gradients for studying transport properties in nonequilibrium MD. A method is described to realize coupling to an external bath with constant temperature or pressure with adjustable time constants for the coupling. The method is easily extendable to other variables and to gradients, and can be applied also to polyatomic molecules involving internal constraints. The influence of coupling time constants on dynamical variables is evaluated. A leap‐frog algorithm is presented for the general case involving constraints with coupling to both a constant temperature and a constant pressure bath.
Investment and uncertainty in a quadratic adjustment cost model : evidence from Brazil

In this paper I assess empirically the sign of the uncertainty-investment relation in Brazil within a quadratic adjustment cost model. It is shown that these variables are negatively related in the Brazilian economy. The implication is that investment can be enlarged with the adoption of a sustainable macroeconomic policy that rules out uncertainty-yielding shocks, like huge devaluation in domestic currency, or defaults in internal and external debts. I also propose a method for estimating the quadratic adjustment cost model when the endogenous variable is I(2) and the forcing variables are I(1). As long as capital stock is typically an I(2) variable, the econometric insight seems particularly suited for models of investment. Neste artigo e avaliado empiricamente o sinal da relacao entre investimento e incerteza no Brasil, utilizando-se do arcabouco dos modelos com custo de ajustamento quadratico. Mostra-se que essas variaveis sao negativamente relacionadas na economia brasileira. A implicacao e que o nivel de investimento pode ser aumentado com a adocao de uma politica macroeconomica sustentavel capaz de evitar choques que geram incerteza, como grandes desvalorizacoes no câmbio ou moratorias nas dividas interna e externa. Propoe-se ainda um metodo para estimar o modelo com custo de ajustamento quadratico quando a variavel endogena e I(2) e as variaveis exogenas sao I(1). Dado que o estoque de capital e tipicamente uma variavel I(2), o procedimento econometrico parece particularmente apropriado para modelos de investimento.
Magnetic resonance imaging following treatment of advanced hepatocellular carcinoma with sorafenib

Hepatocellular carcinomas are highly vascular tumors, showing progressive hypervascularity by the process of neoangiogenesis. Tumor angiogenesis is critical for tumor growth as well as metastatic spread therefore, imaging and quantification of tumor neo-angiogenesis is essential for monitoring response to targeted therapies and predicting disease progression. Sorafenib is a molecular targeting agent used for treating hypervascular tumors. This drug is now the standard of care in treatment of patients with advanced hepatocellular carcinoma. Due to its anti-angiogenic and anti-proliferative actions, imaging findings following treatment with Sorafenib are quite distinct when compared to conventional chemotherapeutic agents. Liver MRI is a widely adopted imaging modality for assessing treatment response in hepatocellular carcinoma and imaging features may reflect pathophysiological changes within the tumor. In this mini-review, we will discuss MRI findings after Sorafenib treatment in hepatocellular carcinoma and review the feasibility of MRI as an early biomarker in differentiating responders from non-responders after treatment with molecular targeting agents.
The Subject, Its Biology, and the Chronic Recurrent Cystitis

Functional disorders in urology are troubling for both patients and physicians. Moreover, advances in recent research promise to provide biological insights into psycho-neuro-endocrino-immunological pathways that are one important facet of chronic urogenital inflammations. We present a case of a middle-aged woman with long-lasting recurrent cystitis for which especially a psychosomatic approach helped to understand and cure the disorder. Altogether, as practitioners treat subjects, not illnesses, a biopsychosocial understanding of human disease should be taken into account in cases of chronic recurrent cystitis.
Incorporating E(cid:14)cient Operations Research Algorithms in Constraint-Based Scheduling

We address the area of scheduling and the di(cid:11)erences between the way operations research and arti(cid:12)cial intelligence approach scheduling. We introduce the concept of constraint programming, and describe how operations research techniques can be integrated in constraint programming. Finally, we give a short overview of the results obtained with our approach
Does Dialectal Variation Matter in Term-Based Feature Selection of Sentiment Analysis?: An Investigation into Multi-dialectal Chinese Microblogs

This paper examines the feature selection procedures of sentiment analysis on a multi-dialectal language. We analyzed a dataset with over 6 million microblogs in China, a multi-dialectal country, deployed sentiment classifier to examine the positive/negative emotion carried by the microblogs, and explored the regional variations in the optimal feature vectors. The results support a localized feature vectors in some China's regions can maximize the classification accuracy and show that geographical distance between provinces and common dialect used contribute to explaining the provincial difference in the feature vectors. This research can be applied to other multicultural countries for feature vector optimization in sentiment analysis.
Weighted Linear Dynamic Logic

We introduce a weighted linear dynamic logic (weighted LDL for short) and show the expressive equivalence of its formulas to weighted rational expressions. This adds a new characterization for recognizable series to the fundamental Sch\"utzenberger theorem. Surprisingly, the equivalence does not require any restriction to our weighted LDL. Our results hold over arbitrary (resp. totally complete) semirings for finite (resp. infinite) words. As a consequence, the equivalence problem for weighted LDL formulas over fields is decidable in doubly exponential time. In contrast to classical logics, we show that our weighted LDL is expressively incomparable to weighted LTL for finite words. We determine a fragment of the weighted LTL such that series over finite and infinite words definable by LTL formulas in this fragment are definable also by weighted LDL formulas.