Miracle-dz/UniFAR-FTUs huggingface dataset:
CernVM-FS: delivering scientific software to globally distributed computing resources

The computing facilities used to process data for the experiments at the Large Hadron Collider at CERN are scattered around the world. The embarrassingly parallel workload allows for use of various computing resources, such as Grid sites of the Worldwide LHC Computing Grid, commercial and institutional cloud resources, as well as individual home PCs in "volunteer clouds". Unlike data, the experiment software cannot be easily split into small work units. Efficient delivery of the complex and frequently changing experiment software is a crucial step to harness heterogeneous resources. Here we present an approach to deliver software on demand using a scalable hierarchy of standard HTTP caches. We show how to tackle this problem by pre-processing software into content-addressable storage. On the worker nodes, we use a specially crafted file system that ensures data integrity and provides fault-tolerance. We show performance figures from large-scale deployment. For the most common case of computing clusters with 10 to 1000 worker nodes, we present a novel state dissemination protocol to support a fully decentralized and distributed memory cache.
Methodology For Building Thematic Indexes In Medicine For French

The aim of this project is to propose a methodology in automatically building thematic index from French medical texts in order to improve the IR process. In this article, we focus on the selection process of relevant terms. Contrary to Bourigault and Charlet (1999) who defend a statistical method followed by human intervention, we propose an automatic method that takes advantage of available a priori medical resources such as the MeSH thesaurus (Lindberg et al., 1993) and GALEN (Rector et al., 1996).
Energy-efficient lightpaths for computational grids

Optical networks have been pointed out as strong candidates to ensure energy-efficiency in the Internet backbone, as well as the distributed applications such as computational grids. In this paper, we propose a distributed framework to address energy-efficient lightpath establishment problem for computational grids over the optical WDM backbone. Each backbone node maintains two thresholds to determine its sleep/wakeup cycle. A node in the sleep mode saves energy by rejecting transient traffic. Each demand is routed based on anycast routing and with the objective of minimum power consumption. Through numerical results, we show that significant energy savings are attained by the proposed framework without requiring centralized information and control. We further show that coordinated sleep and wakeup management in the optical backbone can address the trade-off between propagation delay and energy savings.
Finding Stories in the Archive through

We present research showing the possibility of finding stories in a digital text archive through computational methods. Referring to the concept of "archival bond", we define stories as formed by documents that relate to a target activity. We developed a method called paragraph alignment to find such documents and an interactive visualization to discover connected stories in context with provenance.
An empirical evaluation of design rationale documents

While several studies propose methods and notations for "capturing" design rationale (DR), there is to date little data available on how useful this information is when a designer needs to reuse a previous design. This paper presents the results of an empirical evaluation of DR documents, carried out with six experienced professional designers who were asked to understand and to assess a past design. These designers were provided with documents that described the solution and documents describing the DR. These DR documents were constructed using the QOC method. To determine the usefulness of DR documents, we attempt to answer the three following questions: (1) Do designers confronted with an unknown design need to know the design rationales? (2) How designers use design rationale documents? (3) Do we succeed in capturing the rationales looked for by designers? The results provided by this study lead us to conclude that DR should be useful, at least for some designers who use it as a support to their reasoning, but not sufficient. Indeed, this study exhibits some limitations of the traditional approaches for recording DR. We discuss these limitations and some solutions needed to go beyond them.
Privacy preserving data mining using image slicing and visual cryptography

Slicing geometry (image) into different frames and randomizing them can help in hiding a secret message (key) within the image and also preserve privacy. Authenticated users can use the relevant key and extract the original image at its destination. This field termed as visual cryptography or steganography is one of the most secure forms of message transfer available today and is highly suited for image files. The scope of this paper is to combine steganography and cryptography properties in such a way to make it harder to retrieve the image of the secret message. In this work, the image is sliced and randomized, the stored random slices upon match with a search string (query) the original image is retrieved. Multithread approach is combined in the searching process to reduce the time complexity of the search algorithm. This method is particularly advantageous to store large number of records, categorized into different groups, and each group indexed with specific keys and when used at the receiving side the particular group image(s) becomes accessible.
Epidemiology and Risk Factors Associated with Developing Bacterial Meningitis among Children in Gaza Strip

Abstract Background Bacterial meningitis is still the leading cause of high morbidity and mortality among the children. The present study was conducted to determine the epidemiology, clinical characteristics of bacterial meningitis and to evaluate the risk factors associated with developing the infection. Methods This cross sectional study was conducted in three hospitals of Gaza strip -Palestine during the period 2009. All the children with clinical diagnosis of meningitis /meningoencephalitis admitted to these hospitals were included in the study. They were subjected to clinical examination as well as CSF bacteriological and serological investigations. Results During the period (2009), 1853 patients were admitted to the hospitals with suspect of meningitis by pediatricians, 73 (3.9%) proved by culture to be acute bacterial meningitis, of these patients 62% were males and 38% were females. The common isolated pathogens were Neisseria meningitides (47.9%), Streptococcus pneumonia (15.1%), Haemophilus influenza (13.7%), E. coli (11.0%), Enterobacter spp. (6.8%), Citrobacter spp. (2.7%), Providencia spp. (1.4%), and Pseudomonas aeruginosa (1.4%). The common recorded symptoms were fever (78%), neck stiffness (47%), vomiting (37%), poor feeding (19%), and irritability (16%). Statistical analysis showed that there was statistical significance associated developing of infection with malnutrition (low hemoglobin level), high house crowdness and irritability (P-value <0.05). The ANOVA statistical analysis showed that S. pneumonia has an impact on developing low hemoglobin level and leukocytosis. Conclusion N. meningitides is still dominant and needs vaccination. The risk factors should be taken into consideration in any future plan.
Lower body negative pressure-induced vagal reaction: role for the osmopressor response?

BACKGROUND Water ingestion elicits an osmopressor response in patients with impaired baroreflexes. In young, healthy subjects, water elicits sympathetic vasoconstriction. This study investigated the effect of water on the lower body negative pressure (LBNP)-induced vasovagal reaction and also analyzed its effect on the change of regional cerebral blood flow during LBNP. METHODS Twelve young healthy subjects underwent LBNP (40 mm Hg) tolerance testing for 45 minutes or until presyncopal symptoms occurred. Subjects received either LBNP or no LBNP with or without prior water ingestion. The severity of vasovagal reaction was determined by participant self-report rating of orthostatic symptoms during the LBNP test. Changes of regional cerebral blood flow (rCBF) between LBNP and water ingestion with LBNP groups were assessed using statistical parametrical mapping analyses. RESULTS Water ingestion attenuated the severity of symptomatic scores during LBNP (P = 0.004). Water ingestion increased Total peripheral vascular resistance (P < 0.001) and attenuated the blood pressure drop (P < 0.001) at the cessation of study. LBNP decreased rCBF over the left superior prefrontal gyrus, limbic-parahippocampal gyrus, left sublobar-caudate body, and hypothalamus (P < 0.001). Water increased rCBF significantly over the right frontal lobe, including the inferior and medial prefrontal gyrus, subcallosal, and sublobar insula, during LBNP stimulation (P < 0.001). CONCLUSIONS Water ingestion strongly reduces symptomatic burden of the vasovagal reaction induced by LBNP stimulation. The cortical activation of limbic and prefrontal cortex likely indicates the involvement of osmopressor response in central autonomic cardiovascular physiology. The central cortical activation of osmopressor response might provide insight into the mechanisms by which water ingestion reduces the vasovagal reaction.
Laplacian optimal design for image retrieval

Relevance feedback is a powerful technique to enhance Content-Based Image Retrieval (CBIR) performance. It solicits the user's relevance judgments on the retrieved images returned by the CBIR systems. The user's labeling is then used to learn a classifier to distinguish between relevant and irrelevant images. However, the top returnedimages may not be the most informative ones. The challenge is thus to determine which unlabeled images would be the most informative (i.e., improve the classifier the most) if they were labeled and used as training samples. In this paper, we propose a novel active learning algorithm, called Laplacian Optimal Design (LOD), for relevance feedback image retrieval. Our algorithm is based on aregression model which minimizes the least square error on the measured (or, labeled) images and simultaneously preserves the local geometrical structure of the image space. Specifically, we assume that if two images are sufficiently close to each other, then their measurements (or, labels) are close as well. By constructing a nearest neighbor graph, the geometrical structure of the image space can be described by the graph Laplacian. We discuss how results from the field of optimal experimental design may be used to guide our selection of a subset of images, which gives us the most amount of information. Experimental results on Corel database suggest that theproposed approach achieves higher precision in relevance feedback image retrieval.
Civil War and Human Development: Impacts of Finance and Financial Infrastructure

In this paper interactions between finance, development and armed conflict are explored to demonstrate that financial factors are crucial in sustaining conflict-underdevelopment feedback loops. Military expenditure drains resources, financial instability leads to conflict (and vice versa), war retards the development of financial institutions/infrastructure, and interactions between finance and conflict are exacerbated by distributional struggles. Some of these feedback effects are captured within a two-stage model of war, finance and development and this is used as the basis for an empirical analysis. Econometrically, the model is estimated using panel estimation and two stage Probit least squares (2SPLS) binary dependent variable estimation techniques to control for simultaneity and heterogeneity. The results suggest that financial constraints and financial instability increase the chances of civil war directly and, via negative impacts on development, indirectly too.
Improvements to the scalability of multiobjective clustering

In previous work, the authors have introduced a novel and highly effective approach to data clustering, based on the explicit optimization of a partitioning with respect to two complementary clustering objectives (Handl, et. al., 2004, 2005). In this paper, three modifications were made to the algorithm that improved its scalability to large data sets with high dimensionality and large numbers of clusters. Specifically, new initialization and mutation schemes that enable a more efficient exploration of the search space were introduced, and the null data model that is used as a basis for selecting the most significant solution from the Pareto front was modified. The high performance of the resulting algorithm is demonstrated on a newly developed clustering test suite.
Clustering with Local and Global Consistency ; CU-CS-973-04

Clustering aims at finding hidden structure in data. In this paper we present a new clustering algorithm that builds upon the local and global consistency method (Zhou, et.al., 2003), a semi-supervised learning technique with the property of learning very smooth functions with respect to the intrinsic structure revealed by the data. Starting from this algorithm, we derive an optimization framework that discovers structure in data without requiring labeled data. This framework is capable of simultaneously optimizing all learning parameters, as well as picking the optimal number of clusters. It also allows easy detection of both global outliers and outliers within clusters. Finally, we show that the learned cluster models can be used to add previously unseen points to the clusters, without re-learning the original cluster model. Encouraging experimental results are obtained on a number of toy and real world problems.
Neutral Polymorphisms in Putative Housekeeping Genes and Tandem Repeats Unravels the Population Genetics and Evolutionary History of Plasmodium vivax in India

The evolutionary history and age of Plasmodium vivax has been inferred as both recent and ancient by several studies, mainly using mitochondrial genome diversity. Here we address the age of P. vivax on the Indian subcontinent using selectively neutral housekeeping genes and tandem repeat loci. Analysis of ten housekeeping genes revealed a substantial number of SNPs (n = 75) from 100 P. vivax isolates collected from five geographical regions of India. Neutrality tests showed a majority of the housekeeping genes were selectively neutral, confirming the suitability of housekeeping genes for inferring the evolutionary history of P. vivax. In addition, a genetic differentiation test using housekeeping gene polymorphism data showed a lack of geographical structuring between the five regions of India. The coalescence analysis of the time to the most recent common ancestor estimate yielded an ancient TMRCA (232,228 to 303,030 years) and long-term population history (79,235 to 104,008) of extant P. vivax on the Indian subcontinent. Analysis of 18 tandem repeat loci polymorphisms showed substantial allelic diversity and heterozygosity per locus, and analysis of potential bottlenecks revealed the signature of a stable P. vivax population, further corroborating our ancient age estimates. For the first time we report a comparable evolutionary history of P. vivax inferred by nuclear genetic markers (putative housekeeping genes) to that inferred from mitochondrial genome diversity.
Inferring driver intentions using a driver model based on queuing network

Inferring driver intentions plays an important role in developing human-centric intelligent driver assistance systems. In this paper, we propose a method of inferring the lane-changing intention of drivers by using a driver model based on the queuing network (QN) cognitive architecture. Driver behavior data associated with a range of possible driver intentions are simulated by using the QN-based driver model previously validated. The intentions of drivers are deduced by comparing these sets of simulated behavior data with the collected behavior data of drivers. The experimental results in a driving simulator show that the method can infer typical and rapid lane-changing intention of drivers well.
Dynamic speaker clustering algorithm based on minimal GMM distance tracing

In the field of speaker clustering, most of the clustering algorithms rely heavily on the pre-given thresholds, which is hard work to get the optimal values. This paper proposed a speaker clustering algorithm based on tracing the minimal Bhattacharyya distance between two Gaussian Mixture Models (GMMs), without any pre-given thresholds. In the procedure of clustering, if utterance set A and B has the minimal distance, utterance B is regarded as suspicious set whose utterance may come from the speaker of A. And then, two stage-verification is used. First, a comparative likelihood is used to verify whether the suspicious set B is generated from the speaker or not. Second, a comparative likelihood for each utterance in set B is used to judge whether it is produced by the speaker of set A or not. If the utterance is from the speaker of set A, we move the utterance of set B to set A. And then the models of utterance set A and B are updated. Repeat the above two stages until each speech set is not changed. Experiments, evaluated on Chinese 863 speech database, give 68.97% average cluster purity (ACP), and classification error ratio (CER) is 39%. On the other hand, CER of the K-means and the Iterative Self-Organizing Data Analysis (ISODATA) with the optimal thresholds give 35% and 38% respectively.
Sports anaemia and anthropometric evaluation of footballers at Kwame Nkrumah University of Science and Technology (KNUST)

Introduction Sports anaemia is a physiological activity that occurs amongst footballers and may be due to poor diet, over-training, as well as an increase in plasma volume in endurance training activities. High plasma volume leads to changes in haematological parameters that may impact on endurance of footballers. The objective of the study was to determine the correlation between haematological and an-thropometric indices and their role in sports anaemia in a tropical setting. Methods Venous blood was taken into EDTA for 12 soccer players of KNUST soccer team before training and after training for the first (W1) and fifth (W5) weeks of training sessions. Complete blood count analysis was done for each blood sample and anthropometric parameters such as height, weight, body mass index, body fat percent and lean body mass were also measured. Cross-tabulations with mean and standard deviation or median and range were computed. Paired t-test & and Mann-Whitney test for parametric and non-parametric data computations were carried out and a p-value ≤ 0.05 was taken to rep-resent significant difference between data groups. Results There was significant reduction in haemoglobin (p = 0.003), haematocrit (p = 0.002), mean cell volume (MCV) (p = 0.034) and red blood cell (RBC) count (p = 0.011) as a result of a significant expansion of plasma volume (p= 0.006). Neutrophil, lymphocyte and eosinophil counts were reduced significantly (p= 0.043, 0.001 and 0.007, respectively) after the training at W5. Lean body mass (LBM) inversely correlated with haemoglobin (r = -0.787, p = 0.002) and haematocrit (r = -0.588, p = 0.044). Body fat percentage (BFP) also negatively correlated with lymphocyte count (r = -0.700, p = 0.011). Furthermore, there was a positive correlation between body mass index (BMI) and plasma volume change after the training programme (r = 0.689, p = 0.013). Conclusion The results suggest that sports anaemia was induced by an increase in plasma volume that resulted in changes in haematological parameters.
Closest pair queries in spatio-temporal databases

In recent years, spatio-temporal databases have been studied intensively. This paper proposes how to process k closest pair queries in spatio-temporal databases for the first time. A spatio-temporal k closest pair query continuously searches the k closest pairs between a set of spatial objects and a set of moving objects for a specified time interval of the query. To maintain the order of the k closest pairs, we use a time function that can represent the change in distance between a spatial object and a moving object as time passes. For efficient processing of k closest pair queries, we present an event-based structure, instead of a simple split list structure to avoid unnecessary computations, along with a distance bound used to prune unnecessary node accesses. Our event-based method is 9 to 43 times faster, compared to a method using a simple split list structure. Also, our event-based structure can be applied to process spatio-temporal k nearest neighbor queries. In various experiments, our event-based approach is 11 to 46 times faster than an existing approach for processing spatio-temporal k nearest neighbor queries.
Clustering audiology data

In this paper we describe new results of statistical and neural data mining of audiology patient records, with the ultimate aim of looking for factors influencing which patients would most benefit from being fitted with a hearing aid. We describe how a combination of neural and statistical techniques can usefully subdivide a set of patients into clusters, based on their hearing thresholds at six different frequencies, and then label the clusters with meaningful text labels. In our first experiment, we cluster the patients based on similarities between their audiograms using k-means clustering, resulting in two main clusters. We then use the chi-squared test to label each cluster with the keywords selected from the text comment, diagnosis and hearing aid type associated with each patient which are most typical (and atypical) of each cluster. In our second experiment, we again cluster the patients based on similarities between their audiograms, but this time using a self-organizing map (SOM). Here the locations in the resulting map, corresponding to individual patients, are labeled with the type of hearing aid selected for each patient. We demonstrate that this automatic textual labeling addresses well the heterogeneous character of medical audiology records, since they consist of numeric, structured and free text data.
Weakly supervised semantic segmentation with superpixel embedding

In this paper, we propose to use contexts of superpixels as a prior to improve semantic segmentation by the CRF framework. A graphical model is constructed on over-segmented images. Our main contribution is to take the concept of “superpixel embedding” into consideration, which is formalized as a potential item for optimizing the energy of the whole graph. We also introduce two ways of calculating this embedding potential. Experiments on several popular datasets, e.g., MRSC-21 and PASCAL VOC, illustrate that our approach enhances the performance of a previously proposed segmentation model without embedding. The accuracy results are comparable to some fully supervised methods.
Neural network model for efficient localization of a number of mutually arbitrary positioned stochastic EM sources in far-field

An efficient direction of arrival (DOA) estimation of multiple electromagnetic sources by using artificial neural network (ANN) approach is presented in the paper. Electromagnetic sources considered here are of stochastic radiation nature, mutually uncorrelated and at arbitrary angular distance. The approach is based on training of the ANN in which the calculation of correlation matrix in the far-field scan area is done by using the Green function and the correlation of antenna elements feed currents used to describe stochastic sources radiation and then mapping this matrix to the space of DOA in angular coordinate. Once successfully trained, the neural network model is capable to perform an accurate DOA estimation within the training boundaries. Presented example verifies the accuracy of the proposed neural network model.