Miracle-dz/UniFAR-FTUs huggingface dataset:
CONSUMER CULTURE THEORY AND LIFESTYLE SEGMENTATION

This manuscript compares traditional (AKA quantitative), and Consumer Culture Theory (AKA qualitative), approaches to lifestyle segmentation. We find five key differences: (1) While traditional segmentation aims for brevity, CCT adds more detail; (2) Traditional approaches focus on common brand meanings, whereas CCT examines how meanings differ for different consumers; (3) Traditional favors quantitative methods, while CCT favors qualitative research; (4) While traditional group labels tend to remain obscure, like PRISM’s “belongers”, CCT favors familiar lifestyle groups; (5) Traditional lifestyle segmentation favors the descriptive, whereas CCT emphasizes theory development. Managerial implications are discussed.
Bayesian Spatial Modeling of fMRI Data: A Multiple Subject Analysis.

The aim of this work is to develop a spatial model for multi-subject fMRI data. While there has been much work on univariate modeling of each voxel for singleand multi-subject data, and some work on spatial modeling for single-subject data, there has been no work on spatial models that explicitly account for intersubject variability in activation location. We use a Bayesian hierarchical spatial model to fit the data. At the first level we model “population centers” that mark the centers of regions of activation. For a given population center each subject may have zero or more associated ”individual components”. While most previous work uses Gaussian mixtures for the activation shape, we instead use Gaussian mixtures for the probability that a voxel belongs to an activated region, assuming homogeneous mean intensity within a region. Our approach incorporates the unknown number of mixture components into the model as a parameter whose posterior distribution is estimated by reversible jump Markov Chain Monte Carlo. We demonstrate our method with a fMRI study of visual working memory and show dramatically better precision of localization with our method relative to the standard massunivariate method. Although we are motivated by fMRI data, this model could easily be modified to handle other types of imaging data. Bayesian Spatial Modeling of fMRI data: A Multiple-Subject Analysis Lei Xu1∗ Timothy D. Johnson1 Thomas E. Nichols2,3 1Department of Biostatistics School of Public Health University of Michigan Ann Arbor, MI 48109 2GlaxoSmithKline Clinical Imaging Centre Imperial College London Hammersmith Hospital, W12 0NN United Kingdom 3FMRIB Centre Oxford University Oxford, OX3 9DU United Kingdom Summary. The aim of this work is to develop a spatial model for multi-subject fMRI data. While there has been much work on univariate modeling of each voxel for singleand multi-subject data, and some work on spatial modeling for single-subject data, there has been no work on spatial models that explicitly account for intersubject variability in activation location. We use a Bayesian hierarchical spatial model to fit the data. At the first level we model “population centers” that mark the centers of regions of activation. For a given population center each subject may have zero or more associated “individual components”. While most previous work uses Gaussian mixtures for the activation shape, we instead use Gaussian mixtures for the probability that a voxel belongs to an activated region, assuming homogeneous mean intensity within a region. Our approach incorporates the unknown number of mixture components into the model as a parameter whose posterior distribution is estimated by reversible jump Markov Chain Monte Carlo. We demonstrate our method with a fMRI study of visual working memory and show dramatically better precision of localization with our method relative to the standard mass-univariate method. Although we are motivated by fMRI data, this model could easily be modified to handle other types of imaging data.
No Difference Between Athlete and Non-Athlete Performance on a Spatial Memory Test

by Kaitlin J. Hill Spatial memory is the part of memory devoted to the storage and retrieval of spatial information such as location, distance, and orientation. A quasi-experimental study assessed the spatial memory ability of 26 undergraduate students who participated in a sport that runs plays in high school and 21 undergraduates who played no such sport. Participants completed the Rey Complex Figure Test (Meyers & Meyers, 1995; Rey, 1941) and a short sport history questionnaire. A t-test for independent measures revealed no significant differences between the performance of the athlete and non-athlete group on the Recall Trials of the Test, suggesting there may be no difference in spatial memory ability between the two groups. NO DIFFERENCE BETWEEN ATHLETE AND NON-ATHLETE PERFORMANCE ON A SPATIAL MEMORY TEST by Kaitlin J. Hill A Thesis presented in partial fulfillment of the requirements for the degree of Master of Science in the Department of Psychological Science University of Central Missouri
Traffic classification using en-semble learning and co-training

Classification of network traffic is the essential step for many network researches. However, with the rapid evolution of Internet applications the effectiveness of the port-based or payload-based identification approaches has been greatly diminished in recent years. And many researchers begin to turn their attentions to an alternative machine learning based method. This paper presents a novel machine learning-based classification model, which combines ensemble learning paradigm with co-training techniques. Compared to previous approaches, most of which only employed single classifier, multiple classifiers and semi-supervised learning are applied in our method and it mainly helps to overcome three shortcomings: limited flow accuracy rate, weak adaptability and huge demand of labeled training set. In this paper, statistical characteristics of IP flows are extracted from the packet level traces to establish the feature set, then the classification model is created and tested and the empirical results prove its feasibility and effectiveness.
The Virgo interferometer

The Virgo gravitational wave detector is an interferometer with 3 km long arms in construction near Pisa to be commissioned in the year 2000. Virgo has been designed to achieve a strain sensitivity of a few times at 200 Hz. A large effort has gone into the conception of the mirror suspension system, which is expected to reduce noise to the level of at 10 Hz. The expected signals and main sources of noise are briefly discussed; the choices made are illustrated together with the present status of the experiment.
Refining PCR-based serotyping for detection of vaccine-preventable Streptococcus pneumoniae

Conventional multiplex PCR (cmPCR) reactions have been developed to monitor the most predominant serotypes of Streptococcus pneumoniae causing invasive pneumococcal disease (IPD). Since cmPCR assigns serotypes based on differences in the capsule biosynthesis ( cps ) loci, DNA extracted from clinical specimens can be used directly to monitor changes in serotype distribution and assess the impact of pneumococcal vaccines. Given that cmPCR can require up to eight reactions to assign a serotype, testing is often conducted in sequential algorithms. Sequential cmPCR reactions; however, may not be the most cost effective strategy to determine whether a S. pneumoniae serotype is vaccine-preventable. This study used oligonucleotide permutations in a modified set of cmPCR reactions (termed cmPCRmod) to reduce the number of PCR reactions required to identify S. pneumoniae serotypes covered by the 7- and 13-valent pneumococcal conjugate vaccines (PCV7 and PCV13, respectively) and the 23-valent pneumococcal polysaccharide vaccine (PPV23). While oligonucleotide permutations have previously been reported for regional differences in serotype distribution, the impact on assay performance had not been assessed. This study demonstrated that equivalent analytical sensitivity and specificity was seen when comparing cmPCR and cmPCRmod, and 100% concordance was seen when 308 clinical isolates of S. pneumoniae were evaluated. Compared to cmPCR, cmPCRmod reduced the number and reactions required to detect serotypes covered by PCV7, PCV13, and PPV23. This study demonstrated that conventional multiplex reactions can be reformulated for more efficient detection of vaccine-preventable serotypes, without compromising test performance characteristics. As such, cmPCRmod reactions could provide significant cost savings for large surveillance studies.
EFFICIENT CLASSIFIER FOR PREDICTING STUDENTS KNOWLEDGE LEVEL USING DATA MINING TECHNIQUES

The enormous growth of academic data size in higher education institutions makes learning process tedious to analyze the student’s knowledge level. The main motto of all educational institutions is to provide high quality of education. This can be achieved by predicting the student’s knowledge level of a particular subject. As traditional modelling approaches are unable to make predictions regarding knowledge, data mining methods was adopted to predict the performance of students. In this research work various classification techniques were applied on the Students Modelling Dataset taken from UCI Machine Learning Repository for predicting the student knowledge level. This work mainly focuses on finding best classifiers for predicting the user knowledge level in particular domain of interest. The results of this study indicate the level of accuracy and other performance measures of the algorithms in predicting the performance of student’s knowledge level. The results revealed that Rnd Tree and IBK Classifiers are considered as the best classification algorithms which yields 100 % accuracy on this dataset.
New Discrete Tanaka Sugeno Kang Fuzzy Systems Characterization and Stability Domain

In this paper, an analytical approach to characterize discrete Tanaka Sugeno Kang (TSK) fuzzy systems is presented. This characterization concerns the choice of the adequate conjunctive operator between input variables of discrete TSK fuzzy models, t-norm, and its impact on stability domain estimation. This new approach is based on stability conditions issued from vector norms corresponding to a vector-Lyapunov function. In particular, second order discrete TSK models are considered and this work concludes that Zadeh’s t-norm, logic product min, gives the largest estimation of stability domain.
Immunohistochemical Diagnosis of Pseudorabies (Aujeszky's Disease) in a Cow in Van, Turkey

Aujeszky’s disease, also known as pseudorabies, is caused by an alphaherpesvirus that infects the central nervous system and other system. Disease was first recognized as a fatal viral disease of cattle and dogs in 1902. The natural infections have been reported worldwide in swine, cattle, dogs and variety of wildlife species in more recent years. The study material was a 5 years old cow which was brought with symptoms such as skin itching, weakness, convulsions, fever and neurogical signs. As a result of the clinical examination the animal was suspected for Aujeszky’s disease. Slaughter and necropsy were recommended. Subsequent to necropsy, tissues were fixed in formalin. Samples were evaluated histopathologically and immunohistochemically. According to the histopathological findings; diffuse, nonsuppurative inflammation of brain, spinal cord and spinal nerves were determined. Brain lesions were detected commonly in cerebral and cerebellar cortexes. Grey and white matter were both affected. It was observed marked perivascular lymphoplasmocytic cell infiltration, glial cell proliferation, Hyperemia and hemoragia in veins in brain and light chromatolysis have been identified in some neurons. Intranuclear inclusions were observed in neurons. On the skin itching region of animal, serofibrinous inflammation in the dermis and subcutis was detected. According to the immunohistochemical findings; immunoperoxidase was detected strong positive in cytoplasms of both neurons and glial cells in areas with meningitis in brain. Consequently; after clinical, macroscopical and microscopical examination, this case was diagnosed as Aujeszky’s disease. The aim of this study is presenting this disease immunochemically as it has never been reported in cattle in Turkey. We believe that it would be appropriate to present this case report since it is the first Pseudorabies case in cattle in Turkey.
COMPARISON OF DATA MINING ALGORITHMS, INVERTED INDEX SEARCH AND SUFFIX TREE CLUSTERING SEARCH

New documents are created every day and the number of digital documents in the world is exponentially growing. Search engines do a great job by making these documents easily available to the world population. Data mining works with large amount of data sets and offers data to the end user; it consists of many different techniques and algorithms. These techniques allow faster and better search for large amounts of data. Clustering is one of the techniques used in a data mining process; it is based on data grouping according to the features, or any property they have in common, thus, a search process is faster, and a user gets better search results. On the other hand, an inverted index is a structure that provides fast search too, but this structure does not create clusters or groups of similar data. Instead, it processes all data in a document and measures appearance of specific terms in a document. The goal of this paper is to compare these two algorithms. The authors created applications that use these two algorithms and tested them on the same corpus of documents. For both algorithms, the authors are presenting improvements that provide faster search and better search results.
Mapping energy balance fluxes and root zone soil moisture in the White Volta Basin using optical imagery

Accurate information on the distribution of sensible and latent heat fluxes as well as soil moisture is critical for evaluation of background characteristics. Since these fluxes are subject to rapid changes in time and space, it is nearly impossible to determine their spatial and temporal distributions over large areas from ground measurements alone. Therefore, prediction from remote sensing images is very attractive as it enables extensive area coverage and a high repetition rate. In this study, the Surface Energy Balance Algorithm for Land as implemented at New Mexico Tech (SEBALNM) is used to estimate sensible and latent heat fluxes in the White Volta Basin of Ghana, West Africa. The objectives are (i) to demonstrate a SEBALNM application in a part of the world were ground measurements are very scarce and (ii) to compare evapotranspiration (ET) maps obtained from Landsat and MODIS imagery, respectively. The results of this study demonstrate that SEBALNM can be applied for mapping sensible and latent heat fluxes as well as soil moisture over areas where few or no ground measurements are available using common satellite products (Landsat and MODIS).
Wavelet and multifractal analysis of the nonlinear structures in chaotic processes for hydroecological systems

This paper goes on our investigations of the fractal structures in the chaotic and turbulent processes and connected with a great importance the experimental and theoretical studying of the non-linear dynamical systems with aim to discover the fractal features and elements of dynamical chaos. In this paper on the basis of wavelet analysis and multifractal formalism it is carried out an analysis of fractal structures in the chaotic processes (the time series of the nitrates concentrations in the Small Carpathians river’s watersheds SvidnikOndrava in the Earthen Slovakia) and the spectrum of the fractal dimensions has been computed. It is carried out numerical modelling and fulfilled a comparison of theoretical data with the earlier received estimates on the basis of other fractal formalism algorithm.
Critical Elements that Enhance Assessment of Children with Learning Disabilities in the Remedial Programme in Zimbabwe: A Case of the Harare Metropolitan Province

This study endeavours to look for the critical elements that should be taken into consideration when implementing an effective assessment of children with learning disabilities (LDs) for placement in the remedial programme. Zimbabwe is one of the few countries offering remedial education in Africa. However, the provision has faced resistance since its inception in 1981 because of differences between teachers and other professionals on the type of child who should be placed in the remedial programme. Assessment is crucial in determining the correct candidates. What are the critical elements that improve the assessment so that the correct pupils are placed in the remedial programmes? A case study design using both quantitative and qualitative strategies was employed to collect the data from the participants in their different sites. Purposive sampling was used to identify the participants and the informants. The sample consisted of 120 teachers who included remedial teachers, classroom teachers; head teachers and remedial tutors from the 7 districts in the province. Questionnaires, interviews and document analysis were used to collect the data. The study identified definitions of assessment, learning disabilities, policy, human and material resources as critical elements which ensure the effective provision of remedial education. The study recommends that these elements be addressed to ensure that rights of the children are met in Zimbabwe.
Supporting Privacy Protection in Personalized Web Search - A Survey

Objectives: This survey investigates the several privacy preserving techniques and provides the idea about the new efficient method in the future. The main goal of this work is to assure the privacy guarantee to the user who is involved in the personalized web search. To do this several mechanism which is related to the privacy protection is investigated. Methods: In this manuscript, a survey has been done on the several privacy preserving mechanism that has been used to assure a users privacy guarantee and as well as accurate retrieval. Among the different methodologies that are discussed, it has been found that UPS framework is one of the efficient techniques which guarantees the user privacy and retrieves the contents as per user requirement accurately. Results: This survey expansively studies the problems in the privacy preserving mechanism in the personalized web search environment. The performance of the various methods is evaluated with numerous parameters like accuracy, failure rate and the privacy assurance. The survey analysis conducted were proves that. Conclusion: the UPS framework is one of the efficient methodology which aims to retrieve the user wanted contents with the assurance of privacy guarantee.
Probing QCD with Photons and Jets at the ATLAS detector

Abstract. This contribution gives an overview of the recent measurements of the differential cross sections for final states involving photons and/or jets at the centre-of-mass energies of 8 and 13 TeV, published by the ATLAS Collaboration. The results are compared with several next-to-leading order calculations and with the latest predictions of various Monte Carlo generators. New measurements of transverse energy-energy correlations and their associated asymmetries in multi-jet events at 8 TeV are also presented. Both measurements are used to extract the strong coupling constant and test the renormalization group equation.
Recommendation System Based On Clusteringand Collaborative Filtering

Cluster-based recommendation is best thought of as a variant on user-based recommendation. Instead of recommending items to users, items are recommended to clusters of similar users. This entails a pre processing phase, in which all users are partitioned into clusters. Recommendations are then produced for each cluster, such that the recommended items are most interesting to the largest number of users. The upside of this approach is that recommendation is fast at runtime because almost everything is pre computed. In this paper, we describe the problem of recommending conference sessions to attendees and show how novel extensions to traditional modelbased recommender systems, as suggested in Adomavicius and Tuzhilin can address this problem. We introduce Recommendation Engine by Conjoint Decomposition of items and Users (RECONDITUS)-a technique that is an extension of preference-based recommender systems to recommend items from a new disjoint set to users from a new disjoint set.
ISOCAM mid-infrared spectroscopy and NIR photometry of the HII complex N4 in the Large Magellanic Cloud

Context. We present the analysis of ISOCAM-CVF and the J , H , and K s photometry data for the HII region complex N4 in the Large Magellanic Cloud (LMC). Aims. The aim is twofold: 1) to study the connection between the interstellar medium and the star content of this region; 2) to investigate the effects of the lower-than-Galactic metallicity on dust properties. Methods. A dust-features – gas lines – continuum fitting technique on the whole ISOCAM-CVF data cube allows the production of images in each single emission and detailed analysis of dust (both continuum and bands), and ionized gas. The near infrared (NIR) photometry provides, for the first time, information on the stellar content of N4. Results. The mid-infrared (MIR) spectral characteristics of N4 are those expected for an HII region complex, i.e. very similar to those observed in galactic HII regions. The images in single dust feature bands and gas lines clearly show that the HII region core is completely devoid of the carriers responsible for the aromatic features (AFs). On the other hand, the ionized gas arises almost completely in this dust cavity, where the two main exciting stars of N4 are also located. The HII region models from Stasinska (1982, A&AS, 48, 2999) predict an HII region size that corresponds to the observed size of the dust cavity. We find evidence that the effect of lower-than-Galactic metallicity (although not as extreme as in the case of LMC) on the carriers responsible for the AFs is not to prevent their formation or to modify their chemical properties, but to enhance their destruction by the high and hard interstellar radiation field. We argue that this is the dominant process responsible for the absence of AFs in the HII region core. We show that this mechanism is more efficient on smaller dust particles/molecules thus affecting the dust-size distribution. We argue that effects on dust-size distribution, rather than the different dust properties due to a lower metallicity, should be taken into account when analyzing more distant, relatively low-metallicity galaxies. Finally, the analysis of the stellar content of N4 reveals 7 stars: 4 reddened O MS stars and three stars with envelopes. In particular, one of these seems to be an ultra compact HII region containing an embedded young stellar object.
Ifrs Versus Cz Gaap: Influence of Construction Contracts on Financial Indicators

Abstract In 2009, 117 countries all over the world allowed to prepare financial statements according to International Financial Reporting Standards (IFRS) including the Czech Republic. Between Czech General Accepted Accounting Principles (CZ GAAP) and IFRS are some differences involving reporting of noncurrent fixed assets, leasing, construction contracts etc. Using different accounting procedure we can get different value of assets and assets are one component that is used to calculate the financial performance indicators. Those indicators are then used by investors to assess the financial performance of companies and the application of IFRS instead of CZ GAAP can lead to distinct presentation of enterprise performance. In this paper we examine the influence of construction contract reporting using IFRS and CZ GAAP on financial indicators. IFRS leads to more stable development during the period when the project is in progress. In the last year when the project is finished the figures of both ratios (ROA and Z-score) are getting worse which is due to the lower percentage of the construction that is built in this year.