Miracle-dz/UniFAR-FTUs huggingface dataset:
Higher Educational Attainment for Growth: The MRW Model for Iceland

ABSTRACT This study evaluates Mankiw, Romer and Weil’s augmented neoclassical growth model with human capital for Iceland. Moreover, we differentiate between the impact of upper-secondary and tertiary education. Both unrestricted and restricted regressions show that human capital stimulates real GDP per capita changes. The value of the elasticity of output with respect to human capital proxies calculated by OLS as well as DOLS ranges between 0.22 and 0.47, which is fully compatible with that calculated by the relevant studies on advanced economies applying the same methodology. The regression output is robust to modifications of the baseline model and the method of estimation.
A multi-level dynamic complexity reduction scheme for multiview video coding

In this paper, we propose a novel scheme for dynamically reducing the computational complexity of MVC. Our scheme exploits the coding mode correlation available in the 3D-neighborhood (i.e., spatial, temporal, and view) along with the rate-distortion properties of the neighboring Macroblocks. Our scheme incorporates a multi-level mode decision process based on a mode-ranking mechanism that categorizes more-probable and less-probable coding modes. In order to react to the changing bitrates, our scheme deploys Quantization Parameter based threshold equations which are formulated using an offline statistical analysis. Compared to the exhaustive Rate-Distortion-Optimized Mode Decision (RDO-MD), our scheme achieves a complexity reduction of up to 80% (68% on average) with an average PSNR loss of 0.075 dB. Compared to state-of-the-art fast RDO-MD, our scheme achieves a complexity reduction of up to 34% with an average PSNR gain of 0.007 dB.
Exploring the Trust and Knowledge Obsolescence Relation

There are some contexts in social networks in which obsolescence of knowledge of a person regarding some topic of interest should be considered when establishing trust between parties. However, and as of this writing, obsolescence of knowledge has not been considered as a dimension in trust models. In this work we propose to consider obsolescence of knowledge in trust models. Two studies were run in order to explore the relation between trust and obsolescence of knowledge. Results from these studies show that trust is decreased as obsolescence is incremented and that the relation is not linear.
Extracting Source Models from Java Programs: Parse, Disassemble, or Profile?

Source models of software systems are often created during re-engineering to aid in performing tasks such as reachability analysis and software architecture recovery. It is therefore vital to be able to create source models that are both detailed and accurate. However, in practice the creation of these models is difficult and error prone: extraction tools often tell only part of the story. We have been working on the automated extraction of source models for programs written in Java. Our approach considers Java programs from three points of view: parsing, disassembly, and profiling. We have found that these three techniques have advantages that are complementary. Parsing source code provides the most detailed information, but it is the most complex to implement. Disassembling Java byte code gives similar results to parsing, but is less complex technically. Profiling provides the least amount of detail, but does give important feedback on run-time behaviour, such as polymorphic function calls and reflective instantiation of objects by string input. We have applied our tools to several systems, including Sun’s javac Java compiler and the Jigsaw web server. We compare the source models extracted by each of our tools, and describe reasons for differences in the extracted models.
Improving IR Performance from OCRed Text using Cooccurrence

Information Retrieval performance is hurt to a great extent by OCR errors. Much research has been reported on modelling and correction of OCR errors. However, all the existing systems make use of language dependent resources or training texts to study the nature of errors. No research has been reported on improving retrieval performance from erroneous text when no training data is available. We propose a novel algorithm for automatic detection of OCR errors and improvement of retrieval performance from the erroneous corpus. Our algorithm does not use any training data or any language specific resources like thesaurus. It also does not use any knowledge about the language except that the word delimiter is blank space. We have tested our algorithm on erroneous OCRed Bangla FIRE collection offered in the RISOT 2012 track and obtained about 9% improvement over the OCRed baseline. However, the improvement is not statistically significant.
Accurate multidimensional Poisson-disk sampling

We present an accurate and efficient method to generate samples based on a Poisson-disk distribution. This type of distribution, because of its blue noise spectral properties, is useful for image sampling. It is also useful for multidimensional Monte Carlo integration and as part of a procedural object placement function. Our method extends trivially from 2D to 3D or to any higher dimensional space. We demonstrate results for up to four dimensions, which are likely to be the most useful for computer graphics applications. The method is accurate because it generates distributions with the same statistical properties of those generated with the brute-force dart-throwing algorithm, the archetype against which all other Poisson-disk sampling methods are compared. The method is efficient because it employs a spatial subdivision data structure that signals the regions of space where the insertion of new samples is allowed. The method has O(N log N) time and space complexity relative to the total number of samples. The method generates maximal distributions in which no further samples can be inserted at the completion of the algorithm. The method is only limited in the number of samples it can generate and the number of dimensions over which it can work by the available physical memory.
Long-Term Effect of Mediterranean-Style Diet and Calorie Restriction on Biomarkers of Longevity and Oxidative Stress in Overweight Men

We report the effects of a Mediterranean-style diet, with or without calorie restriction, on biomarkers of aging and oxidative stress in overweight men. 192 men were randomly assigned to either a Mediterranean-style diet or a conventional diet. The intervention program was based on implementation of a Mediterranean dietary pattern in the overweight group (MED diet group), associated with calorie restriction and increased physical activity in the obese group (lifestyle group). Both groups were compared with participants in two matched control groups (advice groups). After 2 years, there was a significant difference in weight loss between groups, which was −14 kg (95% CI −20 to −8) in lifestyle groups and −2.0 kg (−4.4 to 0) in the advice groups, with a difference of −11.9 kg (CI −19 to −4.7 kg, P < .001); moreover, there was a significant difference between groups at 2 years for insulin (P = .04), 8-iso-PGF2α (P = .037), glucose (P = .04), and adiponectin (P = .01). Prolonged adherence to a Mediterranean-style diet, with or without caloric restriction, in overweight or obese men is associated with significant amelioration of multiple risk factors, including a better cardiovascular risk profile, reduced oxidative stress, and improved insulin sensitivity.
Fair background data transfers of minimal delay impact

In this paper we present a methodology for the design of congestion control protocols for background data transfers that have a minimal delay impact on short TCP transfers and compete for a target share of the leftover average capacity with other background TCP transfers. We analytically compute the optimal policy and show that it's delay performance can be well approximated by a weighted TCP policy, which maintains a target proportion of TCPs bandwidth at all times in order to achieve the same share of excess capacity. The relative approximation error is always less than 17.2% while it quickly decreases to zero as the number of coexisting background TCP flows increases. Next, we consider a general utility-based fairness criterion for sharing the leftover average capacity, including a penalty term capturing the delay impact to short flows. The criterion is jointly optimized over all allocations of excess capacity to background flows (including TCP ones) on long timescales, and all bandwidth sharing policies on fast time scales. Even though the delay optimal sharing policy that solves the above optimization problem does not lead to distributed congestion control algorithms and more significantly, requires knowledge of the number of competing background TCP flows, both problems disappear under the weighted TCP policy. A distributed weight adjustment policy is considered where, at equilibrium, the overall performance is nearly optimal, with a vanishing relative optimality error as the number of background TCP flows increases. We illustrate the methodology by giving two examples of congestion control algorithms for background transfers. Both achieve low delay for short flows relative to TCP, but at the same time they present strong incentives for adoption against incumbent low priority solutions in public environments.
FEAPI: A low level feature extraction plugin API

This paper presents FEAPI, an easy-to-use platform-independent plugin application programming interface (API) for the extraction of low level features from audio in PCM format in the context of music information retrieval software. The need for and advantages of using an open and well-defined plugin interface are outlined in this paper and an overview of the API itself and its usage is given.
Low viscosity in the aqueous domain of cell cytoplasm measured by picosecond polarization microfluorimetry

Information about the rheological characteristics of the aqueous cytoplasm can be provided by analysis of the rotational motion of small polar molecules introduced into the cell. To determine fluid-phase cytoplasmic viscosity in intact cells, a polarization microscope was constructed for measurement of picosecond anisotropy decay of fluorescent probes in the cell cytoplasm. We found that the rotational correlation time (tc) of the probes, 2,7-bis-(2-carboxyethyl)-5-(and-6- )carboxyfluorescein (BCECF), 6-carboxyfluorescein, and 8-hydroxypyrene- 1,3,6-trisulfonic acid (HPTS) provided a direct measure of fluid-phase cytoplasmic viscosity that was independent of probe binding. In quiescent Swiss 3T3 fibroblasts, tc values were 20-40% longer than those in water, indicating that the fluid-phase cytoplasm is only 1.2- 1.4 times as viscous as water. The activation energy of fluid-phase cytoplasmic viscosity was 4 kcal/mol, which is similar to that of water. Fluid-phase cytoplasmic viscosity was altered by less than 10% upon addition of sucrose to decrease cell volume, cytochalasin B to disrupt cell cytoskeleton, and vasopressin to activate phospholipase C. Nucleoplasmic and peripheral cytoplasmic viscosities were not different. Our results establish a novel method to measure fluid-phase cytoplasmic viscosity, and indicate that fluid-phase cytoplasmic viscosity in fibroblasts is similar to that of free water.
Human embryonic and neuronal stem cell markers in retinoblastoma

Purpose Retinoblastoma (RB) is the most common intraocular tumor of early childhood. The early onset of RB, coupled with our previous findings of cancer stem cell characteristics in RB, led us to hypothesize that subpopulations of RB tumors harbor markers and behaviors characteristic of embryonic and neuronal origin. Methods Our RB sources included: human pathological tissues, and the human RB cell lines Y79 and WERI-RB27. Microarray screening, single and dual-label immunocytochemistry and RT-PCR were performed to detect embryonic and neuronal stem cell markers, such as Oct3/4, Nanog, CD133, and Musashi-1. To test for functional evidence of stem cell behavior, we examined RB cells for their ability to form neurospheres and retain BrdU label as indicators of self-renewal and slow cell cycling, respectively. Results Microarray comparisons of human RB tumors with normal retinal tissue detected upregulation of a number of genes involved in embryonic development that were also present in Y79 cells, including Oct3/4, Nanog, Musashi-1 and Musashi-2, prominin-1 (CD133), Jagged-2, Reelin, Thy-1, nestin, Meis-1,NCAM, Patched, and Notch4. Expression of Musashi-1, Oct3/4 and Nanog was confirmed by immunostaining and RT-PCR analyses of RB tumors and RB cell lines. CD133 expression was confirmed by PCR analysis. Y79 and WERI-RB27 contained populations of Hoechst-dim/ABCG2-positive cells that co-localized with embryonic stem cell markers Oct3/4-ABCG2 and Nanog-ABCG2. Subpopulations of Y79 and WERI-RB27 cells were label-retaining (as seen by BrdU incorporation) and were able to generate neurospheres, both hallmarks of a stem cell phenotype. Conclusions Small subpopulation(s) of RB cells express human embryonic and neuronal stem cell markers. There are also subpopulations that demonstrate functional behavior (label retention and self-renewal) consistent with cancer stem cells. These findings support the hypothesis that RB is a heterogeneous tumor comprised of subpopulation(s) with stem cell-like properties.
Reliability analysis in component-based development via probabilistic model checking

Engineering of highly reliable systems requires support of sophisticated design methods allowing software architects to competently decide between various design alternatives already early in the development process. Architecture-based reliability prediction provides such capability. The formalisms and analytical methods employed by existing approaches are however often limited to a single reliability measure (the probability of failure on demand) and consideration of behavioural uncertainty (focusing on the uncertainty in model parameters, not the behaviour itself). This paper presents a formal reliability assessment approach for component-based systems based on the probabilistic model checking of various reliability-related properties specified in probabilistic linear temporal logic (PLTL). The systems are formalized as Markov decision processes (MDP), which allows software architects to encode behavioural uncertainties into the models in terms of nondeterministic (scheduler-decided) choices in the MDP.
Agnostic Classification of Markovian Sequences

Classification of finite sequences without explicit knowledge of their statistical nature is a fundamental problem with many important applications. We propose a new information theoretic approach to this problem which is based on the following ingredients: (i) sequences are similar when they are likely to be generated by the same source; (ii) cross entropies can be estimated via "universal compression"; (iii) Markovian sequences can be asymptotically-optimally merged. With these ingredients we design a method for the classification of discrete sequences whenever they can be compressed. We introduce the method and illustrate its application for hierarchical clustering of languages and for estimating similarities of protein sequences.
An LMI approach for robust Iterative Learning Control with Quadratic performance criterion

This paper presents the design of iterative learning control based on Quadratic performance criterion (Q-ILC) for linear systems subject to additive uncertainty. Robust Q-ILC design can be cast as a min-max problem. We propose a novel approach which employs an upper bound of the worst-case error, then formulates a nonconvex quadratic minimization problem to get the update of iterative control inputs. Applying Langrange duality, the Lagrange dual function of the nonconvex quadratic problem is equivalent to a convex optimization over linear matrix inequalities (LMIs). An LMI algorithm with convergence properties is then given for the robust Q-ILC. Finally, we provide a numerical example to illustrate the effectiveness of the proposed method.
Multi-class learning with specific features for pairwise classes

Support vector machine is initially developed for binary classification problem. Multiclass support vector machine (MSVM) is usually realized by using a combination of several binary SVMs. In most of the existing MSVM approaches, all binary SVMs operates on the same feature space. This paper proposed a new approach in which each binary SVM is associated with a specific feature representation. Based on the idea, we developed an algorithm for MSVM named REAL. In the experiment its performance is compared with traditional approaches on 17 real-world multi-class datasets. The good performance achieved by the algorithm clearly verifies the effectiveness of this approach.
Analysis and fuzzy control of chaotic behaviors in buck converter

Existence of chaos in power electronic circuits has received great attention during last two decades. Buck converters are step-down power electronic converters that convert an unregulated DC voltage to a lower level regulated DC voltage. In this work, input voltage of buck converter is considered as a variable parameter and is shown that by increasing input voltage, output voltage leads to chaos. This paper, presents design procedure of a fuzzy logic based Proportional Integral Derivative (PID) controller for control of a Pulse Width Modulation (PWM) based DC-DC buck converter working in Continuous Conduction Mode (CCM). In the simulation results, it is seen that the fuzzy logic based PID control technique prevents chaos in the converter and it gives robust performance under parameter variations.
Automated Text Categorization with Machine Learning and its Application in Multilingual Text Categorization

The automated categorization (or classification) of texts into predefined categories is one of the booming field of text mining. Now a days availability of digital data is very high, and to manage them in predefine categories becomes challenging task. Machine learning is a technique by which we can make automated classifier to classify the document with minimum human assistance. The advantages of this approach over the knowledge engineering approach are effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This paper discusses the Naïve Bayes, Rocchio and kNN methods within machine learning paradigm for automated text categorization of document in predefined categories. We are also like to discuss multilingual text categorization, that consists in classifying documents in different languages according to the same classification tree.
Exploring the Impact of Institutional Distance on Cross-Border iOS Adoption and Assimilation

We set out in this study to analyse the impact of institutional distance on the cross-border interorganizational system (IOS) adoption and assimilation. This study begins with an introduction of two crucial constructs, i.e. institutional distance and interdependence, and then distinguishes IOS adoption from IOS assimilations. These static portraits are the basis by which we explore the relationship between institutional distance and IOS adoption and assimilation. Subsequently we present a theoretical model of the three dimensions of institutional distance’s impact on cross border IOS adoption and IOS assimilation and explore the interplay between organizational interdependence and institutional distances (i.e. regulatory distance, normative distance and cognitive distance) on the adoption and assimilation of cross-border IOS. Finally, we discuss the potential contributions and limitations of the study, as well as ideas about related future research. Throughout the study, we use the institutional theory as a means of integrating discussion and survey approach is chosen to collect data and empirically test the model in future research. The potential theoretical contributions are mainly made to institutional theory and international interorganizational system application. Meanwhile this study provides explanations of why the cross border IOS adoption and assimilation in certain countries outperform those of other countries.
Comparison of Energy Efficiency and Routing Packet Overhead in Single and Multi Path Routing Protocols over S-MAC for Wireless Sensor Network

Wireless Sensor Network (WSN) is a type of intelligent wireless network composed by tens to thousands of nodes. WSN can communicate each other as well as to modify the configuration by itself. In their communication process, WSN needs energy to forward some data packet. Energy of WSN is limited with batteries are not rechargeable. Energy efficiency is an interest case to be analyzed more on WSN. Obviously, With reduce cost of routing process we can save energy on WSN. In this paper, we analyze energy consumption of WSN and routing packet overhead on routing protocol based single path AODV and multipath AOMDV over SMAC in order to find out the most energy efficient between both. By performing simulation with Network Simulator NS-2, the study found that AOMDV perform better in terms of energy efficiency in among intermediate node but overal wasteful of energy with average large number of routing packet overhead compare to AODV.
Audio-visual speaker verification using continuous fused HMMs

This paper examines audio-visual speaker verification using a novel adaptation of fused hidden Markov models, in comparison to output fusion of individual classifiers in the audio and video modalities. A comparison of both hidden Markov model (HMM) and Gaussian mixture model (GMM) classifiers in both modalities under output fusion shows that the choice of audio classifier is more important than video. Although temporal information allows a HMM to outperform a GMM individually in video, this temporal information does not carry through to output fusion with an audio classifier, where the difference between the two video classifiers is minor. An adaptation of fused hidden Markov models, designed to be more robust to within-speaker variation, is used to show that the temporal relationship between video observations and audio states can be harnessed to reduce errors in audio-visual speaker verification when compared to output fusion.
Study on Indoor and Outdoor environment for Mobile Ad Hoc Network : Random Way point Mobility Model and Manhattan Mobility Model

Most of MANET simulations based on random mobility models, e.g. random waypoint model, models insufficient to reflect the environmental constraints. In this paper we have used a combine mobility model to analyze the effect of diverse mobility pattern (Random Waypoint Mobility Model and Manhattan Mobility Model) in indoor and outdoor environment to get a realistic simulation. In these models the movements of mobile nodes are either influenced by building or restricted to street. We demonstrate the utility of our test suite by evaluating various MANET routing protocols, including DSR, AODV and DSDV. Our results show that the protocol performance may vary across mobility models and performance rankings of protocols may vary with the mobility models used. The results shown that the environment factors such as spatial constraints may cause significant impact on the network performance. Keyword: Mobility model, Mobile Adhoc Networks, Simulation.