Miracle-dz/UniFAR-FTUs huggingface dataset:
Capturing non-functional properties through model interlinking

The Software Product Line (SPL) paradigm promotes systematic reuse-based software development and is founded on the idea of capturing commonalities and variabilities between software products of a target domain. Feature model as the main artefact of SPL development mostly captures functional and operational variability of a system. Researchers have argued that connecting intentional variability models such as goal models with feature variability models in a target domain can enrich feature models with valuable quality and non-functional information. Interrelating goal models and feature models has already been proposed in the literature for capturing non-functional properties in software product lines, however, the integration process is cumbersome and tedious. In this paper, we propose a (semi) automated approach that systematically integrates feature models and goal models through domain-specific conceptual models represented by ontologies. We first associate each element of the models with their raison-d'etre, i.e., sections of the domain documents that justify each model element. Then, using texts that are associated with the elements and through a semantics-enabled textual analysis process, the model elements will be semantically annotated with related ontological concepts. Finally, a mapping recommendation process is proposed that would connect feature model and goal model elements through measuring the semantic similarity of their annotated ontological concept. Our proposed approach not only provides the means to systematically interrelate feature models and goal models but also allows domain engineers to identify the mutual impact of features and non-functional properties.
Distributed method for channel assignment in CDMA based "ad-hoc" wireless local area networks

This paper proposes a method that enables the operation of a multichannel wireless local area network without the existence of a base station responsible for proper channel assignment. The method gives each station freedom to choose its own channel even when they can not all communicate with each other, and it also permits message routing between stations. This features allow a wireless LAN to operate in environments that have many obstacles, making it very suitable for IR networks, while multiplying the throughput by balancing the traffic over the available channels. The method is described in general terms, and simulation based results are shown.
A Study on the Effect of Macroeconomic Variables on Indian Mutual Funds

Indian financial markets have witnessed very high levels of volatility in recent months, with a sharp decline in the BSE-SENSEX from a peak of around 21,000 points to a nadir below 11,000 points, with as much as a 700-point fall on one single day. Indian economic conditions have also seemed to stagnate, with an overall slow-down in economic growth, along with the pressures of increasing crude oil prices and increasing inflation. In fact, the overall global scenario has also been quite bleak, especially with the onset of recession in the US. Mutual fund investments, which are generally considered to be less risky than other financial instruments such as shares and debentures, have also suffered in the general atmosphere of volatility. The present study investigates the effect of macroeconomic variables on mutual fund schemes, in terms of returns and volatility. The study uses the Granger causality test to analyze these effects. The results of these causality tests would identify the specific macroeconomic factors which affect the returns and volatility of particular mutual fund schemes, which, on the one hand, would enable fund managers to manage the risk profiles of their portfolios more effectively; and, on the other hand, would enable investors to understand the specific risk factors affecting their investments, so that they can take more informed investment decisions pertaining to mutual funds. The data to be used in the study were the weekly returns and volatilities of different macroeconomic variables, such as market returns (calculated from the BSE-SENSEX), USD/INR and EURO/INR exchange rates, interest rates (Mumbai Inter-Bank Offer rates), inflation rates, and crude oil prices, over the period October '06 - June '08. The weekly returns and volatilities of a sample of major mutual fund schemes over the same period would be considered for the analysis.
The Politics of Rules of Origin

Rules of origin (ROOs) aim to determine whether a particular trade concession will be applied to a given product in international trade. Certain features of ROOs make them particularly susceptible for employment as an instrument of foreign and domestic policies. Though some of those political objectives are desirable, the unrestrained political employment of ROOs embraces some significant hazards, particularly the undermining of the predictability and stability of trade relations. The increasing political employment of ROOs was one of the principal factors leading to the inclusion of this topic in the Uruguay Round agenda and the formulation of the 1994 WTO Agreement on Rules of Origin. The 1994 initial agreement is not effectively coping with the widening political employment of ROOs. Consequently, it is suggested to further restrain the involvement of political considerations in the formulation and implementation of ROOs.
The Effect of Method Origin on Method Invocation Fault Frequency

Recent studies in analyzing bug-fix patterns recorded in software repositories show that method invocation(MI) fixes are very common. Our belief is that these bug fixes could be analyzed further to provide detailed information that allows software engineers to avoid these bugs. For example, developers may have a higher awareness of their intentions in choosing which method to invoke. Unfortunately, previous studies could not provide such information. In this paper, we propose atechnique to accurately identify the precise MI bug-fix changes. We have applied our technique to empirically investigate the origins of MI bug fixes in six open source Java software. We have classified these MI bug-fix origins (e.g. from standard Java library, from third-party library, or in standard code-base). Amajor finding is that MI faults involving Java library methods were under-represented when compared to their usage frequency.
Anisotropic mesh adaptation based upon a posteriori error estimates

An anisotropic mesh adaptation strategy for finite element solution of elliptic differential equations is considered. The adaptation method generates anisotropic adaptive meshes as quasiuniform ones in some metric space. The associated metric tensor is computed by means of a posteriori hierarchical error estimates. A global hierarchical error estimate is employed in this study to obtain reliable directional information of the solution. The exact solution of the corresponding global error problem can be very costly. However, numerical results show that a few iterations of the symmetric Gauß-Seidel method are sufficient for obtaining a reasonably good approximation to the error for use in anisotropic mesh adaptation. The new method is compared with several strategies using local error estimators or recovered Hessians. Numerical results are presented for a selection of test examples and a mathematical model for heat conduction in a thermal battery with large orthotropic jumps in the material coefficients.
Task‐dependent modulation of functional connectivity between hand motor cortices and neuronal networks underlying language and music: a transcranial magnetic stimulation study in humans

Although language functions are, in general, attributed to the left hemisphere, it is still a matter of debate to what extent the cognitive functions underlying the processing of music are lateralized in the human brain. To investigate hemispheric specialization we evaluated the effect of different overt musical and linguistic tasks on the excitability of both left and right hand motor cortices using transcranial magnetic stimulation (TMS). Task‐dependent changes of the size of the TMS‐elicited motor evoked potentials were recorded in 12 right‐handed, musically naive subjects during and after overt speech, singing and humming, i.e. the production of melody without word articulation. The articulation of meaningless syllables served as control condition. We found reciprocal lateralized effects of overt speech and musical tasks on motor cortex excitability. During overt speech, the corticospinal projection of the left (i.e. dominant) hemisphere to the right hand was facilitated. In contrast, excitability of the right motor cortex increased during both overt singing and humming, whereas no effect was observed on the left hemisphere. Although the traditional concept of hemispheric lateralization of music has been challenged by recent neuroimaging studies, our findings demonstrate that right‐hemisphere preponderance of music is nevertheless present. We discuss our results in terms of the recent concepts on evolution of language and gesture, which hypothesize that cerebral networks mediating hand movement and those subserving language processing are functionally linked. TMS may constitute a useful tool to further investigate the relationship between cortical representations of motor functions, music and language using comparative approaches.
Exploring the Role of Uncertainty for Corporate Investment Decisions in Germany

This paper studies the impact of sales uncertainty on firms' investment outlays using the database of the Deutsche Bundesbank's corporate balance sheet statistics. The sample used for estimation is a panel of 4'234 west German manufacturing firms with 33'217 observations, covering the years 1987-1997. In 1996, these firms accounted for 49.3% of the total turnover of German incorporated manufacturers. As uncertainty indicators, we construct firm-specific, time varying volatility measures. Using Within estimators and GMM techniques, we find a moderately strong and consistently negative effect of uncertainty on investment. If the uncertainty indicator is increased by one standard deviation, the predicted investment demand will fall by about 3.7 % of its mean.
An Efficient and Effective Online Sentence Segmenter for Simultaneous Interpretation

Simultaneous interpretation is a very challenging application of machine translation in which the input is a stream of words from a speech recognition engine. The key problem is how to segment the stream in an online manner into units suitable for translation. The segmentation process proceeds by calculating a confidence score for each word that indicates the soundness of placing a sentence boundary after it, and then heuristics are employed to determine the position of the boundaries. Multiple variants of the confidence scoring method and segmentation heuristics were studied. Experimental results show that the best performing strategy is not only efficient in terms of average latency per word, but also achieved end-to-end translation quality close to an offline baseline, and close to oracle segmentation.
Distributional Semantics Approach to Detecting Synonyms in Croatian Language

Identifying synonyms is important for many natural language processing and information retrieval applications. In this paper we address the task of automatically identifying synonyms in Croatian language using distributional semantic models (DSM). We build several DSMs using latent semantic analysis (LSA) and random indexing (RI) on the large hrWaC corpus. We evaluate the models on a dictionarybased similarity test – a set of synonymy questions generated automatically from a machine readable dictionary. Results indicate that LSA models outperform RI models on this task, with accuracy of 68.7%, 68.2%, and 61.6% on nouns, adjectives, and verbs, respectively. We analyze how word frequency and polysemy level affect the performance and discuss common causes of synonym misidentification. Prepoznavanje hrvaških sopomenk s pomočjo distribucijske semantike Prepoznavanje sopomenk je pomembno za številne aplikacije na področju jezikovnih tehnologij in poizvedovanja po informacijah. V pričujočem prispevku se ukvarjamo z avtomatskim prepoznavanjem sopomenk v hrvaščini, pri čemer uporabljamo modele distribucijske semantike (DSM). S pomočjo latentne semantične analize (LSA) in naključnega indeksiranja (RI) iz korpusa hrWaC zgradimo več različnih modelov. Modele nato ovrednotimo s pomočjo testov sinonimije, ki so avtomatsko izluščeni iz strojno berljivega slovarja. Rezultati kažejo, da so modeli, zgrajeni s pomočjo LSA, za to nalogo uspešnejši, njihova natančnost pa je 68,7% za samostalnike, 68,2% za pridevnike in 61,6% za glagole. V prispevku analiziramo tudi, kako pogostost pojavljanja besed v korpusu in stopnja njihove večpomenskosti vplivajo na rezultate in razpravljamo o najpogostejših razlogih za napake, do katerih pri prepoznavanju prihaja.
Cultivation-Independent Methods Reveal Differences among Bacterial Gut Microbiota in Triatomine Vectors of Chagas Disease

Background Chagas disease is a trypanosomiasis whose agent is the protozoan parasite Trypanosoma cruzi, which is transmitted to humans by hematophagous bugs known as triatomines. Even though insecticide treatments allow effective control of these bugs in most Latin American countries where Chagas disease is endemic, the disease still affects a large proportion of the population of South America. The features of the disease in humans have been extensively studied, and the genome of the parasite has been sequenced, but no effective drug is yet available to treat Chagas disease. The digestive tract of the insect vectors in which T. cruzi develops has been much less well investigated than blood from its human hosts and constitutes a dynamic environment with very different conditions. Thus, we investigated the composition of the predominant bacterial species of the microbiota in insect vectors from Rhodnius, Triatoma, Panstrongylus and Dipetalogaster genera. Methodology/Principal Findings Microbiota of triatomine guts were investigated using cultivation-independent methods, i.e., phylogenetic analysis of 16s rDNA using denaturing gradient gel electrophoresis (DGGE) and cloned-based sequencing. The Chao index showed that the diversity of bacterial species in triatomine guts is low, comprising fewer than 20 predominant species, and that these species vary between insect species. The analyses showed that Serratia predominates in Rhodnius, Arsenophonus predominates in Triatoma and Panstrongylus, while Candidatus Rohrkolberia predominates in Dipetalogaster. Conclusions/Significance The microbiota of triatomine guts represents one of the factors that may interfere with T. cruzi transmission and virulence in humans. The knowledge of its composition according to insect species is important for designing measures of biological control for T. cruzi. We found that the predominant species of the bacterial microbiota in triatomines form a group of low complexity whose structure differs according to the vector genus.
CFS in Children and Adolescent: Ten Years of Retrospective Clinical Evaluation

Aim. To estimate number of children being diagnosed with chronic fatigue syndrome (CFS). Methods. For a period of 10 years (2002–2011) data from children being referred for fatigue symptoms were collected retrospectively. Results. Thirty-seven children were referred. Four were excluded due to incorrect coding. Six (18%) patients received other diagnoses at the end of evaluation time. Of the 27 who received the diagnosis G93.3, four had a previous chronic illness, while 23 patients were previously healthy. All patients reported onset of fatigue symptom in relation to an infection, and all tested positive for IgG to either Epstein-Barr virus, cytomegalovirus or borrelia, indicating previous infection. There were 16 (59%) boys among the 27 patients. The mean age at the debut of fatigue symptoms was 141 months (SD 30) for boys and 136 months (SD 31) for girls, respectively. Being underweight, defined as BMI < 17.5, was found in 12 (44%) patients. Conclusion. An increasing number of children and adolescents are evaluated for CFS. The clinical assessment of children and adolescents with possible CFS need systematically evaluation. Nutritional status, possible eating disorder, and psychosocial issues need to be addressed and evaluated carefully. A multidisciplinary approach is essential when assessing CFS in children and adolescents. There is a need for European guidelines.
Scalable pipelined CORDIC architecture design and implementation in FPGA

In Digital Signal Processing, trigonometry and complex multiplications are used in many signal equations, such as synchronization and equalization. Therefore, a fast and an efficient method to calculate trigonometry and complex multiplications are required. Coordinate Rotation Digital Computer (CORDIC) is trigonometric algorithm [1] that is used to transforming data from rectangular to polar and vice versa. CORDIC also can be used other to compute several trigonometry functions, either directly or indirectly [1]. The proposed CORDIC design is based on Pipeline datapath Architecture. By using pipeline architecture, the design is able to calculate continuous input, has high throughput, and doesn't need ROM or registers to save constant angle iteration of CORDIC. The design process is started by modelling CORDIC function, design datapath and control unit, coding to hardware description language using Verilog HDL, synthesized using Quartus II Version 7.2 and implemented on ALTERA Cyclone II DE2 EP2C35F672C6N FPGA. Synthesis result shows that the design is able to work at 81.31 MHz.
Coherency Based Partitioning of a Power System for Intelligent Wide-Area Damping Control

A variety of factors are causing an increase of slow frequency oscillations in power systems. These oscillations reduce the stability of the system, limit power transfers, and can potentially lead to loss of synchronism and widespread blackouts. These oscillations typically involve variables from distant areas accross the system; therefore, conventional local damping controllers such as power system stabilizers might lack the information needed to provide effective damping. Controllers that utilize wide-area measurements can overcome these limitations , but the techniques for developing those controllers are still under development. The use of computational intelligence for wide-area damping control of power systems has been demonstrated in small power system simulations. It has been shown that it is possible to develop intelligent controllers that adapt online to improve their performance and approach optimality over time and that are capable of dealing with the non-linear, stochastic, and time varying nature of power systems. However, the scalability of these algorithms needs to be evaluated and possibly improved if they are to be implemented in realistically sized power systems. Typically, the development of intelligent controllers begins with identifying an accurate and differentiable model of the system to be controlled. However, studies have shown that as the size of the system being identified grows, so does the model and the computational complexity of the algorithms required to tune that model. The shear size of real world power systems makes straightforward implementation of such system identification approaches intractable. The work presented in this paper explores the issue of scalability of intelligent system identification algorithms using simulations of a large portion of the Brazilian Sistema Interligado Nacional. A recently developed concept named the virtual generator is utilized to simplify the online identification of an input/output data driven dynamical model of a large portion of the power system. This model is based on dynamical artificial neural networks and can be used for intelligent online adaptation of controller parameters. Only the system identification methodology is presented.
Genome Holography: Deciphering Function-Form Motifs from Gene Expression Data

Background DNA chips allow simultaneous measurements of genome-wide response of thousands of genes, i.e. system level monitoring of the gene-network activity. Advanced analysis methods have been developed to extract meaningful information from the vast amount of raw gene-expression data obtained from the microarray measurements. These methods usually aimed to distinguish between groups of subjects (e.g., cancer patients vs. healthy subjects) or identifying marker genes that help to distinguish between those groups. We assumed that motifs related to the internal structure of operons and gene-networks regulation are also embedded in microarray and can be deciphered by using proper analysis. Methodology/Principal Findings The analysis presented here is based on investigating the gene-gene correlations. We analyze a database of gene expression of Bacillus subtilis exposed to sub-lethal levels of 37 different antibiotics. Using unsupervised analysis (dendrogram) of the matrix of normalized gene-gene correlations, we identified the operons as they form distinct clusters of genes in the sorted correlation matrix. Applying dimension-reduction algorithm (Principal Component Analysis, PCA) to the matrices of normalized correlations reveals functional motifs. The genes are placed in a reduced 3-dimensional space of the three leading PCA eigen-vectors according to their corresponding eigen-values. We found that the organization of the genes in the reduced PCA space recovers motifs of the operon internal structure, such as the order of the genes along the genome, gene separation by non-coding segments, and translational start and end regions. In addition to the intra-operon structure, it is also possible to predict inter-operon relationships, operons sharing functional regulation factors, and more. In particular, we demonstrate the above in the context of the competence and sporulation pathways. Conclusions/Significance We demonstrated that by analyzing gene-gene correlation from gene-expression data it is possible to identify operons and to predict unknown internal structure of operons and gene-networks regulation.
Forecasting Monthly Tourist Departures from Australia

This paper builds time series forecasting models for monthly international tourist departures from Australia, categorized by the major destinations and by the main purposes of travel. Descriptive and statistical analyses reveal that strong trend and seasonality are the major features of all time series considered. The seasonal ARIMA model is used as a forecasting tool and its trend and seasonal components are specified based on inferential outcomes of the HEGY test for seasonal unit roots. Other alternative forecasting methods used include Holt-Winters' exponential smoothing method and the regression models that involve deterministic trend or seasonal dummy variables. It is found that forecasts generated from seasonal ARIMA models are more accurate than other alternative forecasts in most cases.