Miracle-dz/UniFAR-FTUs huggingface dataset:
Employee Engagement for Sustainable Organizations: Keyword Analysis Using Social Network Analysis and Burst Detection Approach

The issue of sustainability is a vital long-term goal for organizations and as such has formed the basis of much academic research over the last two decades. Organizational sustainability is defined as the ability for an organization to accomplish a range of economic, environmental, and human performance objectives. As one of the most studied topics in organizational science, employee engagement at work is seen as a critical component to achieving sustainable organizational success. In order to better understand the employee engagement discourse, this study examined the keywords that appear in the titles and abstract of the employee engagement research domain using the burst detection and social network analysis techniques. A total of 1406 employee engagement relevant articles that were published from 1990 to 2015 were included and investigated in the study. The results revealed the fading, emerging, and central themes within the employee engagement domain.
MÉTODOS DE ESTIMATIVAS DOS PARÂMETROS DA DISTRIBUIÇÃO LOG-NORMAL A 3 PARÂMETROS PARA A PRECIPITAÇÃO MÁXIMA DIÁRIA EM MUNICIPIOS COM MÉDIO A ALTO POTENCIAL EROSIVO

Estudos probabilisticos de variaveis hidrologicas, como a precipitacao pluvial maxima diaria anual, constituem em um importante elemento de apoio para o planejamento e a gestao de recursos hidricos, principalmente no que diz respeito ao estudo de precipitacoes intensas, as quais apresentam um potencial erosivo. Neste contexto, objetivou-se analisar o desempenho da distribuicao Log Normal a 3 parâmetros, os quais foram ajustados pelos Metodos dos Momentos (MM) e da Maxima Verossimilhanca (MV), aplicadas sobre as series historicas de precipitacao diaria maxima anual de 7 estacoes pluviometricas em regioes com elevado risco potencial erosivo, em Minas Gerais, obtidas do HIDROWEB/ANA com pelo menos 33 anos de observacao. Para a verificacao da melhor estimativa dos parâmetros dos metodos utilizados, foram aplicados os testes de aderencia de Komolgorov Smirnov, Anderson Darlling e Qui-quadrado. Nao se pode identificar qual o melhor metodo de estimativa dos parâmetros, porem ao se comparar os testes de aderencia, o qui-quadrado e o mais restritivo com porcentagens de adequacidade 71,3% para os dois metodos de ajuste, devendo o mesmo ser tomado como base para os estudos, principalmente quando associados ao risco do potencial erosivo de uma precipitacao.
Crack Growth Simulation in Integrally Stiffened Structures Including Residual Stress Effects from Manufacturing. Part II: Modelling and Experiments Comparison

This article is the second part of a two parts paper which presents, compares and discusses the different crack growth simulation models which were introduced for fatigue crack growth assessment during the DaToN project. In the first part, different simulation approaches were applied to determine a calibration of the stress intensity factors as a function of the crack length for a two stiffeners panel with a central crack. Due to the residual stress field promoted by the different manufacturing processes, its influence was included in the numerical models to determine the stress intensity factors. In this second part, the stress intensity factors calibrations are applied in different crack growth models in order to determine the fatigue life under cyclic loads. Paris, Walker, Forman and NASGRO were used for this purpose. The incorporation of the load ratio variation and of the effect of the residual stresses is in general possible in all of them allowing to determine the influence of the residual stress field in the fatigue crack growth. The results were tested and compared with experimental results with the purpose of validation of the models. These numerical models demonstrate that (i) it is possible to predict the fatigue life in stiffened welded panels and (ii) the residual stress field originated by welding processes can be detrimental or beneficial depending on the location where the crack starts.
Addressing the Data-Imbalance Problem in Kernel-Based Speaker Verification via Utterance Partitioning and Speaker Comparison

GMM-SVM has become a promising approach to textindependent speaker verification. However, a problematic issue of this approach is the extremely serious imbalance between the numbers of speaker-class and impostor-class utterances available for training the speaker-dependent SVMs. This data-imbalance problem can be addressed by (1) creating more speaker-class supervectors for SVM training through utterance partitioning with acoustic vector resampling (UP-AVR) and (2) avoiding the SVM training so that speaker scores are formulated as an inner product discriminant function (IPDF) between the target-speaker’s supervector and test supervector. This paper highlights the differences between these two approaches and compares the effect of using different kernels – including the KL divergence kernel, GMM-UBM mean interval (GUMI) kernel and geometric-mean-comparison kernel – on their performance. Experiments on the NIST 2010 Speaker Recognition Evaluation suggest that GMM-SVM with UP-AVR is superior to speaker comparison and that the GUMI kernel is slightly better than the KL kernel in speaker comparison.
HappyJIT: a tracing JIT compiler for PHP

Current websites are a combination of server-generated dynamic content with client-side interactive programs. Dynamically - typed languages have gained a lot of ground in both of these domains. The growth of Web 2.0 has introduced a myriad of websites which contain personalized content, which is specific to the user. PHP or Python programs generate the actual HTML page after querying a database and processing the results, which are then presented by the browser. It is becoming more and more vital to accelerate the execution of these programs, as this is a significant part of the total time needed to present the page to the user. This paper presents a novel interpreter for the PHP language written in RPython, which the PyPy translator then translates into C. The translator integrates into the interpreter a tracing just-in-time compiler which optimizes the hottest loops in the interpreted programs. We also describe a data model that supports all the data types in the PHP language, such as references and iterators. We evaluate the performance of this interpreter, showing that speedups up to a factor of 8 are observed using this approach.
Fast Monte Carlo Algorithms for Matrices I: Approximating Matrix Multiplication

Motivated by applications in which the data may be formulated as a matrix, we consider algorithms for several common linear algebra problems. These algorithms make more efficient use of computational resources, such as the computation time, random access memory (RAM), and the number of passes over the data, than do previously known algorithms for these problems. In this paper, we devise two algorithms for the matrix multiplication problem. Suppose $A$ and $B$ (which are $m\times n$ and $n\times p$, respectively) are the two input matrices. In our main algorithm, we perform $c$ independent trials, where in each trial we randomly sample an element of $\{ 1,2,\ldots, n\}$ with an appropriate probability distribution ${\cal P}$ on $\{ 1,2,\ldots, n\}$. We form an $m\times c$ matrix $C$ consisting of the sampled columns of $A$, each scaled appropriately, and we form a $c\times n$ matrix $R$ using the corresponding rows of $B$, again scaled appropriately. The choice of ${\cal P}$ and the column and row scaling are crucial features of the algorithm. When these are chosen judiciously, we show that $CR$ is a good approximation to $AB$. More precisely, we show that $$ \left\|AB-CR\right\|_F = O(\left\|A\right\|_F \left\|B\right\|_F /\sqrt c) , $$ where $\|\cdot\|_F$ denotes the Frobenius norm, i.e., $\|A\|^2_F=\sum_{i,j}A_{ij}^2$. This algorithm can be implemented without storing the matrices $A$ and $B$ in RAM, provided it can make two passes over the matrices stored in external memory and use $O(c(m+n+p))$ additional RAM to construct $C$ and $R$. We then present a second matrix multiplication algorithm which is similar in spirit to our main algorithm. In addition, we present a model (the pass-efficient model) in which the efficiency of these and other approximate matrix algorithms may be studied and which we argue is well suited to many applications involving massive data sets. In this model, the scarce computational resources are the number of passes over the data and the additional space and time required by the algorithm. The input matrices may be presented in any order of the entries (and not just row or column order), as is the case in many applications where, e.g., the data has been written in by multiple agents. In addition, the input matrices may be presented in a sparse representation, where only the nonzero entries are written.
The Longitudinal Relationship Between Cortisol Responses to Mental Stress and Leukocyte Telomere Attrition

Context: Chronic psychological stress has been associated with shorter telomeres, but the underlying mechanisms are poorly understood. One possibility is that the neuroendocrine responses to stress exposure are involved. Objective: To test the hypothesis that greater cortisol responsivity to acute stressors predicts more rapid telomere attrition. Design: We measured salivary cortisol responses to 2 challenging behavioral tasks. Leukocyte telomere length was measured at the time of mental stress testing and 3 years later. Participants: We studied 411 initially healthy men and women aged 54 to 76 years. Main outcome measure: Leukocyte telomere length. Results: Cortisol responses to this protocol were small; we divided participants into cortisol responders (n = 156) and nonresponders (n = 255) using a criterion (≥20% increase in cortisol concentration) previously shown to predict increases in cardiovascular disease risk. There was no significant association between cortisol responsivity and baseline telomere length, although cortisol responders tended to have somewhat shorter telomeres (β = −0.061; standard error, 0.049). But cortisol responders had shorter telomeres and more rapid telomere attrition than nonresponders on follow-up, after controlling statistically for age, sex, socioeconomic status, smoking, time of day of stress , and baseline telomere length (β = −0.10; standard error, 0.046; P = 0.029). The association was maintained after additional control for cardiovascular risk factors (β = −0.11; P = 0.031). The difference between cortisol responders and nonresponders was equivalent to approximately 2 years in aging. Conclusions: These findings suggest that cortisol responsivity may mediate, in part, the relationship between psychological stress and cellular aging.
Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale

Overview Notions of community quality underlie the clustering of networks. While studies surrounding network clustering are increasingly common, a precise understanding of the realtionship between different cluster quality metrics is unknown. In this paper, we examine the relationship between stand-alone cluster quality metrics and information recovery metrics through a rigorous analysis of four widely-used network clustering algorithms—Louvain, Infomap, label propagation, and smart local moving. We consider the stand-alone quality metrics of modularity, conductance, and coverage, and we consider the information recovery metrics of adjusted Rand score, normalized mutual information, and a variant of normalized mutual information used in previous work. Our study includes both synthetic graphs and empirical data sets of sizes varying from 1,000 to 1,000,000 nodes. Cluster Quality Metrics We find significant differences among the results of the different cluster quality metrics. For example, clustering algorithms can return a value of 0.4 out of 1 on modularity but score 0 out of 1 on information recovery. We find conductance, though imperfect, to be the stand-alone quality metric that best indicates performance on the information recovery metrics. Additionally, our study shows that the variant of normalized mutual information used in previous work cannot be assumed to differ only slightly from traditional normalized mutual information. Network Clustering Algorithms Smart local moving is the overall best performing algorithm in our study, but discrepancies between cluster evaluation metrics prevent us from declaring it an absolutely superior algorithm. Interestingly, Louvain performed better than Infomap in nearly all the tests in our study, contradicting the results of previous work in which Infomap was superior to Louvain. We find that although label propagation performs poorly when clusters are less clearly defined, it scales efficiently and accurately to large graphs with well-defined clusters.
Cooperative D2D communication in downlink cellular networks with energy harvesting capability

Device-to-Device (D2D) communications have been highlighted as one of the promising solutions to enhance spectrum utilization of LTE-Advanced networks. In this paper, we consider a D2D transmitter cooperating with a cellular network by acting as a relay to serve one of the cellular users. We consider the case in which the D2D transmitter is equipped with an energy harvesting capability. We investigate the trade-off between the amount of energy used for relaying and the energy used for decoding the cellular user data at the relaying node. We formulate an optimization problem to maximize the cellular user rate subject to a minimum rate requirement constraint for the D2D link. Moreover, we consider the case when receiving nodes are equipped with successive interference cancellation (SIC) capability and investigate the effect of using SIC on our proposed system performance. Finally, we show via numerical simulations the benefits of our cooperation-based system as compared to the non-cooperative scenario.
Molecular Evolutionary Genetics

Recent developments of statistical methods in molecular phylogenetics are reviewed. It is shown that the mathematical foundations of these methods are not well established, but computer simulations and empirical data indicate that currently used methods such as neighbor joining, minimum evolution, likelihood, and parsimony methods produce reasonably good phylogenetic trees when a sufficiently large number of nucleotides or amino acids are used. However, when the rate of evolution varies exlensively from branch to branch, many methods may fail to recover the true topology. Solid statistical tests for examining'the accuracy of trees obtained by neighborjoining, minimum evolution, and least-squares method are available, but the methods for likelihood and parsimony trees are yet to be refined. Parsimony, likelihood, and distance methods can all be used for inferring amino acid sequences of the proteins of ancestral organisms that have become extinct.
On Nonblocking Multiconnection Networks

In the paper, four theorems describing conditions under which a three-stage multiconnection network is nonblocking for different routing strategies are formulated and proved. These theorems hold for any values of the structural parameters of a network. For some networks having a small number of outer stage switches, these theorems give significantly lower numbers of required middle stage switches than theorems currently known.
Web Based Time-Tunnel: An Interactive Multidimensional Data Visualization Tool Using Genetic Algorithm

This paper presents a Web based Time-tunnel, a visualization system that renders 3D charts of time series multidimensional data in a web browser. The system supports to find differences and similarities among several data by overlapping their charts together. Nonetheless, if many charts are overlapped, it can be difficult to distinguish the charts. Therefore, the authors added new functionality to the system that optimizes data classification by using genetic algorithm, and then, the system came to visualize several charts of data as one group those have a strong relationship among them. In addition, users can share the visualized result with other users by specifying URL generated by the system. In this paper, the authors also justify the usability of the system by indicating visualization results of actual data.
Myc versus USF: discrimination at the cad gene is determined by core promoter elements

Carbamoyl-phosphate synthase/aspartate carbamoyltransferase/dihydroorotase, which is encoded by the cad gene, is required for the first three rate-limiting steps of de novo pyrimidine biosynthesis. It has been previously demonstrated that cad transcription increases at the G1/S-phase boundary, as quiescent cells reenter the proliferative cell cycle. The growth-responsive element has been mapped to an E box at +65 in the hamster cad promoter. Using an in vivo UV cross-linking and immunoprecipitation assay, we show that Myc, Max, and upstream stimulatory factor (USF) bind to the chromosomal cad promoter. To determine whether binding of Myc-Max or USF is critical for cad growth regulation, we analyzed promoter constructs which contain mutations in the nucleotides flanking the E box. We demonstrate that altering nucleotides which flank the cad E box to sequences which decrease Myc-Max binding in vitro correlates with a loss of cad G1/S-phase transcriptional activation. This result supports the conclusion that binding of Myc-Max, but not USF, is essential for cad regulation. Our investigations demonstrate that the endogenous cad E box can be bound by more than one transcription factor, but growth-induced cad expression is achieved only by Myc.
Measurability Aspects of the Compactness Theorem for Sample Compression Schemes

It was proved in 1998 by Ben-David and Litman that a concept space has a sample compression scheme of size d if and only if every finite subspace has a sample compression scheme of size d. In the compactness theorem, measurability of the hypotheses of the created sample compression scheme is not guaranteed; at the same time measurability of the hypotheses is a necessary condition for learnability. In this thesis we discuss when a sample compression scheme, created from com- pression schemes on finite subspaces via the compactness theorem, have measurable hypotheses. We show that if X is a standard Borel space with a d-maximum and universally separable concept class C, then (X,C) has a sample compression scheme of size d with universally Borel measurable hypotheses. Additionally we introduce a new variant of compression scheme called a copy sample compression scheme.
Domain Specific Languages

To match the needs of the fast paced generation, the speed of computing has also increased enormously. But, there is a limit to which the processor speed can be amplified. Hence in order to increase productivity, there is a need to change focus from processing time to programming time. Reduction in programming time can be achieved by identifying the domain to which the task belongs and using an appropriate Domain Specific Language (DSL). DSLs are constrained to use terms and concepts pertaining to an explicit domain making it much easier for the programmers to understand and learn, and cuts down the development time drastically. In this paper, we will understand what a DSL is; explore a number of DSLs spanning various phases of software development life cycle in terms of features that elucidates their advantages over general purpose languages and perform in depth study by practically applying a few open source DSLs: ‘Cascading’, Naked Objects Framework and RSpec.
A Novel Symbolic Algorithm for Maximum Weighted Matching in Bipartite Graphs

The maximum weighted matching problem in bipartite graphs is one of the classic combinatorial optimization problems, and arises in many different applications. Ordered binary decision diagram (OBDD) or algebraic decision diagram (ADD) or variants thereof provides canonical forms to represent and manipulate Boolean functions and pseudo-Boolean functions efficiently. ADD and OBDD-based symbolic algorithms give improved results for large-scale combinatorial optimization problems by searching nodes and edges implicitly. We present novel symbolic ADD formulation and algorithm for maximum weighted matching in bipartite graphs. The symbolic algorithm implements the Hungarian algorithm in the context of ADD and OBDD formulation and manipulations. It begins by setting feasible labelings of nodes and then iterates through a sequence of phases. Each phase is divided into two stages. The first stage is building equality bipartite graphs, and the second one is finding maximum cardinality matching in equality bipartite graph. The second stage iterates through the following steps: greedily searching initial matching, building layered network, backward traversing node-disjoint augmenting paths, updating cardinality matching and building residual network. The symbolic algorithm does not require explicit enumeration of the nodes and edges, and therefore can handle many complex executions in each step. Simulation experiments indicate that symbolic algorithm is competitive with traditional algorithms.
Computation and refinement of statistical bounds on circuit delay

The growing impact of within-die process variation has created the need for statistical timing analysis, where gate delays are modeled as random variables. Statistical timing analysis has traditionally suffered from exponential run time complexity with circuit size, due to arrival time dependencies created by reconverging paths in the circuit. In this paper, we propose a new approach to statistical timing analysis that is based on statistical bounds of the circuit delay. Since these bounds have linear run time complexity with circuit size, they can be computed efficiently for large circuits. Since both a lower and upper bound on the true statistical delay is available, the quality of the bounds can be determined. If the computed bounds are not sufficiently close to each other, we propose a heuristic to iteratively improve the bounds using selective enumeration of the sample space with additional run time. We demonstrate that the proposed bounds have only a small error and that by carefully selecting a small set of nodes for enumeration, this error can be further improved.
A framework for implementing reusable digital signal processing modules

A framework for implementing reusable digital signal processing modules is presented. Based on a cellular structure, it offers a high level of configurability. With its predefined control strategy, and its generic architecture, the framework allows a fast and efficient implementation of digital signal processing applications. As a practical example, the implementation of a frequency estimator, called the Crozier algorithm, is presented. Advantages observed when implementing the algorithm with the proposed framework are demonstrated.
Surface Mosaics of the Bladder Reconstructed From Endoscopic Video for Automated Surveillance

Flexible cystoscopy is frequently performed for recurrent bladder cancer surveillance, making it the most expensive cancer to treat over the patient's lifetime. An automated bladder surveillance system is being developed to robotically scan the bladder surface using an ultrathin and highly flexible endoscope. Such a system would allow cystoscopic procedures to be overseen by technical staff while urologists could review cystoscopic video postoperatively. In this paper, we demonstrate a method for reconstructing the surface of the whole bladder from endoscopic video using structure from motion. Video is acquired from a custom ultrathin and highly flexible endoscope that can retroflex to image the entire internal surface of the bladder. Selected frames are subsequently stitched into a mosaic and mapped to a reconstructed surface, creating a 3-D surface model of the bladder that can be expediently reviewed. Our software was tested on endoscopic video of an excised pig bladder. The resulting reconstruction possessed a projection error of 1.66 pixels on average and covered 99.6% of the bladder surface area.