Miracle-dz/UniFAR-FTUs huggingface dataset:
A Queuing-Network-Based Approach to Performance Evaluation of Service Compositions

Performance prediction is one of the most important research topics of service-oriented systems. To investigate the performance of composite services in queuing condition, this paper introduces a queuing-network-based model. Analytical methods are introduced to evaluate the queue-length, wait-time and completion-duration. The case study (especially the case of airline ticket booking application) shows that the proposed model captures real-world composite services effectively. Through Monte-carlo simulations in the case study, we show analytical models are verified by simulative results.
Challenges for Routine Health System Data Management in a Large Public Programme to Prevent Mother-to-Child HIV Transmission in South Africa

Background Recent changes to South Africa's prevention of mother-to-child transmission of HIV (PMTCT) guidelines have raised hope that the national goal of reducing perinatal HIV transmission rates to less than 5% can be attained. While programmatic efforts to reach this target are underway, obtaining complete and accurate data from clinical sites to track progress presents a major challenge. We assessed the completeness and accuracy of routine PMTCT data submitted to the district health information system (DHIS) in three districts of Kwazulu-Natal province, South Africa. Methodology/Principal Findings We surveyed the completeness and accuracy of data reported for six key PMTCT data elements between January and December 2007 from all 316 clinics and hospitals in three districts. Through visits to randomly selected sites, we reconstructed reports for the same six PMTCT data elements from clinic registers and assessed accuracy of the monthly reports previously submitted to the DHIS. Data elements were reported only 50.3% of the time and were “accurate” (i.e. within 10% of reconstructed values) 12.8% of the time. The data element “Antenatal Clients Tested for HIV” was the most accurate data element (i.e. consistent with the reconstructed value) 19.8% of the time, while “HIV PCR testing of baby born to HIV positive mother” was the least accurate with only 5.3% of clinics meeting the definition of accuracy. Conclusions/Significance Data collected and reported in the public health system across three large, high HIV-prevalence Districts was neither complete nor accurate enough to track process performance or outcomes for PMTCT care. Systematic data evaluation can determine the magnitude of the data reporting failure and guide site-specific improvements in data management. Solutions are currently being developed and tested to improve data quality.
Predicting academic success in Belgium and France Comparison and integration of variables related to student behavior

Having observed low success rates among first-year university students in both Belgium and France, we develop prediction models in this paper in order to identify, at the earliest possible stage, those students who are at risk of failing at the end of the academic year. We applied different data mining techniques to predict the students' academic success. We find that it is very difficult to predict success by only considering the variables related to behavior during classes, and that it is necessary to add variables related to personal history, involvement in and behavior during their studies, and perceptions of academic life, to obtain good-quality results.
An efficient functional verification method for quantum Boolean circuits

Functional verification is an important design method for verifying functional equivalence between a simplified quantum Boolean circuit and original one. During the design process, checking the equivalence of two quantum Boolean circuits is necessary. In this paper, we present an algorithm that can efficiently and easily verify two quantum Boolean circuits by using the back propagation method. For a set of input vectors, the idea of the algorithm is to find the checking vectors that output vectors are different from input vectors by a backward tracking process.
Complete mitochondrial genome of the black-dotted grouper Epinephelus stictus

Abstract The complete mitochondrial genome of the black-dotted grouper Epinephelus stictus was first determined in this study. It is 16,524 bp in length, including 13 protein-coding genes, two ribosomal RNA genes, 22 transfer RNA genes and one control region, with the typical gene arrangement in vertebrates. There are some variations in the initiate and terminal codons among 13 protein-coding genes. The 12S rRNA and 16S rRNA genes are 952 and 1705 bp, respectively. The tRNA-Ser2 gene lacked DHC arm and could not fold into a typical clover-leaf secondary structure. The base composition is different between each portion of mitochondrial genome.
Unsupervised Machine Condition Monitoring Using Segmental Hidden Markov Models

The task of machine condition monitoring is to detect machine failures at an early stage such that maintenance can be carried out in a timely manner. Most existing techniques are supervised approaches: they require user annotated training data to learn normal and faulty behaviors of a machine. However, such supervision can be difficult to acquire. In contrast, unsupervised methods don't need much human involvement, however, they face another challenge: how to model the generative (observation) process of sensor signals. We propose an unsupervised approach based on segmental hidden Markov models. Our method has a unifying observation model integrating three pieces of information that are complementary to each other. First, we model the signal as an explicit function over time, which describes its possible non-stationary trending patterns. Second, the stationary part of the signal is fit by an autoregressive model. Third, we introduce contextual information to break down the signal complexity such that the signal is modeled separately under different conditions. The advantages of the proposed model are demonstrated by tests on gas turbine, truck and honeybee datasets.
Fast Segmentation of Stained Nuclei in Terabyte-Scale, Time Resolved 3D Microscopy Image Stacks

Automated analysis of multi-dimensional microscopy images has become an integral part of modern research in life science. Most available algorithms that provide sufficient segmentation quality, however, are infeasible for a large amount of data due to their high complexity. In this contribution we present a fast parallelized segmentation method that is especially suited for the extraction of stained nuclei from microscopy images, e.g., of developing zebrafish embryos. The idea is to transform the input image based on gradient and normal directions in the proximity of detected seed points such that it can be handled by straightforward global thresholding like Otsu’s method. We evaluate the quality of the obtained segmentation results on a set of real and simulated benchmark images in 2D and 3D and show the algorithm’s superior performance compared to other state-of-the-art algorithms. We achieve an up to ten-fold decrease in processing times, allowing us to process large data sets while still providing reasonable segmentation results.
Egalitarianism and Competitiveness

Competition is a cornerstone of economic life. Some environments are, however, more competitive than others and individuals are often confronted with the decision whether to self-select into a competitive environment. An important example is occupational choice: a self-employed lawyer is in constant competition for clients, whereas a lawyer working as a civil servant in a public authority is not. Likewise, some people vigorously compete for promo tion to better paid jobs associated with a high prestige while others don’t. Understanding who self-selects into competitive environments and who shies away from them is thus important. In this paper, we test the hypothesis that individu als with a preference for egalitarian outcomes are more reluctant to self-select into competitive environments. Our hypothesis is based on (i) the observation that payoff inequalities among winners and losers arise as a natural by-product of competition; and (ii) the empirical literature showing that a nonnegligible share of children and adults is willing to incur costs to reduce earnings inequalities (see, e.g., Christopher Dawes et al. 2007; Ernst Fehr, Helen Bernhard, and Bettina Rockenbach 2008 ). We analyze data from several economic experiments implemented in a household survey study with mothers of preschool children. We
Unbounded Bayesian Optimization via Regularization

Bayesian optimization has recently emerged as a popular and efficient tool for global optimization and hyperparameter tuning. Currently, the established Bayesian optimization practice requires a user-defined bounding box which is assumed to contain the optimizer. However, when little is known about the probed objective function, it can be difficult to prescribe such bounds. In this work we modify the standard Bayesian optimization framework in a principled way to allow automatic resizing of the search space. We introduce two alternative methods and compare them on two common synthetic benchmarking test functions as well as the tasks of tuning the stochastic gradient descent optimizer of a multi-layered perceptron and a convolutional neural network on MNIST.
Pediatric Trichotillomania: Descriptive Psychopathology and an Open Trial of Cognitive Behavioral Therapy

In study 1, 46 children and adolescents with trichotillomania who sought treatment at 2 specialty outpatient clinics were assessed. Most children reported pulling hair from multiple sites on the body, presented with readily visible alopecia, reported spending 30–60 minutes per day pulling or thinking about pulling, and reported experiencing significant distress about their symptoms. Most were described by their parents as having significant problems in school functioning. Few children met criteria for obsessive‐compulsive disorder or tic disorder. Child and family rates of other forms of psychopathology were high. In study 2, 22 of these children were enrolled in an open trial of individual cognitive behavioral therapy with particular attention to relapse prevention. Trichotillomania severity decreased significantly and 77% of children were classified as treatment responders at post‐treatment and 64% at 6‐month follow‐up.
Association of Oxytocin and Parental Prefrontal Activation during Reunion with Infant: A Functional Near-Infrared Spectroscopy Study

Although previous studies have revealed the role of oxytocin (OT) in parental behavior, the role of OT has not been investigated through the direct assessment of prefrontal brain activation during parenting. By using functional near-infrared spectroscopy, we aimed to show the relationship between parental [maternal (N = 15) and paternal (N = 21)] OT levels and the activation of the prefrontal cortex (PFC), while holding their infants after separation. Baseline OT levels were measured in the subjects’ saliva samples before the experiment. Prefrontal brain activation was assessed in participants sitting alone on a chair (i.e., separation from their infant for 120 s) and during the target period (i.e., holding their infant for 45 s), which was done in triplicate. The oxygen hemoglobin (oxy-Hb) dissociation curve significantly increased in 9 out of 22 channels on the PFC when maternal and paternal samples were combined. However, only the fathers showed a correlation between salivary OT and oxy-Hb signal. Furthermore, while holding their infants, high-OT fathers showed left hemispheric dominance compared to low-OT fathers, while high-OT mothers showed right hemispheric dominance compared to low-OT mothers. This study showed that fathers with high-OT levels showed neural activation with left hemispheric dominance, while holding their infants, suggesting that increase of OT level might activate paternal PFC related to parenting behavior, although the same is not true for mothers.
Non-cardiac Surgery in Neonates with Major Congenital Anomalies: a Pilot Study on the Developmental and Psychosocial Effects in Toddlers

Past studies have shown that infants who underwent surgery in the neonatal period because of major congenital anomalies, often show mental and psychomotor developmental delays in later life. Furthermore, less optimal parent-child interaction among these families have been found. The purpose of this pilot study was to gain a first insight in behavior problems, developmental delays and parent-child interaction in non-cardiac surgery requiring CA infants as a group, and outcomes and implications will be considered in the set up of a longitudinal research in the Wilhelmina Children Hospital (WCH) in Utrecht. The current study investigated parents of 36 toddlers (M = 43.6 months, SD = 11.0), who underwent surgery in the WCH in Utrecht because of major non-cardiac congenital anomalies. Being part of the non-cardiac CA group, infants with developmental intrusive syndromes were included. Parents completed questionnaires through which developmental delays (Ages and Stages Questionnaire), behavior problems (Child Behavior Checklist) and parent-child interaction (Nijmeegse Ouderlijke Stress Index) were measured. Results were compared to the tests’ normative groups, except for developmental delays (percentages were given). Contrary to prior research, significantly less behavior problems and better parent-child interaction were found. In addition, nonoptimal parent-child interaction was found to be predictive for developmental delays. Lastly, analyses were run for the non-syndromic infants solely and similar outcomes on behavior problems and parent-child interaction were found. Despite the fact that comparison between non-syndromic children and the whole group of participants on developmental delays was not tested for significance, developmental delays were found to be noticeable lower in the non-syndromic children.
Performance Limits of a Cloud Radio

Cooperation in a cellular network is seen as a key technique in managing other cell interference to observe a gain in achievable rate. In this paper, we present the achievable rate regions for a cloud radio network using a sub-optimal zero forcing equalizer with dirty paper precoding. We show that when complete channel state information is available at the cloud, rates close to those achievable with total interference cancellation can be achieved. With mean capacity gains, of up to 2 fold over the conventional cellular network in both uplink and downlink, this precoding scheme shows great promise for implementation in a cloud radio network. To simplify the analysis, we use a stochastic geometric framework based of Poisson point processes instead of the traditional grid based cellular network model. We also study the impact of limiting the channel state information and geographical clustering to limit the cloud size on the achievable rate. We have observed that using this zero forcing-dirty paper coding technique, the adverse effect of inter-cluster interference can be minimized thereby transforming an interference limited network into a noise limited network as experienced by an average user in the network for low operating signal-to-noise-ratios. However, for higher signal-to-noise-ratios, both the average achievable rate and cell-edge achievable rate saturate as observed in literature. As the implementation of dirty paper coding is practically not feasible, we present a practical design of a cloud radio network using cloud a minimum mean square equalizer for processing the uplink streams and use Tomlinson-Harashima precoder as a sub-optimal substitute for a dirty paper precoder in downlink.
Two-Scale Stochastic Control for Multipoint Communication Systems with Renewables

Increasing threats of global warming and climate changes call for an energy-efficient and sustainable design of future wireless communication systems. To this end, a novel two-scale stochastic control framework is put forth for smart-grid powered coordinated multi-point (CoMP) systems. Taking into account renewable energy sources (RES), dynamic pricing, two-way energy trading facilities and imperfect energy storage devices, the energy management task is formulated as an infinite-horizon optimization problem minimizing the time-average energy transaction cost, subject to the users' quality of service (QoS) requirements. Leveraging the Lyapunov optimization approach as well as the stochastic subgradient method, a two-scale online control (TS-OC) approach is developed for the resultant smart-grid powered CoMP systems. Using only historical data, the proposed TS-OC makes online control decisions at two timescales, and features a provably feasible and asymptotically near-optimal solution. Numerical tests further corroborate the theoretical analysis, and demonstrate the merits of the proposed approach.
Emotional Labor as a Dynamic Process in Service Organizations: Development of a Multi-Perspective, Multilevel Model

In this chapter, we develop and present the Multi-Perspective Multilevel Model of emotional labor in organizations. This model is based on three perspectives: (1) a service requirement, (2) an intra-psychic process, and (3) an emotional display, each involving five levels of analysis: within-person, between persons, in interpersonal exchanges, in groups, and across the organization as a whole. Our model is differentiated from earlier characterizations of emotional labor in that we propose that the phenomenon begins with energy generation instead of energy depletion; and is neither a one-way nor a one-by-one service episode. We further proffer that the intra-psychic processes embedded in emotional labor represent a form of social self-regulation that impacts across multiple levels within service organizations. We conclude by discussing the implications and limitations of our model for emotional labor research.
Multidimensional perfectionism and motivation in sport : potential mediating and moderating variables

Recent research has found that self-oriented and socially prescribed perfectionism have distinct consequences for athletes. The purpose of the thesis was to extend this research by further examining their motivational consequences for athletes and identifying the psychological mechanisms that explain their divergent consequences. The first two studies suggested that the positive relationship between socially prescribed perfectionism and athlete burnout was mediated by the tendency to engage in validation-seeking and utilise avoidant coping, whereas the inverse relationship between self-oriented perfectionism and athlete burnout was mediated by the tendency to utilise problem-focused coping and eschew avoidant coping. Because these initial studies provided little evidence to suggest that self-oriented perfectionism has negative psychological consequences for athletes, the nature of self-oriented perfectionism and its consequences were examined more closely in two subsequent studies. A comparative study examining similarities and differences in the correlates of selforiented perfectionism and conscientious achievement striving found that while both include a commitment to high standards, self-oriented perfectionism also includes a concern over mistakes, fear of failure and negative reactions to imperfection. An experimental study examining the response of student-athletes
Planning Paths for a Flexible Surface Patch

This paper presents a probabilistic planner capable of finding paths for a flexible surface patch. The planner is based on the probabilistic roadmap approach to path planning while the surface patch is modeled as a low degree Bezier surface. We assume that we are dealing with an elastic part and define an approximate energy model for the part. The energy function penalizes excessive shear and bending of the part and we assume that low-energy configurations correspond to reversible elastic deformations of the part. The planner captures the connectivity of a space by building a roadmap, a network of simple paths connecting configurations selected in the space using randomized techniques. We report on the implementation of our planner and show experimental results with examples where the surface patch is required to move through a small hole in its workspace. Our work is a first step towards considering the physical properties of parts when planning paths.
A Protective Role for Complement C3 Protein during Pandemic 2009 H1N1 and H5N1 Influenza A Virus Infection

Highly pathogenic H5N1 influenza infections are associated with enhanced inflammatory and cytokine responses, severe lung damage, and an overall dysregulation of innate immunity. C3, a member of the complement system of serum proteins, is a major component of the innate immune and inflammatory responses. However, the role of this protein in the pathogenesis of H5N1 infection is unknown. Here we demonstrate that H5N1 influenza virus infected mice had increased levels of C5a and C3 activation byproducts as compared to mice infected with either seasonal or pandemic 2009 H1N1 influenza viruses. We hypothesized that the increased complement was associated with the enhanced disease associated with the H5N1 infection. However, studies in knockout mice demonstrated that C3 was required for protection from influenza infection, proper viral clearance, and associated with changes in cellular infiltration. These studies suggest that although the levels of complement activation may differ depending on the influenza virus subtype, complement is an important host defense mechanism.
Validating the Use of Topic Models for Software Evolution

Topics are collections of words that co-occur frequently in a text corpus. Topics have been found to be effective tools for describing the major themes spanning a corpus. Using such topics to describe the evolution of a software system’s source code promises to be extremely useful for development tasks such as maintenance and re-engineering. However, no one has yet examined whether these automatically discovered topics accurately describe the evolution of source code, and thus it is not clear whether topic models are a suitable tool for this task. In this paper, we take a first step towards deter-mining the suitability of topic models in the analysis of software evolution by performing a qualitative case study on 12 releases of JHot Draw, a well studied and documented system. We define and compute various metrics on the identified topics and manually investigate how the metrics evolve over time. We find that topic evolutions are characterizable through spikes and drops in their metric values, and that the large majority of these spikes and drops are indeed caused by actual change activity in the source code. We are thus encouraged by the use of topic models as a tool for analyzing the evolution of software.
Environmental Sounds Spectrogram Classification using Log-Gabor Filters and Multiclass Support Vector Machines

This paper presents novel approaches for efficient feature extraction using environmental sound magnitude spectrogram. We propose approach based on the visual domain. This approach included three methods. The first method is based on extraction for each spectrogram a single log-Gabor filter followed by mutual information procedure. In the second method, the spectrogram is passed by the same steps of the first method but with an averaged bank of 12 log-Gabor filter. The third method consists of spectrogram segmentation into three patches, and after that for each spectrogram patch we applied the second method. The classification results prove that the second method is the most efficient in our environmental sound classification system.