Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Meaning of Noise Annoyance Modifiers: A Fuzzy Set Theoretical Approach

Summary This paper reports on a fuzzy analysis of information gathered by many colleagues on the precise meaning of noise annoyance modifiers in 9 different languages. It is shown how fuzzy set theory can help us to construct a mathematical background for translating these modifiers between the languages concerned. A second goal of annoyance modifier research is to define labels to be used in noise annoyance surveys in order to obtain accurate and comparable results. Similarity measures used to compare fuzzy sets associated with verbal descriptors of annoyance levels indicate to what extent previously proposed labels [1] match between the languages considered. An ideal language from the fuzzy point of view where a continuous annoyance scale is exactly divided into equal parts is translated to these natural languages and results in an alternative selection of labels that are better suited for fuzzy calculus. In general this selection of labels corresponds quite well with the set proposed in [1] which is rather surprising since the fuzzy set approach lacks most of the human input used in the ICBEN selection procedure.
POIKILO: A Tool for Evaluating the Results of Diversification Models and Algorithms

Search result diversification has attracted considerable attention as a means of improving the quality of results retrieved by user queries. In this demonstration, we present Poikilo, a tool to assist users in locating and evaluating diverse results. We provide implementations of a wide suite of models and algorithms to compute and compare diverse results. Users can tune various diversification parameters, combine diversity with relevance and also see how diverse results change over time in the case of streaming data.
Empirical evaluations of regression test selection techniques: a systematic review

Regression testing is the verification that previously functioning software remains after a change. In this paper we report on a systematic review of empirical evaluations of regression test selection techniques, published in major software engineering journals and conferences. Out of 2,923 papers analyzed in this systematic review, we identified 28 papers reporting on empirical comparative evaluations of regression test selection techniques. They report on 38 unique studies (23 experiments and 15 case studies), and in total 32 different techniques for regression test selection are evaluated. Our study concludes that no clear picture of the evaluated techniques can be provided based on existing empirical evidence, except for a small group of related techniques. Instead, we identified a need for more and better empirical studies were concepts are evaluated rather than small variations. It is also necessary to carefully consider the context in which studies are undertaken.
CAN SER VICE QUALITY, TRUST, AND CUSTOMER SATISFACTION ENGENDER CUSTOMERS LOYALTY?

This research has proposed a conceptual framework to investigate the effects of customers’ perceived service quality, trust, and customer satisfaction on customer loyalty. To test the conceptual framework, structural equation modeling (SEM) has been used to analyze the data collected fr om 304 customers of a major private telecommunication company operating in Bangladesh. The r esults of the study indicate that trust and customer satisfaction are significantly and positively related to customer loyalty. Customer satisfaction has found to be an impor tant mediator between perceived service quality and customer loyalty. A clear understanding of the postulated relationships among the studied variables might encourage the mobile ser vice provider(s) to figur e out appropriate course of action to win customers’ trust by providing better services in order to create a loyal customer base.
High Prevalence of Panton-Valentine Leukocidin (PVL) Genes in Nosocomial-Acquired Staphylococcus aureus Isolated from Tertiary Care Hospitals in Nepal

Methicillin-resistant Staphylococcus aureus (MRSA) carrying the important virulence determinant, Panton-Valentine leukocidin (PVL), is an emerging infectious pathogen associated with skin and soft tissue infections as well as life-threatening invasive diseases. In carrying out the first PVL prevalence study in Nepal, we screened 73 nosocomial isolates of S. aureus from 2 tertiary care Nepali hospitals and obtained an overall PVL-positivity rate of 35.6% among the hospital isolates: 26.1% of MRSA and 51.9% of methicillin sensitive S. aureus (MSSA) isolates were found to be positive for the PVL genes. PVL prevalence was not associated with a specific (i) infection site, (ii) age group, or (iii) hospital of origin. It was found to be positively associated with heterogeneous MRSA (73.3%) compared to homogeneous MRSA (3.2%) and MSSA (51.9%); negatively associated with multiresistant MRSA (22%) compared to nonmultiresistant MRSA (60%) and MSSA (51.9%); and positively associated with macrolide-streptogramin B resistance (93.8%) compared to macrolide-lincosamide-streptogramin B resistance (0%) and no-resistance (45.8%) types. Macrolide-streptogramin B resistance was confirmed by the presence of msr(A) gene. Restriction pattern analyses provided evidence to support the circulation of a limited number of clones of PVL-positive MRSA, arguing for the adaptability of these isolates to a hospital setting.
Uncertainty‐Aware Exploration of Continuous Parameter Spaces Using Multivariate Prediction

Systems projecting a continuous n‐dimensional parameter space to a continuous m‐dimensional target space play an important role in science and engineering. If evaluating the system is expensive, however, an analysis is often limited to a small number of sample points. The main contribution of this paper is an interactive approach to enable a continuous analysis of a sampled parameter space with respect to multiple target values. We employ methods from statistical learning to predict results in real‐time at any user‐defined point and its neighborhood. In particular, we describe techniques to guide the user to potentially interesting parameter regions, and we visualize the inherent uncertainty of predictions in 2D scatterplots and parallel coordinates. An evaluation describes a real‐world scenario in the application context of car engine design and reports feedback of domain experts. The results indicate that our approach is suitable to accelerate a local sensitivity analysis of multiple target dimensions, and to determine a sufficient local sampling density for interesting parameter regions.
Robust Locality Preserving Projections with Pairwise Constraints

Dimensionality reduction is one of the key processes of high dimensional data analysis, including machine learning and pattern recognition. Constrained Locality Preserving Projections (CLPP) is a variant of Locality Preserving Projections (LPP) plus with pairwise constraints and constraints propagation. Like LPP, however, CLPP is still sensitive to noise and parameters. To overcome these problems, we propose a Robust LPP (RLPP) with pairwise constraints based on robust path based similarity. In contrast to CLPP, RLPP is less sensitive to noise and outliers. Besides, it capitalizes on pairwise constraints more efficiently. Experimental results on real world datasets confirm its effectiveness.
HMM-Web: A Framework for the Detection of Attacks Against Web Applications

Nowadays, the web-based architecture is the most frequently used for a wide range of internet services, as it allows to easily access and manage information and software on remote machines. The input of web applications is made up of queries, i.e. sequences of pairs attribute←value. A wide range of attacks exploits web application vulnerabilities, typically derived from input validation flaws. In this work we propose a new formulation of query analysis through Hidden Markov Models (HMM) and show that HMM are effective in detecting a wide range of either known or unknown attacks on web applications. In addition, despite previous works, we explicitly address the problem related to the presence of noise (i.e., attacks) in the training set. Finally, we show that performance can be increased when a sequence of symbols is modelled by an ensemble of HMM. Experimental results on real world data, show the effectiveness of the proposed system in terms of very high detection rates and low false alarm rates.
Proteomic Screening of Antigenic Proteins from the Hard Tick, Haemaphysalis longicornis (Acari: Ixodidae)

Proteomic tools allow large-scale, high-throughput analyses for the detection, identification, and functional investigation of proteome. For detection of antigens from Haemaphysalis longicornis, 1-dimensional electrophoresis (1-DE) quantitative immunoblotting technique combined with 2-dimensional electrophoresis (2-DE) immunoblotting was used for whole body proteins from unfed and partially fed female ticks. Reactivity bands and 2-DE immunoblotting were performed following 2-DE electrophoresis to identify protein spots. The proteome of the partially fed female had a larger number of lower molecular weight proteins than that of the unfed female tick. The total number of detected spots was 818 for unfed and 670 for partially fed female ticks. The 2-DE immunoblotting identified 10 antigenic spots from unfed females and 8 antigenic spots from partially fed females. Matrix Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF) of relevant spots identified calreticulin, putative secreted WC salivary protein, and a conserved hypothetical protein from the National Center for Biotechnology Information and Swiss Prot protein sequence databases. These findings indicate that most of the whole body components of these ticks are non-immunogenic. The data reported here will provide guidance in the identification of antigenic proteins to prevent infestation and diseases transmitted by H. longicornis.
Evaluating Locations for Outpatient Medical Care Facilities

The distance from patient to provider has always been recognized as an important factor to consider when locating medical facilities. Past locational studies have focused on minimizing aggregate patient-provider distance and/or including travel cost estimates when designing cost-minimizing systems of medical facilities. ' Recently Acton has pioneered the use of distance (or some variant based on distance) as a price in the estimation of the demand for outpatient medical services.2 Acton's empirical results indicate that the "time price" faced by a patient can be a significant factor in his decision to purchase medical care. Furthermore, he suggests that his approach to estimating consumer demand may have policymaking implications for locating outpatient medical care facilities.3 In this paper, demand relationships based on a "time price" similar to Acton's are employed to estimate consumer willingness-to-pay for several different facility sites.4 It is hypothesized that aggregate willingness-to-pay for a particular outpatient facility location is based on the following factors: (1) Travel cost; since part of the total price of a visit to a medical facility is the consumer's travel cost, economic theory and Acton's empirical results predict that a reduction in travel cost leads to an increase in the quantity of visits purchased by the consumer. To obtain a new medical facility at a given site, the consumer would potentially be willing to pay an amount equal to the net value he places on his additional visits. This net value can be approximated using estimated demand curves for medical services. (2) "Option value"; since the existence of some medical problems is highly uncertain, the demand for their treatment is also uncertain. However, each potential consumer may be willing to pay to guarantee the presence of a physician in the event of medical emergency.s The exact amount depends on an individual's
A CROSS SECTIONAL STUDY OF FREIGHT TRANSPORT DEMAND AND RAIL-TRUCK COMPETITION IN CANADA

This article derives a rail-truck freight demand model that is consistent with the economic theory of modal choice in the price-speed-reliability space. The translog model is estimated from the cross sectional data of Canadian interregional freight flows for the eight selected commodities. Major empirical findings are: (i) The quality attributes of service significantly influence modal choice only for the relatively high-value commodities. (ii) Both the price and quality elasticities of demand and the elasticity of rail-truck substitution vary substantially from route to route as well as from commodity to commodity. This implies that CES models including Cobb-Douglas form should not be used for freight demand studies. (iii) For the relatively high-value commodities, short-haul traffic is largely dominated by the truck mode, and significant rail-truck competition exists only in the medium and long-haul markets. (iv) For the relatively low-value commodities, effective rail-truck competition exists only in the short-haul markets. Hence, the medium and long-haul markets are largely rail-dominated.
Is there an emotion signature in into it be used in syn

Intonation is often considered to play an important role in the vocal communication of emotion. Early studies using pitch manipulation have supported this view. However, the properties of pitch contours involved in marking emotional state remain largely unidentified. In this contribution, a corpus of actor-generated utterances for 8 emotions was used to measure intonation (pitch contour) by identifying key features of the F0 contour. The data show that the profiles obtained vary reliably with respect to F0 level as a function of the degree of activation of the emotion concerned. However, there is little evidence for qualitatively different forms of profiles for different emotions. Results of recent collaborative studies on the use of the F0 patterns identified in this research with synthesized utterances are presented. The nature of the contribution of F0/pitch contours to emotional speech is discussed; it is argued that pitch contours have to be considered as configurations that acquire emotional meaning only through interaction with a linguistic and paralinguistic context.
Early Thiopurines Versus Conventional Step-Care Therapy for Modifying the Disease Course of Early Crohn's Disease

Abstract The impact of thiopurines (TP) on the long-term outcome of early Crohn disease (CD) is still controversial. The present study designed as a comparison of conventional step-care to alternative treatment paradigms for disease progression. This longitudinal cohort study examined the established CD patients from a university-based inflammatory bowel disease referral center. Outcomes of mucosal healing (MH), CD-related surgery or hospitalization, and clinical remission were compared based on timing of initiation of TP therapy. The cumulative incidence of events was estimated by Kaplan–Meier method. One-hundred ninety patients with early CD were included. After a median follow-up of 57 months (interquartile range, 31.3–76.2), 29 patients undergone abdominal surgeries, 48 patients hospitalized, and 68 patients experienced clinical flares. A higher cumulative proportion of patients in the top-down (TD) group achieving MH than both the accelerated step-up (AC) group and conventional management (CM) group at month 36 (78.8% vs 39.9% and 42.2%, respectively; P = 0.001). There was a trend, albeit not significant, for an increased proportion of patients free of CD-related intestinal surgery in the TD group at month 60 (P = 0.16). However, among secondary outcomes, an early TP-based AC or TD strategy was not associated with improvement in clinical remission rates compared with a CM strategy at month 60 (P = 0.79). No significant difference was observed between early TP and CM for rates of MH, CD-related intestinal surgery or hospitalization, and clinical remission. Both AC and CM strategy were minimally effective for disease modification. TD strategy has the potential of achieving higher rates MH. Our results support the TD strategy in patients with early CD at risk for a disabling course.
Control of a Step Walking Combined to Arms Swinging for a Three Dimensional Humanoid Prototype

Problem statement: Present researches focus to make humanoid robots more and more autonomous so they can assist human in daily works like taking care of children, aged or disabled persons. In such social activities, the contemporar y humanoid robots are expected to have human like morphology and gait. Studies on bipedal locomotion for humanoid robots are then part of the hottest topics in the field of robotic researches. Knowing the benefits of arm swinging for human gait, we propose in this study a new prototype of female humanoid robot morphology having the capabilities to swing arms during step walking. Approach: A new humanoid robot prototype had been introduced based on a human morphology corresponding to a woman whose weight is 70 kg and height is 1,73 m and using realistic gait parameters of a women. The female hu manoid robot prototype was composed of fifteen links associated to twenty-six degrees of freedom. Winter statistical model had been applied to determ ine all physical parameters corresponding to each link. Modeling the proposed humanoid robot implies first to establish the kinematic model basically founded on Euler's transformation matrix and then to set th e dynamic model computed using the Newton-Euler method. To show how the arms played an important role in bipedal gait, we had chosen to consider the whole body as two independent robotic systems: the upper body and the lower body. Results: Both three dimensional kinematic and dynamic models of the humanoid robot had been developed. The three dimensional humanoid robot was controlled via a feedback linearization control during the single su pport, impact and double support phases. The simulation results showed the arm swing during the step of walking. Conclusion: The humanoid robot proposed has a human like morphology and ensures the function of a step walking with arm swinging. The applied control laws have ensured to the robot desired performances during a step walking.
Detectability of small communities in multilayer and temporal networks: Eigenvector localization, layer aggregation, and time series discretization

Inspired by real-world networks that are naturally represented by layers encoding different types of connections, such as different instances in time, we study the detectability of small-scale communities that are anomalies in that they are hidden in a multilayer network. Letting $K$ and $T$ respectively denote the community size and the number of layers in which it is present, we assume that it is unknown which of the $N\gg K$ nodes are involved in the community or in which of the $L\ge T$ layers it is present. We study fundamental limitations on the detectability of small communities by developing random matrix theory for the dominant eigenvectors of a modularity matrix that is associated with an aggregation of layers. We identify a phase transition in detectability that is caused by an eigenvector localization phenomenon that is analogous to localization arising for disordered media and occurs when $K$ surpasses a critical size $K^*\varpropto \sqrt{T^{-2}NL}$. We highlight several consequences of this scaling behavior and compare different methods of layer aggregation including optimal strategies for summation and thresholding. As one concrete application, we identify good practices with respect to small-community detection for how to bin (i.e., discretize) temporal network data into time windows. Using this analysis as a guide, we introduce an eigenvector-localization-based procedure for community detection in temporal networks, which we apply to the Enron email corpus as an example.
From Vulnerability to Growth: Positive and Negative Effects of Stressful Life Events

Severely stressful life events can have a substantial impact on those who experience them. For some, experience with a traumatic life event can leave them confused, withdrawn, depressed, and increasingly vulnerable to the next stressful situation that arises. The clinical literature, for example, has found various stressful life events to be risk factors for the development of depression, anxiety, and in extreme cases, posttraumatic stress disorder (PTSD). For other individuals, a traumatic experience can serve as a catalyst for positive change, a chance to reexamine life priorities or develop strong ties with friends and family. Recent research has explored the immediate and long-term positive effects of similarly severe life events, such as cancer, bereavement, and HIV-infection, to identify the factors and processes that appear to contribute to resilience and growth. These two lines of research, however, have developed largely independent of each other and a number of questions remain to be explored in their integration. For example, do the roots of these apparently divergent patterns lie in the events themselves or in the people who experience them? Do some experiences typically lead to negative outcomes, whereas others contribute to the development of positive changes? What psychological factors appear to moderate these outcomes? How do positive outcomes, such as perceptions of stress-related growth and benefit, relate to measures of negative adjustment? To address these questions, we begin with a review of positive outcomes that have been reported in response to stressful life events, such as the perceptions of stressrelated growth and benefit and theories that help to explain these changes. We then
Continuous pattern detection over billion-edge graph using distributed framework

Continuous pattern detection plays an important role in monitoring-related applications. The large size and dynamic update of graphs, along with the massive search space, pose huge challenges in developing an efficient continuous pattern detection system. In this paper, we leverage a distributed graph processing framework to approximately detect a given pattern over a large dynamic graph. We aim to improve the scalability and precision, and reduce the response time and message cost in the detection. We convert a given query pattern into a Single-Sink DAG (Directed Acyclic Graph), and propose an evaluation plan with message transitions on the DAG, which is shorten by SSD plan, to detect the pattern in a large dynamic graph. SSD plan can guide the data graph exploration via messages, and the messages will converge at data sink vertices, which then detect existences of the query pattern. We also conduct join operations over partial vertices during the graph exploration to improve the precision of pattern detection. In addition, we show that SSD plan can support the continuous query over dynamic graphs with slight extensions. We further design various sink vertex selection strategies and neighborhood based transition rule attachment to lower the evaluation cost. The experiments on billion-edge real-life graphs using Giraph, an open source implementation of Pregel, illustrate the efficiency and effectiveness of our method.