Miracle-dz/UniFAR-FTUs huggingface dataset:
Mixing Scores from Artificial Neural Network and Social Network Analysis to Improve the Customer Loyalty

Due to the increased competition in the telecommunications, customer relation and churn management is one of the most crucial aspects for companies in this sector. Over the last decades, researchers have proposed many approaches to detect and model historical events of churn. Traditional approaches, like neural networks, aim to identify behavioral pattern related to the customers. This kind of supervised learned model is suitable to establish likelihood assigned to churn. Although these models can be effective in terms of predictions, they just present the isolated likelihood about the event. However these models do not consider the influence among the customers. Based on the churn score, companies are able to perform an efficient process to retain different types of customer, according to their value in any corporate aspects. Social network analysis can be used to enhance the knowledge related to the customers' influence in an internal community. This new proposition to valuate the customers can arise distinguishes aspects about the virtual communities inside the telecom networks, allowing companies to establish more effective action plans to enhance the customer loyalty process. Combined scores from predictive modeling and social network analysis can create a new customer centric view, based on individual pattern recognition and community overview understanding. The combination of scores provided by the predictive model and the social network analysis can optimize the offerings to retain the customer, increasing the profit and decreasing the cost assigned to the marketing campaigns.
Strictly nonclassical behavior of a mesoscopic system

We experimentally demonstrate the strictly nonclassical behavior in a many-atom system using a recently derived criterion [E. Kot et al., Phys. Rev. Lett. 108, 233601 (2012)] that explicitly does not make use of quantum mechanics. We thereby show that the magnetic moment distribution measured by McConnell et al. [R. McConnell et al., Nature 519, 439 (2015)] in a system with a total mass of $2.6\times 10^5$ atomic mass units is inconsistent with classical physics. Notably, the strictly nonclassical behavior affects an area in phase space $10^3$ times larger than the Planck quantum $\hbar$.
A Sixth-Order PWM Modulator for Digital Input Class-D Audio Amplifiers

A digital Class-D audio amplifier is presented in this paper. This amplifier uses a sixth-order PWM modulator to generate the driver signal of the output stage of the amplifier. The transformation of pulse-amplitude-modulation (PAM) signal to pulse-width-modulation (PWM) signal is moved into the closed sigma-delta modulator loop that the noise and distortion generated during the transformation of PAM signal to PWM signal is suppressed by the high gain of the forward loop of the sigma-delta modulator. Therefore, at the output of the digital modulator, a very clean PWM signal is generated to drive the power stage of the class-D amplifier. This class-D amplifier achieves 110dB dynamic range (DR), 100dB signal to noise rate (SNR) and 0.005% total harmonic distortion plus noise (THD+N).
Meta-analysis of the effectiveness of psychological and medical treatments for binge-eating disorder (MetaBED): study protocol

Introduction Binge-eating disorder (BED) was included as its own diagnostic entity in the Fifth Edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). An increasing number of treatment studies have been published, but an up-to-date comprehensive meta-analysis on diverse treatment approaches for BED is lacking. In an updated and extension of a previous meta-analysis, the goals of this study are to assess the short-term and long-term effectiveness of psychological and medical treatments for BED. Methods and analysis We will search bibliographic databases and study registries, including manual searches for studies published before January 2016. The search strategy will include terms relating to binge eating and diverse forms of psychological and medical interventions. Language will be restricted to English. The studies included will be treatment studies, that is, randomised-controlled trials, and non-randomised and non-controlled studies, for individuals with BED (DSM-IV or DSM-5), and studies that provided a pre-treatment and at least one post-treatment or follow-up assessment of binge eating. The primary outcomes will be the number of binge-eating episodes, abstinence from binge eating and diagnosis of BED at post-treatment and/or follow-up(s), and changes from pre-treatment to post-treatment and/or follow-up(s). Likewise, as secondary outcomes, eating disorder and general psychopathology, quality of life, and body weight will be analysed and adverse events and treatment drop-out will be examined. Study search, selection and data extraction, including risk of bias assessment, will be independently performed by 2 reviewers and consensus will be sought. Moderator analyses will be conducted, and equity aspects will be considered. Sensitivity analyses will be conducted to determine the robustness of the results. Ethics and dissemination Ethical approval is not required for this meta-analysis. Published in a peer-reviewed journal and disseminated electronically and in print, this meta-analysis will form the basis of the renewal of the German evidence-based S3 Guidelines of Diagnosis and Treatment of Eating Disorders, specifically BED. Trial registration number CRD42016043604.
A Long-Term Follow-Up Programme for Maintenance of Motor Function after Stroke: Protocol of the life after Stroke—The LAST Study

Background. There are no evidence-based strategies that have been shown to be superior in maintaining motor function for months to years after the stroke. The LAST study therefore intends to assess the effect of a long-term follow-up program for stroke patients compared to standard care on function, disability and health. Design. This is a prospective, multi-site randomised controlled trial, with blinded assessment 18 months after inclusion. A total of 390 patients will be recruited and randomised to a control group, receiving usual care, or to an intervention group 10 to 16 weeks after onset of stroke. Patients will be stratified according to stroke severity, age above 80, and recruitment site. The intervention group will receive monthly coaching on physical activity by a physiotherapist for 18 consecutive months after inclusion. Outcomes. The primary outcome is motor function (Motor Assessment Scale) 18 months after inclusion. Secondary outcomes are: dependency, balance, endurance, health-related quality of life, fatigue, anxiety and depression, cognitive function, burden on caregivers, and health costs. Adverse events and compliance to the intervention will be registered consecutively during follow-up.
Evaluating the enjoyability of the ghosts in Ms Pac-Man

The video games industry is one of the fastest-growing industries in the world, bolstered by sophisticated technology in gaming consoles and modern trends such as mobile and social gaming. The goal of most video games is to entertain the gamer and in most games this stems from the interaction between the gamer and the non-player characters (NPCs): it is no longer sufficient for a game to be visually appealing but instead, the gamer must be challenged at the right level of difficulty to be engaged by the game. It is thus necessary to develop suitable NPCs that are fun to play against and the realm of computational intelligence offers a variety of techniques to do so. However, the perception of fun is clearly subjective and indeed, difficult to quantify. In this paper we make use of the Ms Pac-Man vs Ghosts gaming competition to gather and analyse data from human gamers regarding their preference of opponent: each gamer plays two games against different ghost teams, indicating their preference at the end. We subsequently use this data to establish which ghost teams are generally preferred and demonstrate that there are measurable differences between these ghost teams. These differences are sufficient to group the ghosts into different categories with a good degree of accuracy. This work is a first step in better understanding the attributes required by NPCs for players to be engaged.
When Do Biological Attributions of Mental Illness Reduce Stigma? Using Qualitative Comparative Analysis to Contextualize Attributions

Individuals increasingly have encountered messages that mental illness is explained by biological factors such as chemical imbalance or genetic abnormality. Many assumed this “biological turn” would lessen stigma toward mental illness, but stigma generally has remained stable or even increased. Given how nonbiological illness explanations (e.g., way one is raised, bad character, life stressors) often are endorsed even among those who support biological explanations, we contend that combinations or configurations of beliefs integrating distinct types of explanation may hold a key to understanding why biological beliefs have not succeeded in lessening stigma. Using qualitative comparative analysis (QCA) on national vignette data (2006 General Social Survey; N = 968), we find that not blaming an individual’s character is essential to lowering depression stigma whenever biological explanations also are endorsed and that blaming character unconditionally contributes to stigmatizing alcoholism. For schizophrenia and alcoholism, biological explanations may lower stigma contingent on several other beliefs.
Stochastic Extraction of Elongated Curvilinear Structures With Applications

The automatic extraction of elongated curvilinear structures (CLSs) is an important task in various image processing applications, including numerous remote sensing, and biometrical and medical problems. To address this task, we develop a stochastic approach that relies on a fixed-grid, localized Radon transform for line segment extraction and a conditional random field model to incorporate local interactions and refine the extracted CLSs. We propose several different energy data terms, the appropriate choice of which allows us to process images with different noise and geometry properties. The contribution of this paper is the design of a flexible and robust elongated CLS extraction framework that is comparatively fast due to the use of a fixed-grid configuration and fast deterministic Radon-based line detector. We present several different applications of the developed approach, namely: 1) CLS extraction in mammographic images; 2) road networks extraction from optical remotely sensed images; and 3) line extraction from palmprint images. The experimental results demonstrate that the method is fairly robust to CLS curvature and can accurately extract blurred and low-contrast elongated CLS.
Asymptotic normality for deconvolution kernel density estimators from random fields

The paper discusses the estimation of a continuous density function of the target random field $X_{\bf{i}}$, $\bf{i}\in \mathbb {Z}^N$ which is contaminated by measurement errors. In particular, the observed random field $Y_{\bf{i}}$, $\bf{i}\in \mathbb {Z}^N$ is such that $Y_{\bf{i}}=X_{\bf{i}}+\epsilon_{\bf{i}}$, where the random error $\epsilon_{\bf{i}}$ is from a known distribution and independent of the target random field. Compared to the existing results, the paper is improved in two directions. First, the random vectors in contrast to univariate random variables are investigated. Second, a random field with a certain spatial interactions instead of i. i. d. random variables is studied. Asymptotic normality of the proposed estimator is established under appropriate conditions.
A Mathematical Model for the Sulphur Dioxide Aggression to Calcium Carbonate Stones: Numerical Approximation and Asymptotic Analysis

We introduce a degenerate nonlinear parabolic system that describes the chemical aggression of calcium carbonate stones under the attack of sulphur dioxide. For this system, we present some finite element and finite difference schemes to approximate its solutions. Numerical stability is given under suitable CFL conditions. Finally, by means of a formal scaling, the qualitative behavior of the solutions for large times is investigated, and a numerical verification of this asymptotics is given. Our results are in qualitative agreement with the experimental behavior observed in the chemical literature.
A Technique for Accelerating Injection of Transient Faults in Complex SoCs

This paper presents a technique for reducing CPU time to perform simulation-based fault-injection experiments in complex SoCs. This technique is fully compatible with commercial HDL simulators with no requirement to develop dedicated compilers. This approach can be easily applied to complex SoC models, as it is not required to modify the top-level modules of design, moreover, it can inject a wide range of fault models in the design and finally it can achieve a competitive reduction in terms of CPU time compared with other time-accelerated simulation-based approaches. These goals are achieved by using simulator-commands along with partial code modification techniques. The experimental results show that the proposed technique is able to reduce the CPU time by a factor ranging from 27% to 67% compared with typical simulation-based fault-injection approaches and by a factor of 10% compared with time-accelerating simulation-based techniques.
Is conflict adaptation triggered by feature repetitions? An unexpected finding

For decades, cognitive adaptation to response conflict has been considered to be the hallmark of cognitive control. Notwithstanding a vast amount of evidence ruling out low-level interpretations of these findings, disbelief still exists with regard to the underlying cause of the observed effects. Especially when considering cognitive adaptation to unconscious conflict, it is still a matter of debate whether repetitions of features between trials might explain this intriguing finding rather than the involvement of unconscious control. To this purpose, we conducted two masked priming experiments in which four different responses to four different stimuli were required. This allowed us to completely eliminate repetitions of prime and target over consecutive trials. Independent of whether conflicting information was presented clearly visible or almost imperceptible, the results showed an unexpected pattern. Contrary to the regular congruency sequence effect (CSE; i.e., classic Gratton effect), in both experiments the congruency effect increased following incongruent trials. Interestingly, this reversed effect completely disappeared when we eliminated all trials with feature repetitions from the analysis. A third experiment, in which feature repetitions were excluded a priori, showed a small but regular CSE in the error rates only. Given that feature repetitions are theoretically thought to create a regular CSE, our results are not in line with an interpretation in terms of feature repetitions nor with an interpretation in terms of cognitive control. We conclude that examining cognitive adaptation with or without feature repetitions might be more difficult to conceive than is often suggested in the literature.
DNA Scaffolded Silver Clusters: A Critical Study

Fluorescent silver nanoclusters (Ag-NCs) are in prominence as novel sensing materials due to their biocompatibility, photostability, and molecule-like optical properties. The present work is carried out on an array (17 sequences) of 16 bases long cytosine rich, single stranded DNA templates 5′-C3XiC3XiiC3XiiiC3Xiv-3′ where i, ii, iii, iv correspond to T/G/C deoxynucleobases (with default base A). Among all the oligonucleotides, a sequence C3AC3AC3TC3G (3T4G) has been identified, which grows three different near-infrared-emitting NC species with absorption/emission maxima at ~620/700 (species I), 730/800 (species II), and 830 (Species III) nm, respectively. The nature of the spectral profiles, along with relevant parameters namely absorption maximum (λabsmax), emission maximum (λemmax), anisotropy (r), lifetime (τ), circular dichroism spectral data are used to understand the microenvironments of the fluorescent NC species I, II, and III. DNA:Ag stiochiometric, pH and solvent dependent studies proved that i-motif scaffolds with different folding topologies are associated with the growth of these three species and a certain concentration of silver and H+ favor the growth of species III. Size exclusion chromatographic measurements provided similar indications that a folded, more compact, classic i-motif template is associated with the formation of the longer NIR (~830 nm) absorbing species. This study provides a more definitive approach to design and obtain a targeted DNA templated Ag-NC with required emission properties for biophysical and cellular applications.
Source identification of turbine vibration based on Independent Component Analysis with virtual signal channels

The basic Independent Component Analysis (ICA) has its own limitation, if N components are expected to be separated, the number of observed composite signals M must at least be equal to or more than N, that is M≥N, that is difficulty for implementation. This paper proposes a novel generalized ICA model, with which more independent components can be separated than the number of the input signals by means of the additional virtual channels, that is, it is available for ICA separation by M≪N. The virtual channels are build up under the condition of the prior information of possible sources. Tests of this ICA model with virtual channels were carried out by using the real measured multi-channel vibration signals of a steam turbine to demonstrate its effectiveness.
An integration framework for heterogeneous automatic software tests

: Developing and maintaining large software systems can require the usage of a variety of different automatic test tools. The complexity of the tools leads to considerable overheads for administration and maintenance of the test cases, as well as for the analysis of the test results. The necessary know-how to handle the test tools may limit their acceptance. This paper describes an integration framework for heterogeneous automatic test tools that unifies the test case administration, test execution, and reporting of the test results
Identification of residential load patterns based on neural networks and PCA

Knowledge about residential load profile is a key information to smart grid allowing to control and minimize residential energy consumption. This paper presents a method to nonlinear residential load identification based on Artificial Neural Networks (ANN) and Principal Component Analysis (PCA). The automatic characterization, monitoring and control of loads is a main requirement to smart homes. The proposed method uses ANNs as pattern classifier and PCA is applied to choose the number of features considered in the classifier, thus reducing system complexity. An experimental test bed composed by different lighting loads has been assembled in order to validate the method that attains a 85 % of correct classification of loads.
Leadership Development: Return of Investment. Calculating the Monetary Value of the Managerial Assessment of Proficiency Program for the Georgia Extension System.

Measuring the Return On Investment (ROI) in training and development has consistently earned a place among the critical issues in the Human Resources Development (HRD) field. The topic appears routinely on conference agendas and at professional meetings. Journals and newsletters regularly embrace the concept with increasing print space. At least a dozen books provide significant coverage of the topic. Even top executives have developed an appetite for ROI information (Phillips, 1997). Leadership educators may soon find that program sponsors and administrators will be asking for Return On Investment information as well.
Semi-supervised image classification in large datasets by using random forest and fuzzy quantification of the salient object

In this paper we are interested in the semi-supervised image classification in large datasets. The main originality of the proposed technique resides in the fuzzy quantification of the salient object in each image in order to guide the semi-supervised learning process during the classification. Indeed, we detect the salient object in each image using soft image abstraction, which allows the subsequent global saliency cues to uniformly highlight entire salient regions. Then, fuzzy quantification was involved for the purpose of improving the correct belonging of pixels to the salient object in each image. For classification, ensemble projection is used, while training a random forest classifier on labeled images with the learned features to classify the unlabeled ones. Experimental results on two challenging large benchmarks show the accuracy and the efficiency of the proposed technique.
Optimizing Re-planning Operation for Smart House Applying Solar Radiation Forecasting

This paper proposes the re-planning operation method using Tabu Search for direct current (DC) smart house with photovoltaic (PV), solar collector (SC), battery and heat pump system. The proposed method is based on solar radiation forecasting using reported weather data, Fuzzy theory and Recurrent Neural Network. Additionally, the re-planning operation method is proposed with consideration of solar radiation forecast error, battery and inverter losses. In this paper, it is assumed that the installation location for DC smart house is Okinawa, which is located in Southwest Japan. The validity of proposed method is confirmed by comparing the simulation results.
Enhanced Gender Recognition System Using an Improved Histogram of Oriented Gradient (HOG) Feature from Quality Assessment of Visible Light and Thermal Images of the Human Body

With higher demand from users, surveillance systems are currently being designed to provide more information about the observed scene, such as the appearance of objects, types of objects, and other information extracted from detected objects. Although the recognition of gender of an observed human can be easily performed using human perception, it remains a difficult task when using computer vision system images. In this paper, we propose a new human gender recognition method that can be applied to surveillance systems based on quality assessment of human areas in visible light and thermal camera images. Our research is novel in the following two ways: First, we utilize the combination of visible light and thermal images of the human body for a recognition task based on quality assessment. We propose a quality measurement method to assess the quality of image regions so as to remove the effects of background regions in the recognition system. Second, by combining the features extracted using the histogram of oriented gradient (HOG) method and the measured qualities of image regions, we form a new image features, called the weighted HOG (wHOG), which is used for efficient gender recognition. Experimental results show that our method produces more accurate estimation results than the state-of-the-art recognition method that uses human body images.