Miracle-dz/UniFAR-FTUs huggingface dataset:
Decision tree-based credit decision support system

In this article, our aim is to demonstrate a new approach on financial credit decision support based on artificial intelligence, which, beside the classic methods, gets more and more prevalent in economics [1]. Our goal was to work out and achieve a decision tree-based credit decision method which is able to process and appraise data both in large numbers and one at a time. Beside the quick and robust solution, the efficiency of the algorithm was also an important aspect. Our improved algorithm of the traditional decision tree building is going to be demonstrated, and we will also touch upon the incidental problems that have occurred during the development, and their solutions. The efficiency of the algorithm in different situations is also going to be demonstrated with tests, and the results of the algorithm are going to be compared with a bank system's results on the same set of data.
In-house HIV-1 RNA real-time RT-PCR assays: principle, available tests and usefulness in developing countries

The principle of currently available licensed HIV-1 RNA assays is based on real-time technologies that continuously monitor the fluorescence emitted by the amplification products. Besides these assays, in-house quantitative (q) real-time reverse transcription (RT)-PCR (RT-qPCR) tests have been developed and evaluated particularly in developing countries, for two main reasons. First, affordable and generalized access to HIV-1 RNA viral load is urgently needed in the context of expected universal access to prevention and antiretroviral treatment programs in these settings. Second, since many non-B subtypes, circulating recombinant forms and unique recombinant forms circulate in these areas, in-house HIV-1 RNA RT-qPCR assays are ideal academic tools to thoroughly evaluate the impact of HIV-1 genetic diversity on the accuracy of HIV-1 RNA quantification, as compared with licensed techniques. To date, at least 15 distinct in-house assays have been designed. They differ by their chemistry and the HIV-1 target sequence (located in gag, Pol-IN or LTR gene). Analytical performances of the tests that have been extensively evaluated appear at least as good as (or even better than) those of approved assays, with regard to HIV-1 strain diversity. Their clinical usefulness has been clearly demonstrated for early diagnosis of pediatric HIV-1 infection and monitoring of highly active antiretroviral therapy efficacy. The LTR-based HIV-1 RNA RT-qPCR assay has been evaluated by several groups under the auspices of the Agence Nationale de Recherches sur le SIDA et les hépatites virales B et C. It exists now as a complete standardized commercial test.
Recurrence networks—a novel paradigm for nonlinear time series analysis

This paper presents a new approach for analysing the structural properties of time series from complex systems. Starting from the concept of recurrences in phase space, the recurrence matrix of a time series is interpreted as the adjacency matrix of an associated complex network, which links different points in time if the considered states are closely neighboured in phase space. In comparison with similar network-based techniques the new approach has important conceptual advantages, and can be considered as a unifying framework for transforming time series into complex networks that also includes other existing methods as special cases. It has been demonstrated here that there are fundamental relationships between many topological properties of recurrence networks and different nontrivial statistical properties of the phase space density of the underlying dynamical system. Hence, this novel interpretation of the recurrence matrix yields new quantitative characteristics (such as average path length, clustering coefficient, or centrality measures of the recurrence network) related to the dynamical complexity of a time series, most of which are not yet provided by other existing methods of nonlinear time series analysis.
The Crohn's and colitis knowledge score: a test for measuring patient knowledge in inflammatory bowel disease

OBJECTIVE:The aim of this study was to develop a valid and reliable questionnaire assessing patient knowledge of inflammatory bowel disease (IBD) and its treatment—the Crohn's and Colitis Knowledge (CCKNOW) Score.METHODS:A total of 30 multiple choice questions were constructed into a draft questionnaire. This was piloted on a random selection of participants with differing IBD knowledge levels; junior doctors, nurses, and ward clerks. Factor analysis eliminated questions with poor discriminant ability. The resulting 24-item questionnaire (CCKNOW score) was retested on the three groups, and a Kruskal-Wallis test determined the questionnaire's ability to discriminate between the groups. Reliability and readability were tested using Cronbach's α and the Flesch Kincaid reading score. The validated CCKNOW was then tested on patients from the Leicestershire IBD database.RESULTS:CCKNOW scores differed significantly across the groups of doctors, nurses, and ward clerks (median 22, 16, and five, respectively) T = 40.35, p < 0.0001. The reliability was very good with a Cronbach's α of 0.95 and the readability was also high. The median score on the CCKNOW for IBD patients was 10, with no significant difference between ulcerative colitis and Crohn's disease. Patients who are members of NACC (National Association of Crohn's and Colitis) achieve statistically significantly higher scores than do nonmembers (difference in medians 4, 95% confidence interval 4–6, p < 0.0001). CONCLUSIONS:The CCKNOW score provides a valuable index of overall knowledge. It is self-administered and psychometric tests show it to be valid, reliable, and readable. It may be used in the future as a tool to evaluate patient education programs.
A 2D-DFT Based Channel Estimation Scheme in Indoor 60GHz Communication Systems with Large-Scale Multiple-Antenna

The paper presents a new channel estimation scheme for downlink 60GHz indoor environment with Massive Multiple Input Multiple Output (Massive MIMO) systems by exploiting the physical characteristics of the uniform planar array (UPA) and 2-dimensional discrete Fourier Transform (2D-DFT) operation. The proposed channel estimation algorithm can be carried out with very small number of training resources, which significantly reduces the training overhead and the feedback cost. Meanwhile, the number of user terminals (UTs) served simultaneously can be increased by exploiting the spatial pattern of different UTs. Compared to the existing channel estimation algorithms, the newly proposed one does not require any knowledge of channel statistics and can be efficiently deployed by the fast Fourier transform (FFT). Finally, simulation results are provided to corroborate the proposed studies.
A NEW NOISE REDUCTION METHOD USING LINEAR PREDICTION AND SYSTEM IDENTIFICATION

A technique that uses linear prediction and system identification to achieve noise reduction in a voice signal mixed with a background noise is proposed. In this method, the linear prediction error filter (LPEF) whose coefficients will converge such that the prediction error signal becomes white, removes the spectrum of the voice from a voice mixed with noise. Thereby, the prediction error signal has not crosscorrelation to the voice. The background noise can be reconstructed from the prediction error signal by using the adaptive digital filter (ADF) which is used as one for the system identification. The coefficients of the ADF will converge such that the error signal which is obtained by subtracting the output of the ADF from a voice mixed with noise, is minimized. Then, the ADF estimates only the background noise since that the input of the ADF (the prediction error signal) has not cross-correlation to the voice. Noise reduction is achieved by subtracting the output of the ADF from the voice mixed with noise.
Maternal Autonomy‐ and Connectedness‐oriented Parenting Behaviors as Predictors of Children's Social Behaviors in China

The purpose of the study was to examine the contributions of maternal encouragement of autonomy and maternal encouragement of connectedness to the prediction of children's social behaviors. A sample of children (N = 94), initially aged two years, and their mothers in China participated in the two-year longitudinal study. Child autonomy and connectedness and maternal goal-oriented behaviors were assessed at two years of age through the observation of mother–child interactions. Data on children's social participation, prosocial behavior, aggression, and organization/leadership in peer interactions were collected in a follow-up study when the children were four years of age. It was found that maternal encouragement of connectedness significantly predicted children's sociability and organization/leadership behavior, especially for children who were relatively high on autonomy. The results indicated the developmental significance of socialization goal-oriented behaviors in cultural context.
Implications of Dopant-Fluctuation-Induced $V_{\rm t}$  Variations on the Radiation Hardness of Deep Submicrometer CMOS SRAMs

Accurately analyzing the single-event (SE) vulnerability of static random-access memory (SRAM) cells leads to precisely calculated soft-error rates (SERs). Random dopant-fluctuation-induced Vt variations affect the SE vulnerability of these memory cells and increase the intercell spread in critical charge (Qcrit), which cause SE upsets. This might consequently lead to higher SERs than would be calculated, assuming a single critical charge. Monte Carlo simulations in the IBM 130- and 90-nm technologies quantify this spread in Qcrit and in SRAM soft-error cross sections with increasing variance. For a radiation-tolerant design, a statistical-design methodology must be used to validate existing hardening schemes and to obtain the expected tolerance levels.
Link-load balance aware mapping and routing for NoC

The paper presents a novel mapping and routing technique for the mesh based NoC design problem with an objective of minimizing the energy consumption and normalized worst link-load. The proposed algorithm PLBMR is a particle swarm optimization (PSO) based two phases process, one is mapping core onto NoC to minimize the NoC communication energy consumption, and another is the allocation of routing path for keeping the link-load balance. and the paper presents the detail implementation of the PLBMR algorithm. Experimental results show that the proposed technique can reduce the normalized worst link-load by 20% on average while guarantee a low energy consumption.
Cervical spinal metastasis: anterior reconstruction and stabilization techniques after tumor resection.

OBJECT In a review of the literature, the authors provide an overview of various techniques that have evolved for reconstruction and stabilization after resection for metastatic disease in the subaxial cervical spine. METHODS Reconstruction and stabilization of the cervical spine after vertebral body (VB) resection for metastatic tumor is an important goal in the surgical management of spinal metastasis. Generally, the VB defect is reconstructed with bone autograft or allograft, polymethylmethacrylate (PMMA), interbody spacers, and/or cages. In cases of PMMA-assisted reconstruction, internal devices are used to augment the fixation of PMMA. Stabilization is then achieved with anterior instrumentation, usually an anterior cervical locking plate. In some cases, posterior instrumentation may be necessary to supplement the anterior construct. CONCLUSIONS Anterior cervical corpectomy followed by reconstruction and stabilization is an effective strategy in the management of spinal metastases in patients.
Women Coming Home: Long‐Term Patterns of Recidivism

Drawing on recent scholarship on prisoner reentry and gendered pathways to crime, this research explores how social relationships, incarceration experiences, and community context, and the intersection of these factors with race, influence the occurrence and timing of recidivism. Using a large, modern sample of women released from prison, we find that women who are drug dependent, have less education, or have more extensive criminal histories are more likely to fail on parole and to recidivate more quickly during the eight year follow‐up period. We also observe racial variation in the effect of education, drug use, and neighborhood concentrated disadvantage on recidivism. This study highlights the importance of an intra‐gender, theoretical understanding of recidivism, and has import for policy aimed at female parolees.
The Use of Ultra-Sensitive Molecular Assays in HIV Cure-Related Research

Ultra-sensitive laboratory assays based on the Polymerase Chain Reaction (PCR) are playing an increasingly important role in HIV cure-related research. This article reviews the different assays available and how they have evolved. There is a great need for their standardization and for the establishment of reference reagents and testing algorithms to evaluate potential HIV cure-related treatments.
Finding Poly-Curves of Straight Line and Ellipse Segments in Images Segmentierung von Pixelketten in Geraden- und Ellipsenelemente

Simplification of given polygons has attracted many researchers. Especially, finding circular and elliptical structures in images is relevant in many applications. Given pixel chains from edge detection, this paper proposes a method to segment them into straight line and ellipse segments. We propose an adaption of Douglas-Peucker’s polygon simplification algorithm using circle segments instead of straight line segments and partition the sequence of points instead the sequence of edges. It is robust and decreases the complexity of given polygons better than the original algorithm. In a second step, we further simplify the poly-curve by merging neighbouring segments to straight line and ellipse segments. Merging is based on the evaluation of variation of entropy for proposed geometric models, which turns out as a combination of hypothesis testing and model selection. We demonstrate the results of circlePeucker as well as merging on several images of scenes with significant circular structures and compare them with the method of PATRAUCEAN et al. (2012). Zusammenfassung: . ie tion runder und elliptischer Strukturen ist relevant für viele Anwendungen. Die Reduktion der Komplexität gegebener Polygone ist für sich ein interessantes Forschungsthema. Diese Arbeit stellt ein Verfahren zur Segmentierung von Pixelketten einer Kantendetektion in Geradenund Ellipsensegmente vor. Der erste Schritt besteht in einer Adaption des Douglas-Peucker Algorithmus, in der Kreise anstelle von Geraden zur Partitionierung verwendet werden und die Punktstatt der Kantensequenz partitioniert wird. Das Verfahren ist robust und reduziert die Komplexität der gegebenen Polygone stärker als der originale Algorithmus. In einem zweiten Schritt vereinfachen wir diese Vorsegmentierung durch das Verschmelzen benachbarter Segmente zu Geradenund Ellipsensegmenten und stützen uns dabei auf die Entropieänderung. Wir zeigen die Ergebnisse der Vorsegmentierung als auch der folgenden Vereinfachung anhand verschiedener Bilder von Szenen, die signifikante kreisförmige Strukturen aufweisen und vergleichen sie mit dem Algorithmus von PATRAUCEAN et al. (2012).
Spatial clustering for determining rescue shelter of flood disaster in South Bandung using CLARANS Algorithm with Polygon Dissimilarity Function

In this research, we provide a solution to the problem of handling the recent flooding in South Bandung, West Java, Indonesia. We offer the solution in determining the locations of the rescue posts. The analysis uses a spatial data clustering algorithm known as CLARANS Algorithm and the spatial similarity is measured using Polygon Dissimilarity Function (PDF). Results showed that clustering of two clusters gives the strongest Silhouette index value of 0.9, and clustering of 4 and 5 clusters have a Silhouette index value of 0.8. Clustering process is done quickly, less than 3 seconds for 2 clusters and less than 4 seconds for 5 clusters.
Power Aware Progressive Energy Routing Protocol for MANET

Mobile ad hoc networks are infrastructure-less networks used for communication between two or more nodes without a common access point. Due to frequent changes within the topology of the network, Routing protocol design with energy efficiency is an important issue. Our proposed scheme MPEER integrates Energy efficient routing scheme to improve the performance during path discovery and in mobility scenarios. A discrete level of power control and priority based packet scheduling is also done. The proposed routing method achieves energy efficient routing by considering shortest path, avoids retransmission with reduced overhead and path setup delay as compared to other conventional routing protocols.
Efficient Storage and Processing of High-Volume Network Monitoring Data

Monitoring modern networks involves storing and transferring huge amounts of data. To cope with this problem, in this paper we propose a technique that allows to transform the measurement data in a representation format meeting two main objectives at the same time. Firstly, it allows to perform a number of operations directly on the transformed data with a controlled loss of accuracy, thanks to the mathematical framework it is based on. Secondly, the new representation has a small memory footprint, allowing to reduce the space needed for data storage and the time needed for data transfer. To validate our technique, we perform an analysis of its performance in terms of accuracy and memory footprint. The results show that the transformed data closely approximates the original data (within 5% relative error) while achieving a compression ratio of 20%; storage footprint can also be gradually reduced towards the one of the state-of-the-art compression tools, such as bzip2, if higher approximation is allowed. Finally, a sensibility analysis show that technique allows to trade-off the accuracy on different input fields so to accommodate for specific application needs, while a scalability analysis indicates that the technique scales with input size spanning up to three orders of magnitude.
Ranking and classifying attractiveness of photos in folksonomies

Web 2.0 applications like Flickr, YouTube, or Del.icio. us are increasingly popular online communities for creating, editing and sharing content. The growing size of these folksonomies poses new challenges in terms of search and data mining. In this paper we introduce a novel methodology for automatically ranking and classifying photos according to their attractiveness for folksonomy members. To this end, we exploit image features known for having significant effects on the visual quality perceived by humans (e.g. sharpness and colorfulness) as well as textual meta data, in what is a multi-modal approach. Using feedback and annotations available in the Web 2.0 photo sharing system Flickr, we assign relevance values to the photos and train classification and regression models based on these relevance assignments. With the resulting machine learning models we categorize and rank photos according to their attractiveness. Applications include enhanced ranking functions for search and recommender methods for attractive content. Large scale experiments on a collection of Flickr photos demonstrate the viability of our approach.
Prototyping and In-Depth Analysis of Big Data Benchmarking

Today's digital age has witnessed an explosion of data and information. This has resulted into changing the nature of data from being a medium of supporting transactions to becoming a transactional commodity itself. The consequential increase in the value of data has led to many innovations in both academic and industrial circles. The main focus remains on finding efficient ways to analyse data and derive meaningful results out of it. An efficient way of doing so is constructing benchmarks in order to effectively evaluate the performances of existing and upcoming data systems. A successful benchmark should cover all the major big data system application domains and there workloads. A prototype, outlining a small and diverse benchmark, which takes minimum time to cover a wide range of applications needs to be developed. In designing this prototype the four cornerstones of big data namely volume, veracity, velocity and variety should also be maintained. In addition to this the workload of a benchmark set should be carefully selected. It should represent a wide spectrum of application domains, have diversity of data characteristics and should not have any redundancy. Lastly, there should be a metric to evaluate the benchmarks so as to give them validity.
A Data-Based Nested LUT Model for RF Power Amplifiers Exhibiting Memory Effects

In this letter, a new model is proposed for radio frequency power amplifiers exhibiting memory effects. The model is based on nested look-up tables that compute the estimated amplifier output signal, based on the current input sample and the preceding ones. The model is validated experimentally, using input and output waveforms of a high-power LDMOS based amplifier driven by a multicarrier WCDMA signal. It is shown that the performance of the proposed model is comparable to that of the conventional memory polynomial model. However, the nested look-up table based model significantly reduces the complexity of the identification of the model's parameters, as well as the computational complexity in calculating the estimated output signal.
Children's body knowledge: Understanding 'life' as a biological goal

Two studies assessed the development of children's understanding of life as a biological goal of body functioning. In Study 1, 4-to-10-year-old children were given an interview consisting of a series of structured questions about the location and function of various body organs. Their responses were coded both for factual correctness and for appeals to the goal of maintaining life. The results showed a gradual increase in children's factual knowledge across this age range but an abrupt increase in appeals to life between the ages of 4 and 6. Analyses of the 4-year-olds' responses suggested that appeals to life were associated with increased knowledge of organ function, but not of organ location. Study 2 was designed to replicate the pattern found in Study I. A continuous sample of 4-to 5-year-old children was administered an abbreviated version of the interview from Study 1. Children's understanding of life as a biological goal was again found to be predictive of their knowledge of organ function, but not of organ location. These results indicate a reorganization in children's understanding of the body between the ages of 4 and 6, which coincides with children's discovery of 'life' as a biological goal for bodily function.
Comprehensive performance analysis of road detection algorithms using the common urban Kitti-road benchmark

The navigation of an autonomous vehicle is a highly complex task and the dynamic environment is used as a source for reasoning. Road detection is a major issue in autonomous systems and advanced driving assistance systems applied for inner-city. Uncertainty may arise in environments with unmarked or weakly marked roads or poor lightning conditions. Moreover, when a common benchmark is not used, it is hard to decide which approach performs better on the road detection problem. This paper introduces a comprehensive performance analysis of two road recognition approaches using the urban Kitti-road benchmark. The first approach makes the extraction of a feature set based on statistical measures of 2D and 3D information from each superpixel. An Artificial Neural Network is used to detect the road pattern. The second approach extracts the feature set based on a multi-normalized histogram of Textons and Disptons for each superpixel. This feature set is used as a source for a Joint Boosting algorithm to model the road pattern. The proposed work presents a detailed evaluation highliting the pros and cons of each approach.