Miracle-dz/UniFAR-FTUs huggingface dataset:
Spotify Me: Facebook-assisted automatic playlist generation

We design a novel method for automatically generating a playlist of recommended songs in the popular social music sharing application Spotify that are liked with high probability by a user. Our method employs multiple seed artists as an input that are obtained via the Facebook likes of artists and the listening history of songs of a Spotify user. First, we construct an input vector comprising all the artists that the user likes on Facebook and listens to in Spotify. Then, we search for other artists and bands related to them using EchoNest, an online state-of-the-art machine learning platform. We assign a score to every artist in the thereby obtained collection, based on the frequency of his/her appearance. Finally, we construct a playlist comprising randomly selected popular songs associated with the most frequently cited artists. We examine the recommendation performance of our algorithm by computing its WTF score (fraction of disliked songs) and novelty factor (fraction of new liked songs) on playlists generated for different seed input sizes. We observe that our approach substantially outperforms the built-in Spotify Radio recommender. On 30 song playlists, we are able to improve the WTF score by 49% and the novelty factor by 42%, on average. Due to its general design, our method is broadly applicable to a variety of personal content management scenarios.
Simvastatin attenuates radiation-induced tissue damage in mice

The aim of this study was to investigate the protective effect of simvastatin against radiation-induced tissue injury in mice. Mice were radiated with 4 Gy or 8 Gy after 20 mg/kg/d simvastatin treatment over 2 weeks. Morphological changes were observed in the jejunum and bone marrow, and apoptotic cells were determined in both tissues. Peripheral blood cells were counted, and the superoxide dismutase (SOD) activity and the malondialdehyde (MDA) level in tissues of both thymus and spleen were measured. Compared with the radiation-only group, 20 mg/kg/d simvastatin administration significantly increased the mean villi height and decreased apoptotic cells in jejunum tissue, and stimulated regeneration and reduced apoptotic cells in bone marrow. Peripheral blood cell analysis revealed that simvastatin treatment induced a larger number of red blood cells and increased the hemoglobin level present after 4 Gy of radiation. Interestingly, it was also found that the number of peripheral endothelial progenitor cells was markedly increased following simvastatin administration. Antioxidant determination for tissues displayed that simvastatin therapy increased the SOD activity after both 4 and 8 Gy of radiation, but only decreased the MDA level after 4 Gy. Simvastatin ameliorated radiation-induced tissue damage in mice. The radioprotective effect of simvastatin was possibly related to inhibition of apoptosis and improvement of oxygen-carrying and antioxidant activities.
Sex is not a problem: The erasure of pleasure in sexual science research

Guided by feminist and a queer intersectional framework, this article explores the discursive production of sexuality in contemporary sexual science research. Specifically, this article examines the absence of pleasure as a topic in research on human sexuality in the sexual sciences. Articles from 2010 to 2015 were sampled from The Journal of Sex Research (JSR) N = 300 and discourse analysis was performed. Contemporary research on sexuality in this journal focuses on risk, disease, and dysfunction and reinforces heteronormativity. This focus examines sexuality from a limited and negative vantage point and, as a result, does not provide us with a holistic portrait of human sexuality. Researchers must discuss pleasure and should make greater efforts to ensure more inclusivity and diversity around issues of gender, race, nationality, age, and sexual identity. Importantly, I show how the three main focal points of this article (the erasure of sexual pleasure, the reproduction of heteronormativity, and the erasure of marginalized racial, gendered, classed, and sexual identities) are mutually reinforcing. Scholars in the sexual sciences can avoid these issues by using feminist and queer intersectional frameworks. Finally, because the empirical findings of scientific research often inform political policy, healthcare policies, workplace policies, and larger societal understandings of human life and experience, we must appreciate that the limited frameworks used by sexual scientists will have an impact on people’s lives and their access to the resources and services they need to survive, and to lead pleasurable—not just healthy—lives.
Stereo image based object localization framework for visually impaired people using edge orientation histogram and co-occurrence matrices

A new framework that uses internet-based images for detecting objects and estimating real world location of the objects via stereo images is proposed. This framework provides a self-learning ability for detecting desired objects in the scene without pre-prepared classifiers by harvesting sample images of the objects from the internet. Histogram and co-occurrence matrices of edge orientation are used as features. The objects are recognized based on likelihood scores and distance in the feature space between every window in the scene and k-nearest prototypes. A local feature matching is used to match the feature points in stereo pair. Disparities from stereo images are used to estimate real world distance and direction of the objects. The experiments on 120 pairs of stereo images from three object classes show the satisfying results in comparison to baseline methods.
The acceptance of e-Learning using SEM approach: A case of IT Literacy development for PIM students

TAM theory is applied in this research and the research is to study the factors that will lead to acceptance and participation of e-Learning system for the development of IT Literacy for Thai students. It uses the technique of SEM and the result from collecting data of 420 students found that Goodness of fit indicators which also included a chi-square value of 267.84, CMIN/ DF = 1.87, SRMR = . 05, GFI = . 93, TLI = . 97, CFI = . 97, RMSEA = . 05 and HOELTER = 269. Additionally, there are 2 factors which include; Perceived usefulness (PU) and Perceived ease of use (PEOU). Both factors have positive (+) direct influence on behavioral intention (BI). Moreover, there are 3 factors including Information quality (IQ), Functionality (FL) and Accessibility (A) which have a positive (+) indirect influence on behavioral intention (BI). The BI have been determined based on (PU) perceived usefulness along with perceived ease of use (PEOU). This lead to result in a R2= . 80. Therefore, the variables described earlier lead to the explanation that 80% of variance on behavioral intention (BI).
Nucleosynthesis of s-elements in zero-metal AGB stars

Contrary to previous expectations, recent evolutionary models of zero-metallicity stars show that the development of mixing episodes at the beginning of the AGB phase allows low- and intermediate-mass stars to experience thermal pulses. If these stars, like their metal-rich counterparts, also experience partial mixing of protons from the H-rich envelope into the C-rich layers at the time of the third dredge-up, an extensive neutron capture nucleosynthesis leads to the production of s-process nuclei up to Pb and Bi. Nucleosynthesis calculations based on stellar AGB models are performed assuming a parameterized H-abundance prole below the convective envelope at the time of the third dredge-up. Despite the absence of Fe-group elements, the large neutron flux resulting from the 13 C( ;n) 16 O reaction leads to an ecient production of s-process elements starting from the neutron captures on the C-Ne isotopes. Provided partial mixing of protons takes place, it is shown that population III AGB stars should be enriched in s-process elements and overall in Pb and Bi.
Whispered Speech Detection Using Fusion of Group-Delay-Based Subband Modulation Spectrum and Correntropy Features

In this letter, we propose a novel fusion feature for detection of whispered speech in noisy environment using a group-delay-based instantaneous spectrum analysis. The fusion feature involves two individual components, namely, subband modulation spectrum (SMS)-based features and subband correntropy (SCE) features, both extracted from the instantaneous spectrum. The instantaneous spectrum estimation involves zero-time windowing for improved temporal resolution and group-delay computation for improved spectral resolution, as compared to the traditional discrete-Fourier-transform-based spectrum estimation. The SMS features capture the spectral representation of the subband energy time trajectories, while the SCE features model the fluctuations in the subband energy time trajectories. The SMS captures both the short-term as well as long-term spectral characteristics of whispered speech and is known to provide good separation between speech and noise components. The correntropy features help capture the dynamics of the vocal tract system to discriminate noisy whisper from noise. Whisper speech detection experiments using support vector machine models and the proposed features indicate promising performance under low signal-to-noise conditions.
Functional and dysfunctional retention: the impact of trust and cynicism

In light of current workforce demographics, such as an aging workforce and increasingly dynamic and competitive labour markets, retention is a major concern to organizations, yet the concept of retention is not well defined in organizational theory. The purpose of this study was to explore the role of trust and cynicism in functional and dysfunctional retention. It was expected that trust and cynicism were components of the same underlying latent factor. Models of functional and dysfunctional retention that featured trust and cynicism as predictors of important performance outcomes were tested on a sample of 1,472 members of the Canadian Forces from various occupations across Canada who had indicated their intentions to stay with the organization for the next three to five years. Trust was elemental in encouraging the functional retention outcomes of initiative and organizational citizenship behaviour, and in discouraging the dysfunctional retention outcome of psychological withdrawal. Cynicism was also a significant predictor of psychological withdrawal. The results emphasize the need to build and maintain trust in organizations and to prevent the development of cynicism in order to have functionally retained employees that contribute to organizational effectiveness.
Spatio-temporal correlation based outlier detection algorithm in sensor network

Sensor nodes are prone to produce faulty sample data because of poor quality and harsh environment. In this paper we put forward STCOD algorithm which uses spatial and temporal correlations between sensor nodes to distinguish faulty sample data from outlier data. STCOD algorithm includes three child algorithms which are outlier self-detection algorithm, neighbor-voting algorithm and outlier confirming algorithm. Experiments show that STCOD outperforms majority voting algorithm.
DGA CapsNet: 1D Application of Capsule Networks to DGA Detection

Domain generation algorithms (DGAs) represent a class of malware used to generate large numbers of new domain names to achieve command-and-control (C2) communication between the malware program and its C2 server to avoid detection by cybersecurity measures. Deep learning has proven successful in serving as a mechanism to implement real-time DGA detection, specifically through the use of recurrent neural networks (RNNs) and convolutional neural networks (CNNs). This paper compares several state-of-the-art deep-learning implementations of DGA detection found in the literature with two novel models: a deeper CNN model and a one-dimensional (1D) Capsule Networks (CapsNet) model. The comparison shows that the 1D CapsNet model performs as well as the best-performing model from the literature.
Kernelized log linear models for continuous speech recognition

Large margin criteria and discriminative models are two effective improvements for HMM-based speech recognition. This paper proposed a large margin trained log linear model with kernels for CSR. To avoid explicitly computing in the high dimensional feature space and to achieve the nonlinear decision boundaries, a kernel based training and decoding framework is proposed in this work. To make the system robust to noise a kernel adaptation scheme is also presented. Previous work in this area is extended in two directions. First, most kernels for CSR focus on measuring the similarity between two observation sequences. The proposed joint kernels defined a similarity between two observation-label sequence pairs on the sentence level. Second, this paper addresses how to efficiently employ kernels in large margin training and decoding with lattices. To the best of our knowledge, this is the first attempt at using large margin kernel-based log linear models for CSR. The model is evaluated on a noise corrupted continuous digit task: AURORA 2.0.
Efficient Component Labeling of Images of Arbitrary Dimension Represented by Linear Bintrees

An algorithm is presented to perform connected-component labeling of images of arbitrary dimension that are represented by a linear bintree. The bintree is a generalization of the quadtree data structure that enables dealing with images of arbitrary dimension. The linear bintree is a pointerless representation. The algorithm uses an active border which is represented by linked lists instead of arrays. This results in a significant reduction in the space requirements, thereby making it feasible to process three- and higher-dimensional images. Analysis of the execution time of the algorithm shows almost linear behavior with respect to the number of leaf nodes in the image, and empirical tests are in agreement. The algorithm can be modified easily to compute a (d-1)-dimensional boundary measure (e.g. perimeter in two dimensions and surface area in three dimensions) with linear performance. >
A covariance fitting approach to parametric localization of multiple incoherently distributed sources

In this paper, a new algorithm for parametric localization of multiple incoherently distributed sources is presented. This algorithm is based on an approximation of the array covariance matrix using central and noncentral moments of the source angular power densities. Based on this approximation, a new computationally simple covariance fitting-based technique is proposed to estimate these moments. Then, the source parameters are obtained from the moment estimates. Compared with earlier algorithms, our technique has lower computational cost and obtains the parameter estimates in a closed form. In addition, it can be applied to scenarios with multiple sources that may have different angular power densities, while other known methods are not applicable to such scenarios.
Genome-Scale Transcriptome Analysis of the Desert Shrub Artemisia sphaerocephala

Background Artemisia sphaerocephala, a semi-shrub belonging to the Artemisia genus of the Compositae family, is an important pioneer plant that inhabits moving and semi-stable sand dunes in the deserts and steppes of northwest and north-central China. It is very resilient in extreme environments. Additionally, its seeds have excellent nutritional value, and the abundant lipids and polysaccharides in the seeds make this plant a potential valuable source of bio-energy. However, partly due to the scarcity of genetic information, the genetic mechanisms controlling the traits and environmental adaptation capacity of A. sphaerocephala are unknown. Results Here, we present the first in-depth transcriptomic analysis of A. sphaerocephala. To maximize the representation of conditional transcripts, mRNA was obtained from 17 samples, including living tissues of desert-growing A. sphaerocephala, seeds germinated in the laboratory, and calli subjected to no stress (control) and high and low temperature, high and low osmotic, and salt stresses. De novo transcriptome assembly performed using an Illumina HiSeq 2500 platform resulted in the generation of 68,373 unigenes. We analyzed the key genes involved in the unsaturated fatty acid synthesis pathway and identified 26 A. sphaerocephala fad2 genes, which is the largest fad2 gene family reported to date. Furthermore, a set of genes responsible for resistance to extreme temperatures, salt, drought and a combination of stresses was identified. Conclusion The present work provides abundant genomic information for functional dissection of the important traits of A. sphaerocephala and contributes to the current understanding of molecular adaptive mechanisms of A. sphaerocephala in the desert environment. Identification of the key genes in the unsaturated fatty acid synthesis pathway could increase understanding of the biological regulatory mechanisms of fatty acid composition traits in plants and facilitate genetic manipulation of the fatty acid composition of oil crops.
Fast Bayesian Semiparametric Curve-Fitting and Clustering in Massive Data With Application to Cosmology

Recent technological advances have led to a ood of new data on cosmology rich in information about the formation and evolution of the universe, e.g., the data collected in Sloan Digital Sky Survey (SDSS) for more than 200 million objects. The analyses of such data demand cutting edge statistical technologies. Here, we have used the concept of mixture model within Bayesian semiparametric methodology to t the regression curve with the bivariate data for the apparent magnitude and redshift for Quasars in SDSS (2007) catalogue. Associated with the mixture modeling is a highly ecient curve-tting procedure, which is central to the application considered in this paper. Moreover, we adopt a new method for analysing the posterior distribution of clusterings, also generated as a by-product of our methodology. The results of our analysis of the cosmological data clearly indicate the existence of four change points on the regression curve and posssibiltiy of clustering of quasars specially at high redshift. This sheds new light not only on the issue of evolution, existence of acceleration or decceleration and environment around quasars at high redshift but also help us to estimate the cosmological parameters related to acceleration or decceleration.
Video object extraction and shape coding using B-splines

This work presents a system for semi-automatic video object extraction and efficient shape coding using cubic B-splines. In the framework of object-based layered coding of image sequences, shape information is essential for content-based access to video objects, and its efficient encoding needs to be investigated. We present a rate and distortion controlled algorithm for video object shape approximation by variable number of cubic B-spline segments and motion compensated inter-frame coding of B-spline control points. Video object masks are generated using a semi-automatic tracking algorithm. Given an outline of the object in the first frame of the sequence, color and shape models for video object are generated. The adjustment of object's shape and position in the following frames is based on motion estimation and local color information in the neighborhood of the object's boundary. Ratedistortion efficiency of the proposed shape coding algorithm is compared to MPEG-4 context arithmetic encoding.
Health of Nurses with Leadership and Management Functions: An Integrative Review

The purpose of this paper is to present findings of an integrative literature review related to health and wellbeing of nurses with leadership and management functions and its associated factors, in order to provide the best available evidence on this topic. A large body of knowledge related to nursing health and well-being exists, but little we know about this topic in a nurse leadership and management role. It is a complex role in nursing that implies both clinical and leadership knowledge and abilities. A comprehensive search of electronic databases, focusing on articles published in English and Italian languages during 2009-2018, was completed. A three-step approach was used and from the original 1345 studies, 17 papers were included in this systematic literature review. Two independent reviewers analyzed these papers for critical appraisal on methodological quality and their topics. Narrative synthesis of the empirical literature allowed highlighting the most important results related to the topic investigated. This integrative review has shown that nurse leader and manager roles are stressful mainly for its specific functions and job demands. Directions for future high-quality research are suggested both to deepen the relative importance of the specific risk and protective health factors in nursing leadership roles.
Factors Influencing the Adoption of Mobile Banking: Perspective Bangladesh

With the convergence of banking services and mobile technologies, users are able to conduct banking services at any place and at any time through mobile banking. This research examines the factors influencing the adoption of mobile banking in Bangladesh, with a special focus on trust, perceived cost and perceived risk including the facets of perceived risks: performance risk, security/privacy risk, time risk, social risk and financial risk. The research model includes the original variables of extended technology acceptance model (TAM2). Data for this study was collected through a structured questionnaire survey in townships around Dhaka. The research has found that customers will consider adopting mobile banking as long as it is perceived to be useful and easy to use. But the most critical factor for the customer is cost; the service should be affordable. Trust was found to be significantly negatively correlated to perceived risk. Thus, trust plays a role in risk mitigation and in enhancing customer loyalty.
Probabilistic Modeling of Human Movements for Intention Inference

Inference of human intention may be an essential step towards understanding human actions and is hence important for realizing efficient human-robot interaction. In this paper, we propose the Intention-Driven Dynamics Model (IDDM), a latent variable model for inferring unknown human intentions. We train the model based on observed human movements/actions. We introduce an efficient approximate inference algorithm to infer the human’s intention from an ongoing movement. We verify the feasibility of the IDDM in two scenarios, i.e., target inference in robot table tennis and action recognition for interactive humanoid robots. In both tasks, the IDDM achieves substantial improvements over state-of-the-art regression and classification.