Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on the Effect of Emotional Speech on Language Identification

Identifying language information from speech utterance is referred to as spoken language identification. Language Identification (LID) is essential in multilingual speech systems. The performance of LID systems have been studied for various adverse conditions such as background noise, telephonic channel, short utterances, so on. In contrast to these studies, for the first time in the literature, the present work investigated the impact of emotional speech on language identification. In this work, different emotional speech databases have been pooled to create the experimental setup. Additionally, state-of-art i-vectors, time-delay neural networks, long short term memory, and deep neural network x-vector systems have been considered to build the LID systems. Performance of the LID system has been evaluated for speech utterances of different emotions in terms of equal error rate and Cavg. The results of the study indicate that the speech utterances of anger and happy emotions degrades performance of LID systems more compared to the neutral and sad emotions.
Male Fertility Evaluation Using Biomarkers in Livestock

While much attention is paid to the fertility of breeding females in livestock production systems, less attention is given to the fertility of breeding males. Male fertility is a complex trait, and while semen quality assessment is an important tool, it is more suitable for identifying males with reproductive problems than predicting relative fertility. The cascade of events required for fertilization and embryonic development limit the usefulness of functional assays which measure a single trait. However, there is potential in the use of genomic and proteomic markers to better predict relative fertility performance of sires. This review will describe current methods for evaluating male fertility in livestock, the physiology underlying these fertility outcomes as well as potential biological markers for predicting fertility performance of sires in livestock production
A New Source of Stem Cells: Mesenchymal Stem Cells from Menstrual Blood

Menstrual blood is a bodily fluid that consists endometrial cells, and, therefore, contains mesenchymal stem cells (MSCs). Over the past few years, these cells have been catching the attention of researchers and medical professionals due to the advantages they hold over alternative stem cell sources, such as the frequent production, pain free collection process, and lack of complex, ethical concerns associated with collecting the fluid. Menstrual blood-derived mesenchymal stem cells (MenSCs), still demonstrate the typical characteristics of mesenchymal stem cells, but research has found that they have an even faster rate of replication, as well as an increased proliferation and differentiation potential compared to other stem cell sources. Though MenSCs have been gaining more attention and there is evidence showing that would work well to help slow the progression or cure many diseases, there are still several unanswered questions and the occasional concerning data, which creates a need for further preclinical and clinical research before it is a well-accepted tool in medicine.
Non-parametric Estimator for a Finite Population Total Based on Edgeworth Expansion

In survey sampling, the main objective is to make inference about the entire population parameters using the sample statistics. In this study, a nonparametric estimator of finite population total is proposed and the coverage probabilities using the Edgeworth expansion explored. Three properties; unbiasedness, efficiency and the confidence interval of the proposed estimator are studied. There is a lot of literature on study of two properties; unbiasedness and efficiency of the finite population total. This study therefore has more focus on confidence interval and coverage probability. The amount of bias and MSE are studied partially analytically, followed by an empirical study on the two properties and the confidence interval of the proposed estimator. Based on the empirical study with simulations in R, the proposed estimator resulted into smaller bias and MSE compared to the nonparametric estimator due to [6], the design-based Horvitz-Thompson estimator and the model-based ratio estimator. Further, the proposed estimator is tighter compared to the other three considered in this study and has higher converging coverage probabilities.
Magnetic Racetrack Memory: From Physics to the Cusp of Applications Within a Decade

Racetrack memory (RTM) is a novel spintronic memory-storage technology that has the potential to overcome fundamental constraints of existing memory and storage devices. It is unique in that its core differentiating feature is the movement of data, which is composed of magnetic domain walls (DWs), by short current pulses. This enables more data to be stored per unit area compared to any other current technologies. On the one hand, RTM has the potential for mass data storage with unlimited endurance using considerably less energy than today’s technologies. On the other hand, RTM promises an ultrafast nonvolatile memory competitive with static random access memory (SRAM) but with a much smaller footprint. During the last decade, the discovery of novel physical mechanisms to operate RTM has led to a major enhancement in the efficiency with which nanoscopic, chiral DWs can be manipulated. New materials and artificially atomically engineered thin-film structures have been found to increase the speed and lower the threshold current with which the data bits can be manipulated. With these recent developments, RTM has attracted the attention of the computer architecture community that has evaluated the use of RTM at various levels in the memory stack. Recent studies advocate RTM as a promising compromise between, on the one hand, power-hungry, volatile memories and, on the other hand, slow, nonvolatile storage. By optimizing the memory subsystem, significant performance improvements can be achieved, enabling a new era of cache, graphical processing units, and high capacity memory devices. In this article, we provide an overview of the major developments of RTM technology from both the physics and computer architecture perspectives over the past decade. We identify the remaining challenges and give an outlook on its future.
EUROPEAN DEPOSIT INSURANCE SYSTEMS IN THE TIME OF CRISIS

Deposit insurance system was primarely created to protect depositors and their deposits. It creates depositors̕ confidence in the banking system, thus preventing depositor panic and bank run of bank deposits especially in the time of crisis. Since most depositors do not possess adequate knowledge and professional experience, which is necessary to assess bank risks, the operation of deposit insurance system is justified in order to create trust in the banking system. It is believed that the depositor's trust in the banking system and the deposit insurance system ultimately contribute to the maintenance of banking stability. Despite the these significant positive effects of deposit insurance system there are also some negative aspects that have to be taken into account effects on bank stability. They refer to risky bank operations, reduced market discipline, moral hazard, negative selection and the principal-agent problem. Financial crisis in Europe started in 2008, points on the risk operations of banks in certain eurozone countries, which is often associated with high level protection of the deposit insurance system. To prevent depositor panic and further systemic banking crisis, in this period, national deposit insurance systems became more generous by introducing additional forms of protection (paybox plus protection, foreign currency deposits insurance, interbank deposits insurance and government support in raising funds regardless of the management of the system), increasing the amount of protection and leaving co-insurance. Therefore, the aim of this paper is to discuss about changes in the implemetation of the deposit insurance systems in Europen Union (EU) countries and selected Southeastern (SE) European countries in the time of financial crisis as well as changes resulting from the requirements of the banking union.
A SIFT extension to radial distorted images

Image keypoints are widely used in computer vision ranging from recognition to 3D reconstruction. Unfortunately, the standard approaches to extract keypoints are not invariant to the radial distortion present in images acquired by cameras with wide ﬁeld-of-view. This work proposes modiﬁcations to the standard SIFT algorithm that relieves this problem for considerable amounts of this non-linear geometric deformation. We provide experimental evidences that our method outperforms image rectiﬁcation for considerable amounts of radial distortion (RD).
Back To The Core: Alternative Performance Measurement

The reporting of alternative performance measures (APMs) is currently the focus of much debate in the world. In this paper, we analyse APMs basics, e.g. financial statements as the basis for alternative performance measurement. Accounting, with the basic concepts of documentation and verifiability, provides wide intervals of evaluation of all positions of all financial statements that have found economic expression in accounting policies. Using a variety of alternative accounting policies brings about different effects on the stated financial results and the value of the company. Each of the standard financial statements users has different information needs and their understanding of fair financial reporting. Which is why, APMs and financial ratios mustn´t be basis for financial decisions without deep insight into the numbers from which ratios have been made. In this paper is given an overall literature review of accounting conservatism models, which should be used in assessment of bias in financial reporting.
The Price is (Not) Right: Comparing Privacy in Free and Paid Apps

Abstract It is commonly assumed that “free” mobile apps come at the cost of consumer privacy and that paying for apps could offer consumers protection from behavioral advertising and long-term tracking. This work empirically evaluates the validity of this assumption by comparing the privacy practices of free apps and their paid premium versions, while also gauging consumer expectations surrounding free and paid apps. We use both static and dynamic analysis to examine 5,877 pairs of free Android apps and their paid counterparts for differences in data collection practices and privacy policies between pairs. To understand user expectations for paid apps, we conducted a 998-participant online survey and found that consumers expect paid apps to have better security and privacy behaviors. However, there is no clear evidence that paying for an app will actually guarantee protection from extensive data collection in practice. Given that the free version had at least one thirdparty library or dangerous permission, respectively, we discovered that 45% of the paid versions reused all of the same third-party libraries as their free versions, and 74% of the paid versions had all of the dangerous permissions held by the free app. Likewise, our dynamic analysis revealed that 32% of the paid apps exhibit all of the same data collection and transmission behaviors as their free counterparts. Finally, we found that 40% of apps did not have a privacy policy link in the Google Play Store and that only 3.7% of the pairs that did reflected differences between the free and paid versions.
International Journal of Soft Computing and Engineering

This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbour algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Repositioning nutrition as central to development: a strategy for large-scale action.

It has long been known that malnutrition undermines economic growth and perpetuates poverty. Yet the international community and most governments in developing countries have failed to tackle malnutrition over the past decades even though well-tested approaches for doing so exist. The consequences of this failure to act are now evident in the worlds inadequate progress toward the Millennium Development Goals (MDGs) and toward poverty reduction more generally. Persistent malnutrition is contributing not only to widespread failure to meet the first MDG--to halve poverty and hunger--but to meet other goals in maternal and child health HIV/AIDS education and gender equity. The unequivocal choice now is between continuing to fail as the global community did with HIV/AIDS for more than a decade or to finally make nutrition central to development so that a wide range of economic and social improvements that depend on nutrition can be realized. (excerpt)
Effect of Masking of Imagery with Picture-Stimuli on the Memory of Students of Architecture and Humanities.

The current study investigates the effect of masking of mental images with congruent and incongruent picture-stimuli among the students of Architecture and Humanities. Students from these two streams were chosen (N=60, mean age = 19 yrs.). Participants were shown a list of concrete words and were urged to make mental images. The images made by the participants were covered by the Experimenter by the congruent and incongruent pictures. The effect of masking was compared among the high and low imagery usage participants. Two t tests were computed to understand the significant differences between the means. A non-significant t of 1.031 was obtained for the high imagery expert group while, a significant t of 4.699, p < 0.001 with df 58 was seen for low imagery expert group. Additionally, a two-way ANOVA was computed followed by Tukey’s post-hoc test to understand the difference in the overall recognition scores between the high and low imagery usage groups, the effect of masking on memory, and the interaction effect of both the variables. The findings have been discussed and interpreted with limitations and suggestions
Continuous user verification in social networking sites using facial Recognition

-This paper proposes a service operator-aware trust scheme (SOTS) for resource matchmaking across multiple clouds. Through analyzing the built-in relationship between the users, the broker, and the service resources, this paper proposes a middleware framework of trust management that can effectively reduce user burden and improve system dependability. Based on multi-dimensional resource service operators, we model the problem of trust evaluation as a process of multi-attribute decision-making, and develop an adaptive trust evaluation approach based on information entropy theory. This adaptive approach can overcome the limitations of traditional trust schemes, whereby the trusted operators are weighted manually or subjectively. As a result, using SOTS, the broker can efficiently and accurately prepare the most trusted resources in advance, and thus provide more dependable resources to users. Our experiments yield interesting and meaningful observations that can facilitate the effective utilization of SOTS in a large-scale multi-cloud environment.
Comparative study of DWT and DTCWT for 3D Medical Image Registration

: Image Registration is one of the most important step in medical diagnosis and treatment planning, an accurate and efficient alignment of images is needed to improvise the planning process. Medical image registration is the area of active research. Many algorithms have already been proposed and implemented in various domain, wavelet transform is found as one of them that has been proved very simple and effective, which is a multiresolution analysis of an image using a set of analyzing functions that are dilations and translations functions. Discrete Wavelet Transform (DWT) and Dual Tree Complex Wavelet Transform (DTCWT) are most popular techniques for medical image registration. DTCWT has been found better than DWT due to its properties like shift invariance, less aliasing and better directionality than DWT. These properties play an important role in medical image registration. To gain some improvements in image registration process, DWT is replaced by Dual tree complex wavelet transform. Experimental results are presented to illustrate the comparison of DWT and DTCWT to a set of medical images.
DIABETIC RETINOPATHY IMAGE CLASSIFICATION USING DEEP NEURAL NETWORK

Healthcare is an important field where image classification has an excellent value. An alarming healthcare problem recognized by the WHO that the world suffers is diabetic retinopathy (DR). DR is a global epidemic which leads to the vision loss. Diagnosing the disease using fundus images is a timeconsuming task and needs experience clinicians to detect the small changes. Here, we are proposing an approach to diagnose the DR and its severity levels from fundus images using convolutional neural network algorithm (CNN). Using CNN, we are developing a training model which identifies the features through iterations. Later, this training model will classify the retina images of patients according to the severity levels. In healthcare field, efficiency and accuracy is important, so using deep learning algorithms for image classification can address these problems efficiently.
Nutritional Status of Lung Cancer Patients in Benghazi City of Libya

Lung cancer is one of the most common type of cancer in the world and Libya is no exception. Nutrition intervention is often needed for nutrition-related complications of cancer treatment, which include but are not limited to vomiting, anorexia, nausea and, and esophagitis. The purpose of this paper is to identify the nutritional status of lung cancer patients in Benghazi city of Libya via crosssectional study using Patient Generated Subjective Global Assessment (PG‐SGA). The study was carried out on 121 lung cancer patients. Out of 121 lung cancer patients; Only7.5% of the subjects were well nourished. 22.3% of were severe ly malnourished; while 70.2% were either at risk of malnutrition or suspected to develop malnutrition in the future. The mean age of the subjects’ ±SD was 49.5±0.6. Age, family income, Original Research Article Nouh et al.; JCTI, 6(3): 1-10, 2017; Article no.JCTI. 38345 2 and physical activity levels were associated with nutritional status of the subjects at (P< 0.05). All sections of the PG-SGA has a significant statistical positive correlation with its total score (r = 0. 96 – 0.51, P< 0.05). Dietitians played a very limited role in patients’ nutritional care.
Exact solutions of space-time fractional reaction-diffusionequations by (G'/G)-expansion method

In the last few years, the nonlinear fractional differential equations in mathematical physics have been considerable interest in many fields such as biophysics, blood flow phenomena, signal processing, control theory, electrical circuits and finance [1-6]. The fractional partial differential equations have been investigated by many authors [7-9]. In recently, some effective methods have proposed for determination of the exact solutions for some space and time fractional differential equations, such as the exp-function method [10-11], the fractional sub-equation method [12-15], the (G'/G)-expansion method [16-20] and the first integral method [21].
Student Paramedic Anticipation, Confidence and Fears: Do Undergraduate Courses Prepare Student Paramedics for the Mental Health Challenges of the Profession?

Introduction This study explores the preparedness of undergraduate student paramedics for the mental health challenges of the paramedic profession from the perspective of course coordinators and their students. Methods Two surveys were developed and administered to course coordinators and students of the 16 undergraduate degree paramedicine courses across Australia and New Zealand. Sixteen course coordinators and 302 students responded. Results Results illustrate there was widespread recognition for the need to include preparation for the mental health challenges of the profession within undergraduate courses. Furthermore, most course coordinators and students had a preference for this topic to be taught using multiple teaching modes with particular preference for teaching the topic via discussion and activity based education. Teaching the topic as a standalone unit was supported by more than a third of course coordinators (43%) and a third of students (32%). Conclusion Six themes were identified as positive by anticipants: caring for people, high acuity work, diversity of work and patients, making a difference to patients and their families, using clinical skills and knowledge and engaging with the community. Students were most confident about communicating with patients and using clinical skills and knowledge. Students were least confident about clinical decision making and the most commonly cited fear was making a clinical mistake. A significant proportion of students (16%) feared for their personal mental wellbeing and 14% reported they were least confident about personal mental health within the profession.
Polysome fractionation analysis reveals features important for human nonsense-mediated mRNA decay

Nonsense-mediated mRNA decay (NMD) is a translation-dependent mRNA surveillance pathway that eliminates transcripts with premature termination codons. Several studies have tried defined the features governing which transcripts are targeted to NMD. However, these approaches often rely on inhibiting core NMD factors, which often have roles in non-NMD processes within the cell. Based on reports that NMD-targeted transcripts are often bound by a single ribosome, we analyzed RNA-Seq data from a polysome fractionation experiment (TrlP-Seq) to characterize the features of NMD-targeted transcripts in human cells. This approach alleviates the need to inhibit the NMD pathway. We found that the exon junction complex (EJC) model, wherein an exon-exon junction located ≥50 nucleotides downstream of a stop codon is predicted to elicit NMD, was a powerful predictor of transcripts with high abundance in the monosome fraction (bound by a single ribosome). This was also true for the presence of an upstream open reading frame. In contrast, as 3’ UTR lengths increase, the proportion of transcripts that are most abundant in the monosome fraction does not increase. This suggests that either longer 3’ UTRs do not consistently act as potent triggers of NMD or that the degradation of these transcripts is mechanistically different to other NMD-targeted transcripts. Of the ribosome-associated transcripts annotated as “non-coding”, we find that a majority are bound by a single ribosome. Many of these transcripts increase in response to NMD inhibition, including the oncogenic SHNG15, suggesting many might be NMD targets. Finally, we found that retained intron transcripts without a premature termination codon are over-represented in the monosome fraction, suggesting an alternative mechanism is responsible for the low level of translation of these transcripts. In summary, our analysis finds that the EJC model is a powerful predictor of NMD-targeted transcripts, while the presence of a long 3’ UTR is not.