nils-herrmann/clean_scirepeval huggingface dataset:
A comparison between a conventional LSTM network and a grid LSTM network applied on speech recognition

In this paper, a comparision between the conventional LSTM network and the one-dimensional grid LSTM network applied on single word speech recognition is conducted. The performance of the networks are measured in terms of accuracy and training time. The conventional LSTM model is the current state of the art method to model speech recognition. However, the grid LSTM architecture has proven to be successful in solving other emperical tasks such as translation and handwriting recognition. When implementing the two networks in the same training framework with the same training data of single word audio files, the conventional LSTM network yielded an accuracy rate of 64.8 % while the grid LSTM network yielded an accuracy rate of 65.2 %. Statistically, there was no difference in the accuracy rate between the models. In addition, the conventional LSTM network took 2 % longer to train. However, this difference in training time is considered to be of little significance when tralnslating it to absolute time. Thus, it can be concluded that the one-dimensional grid LSTM model performs just as well as the conventional one.
Factors affecting consumers' perception of electronic payment: an empirical analysis

In view of the promising growth of e-payment in Malaysia, this study aims to discover the factors influencing perception towards electronic payment (e-payment) from the Malaysian consumers’ perspective.  Literature indicates that factors such as benefits, trust, selfefficacy, ease of use, and security influence consumers’ perception towards e-payment. A self-reporting questionnaire was developed and disseminated to 200 respondents, out of which 183 valid responses were considered for further statistical analysis.  The multiple linear regression results reveal that benefits, self-efficacy, and ease of use exert significant influences on consumers’ perception towards e-payment. However, the insignificant results obtained for trust and security warrant further investigation.  This study proposes five factors for measuring consumers’ perception towards e-payment which is replicable across different economies. However, the small sample size raises the issue of generalizability which future studies should seek to address.  The use of e-payment by the majority of respondents confirms that there is a great potential for future expansion of such payment devices. The challenge is to ensure that it continues to meet consumers’ expectations which will subsequently lead to its increased adoption and use.  This study has advanced knowledge for it has provided information on the current state of e-payment acceptance and use, particularly among Malaysians. The significant factors identified are beneficial to the policy maker, banking institutions, online transaction facilities providers, and software developers as they develop strategies directed at increasing e-payment acceptance and use.
Dual-energy computed tomography (DECT) in emergency radiology: basic principles, techniques, and limitations

Recent advances in computed tomography (CT) technology allow for acquisition of two CT datasets with different X-ray spectra. There are different dual-energy computed tomography (DECT) technical approaches such as: the dual-source CT, the fast kilovoltage-switching method, and the sandwich detectors technique. There are various postprocessing algorithms that are available to provide clinically relevant spectral information. There are several clinical applications of DECT that are easily accessible in the emergency setting. In this review article, we aim to provide the emergency radiologist with a discussion on how this new technology works and how some of its applications can be useful in the emergency room setting.
Blind detection of interleaver parameters

Interleaving is a key component of many digital communication systems involving error correction schemes. It provides a form of time diversity to guard against bursts of errors. Recently, interleavers have become an even more integral part of the code design itself, if we consider for example turbo and turbo-like codes. In a non-cooperative context, such as passive listening, it is a challenging problem to estimate the interleaver parameters. In this paper we propose an algorithm that allows us to estimate the parameters of the interleaver at the output of a binary symmetric channel and to locate the codewords in the interleaved block. This gives us some clues about the interleaving function used.
The Evaluation Process of a Computer Security Incident Ontology

Ontologies have been developed and used by several researchers in different knowledge domains aiming to ease the structuring and management of knowledge, and to create a unique standard to represent concepts of such a knowledge domain. Considering the computer security domain, several tools can be used to manage and store security information. These tools generate a great amount of security alerts, which are stored in different formats. This lack of standard and the amount of data make the tasks of the security administrators even harder, because they have to understand, using their tacit knowledge, different security alerts to make correlation and solve security problems. Aiming to assist the administrators in executing these tasks efficiently, this paper presents the main features of the computer security incident ontology developed to model, using a unique standard, the concepts of the security incident domain, and how the ontology has been evaluated.
The Dark Triad : Facilitating a Short-Term Mating Strategy in Men

This survey (N1⁄4 224) found that characteristics collectively known as the Dark Triad (i.e. narcissism, psychopathy and Machiavellianism) were correlated with various dimensions of short-term mating but not long-term mating. The link between the Dark Triad and shortterm mating was stronger for men than for women. The Dark Triad partially mediated the sex difference in short-term mating behaviour. Findings are consistent with a view that the Dark Triad facilitates an exploitative, short-term mating strategy in men. Possible implications, including that Dark Triad traits represent a bundle of individual differences that promote a reproductively adaptive strategy are discussed. Findings are discussed in the broad context of how an evolutionary approach to personality psychology can enhance our understanding of individual differences.
Gifi Methods for Optimal Scaling in R : The Package homals

Homogeneity analysis combines the idea of maximizing the correlations between variables of a multivariate data set with that of optimal scaling. In this article we present methodological and practical issues of the R package homals which performs homogeneity analysis and various extensions. By setting rank constraints nonlinear principal component analysis can be performed. The variables can be partitioned into sets such that homogeneity analysis is extended to nonlinear canonical correlation analysis or to predictive models which emulate discriminant analysis and regression models. For each model the scale level of the variables can be taken into account by setting level constraints. All algorithms allow for missing values.
A Wideband Balun for HF, VHF, and UHF Applications

There is an industry need for wideband baluns to operate across several decades of bandwidth covering the HF, VHF, and UHF spectrum. For readers unfamiliar with the term "balun," it is a compound word that combines the terms balanced and unbalanced. This is in reference to the conversion between a balanced source and an unbalanced load, often requiring an impedance transformation of some type. It's common in literature to see the terms "balanced" and "unbalanced" used interchangeably with the terms "differential" and "single-ended," and this article will also share this naming convention. These devices are particularly useful in network matching applications and can be constructed at low cost and a relatively small bill of materials. Wideband baluns first found widespread use converting the balanced load of a dipole antenna to the unbalanced output of a single-ended amplifier. These devices can also be found in solid-state differential circuits such as amplifiers and mixers where network matching is required to achieve the maximum power transfer to the load. In the design of RF power amplifiers, wideband baluns play a critical role in an amplifier's performance, including its input and output impedances, gain flatness, linearity, power efficiency, and many other performance characteristics.This article describes the theory of operation, design procedure, and measured results of the winning wideband balun presented at the 2013 IEEE Microwave Theory and Techniques Society (MTT-S) International Microwave Symposium (IMS2013), sponsored by the MTT-17 Technical Coordinating Committee on HF-VHF-UHF technology. The wideband balun was designed to deliver a 4:1 impedance transformation, converting a balanced 100 Ω source to an unbalanced 25 Ω load. It was constructed using a multiaperture ferrite core and a pair of bifilar wires with four parallel turns.
Comparison of data mining classification algorithms for breast cancer prediction

Data mining is an area of computer science with a huge prospective, which is the process of discovering or extracting information from large database or datasets. There are many different areas under Data Mining and one of them is Classification or the supervised learning. Classification also can be implemented through a number of different approaches or algorithms. We have conducted the comparison between three algorithms with help of WEKA (The Waikato Environment for Knowledge Analysis), which is an open source software. It contains different type's data mining algorithms. This paper explains discussion of Decision tree, Bayesian Network and K-Nearest Neighbor algorithms. Here, for comparing the result, we have used as parameters the correctly classified instances, incorrectly classified instances, time taken, kappa statistic, relative absolute error, and root relative squared error.
Alveolar bone development after decoronation of ankylosed teeth

Decoronation of ankylosed teeth in infraposition was introduced in 1984 by Malmgren and co-workers (1). This method is used all over the world today. It has been clinically shown that the procedure preserves the alveolar width and rebuilds lost vertical bone of the alveolar ridge in growing individuals. The biological explanation is that the decoronated root serves as a matrix for new bone development during resorption of the root and that the lost vertical alveolar bone is rebuilt during eruption of adjacent teeth. First a new periosteum is formed over the decoronated root, allowing vertical alveolar growth. Then the interdental fibers that have been severed by the decoronation procedure are reorganized between adjacent teeth. The continued eruption of these teeth mediates marginal bone apposition via the dental-periosteal fiber complex. The erupting teeth are linked with the periosteum covering the top of the alveolar socket and indirectly via the alveolar gingival fibers, which are inserted in the alveolar crest and in the lamina propria of the interdental papilla. Both structures can generate a traction force resulting in bone apposition on top of the alveolar crest. This theoretical biological explanation is based on known anatomical features, known eruption processes and clinical observations.
Improving Sentiment Analysis in Arabic Using Word Representation

The complexities of Arabic language in morphology, orthography and dialects makes sentiment analysis for Arabic more challenging. Also, text feature extraction from short messages like tweets, in order to gauge the sentiment, makes this task even more difficult. In recent years, deep neural networks were often employed and showed very good results in sentiment classification and natural language processing applications. Word embedding, or word distributing approach, is a current and powerful tool to capture together the closest words from a contextual text.In this paper, we describe how we construct Word2Vec models from a large Arabic corpus obtained from ten newspapers in different Arab countries. By applying different machine learning algorithms and convolutional neural networks with different text feature selections, we report improved accuracy of sentiment classification (91%-95%) on our publicly available Arabic language health sentiment dataset .
Analysis and Enhancement of Random Number Generator in FPGA Based on Oscillator Rings

A true random number generator (TRNG) is an important component in cryptographic systems. Designing a fast and secure TRNG in an FPGA is a challenging task. In this paper we analyze the TRNG designed by Sunar et al. based on XOR of the outputs of many oscillator rings. We propose an enhanced TRNG that does not require post-processing to pass statistical tests and with better randomness characteristics on the output. We have shown by experiment that the frequencies of the equal length oscillator rings in the TRNG are not identical but different due to the placement of the inverters in the FPGA. We have implemented our proposed TRNG in an Altera Cyclone II FPGA. Our implementation has passed the NIST and DIEHARD statistical tests with a throughput of 100 Mbps and with a usage of less than 100 logic elements in the FPGA.
Positive Definite Matrices : Data Representation and Applications to Computer Vision

Numerous applications in computer vision and machine learning rely on representations of data that are compact, discriminative, and robust while satisfying several desirable invariances. One such recently successful representation is offered by symmetric positive definite (SPD) matrices. However, the modeling power of SPD matrices comes at a price: rather than a flat Euclidean view, SPD matrices are more naturally viewed through curved geometry (Riemannian or otherwise) which often complicates matters. We focus on models and algorithms that rely on the geometry of SPD matrices, and make our discussion concrete by casting it in terms of covariance descriptors for images. We summarize various commonly used distance metrics on SPD matrices, before highlighting formulations and algorithms for solving sparse coding and dictionary learning problems involving SPD data. Through empirical results, we showcase the benefits of mathematical models that exploit the curved geometry of SPD data across a diverse set of computer vision applications.
Three-Dimensional Bandpass Frequency-Selective Structures With Multiple Transmission Zeros

A new class of three-dimensional (3-D) bandpass frequency-selective structures (FSSs) with multiple transmission zeros is presented to realize wide out-of-band rejection. The proposed FSSs are based on a two-dimensional (2-D) array of shielded microstrip lines with shorting via to ground, where two different resonators in the substrate are constructed based on the excited substrate mode. Furthermore, metallic plates of rectangular shape and “T-type” are inserted in the air region of shielded microstrip lines, which can introduce additional resonators provided by the air mode. Using this arrangement, a passband with two transmission poles can be obtained. Moreover, multiple transmission zeros outside the passband are produced for improving the out-of-band rejection. The operating principles of these FSSs are explained with the aid of equivalent circuit models. Two examples are designed, fabricated, and measured to verify the proposed structures and circuit models. Measured results demonstrate that the FSSs exhibit high out-of-band rejection and stable filtering response under a large variation of the incidence angle.
Memory-Efficient On-The-Fly Voxelization of Particle Data

In this paper we present a novel GPU-friendly real-time voxelization technique for rendering homogeneous media that is defined by particles, e.g. fluids obtained from particle-based simulations such as Smoothed Particle Hydrodynamics (SPH). Our method computes view-adaptive binary voxelizations with on-the-fly compression of a tiled perspective voxel grid, achieving higher resolutions than previous approaches. It allows for interactive generation of realistic images, enabling advanced rendering techniques such as ray casting-based refraction and reflection, light scattering and absorption, and ambient occlusion. In contrast to previous methods, it does not rely on preprocessing such as expensive, and often coarse, scalar field conversion or mesh generation steps. Our method directly takes unsorted particle data as input. It can be further accelerated by identifying fully populated simulation cells during simulation. The extracted surface can be filtered to achieve smooth surface appearance.
Design and study of wide-band patch antenna fed by meandering probe

A wide-band probe fed patch antenna with low cross polarization and symmetrical broadside radiation pattern is proposed and studied. By employing a novel meandering probe feed and locating a patch about 0.1/spl lambda//sub 0/ above a ground plane, a patch antenna with 30% impedance bandwidth (SWR<2) and 9 dBi gain is designed. The far field radiation pattern of the antenna is stable across the operating bandwidth. Parametric studies and design guidelines of the proposed feeding structure are provided.
Using machine learning techniques for grapheme to phoneme transcription

The renewed interest in grapheme to phoneme conversion (G2P), due to the need of developing multilingual speech synthesizers and recognizers, suggests new approaches more efficient than the traditional rule&exception ones. A number of studies have been performed to investigate the possible use of machine learning techniques to extract phonetic knowledge in a automatic way starting from a lexicon. In this paper, we present the results of our experiments in this research field. Starting from the state of art, our contribution is in the development of a language-independent learning scheme for G2P based on Classification and Regression Trees (CART). To validate our approach, we realized G2P converters for the following languages: British English, American English, French and Brazilian Portuguese.
A Survey on Lung Segmentation Methods

Lung diseases are the deadliest disease in the world. The computer aided detection system in lung diseases needed accurate lung segmentation to preplan the pulmonary treatment and surgeries. The researchers undergone the lung segmentation need a deep study and understanding of the traditional and recent papers developed in the lung segmentation field so that they can continue their research journey in an efficient way with successful outcomes. The need of reviewing the research papers is now a most wanted one for researches so this paper makes a survey on recent trends of pulmonary lung segmentation. Seven recent papers are carried out to analyze the performance characterization of themselves. The working methods, purpose for development, name of algorithm and drawbacks of the method are taken into consideration for the survey work. The tables and charts are drawn based on the reviewed papers. The study of lung segmentation research is more helpful to new and fresh researchers who are committed their research in lung segmentation.
Impact of hyperketonemia in early lactation dairy cows on health and production.

Data from 1,010 lactating lactating, predominately component-fed Holstein cattle from 25 predominately tie-stall dairy farms in southwest Ontario were used to identify objective thresholds for defining hyperketonemia in lactating dairy cattle based on negative impacts on cow health, milk production, or both. Serum samples obtained during wk 1 and 2 postpartum and analyzed for beta-hydroxybutyrate (BHBA) concentrations that were used in analysis. Data were time-ordered so that the serum samples were obtained at least 1 d before the disease or milk recording events. Serum BHBA cutpoints were constructed at 200 micromol/L intervals between 600 and 2,000 micromol/L. Critical cutpoints for the health analysis were determined based on the threshold having the greatest sum of sensitivity and specificity for predicting the disease occurrence. For the production outcomes, models for first test day milk yield, milk fat, and milk protein percentage were constructed including covariates of parity, precalving body condition score, season of calving, test day linear score, and the random effect of herd. Each cutpoint was tested in these models to determine the threshold with the greatest impact and least risk of a type 1 error. Serum BHBA concentrations at or above 1,200 micromol/L in the first week following calving were associated with increased risks of subsequent displaced abomasum [odds ratio (OR) = 2.60] and metritis (OR = 3.35), whereas the critical threshold of BHBA in wk 2 postpartum on the risk of abomasal displacement was >or=1,800 micromol/L (OR = 6.22). The best threshold for predicting subsequent risk of clinical ketosis from serum obtained during wk 1 and wk 2 postpartum was 1,400 micromol/L of BHBA (OR = 4.25 and 5.98, respectively). There was no association between clinical mastitis and elevated serum BHBA in wk 1 or 2 postpartum, and there was no association between wk 2 BHBA and risk of metritis. Greater serum BHBA measured during the first and second week postcalving were associated with less milk yield, greater milk fat percentage, and less milk protein percentage on the first Dairy Herd Improvement test day of lactation. Impacts on first Dairy Herd Improvement test milk yield began at BHBA >or=1,200 micromol/L for wk 1 samples and >or=1,400 micromol/L for wk 2 samples. The greatest impact on yield occurred at 1,400 micromol/L (-1.88 kg/d) and 2,000 micromol/L (-3.3 kg/d) for sera from the first and second week postcalving, respectively. Hyperketonemia can be defined at 1,400 micromol/L of BHBA and in the first 2 wk postpartum increases disease risk and results in substantial loss of milk yield in early lactation.
Polyelectrolyte complexes: bulk phases and colloidal systems.

When aqueous solutions of polycations and polyanions are mixed, polyelectrolyte complexes form. These are usually insoluble in water, so that they separate out as a new concentrated polymer phase, called a complex coacervate. The behavior of these complexes is reviewed, with emphasis on new measurements that shed light on their structural and mechanical properties, such as cohesive energy, interfacial tension, and viscoelasticity. It turns out that stoichiometric complexes can be considered in many respects as pseudo-neutral, weakly hydrophobic polymers, which are insoluble in water, but become progressively more soluble as salt is added. In fact, the solubility-enhancing effect of salt is quite analogous to that of temperature for polymers in apolar solvents. Since two-phase systems can be prepared in colloidal form, we also discuss several kinds of colloids or 'microphases' that can arise due to polyelectrolyte complexation, such as thin films, 'zipper' brushes, micelles, and micellar networks. A characteristic feature of these charge-driven two-phase systems is that two polymeric ingredients are needed, but that some deviation from strict stoichiometry is tolerated. This turns out to nicely explain how and when the layer-by-layer method works, how a 'leverage rule' applies to the density of the 'zipper brush', and why soluble complexes or micelles appear in a certain window of composition. As variations on the theme, we discuss micelles with metal ions in the core, due to incorporation of supramolecular coordination polyelectrolytes, and micellar networks, which form a new kind of physical gels with unusual properties.