Miracle-dz/UniFAR-FTUs huggingface dataset:
The Deep Neural Network and Content Transcription-based Speech Recognition Algorithm in Keyword Detection

Objective: It is hoped that the DNN-Hidden Markov Model (HMM) acoustic model and the long-short-term memory neural network-Hidden Markov (LSTM-HMM) acoustic model can be used to improve the accuracy rate of speech recognition in keyword detection. Method: First, the principles of speech recognition and related algorithms are introduced. Then, a DNN algorithm is applied to the speech recognition system, and a keyword detection system is established based on the DNN-HMM acoustic model. As for the experimental comparison, the proposed DNN model is used for acoustic modeling, and the effect of the DNN algorithm on the performance of the recognition system is analyzed experimentally. Results: The size of the training parameters of the proposed LSTM model is 436570, the size of the training parameters of the deep neural network (DNN) is 698100, and the size of the training parameters of the Gaussian mixture model (GMM) is 1226700. The accuracy of speech recognition based on the LSTM-HMM model and DNN-HMM model is 965% and 91.6%, respectively, which is significantly higher than 78.5% of the traditional speech recognition model (GMM-HMM). Conclusion: The speech recognition technology based on LSTM-HMM and DNN-HMM models has higher accuracy and is more suitable for speech keyword detection.
A 3D reconstruction method of image sequence based on deep learning

Camera calibration, image feature detection, matching and other aspects have become barriers that traditional 3D reconstruction methods are difficult to break through. The important role of deep learning in data detection and classification has a deep impact on the real world, and has become a research hotspot at home and abroad to deal with this problem. In this paper, a 3D reconstruction method of image sequence based on depth learning is proposed. Firstly, the principle and method of 3D reconstruction are introduced. Then, the new method of 3D reconstruction based on image is studied and discussed in combination with deep learning theory. Finally, the conclusion and prospect are given.
X-ray diffraction analysis of the distribution of spherical crystallites in polydisperse systems

The authors suggest a method permitting, with minimal quantity of data on the broadening and shape of the x-ray diffraction lines, to obtain the distribution of the diameters of spherical crystallites in polydisperse systems. The method was tested on numerical models and a real object (nickel oxide) by comparison of the shape of the lines with electron-microscopy and Fourier-analysis data, and can be used for analysis of ultradisperse crystalline media with particle dimensions less than 1000 A, for which electron-microscopy methods are difficult
Method of Moments Based Modelling of Race-Track Fluxgate Sensitivity Dependence on Track and Core Width

Intensive development of thin-layer based fluxgate sensors produced as printed circuit boards demand proper tools for modelling their functional characteristics. This paper presents the application of the method of moments utilizing uniform square meshes to avoid problems with thin layer modelling utilizing finite elements method. Exemplary results of modelling indicate that the sensitivity of race-track shaped fluxgate sensor strongly depends on the width of the track, whereas the influence of the width of the core on the fluxgate sensitivity is about 3%. This information is important for development of printed circuit boards based fluxgate sensors with racetrack-shaped cores, as it can lead to significant reduction of the sensor’s perpendicular dimension.
DEVELOPMENT AND CLINICAL STUDY OF A PROJECTION IMAGES PROGRAM TO ENCOURAGE ARM MOVEMENTS FOR REHABILITATION

Abstract In a program we developed called “Let's keep wiping to draw pictures! ”, projected graphic images change according to rehabilitation movements for upper limbs, and the levels of exercise amount and quality of movement achieved by patients are reflected in the outcome of the artwork as feedback. At a rehabilitation hospital, inpatients who used the program to perform rehabilitation exercises showed higher levels of satisfaction and expectation in the exercises, and performed simple and repetitive movements more willingly. The program can expect to maintain motivation towards rehabilitation.
A mini-review on importance and role of trace elements in the human organism

Trace elements are minerals present in living tissues in minute quantities. Some of them are known to be nutritionally essential and the remainder is considered to be nonessential. The body requires certain essential elements and their deficiency or excess may result in serious dysfunction of the body and even death in extreme cases. The low intakes dietary of trace element produce changes in biochemical pathways that can raise the risk of diseases over time. On the other hand, excessive levels, a level higher than needed for biological functions, of these elements can be toxic for the body health. This review evaluates the role and importance of the essential trace element in the human organism. The potential importance of key essential trace elements; Magnesium, Manganese, Iron, Zinc, Copper, Cobalt, Iodine, Selenium, Nickel, Molybdenum and Chromium; and nonessential trace such as Cadmium, Lead, Arsenic and Mercury are discussed.
A novel CNN model for fine-grained classification with large spatial variants

Convolutional Neural Networks (CNN) have achieved great performance in many visual tasks. However, CNN models are sensitive to samples with large spatial variants, especially severe in fine-grained classification task. In this paper, we propose a novel CNN model called ST-BCNN to solve these problems. ST-BCNN contains two functional CNN modules: Spatial Transform Network (STN) and Bilinear CNN(BCNN). Firstly, STN module is used to select key region in input samples and get it spatially modified. Since the adoption of STN will cause an information loss phenomenon called boundary loss, we design a brand-new IOU loss method to solve it. We make a theoretical analysis of the IOU loss method. Secondly, to discover discriminative features for fine-grained classification task, BCNN module is applied. BCNN interacts CNN features from different channels to produce more discriminative bilinear features than fully connected features of CNN. ST-BCNN works by reducing irrelevant spatial states and producing fine-grained features. We evaluate our model on 3 public fine-grained classification datasets with large spatial variants: CUB200-2011, Fish100 and UAV43. Experiments show that the IOU loss method can reduce boundary loss and make STN module output spatial transformed image appropriately. Our proposed ST-BCNN model outperforms other advanced CNN models on all three datasets.
The effects of dietary lipids and roughage level on dairy goat performance, milk physicochemical composition, apparent transfer efficiency and biohydrogenation rate of milk fatty acids

Abstract The study was conducted to investigate the effects of fish or palm oil diets with different roughage levels on dairy performance, milk physicochemical composition and apparent transfer efficiency of fatty acids (FA) in goat milk. The experiment was conducted with 40 Aleppo goats with a mean parity of 2.53 ± 0.8 (multiparous), mean initial body weight of 47.23 kg and 25 ± 5 days in milk which were allocated to four (2 × 2) experimental diets with two oil sources (fish or palm oil) at 25.6 g/kg of dietary dry matter and forage levels (400 or 600 g/kg). The experimental data were analysed by repeated measures analysis, using the MIXED procedure. The concentrations of saturated FA decreased with high forage level and fish oil diets; however, the fish oil diets caused an increase in C14 saturated FA. Fish oil diets with high roughage levels more efficiently increased conjugated linoleic acid, n-6 (18 : 2), and n-3 (20 : 5). The apparent transfer efficiency of 18 : 1, 18 : 2, 18 : 3 and 20 : 5 decreased and the transfer efficiency of 22 : 6 increased with the use of fish oil in the diet. The roughage level did not affect the apparent transfer efficiency of 18 : 1 and 18 : 2, but the low roughage level increased the apparent transfer efficiency of 20 : 5. High roughage diets improved milk quality parameters through increasing eicosapentaenoic acid, polyunsaturated fatty acids (PUFA), PUFA/saturated FA and atherogenicity index, thus it was concluded that dietary roughage level could be considered as an important designator of milk quality when a supplement of fish oil and palm oil was supplied to goats.
Research on hotel online sales forecast model based on improved WaveNet

In recent years, with the rapid development of the Internet in China, online transactions have grown greatly. For example, OTAs with a large number of hotels have accumulated a large amount of hotel data and user consumption data. And the online sales of hotels is the basis and core of revenue management. Time series prediction has always been one of the main application fields of machine learning algorithm. From the classical traditional time series prediction methods to long-term and short-term memory networks and closed-loop neural networks, the prediction ability is constantly improving. With the development of deep neural networks, convolution neural networks show superior performance in the prediction of time series. This paper proposes a new prediction model based on the improved WaveNet using not only the parameters of historical sales and hotel property, but also the parameters of holiday time and time position in the prediction range, which are processed by serialization. Simulation results are presented in details in this paper, where these results indicate the effectiveness of the proposed forecasting tool as an accurate technique.
Proposal of a composite indicator for measuring social media presence in the wine market

This work was carried out within the framework of a Spanish research project ‘eMarketwine: diseno de un metodo y una herramienta de online information intelligence orientada a la recomendacion geolocalizada para el mercado del vino’ (Ref. CSO2016-78775-R), founded by the Ministerio de Economia y Competitivad (Spanish Ministry of Economy and Competitiveness).
The hidden side of the Allee effect: correlated demographic traits and extinction risk in experimental populations

Because Allee effects have major impacts on the dynamics of small populations, they are routinely included in demographic models for the evaluation of extinction risks. However, the structure of most common models implies that other demographic parameters (like the maximum growth rate) are modified by the inclusion of an Allee effect, which also affects in return the extinction risk of the population. Whether such correlations between demographic traits occur in natural populations or merely reflect a practical constraint related to model formalism is of primary importance to understand better the dynamics of small populations. We investigated this question using 20 populations of Trichogramma wasps raised under similar conditions, of which 3 were subject to an Allee effect. We showed that these 3 populations were also characterized by lower maximum growth rate and lower carrying capacity, and that their extinction probability was higher than for non-Allee populations. These results provide the first empirical demonstration of a correlation between the presence of positive density-dependence and impaired demographic performance, which increases the extinction risk of population, especially during the establishment phase.
Real-time detection of UAV detection image of power line insulator bursting based on YOLOV3

The insulator fault of high voltage line is the main factor of transmission accidents, so positioning and detection of burst insulator of power line has become an important part of routine detection. Traditional UAV detection is still mainly to evaluate the image transmitted by UAV manually, which is not only time-consuming, but also not accurate. In this paper, UAV aerial images are used to make datasets, the problem of insulator class imbalance (normal insulator and burst insulator) in the model training process is solved by using data augmentation, an improved depth learning algorithm is proposed based on YOLOv3, which provides rich semantic information for prediction layer by adding feature mapping module. At the same time, the residual network is introduced into the feature extraction, which improves the detection accuracy of small objects, more effectively extracts the object features of burst insulator, carries out real-time object detection and positioning, and completes the daily detection of insulator state of power transmission lines. The experimental results show that the detection accuracy (mAP) of the improved YOLOv3 algorithm is 91.22%, and the detection speed is 28 frames/s. The recall rate and Intersection Over Union (IOU) are also improved.
Systems of Innovation and Economic Theory

The volume of literature which emerged around innovation and systems of innovation in the 1980s and the first half of the 1990s held the promise of the emergence of an account of the general economy which could challenge the mainstream. The central argument of this paper is that the failure of that promise to materialise was due to the identification of neoclassical economics as the mainstream against which the system of innovation approach would be counterpoised, rather than neoliberal economics. While the former constitutes the disciplinary base of mainstream economics, the latter has formed its discourse. This paper looks at the contradictions between discipline and discourse in the mainstream and the relegation of the economic understanding of innovation to a peripheral area of study within economics.
EPIDEMIOLOGY OF BARLEY YELLOW DWARF AND SOURCES OF RESISTANCE GENES IN BARLEY IN ETHIOPIA

Barley yellow dwarf viruses (BYDVs), maize yellow dwarf virus (MYDV) and cereal yellow dwarf viruses (CYDVs), collectively known as yellow dwarf viruses (YDVs), are causing the most economically important virus diseases of cereal crops throughout the world. It is an important disease constraining highland grown barley in Ethiopia. However, YDVs were not well characterized using state-of-the-art diagnostic method(s). Biotic components of epidemics viz. alternative grasses hosting both the viruses and associated vectors, distribution and composition of potential aphid vectors and reaction of local cultivars to the disease were not addressed. Besides, information on the physical environment, climate and farming systems affecting YD epidemic development were either lacking or what known was not systematically analyzed and made available for public consumption. The objectives of this study were to: 1) determine the relative proportion and composition of Ethiopian YDVs with respect to agroecology or altitude rage; 2) identify potential aphid vectors and alternative hosts of YDVs in Ethiopia; 3) molecular identification of Ethiopian BYDVs, CYDV and MYDV isolates based on coat protein gene sequences; and 4) identify gene(s) effective to BYDVs in barley. Results of objectives 1-3 were achieved based on field surveys accompanied by laboratory testing using TBIA, RT-PCR and sequencing, while the fourth objective was performed based on field evaluation, greenhouse study and laboratory analysis. Results revealed that recurrent YD epidemics occurred in highland cereals growing districts of West Shewa, Arsi and Bale Zones, with visual field incidences reaching as high as 90, 85 and 100%, respectively. In all other cereal growing areas assessed in northwest and central regions, YD symptoms were either absent or trace or recorded at low to medium level. Laboratory testing of random samples revealed that BYDV-PAV remained the predominant species identified in Ethiopia, with overall incidences of 28.5, 23.9 and 15%, respectively, in
Effect of HR Practices on Employee Retention: The Role of Perceived Supervisor Support

Now a days managing human resource (HR) effectively is the priority of the organizations. The relationship between of human resource management (HRM) practices and employee retention is investigated in this study. This study also scrutinized the perceived supervisor support’s (PSS) moderating effect between HRM practices and employee retention. The data was collected from employees of textile industry using questionnaire. Results show significant relationship between HRM practices and employee retention. Except employee empowerment, PSS is also moderating between HRM practices and employee retention. Organizations should implement these Human Resource Practices to keep employees motivated and positive towards work, this will decrease employee turnover and increase retention of skilled and motivated employees in an organization.
Gut Microbiome Analysis In Adult Tropical Gars (Atractosteus tropicus)

Tropical gar (Atractosteus tropicus), is freshwater and estuarine fish that has inhabited the Earth since the Mesozoic era, undergoing limited physiological variation ever since. This omnivorous fish is endemic to southern Mexico and part of Central America. Besides its recognized cultural and scientific relevance, the species has seen remarkable growth in its economic impact due to pisciculture. Previous studies have highlighted the role of microbial communities in fish, particularly those in the gut microbiome, in maintaining their host homeostasis or disease. In this study, we present the first report of the whole taxonomic composition of microbial communities in gut contents of adults’ A. tropicus, by sex (female/male) and origin (wild/cultivated). Using culture-independent techniques, we extracted metagenomic DNA that was used for high throughput 16S rDNA profiling by amplifying the V4 – V5 hypervariable regions of the bacterial gene. A total of 364,735 total paired-end reads were obtained on an Illumina MiSeq sequencing platform, belonging to 508 identified genera, with the most and least abundant are Cetobacterium, Edwardsiella, Serratia, Clostridium sensu stricto, Paludibacter and Campylobacter, Snodgrassella, Albirhodobacter, Lentilitoribacter, respectively. We detected that, by sex and origin, Proteobacteria, Fusobacteria, Firmicutes and Bacteroidetes phyla are the core gut microbiome of the adults’ A. tropicus. We discover the Deinococcus-Thermus phylum sequence, wildtype males only, with extremophile capacity in another freshwater fish. We also identified the species Lactococcus lactis strains CAU929 and CAU6600, Cp6 and CAU9951, Cetobacterium strain H69, Aeromonas hydrophila strains P5 and WR-5-3-2, Aeromonas sobria strain CP DC28 and Aeromonas hydrophila with probiotic potential in aquaculture within the three dominant phyla, especially in wild-type organisms.
Molecular dynamics simulation of latent track formation in α-quartz

The latent ion track in α-quartz is studied by molecular dynamics simulations. The latent track is created by depositing electron energies into a cylindrical region with a radius of 3nm. In this study, the electron stopping power varies from 3.0keV/nm to 12.0keV/nm, and a continuous latent track is observed for all the simulated values of electron stopping power except 3.0keV/nm. The simulation results indicate that the threshold electron stopping power for a continous latent track lies between 3.0keV/nm and 3.7 keV/nm. In addition, the coordination defects produced in the latent track are analyzed for all the simulation conditions, and the results show that the latent track in α-quartz consists of an O-rich amorphous phase and Si-rich point defects. At the end of this paper, the influence of the energy deposition model on the latent track in α-quartz is investigated. The results indicate that different energy deposition models reveal similar latent track properties. However, the values of the threshold electron stopping power and the ion track radius are dependent on the choice of energy deposition model.
Generative Adversarial Network for Market Hourly Discrimination

In this paper, we consider 2 types of instruments traded on the markets, stocks and cryptocurrencies. In particular, stocks are traded in a market subject to opening hours, while cryptocurrencies are traded in a 24-hour market. What we want to demonstrate through the use of a particular type of generative neural network is that the instruments of the non-timetable market have a different amount of information, and are therefore more suitable for forecasting. In particular, through the use of real data we will demonstrate how there are also stocks subject to the same rules as cryptocurrencies.
A Very Fast String Search Algorithm

A very fast algorithm to perform pattern matching in strings was described. The proposed match algorithm combines the advantages of Karp-Rabin’s algorithm and Boyer-Moore’s algorithm. Instead of checking at each position of the text if the pattern occurs, it is only to check the resemblance between the contents of the string and the pattern by using a hashing function and can skips as many characters as possible. In the cases of short patterns its time complexity can theoretically reach O(n) even in the worst cases. In actual experiments, the time it takes to search a string is about 1/10 of the time KR algorithm takes.
Impact of cognitive and metacognitive strategies on learning performance in digital learning: What’s working and what’s not in the age of brilliant technology

Ineffective cognitive processes result in learning difficulties online. Hence, how cognitive learning processes influence the way students engage and learn online needs further investigation. This study, therefore, examined cognitive engagement and metacognitive knowledge strategies on learning performance among Malaysian IT Undergraduates. A sample of 563 IT undergraduates from private higher education in Malaysia participated in this quantitative study that were collected through a questionnaire. Data was analysed using PLS-SEM. The findings of this study constitutes essential results where all cognitive engagement strategies and two of metacognitive knowledge strategies positively influence the students’ perception of learning performance. Nevertheless, one metacognitive strategy resulted in a negative relationship with learning performance which have led to a new finding. This study concluded that the acquisition of cognitive and metacognitive skills are needed to excel in digital learning for deeper learning. Lecturers can use the findings of this study to integrate the necessary cognitive and metacognitive learning approaches across all key learning areas in the digital learning without altering the curriculum.
ASSESSING THE DRIVER'S RISK PERCEPTION DURING AUTONOMOUS DRIVING

Abstract Advanced Driver-Assistance Systems were created to address the driver's failures. All these ADAS are a part of the evolution of the vehicles towards whole automation. To complete its launch in the automotive market, autonomous vehicles have to pass safety tests to acquire the consumers’ trust. To receive the approval of the public, the self-driving car has to take into account the human feeling. The risk perceived by the driver is one of the new emotional form to integrate at the validation plan. The purpose of this study is to examine the perception of the risk of a self-driving car's driver.