Miracle-dz/UniFAR-FTUs huggingface dataset:
Data imputation on IoT gateways using machine learning

IoT (Internet of Things) gateways receive data from thousands of sensors and send it to the cloud, which runs intelligent services. However, collected data might have missing or anomalous values due to various reasons, such as network problems, damaged sensors, or security attacks. Missing and noisy data can affect future decision-making, so IoT gateways need to transmit consistent data to the cloud. This work proposes a method to impute missing data on IoT gateways based on neural network regression. We validate this method using six years of weather data from a station located in Rio de Janeiro, considering different percentages of missing data. The results show that the regression models have more than a 0.92 R-squared score and low errors when predicting sensor measurements. Furthermore, we show that the neural network implementation can run on IoT gateways due to its short execution time and low memory utilization. Finally, we show that a single model performs well even when 50% of the data is missing, highlighting the proposed approach's generality.
Regulatory sanction to an audit firm: An analysis of client stock market reaction

Regulatory compliance is a significant element of environmental security, the safeguarding of stable state revenues and the achievement of other key public objectives. Capital market effects of enforcement have received a lot of attention, but it has not been much reviewed and explored within the scope of Indonesian capital market. Specifically, this research aims to provide an in-depth review of the regulatory sanction imposed on a major audit firm in Indonesia and to analyze the impact of investor valuations by looking at the stock prices of each client company of the audit firm. We examine the stock market effects of the regulatory sanction imposed upon one of the Big Four auditors in Indonesia, namely KAP Tanubrata, Sutanto, Fahmi, Bambang & Rekan as a member of BDO International Limited for actions related to its audit of PT Garuda Indonesia (Persero) Tbk (Garuda Indonesia). The shares of 37 BDO auditees and 37 non-BDO auditees are analyzed and their respective cumulative abnormal returns (CAR) are calculated in order to observe their reaction upon the event, i.e. the imposition of sanction by regulator towards BDO. Two event windows are examined in our analysis April 26th, 2019 is the date on which the issue was informally reported by the media/press and June 28th, 2019 is the date on which the regulator officially imposes its sanction. Using the event study methodology, we calculated the CAR and obtained results that showed significant differences in the CAR figures for BDO and non-BDO clients. Our findings suggest that a significant difference in the CARs of BDO and non-BDO clients only exists over a brief period of time, indicating a lack of major concerns for the case. Therefore, we see that BDO has no reputational loss in relation to the Garuda Indonesia case, both on informal and formal dates.
Birds Identification System using Deep Learning

Identifying birds is one of challenging role for bird watchers due to the similarity of the birds’ forms/image background and the lack of experience for watchers. So, it needs a computer system based images to help birdwatchers in order to identify birds. This study aims at investigating the use of deep learning for birds’ identification using convolutional neural network for extracting features from images. The investigation was performed on database contained 4340 images that collected by the paper author from Jordan. The Principal Component Analysis (was applied on layer 6 and 7, as well as on the statistical operations of merging the two layers like: average, minimum, maximum and combine of both layers. The datasets were investigated by the following classifiers: Artificial neural networks, K-Nearest Neighbor, Random Forest, Naïve Bayes and Decision Tree. Whereas, the metrics used in each classifier are: accuracy, precision, recall, and F-Measure. The results of investigation include and not limited to the following, the PCA used on the deep features does not only reduce the dimensionality, and therefore, the training/testing time is reduced significantly, but also allows for increasing the identification accuracy, particularly when using the Artificial Neural Networks classifier. Based on the results of classifiers; Artificial neural networks showed high classification accuracy (70.9908), precision (0.718), recall (0.71) and F-Measure (0.708) compared to other classifiers. Keywords—Birds identification; deep learning convolutional neural networks (CNN); VGG-19; principal component analysis (PCA)
Soret and Dufour effects on MHD flow about a rotating vertical cone in presence of radiation

In this paper a numerical study of Soret and Dufour effect on MHD flow in presence of thermal radiation about a rotating vertical cone has been investigated. The governing equations are nonlinear partial differential equations and so by using similarity transformations they are converted to ordinary differential equations. MATLAB’s built in solver bvp4c is employed to solve numerically the ODE’s. The graph of velocity, temperature and concentration of the fluid are illustrated. To verify the accuracy of the numerical solution, a comparison for wall shear stress in tangential and circumferential direction with the present result and the one available in literature is done and the outcomes are in good match. Also local rate of surface heat transfer and mass transfer for different values of parameters are obtained.
Effect of mesenchymal stem cells combined with chondroitin sulfate in an osteoarthritis in vitro model

Introduction: Osteoarthritis (OA) is a degenerative joint disease which affects the whole joint structure. Many authors have focused on the factors responsible for the development of inflammatory processes involved in OA. Adipose tissue-derived mesenchymal stem cells (ASCs) represent a promising alternative of cell-based therapy strategy in the treatment of OA which could be combined with any other drug. Chondroitin sulfate plays a protective role in the joint based on the decrease of pro-inflammatory cytokines, thus having an important role in activating and inhibiting the metabolic pathways in chondrocytes. Aims: The effectiveness of chondroitin sulfate and ASCs combined in an in vitro model of OA has been evaluated in this study. Materials: Cytokines and factors which are involved in OA as well as specific cartilage gene expression after adding ASCs and chondroitin sulfate have been discussed in detail. Results: Our results show a decrease in the expression of all genes related to the pro-inflammatory cytokines analysed. Although there was no increase in the expression of the specific genes of the cartilage matrix, such as collagen type II and aggrecan. Conclusions: This study shows the effectiveness of association of ASCs and chondroitin sulfate for the treatment of OA.
Effect of Phytochemical Components, Morphological and Histological Leaf Structure of Five Tomato Hybrids on Tetranychus urticae Koch Infestation

Experiments were conducted to estimate the susceptibility of five tomato hybrids (Supper-gekal, F1Gs-12, Marwa, El-basha 1077 and Salymia 65010) to infestation with Tetranychus urticae Koch and its population fluctuation during the two successive seasons, summer 2013 and 2014, at Giza governorate. Supper-gekal and F1Gs-12 hybrids were the most highly significant susceptible to infestation. It recorded 31.16 (25.10%) & 24.61 (24.82%) and 28.10 (22.63%) & 22.09 (22.28%) moving mite stages / leaflet during 2013 and 2014 seasons, respectively, followed by moderate infestation observed on hybrids (El-basha 1077 and Marwa), being 25.20 (20.29%) & 19.80 (19.96%) for the former; 21.32 (17.17%) & 17.67 (17.82%) for the latter hybrid during the two seasons, respectively. The lowest infestation was observed on Salymia (65010) hybrid which recorded, 18.38 (14.81%) and 15.00 (15.13%) during the two successive seasons, respectively. Susceptibility of five tomato hybrids to infestation with T. urticae may be affected by plant leaf morphological and histological characteristics and its chemical contents. The number of leaf trichomes /cm in upper and lower surfaces averaged (2606&8606), (3939&10557), (5788&9000), (7030&13870) and (10970&16818) for Suppergekal, F1Gs-12, El-basha 1077, Marwa and Salymia 65010, respectively, the less number of trichomes the more mite infestation. Also, the mean thickness of lower epidermis was 19.61, 18.85, 27.57, 22.63 and 26.55 μm, while the upper epidermis was 25.64, 28.58, 27.77, 26.78 and 30.50 μm for the aforementioned hybrids, respectively. Positive relationship occurred between mite infestation levels and total carbohydrates in tomato leaves, while a negative relationship were found with alkaloids and total phenolic compounds, total flavonoids and total carotenes. Mite populations reached its peak in the 1 week of October in the first season, while during the second season occurred in the 2 week of September and the 1 week of October. The high population density of T. urticae found in 2013 compared with 2014 might be due to environmental conditions.
A compact dual-band semi-flexible antenna at 2.45 GHz and 5.8 GHz for wearable applications

In this work, a compact dual-band semi-flexible antenna operating at 2.45 GHz and 5.8 GHz for the industrial, scientific and medical (ISM) band is presented. The antenna is fabricated on a semi-flexible substrate material, Rogers Duroid RO3003™ with a low-profile feature with dimensions of 30×38 mm2 which makes it a good solution for wearable applications. Bending investigation is also performed over a vacuum cylinder and the diameters are varied at 50 mm, 80 mm and 100 mm, that represents the average human arm’s diameter. The bending investigation shows that reflection coefficients for all diameters are almost similar which imply that the antenna will operate at the dual-band resonant frequencies, even in bending condition. The simulated specific absorption rate (SAR) in CST MWS® software shows that the antenna obeys the FCC and ICNIRP guidelines for 1 mW of input power. The SAR limits at 2.45 GHz for 1 g of human tissue is simulated at 0.271 W/kg (FCC standard: 1.6 W/kg) while for 10 g is at 0.0551 W/kg (ICNIRP standard: 2 W/kg. On the other hand, the SAR limits at 5.8 GHz are computed at 0.202 W/kg for 1 g and 0.0532 W/kg for 10 g.
Short Communication Anatomical and physiological effects of Gynaikothrips uzeli Zimmerman (Thysanoptera:Phlaeothripidae) feeding on Ficus benjamina L. leaves

Feeding by Gynaikothrips uzeli on Ficus benjamina causes extensive morphological changes (leaf curling and galling) to its leaves and reduced plant vigour. This in turn decreases the aesthetic horticultural appeal and economic value of the plant. In an effort to determine the extent of these changes, histological sections were made from mature healthy and galled leaves of F. benjamina. In addition, physiological parameters viz., leaf osmotic potential, stomatal conductance, chlorophyll content, leaf specific area and leaf dry matter content were assessed. Histological comparisons indicated that the lower epidermis, upper hypodermis, palisade and spongy mesophyll layers were significantly thinner in galled leaves than in healthy leaves. Additionally, stomatal density in galled leaves was approximately half that in healthy leaves. Leaf area and specific leaf area were significantly lower in infested leaves than in healthy leaves while osmotic potential was significantly more negative in infested than in healthy leaves. However, while levels of chlorophylls a and b decreased significantly in galled leaves compared to healthy leaves, the chlorophyll a/b ratio was not significantly different. The current study has identified several significant anatomical and physiological changes to leaves of F. benjamina infested with G. uzeli.
Taguchi Optimization of Fused Deposition Modeling Process Parameters on Mechanical Characteristics of PLA+ Filament Material

This study was realized to determine effects of Fused Deposition Modeling (FDM) process parameters on mechanical characteristics with Taguchi optimization method. Three different FDM process parameters used for modified Polylactic Acid (PLA+) filament material; filling structures (Rectilinear, Triangular, and Full Honeycomb), occupancy rates (10, 30, and 50 %) and table orientation (0, 60, and - 45°) was specified as variable parameters for experiments. Other parameters kept fixed for each tensile and izod impact test samples were printed according to the ISO 527 – Type IV and ISO 180-Type I standards. The results found tensile strength values and izod impact values directly proportionate with occupancy rate. The difference between the estimation model and the results of experiments did not exceed the maximum value of 1.8 %. Thus, using the equations derived from this optimization, printing parameters can be determined for the desired tensile strength and izod impact values. By improving the material properties using modified PLA+ filament material as observed in the results, it will be possible to provide support for researchers, design engineers and manufacturer to optimize raw-material usage and margin.
A Chorus-Section Detection Method for Lyrics Text

This paper addresses the novel task of detecting chorus sections in English and Japanese lyrics text. Although chorus-section detection using audio signals has been studied, whether chorus sections can be detected from text-only lyrics is an open issue. Another open issue is whether patterns of repeating lyric lines such as those appearing in chorus sections depend on language. To investigate these issues, we propose a neural network-based model for sequence labeling. It can learn phrase repetition and linguistic features to detect chorus sections in lyrics text. It is, however, difficult to train this model since there was no dataset of lyrics with chorus-section annotations as there was no prior work on this task. We therefore generate a large amount of training data with such annotations by leveraging pairs of musical audio signals and their corresponding manually time-aligned lyrics; we first automatically detect chorus sections from the audio signals and then use their temporal positions to transfer them to the line-level chorus-section annotations for the lyrics. Experimental results show that the proposed model with the generated data contributes to detecting the chorus sections, that the model trained on Japanese lyrics can detect chorus sections surprisingly well in English lyrics and that patterns of repeating lyric lines are language-independent.
Network Sorting and Labor Market Outcomes: Evidence from the Chaotic Dispersal of the Viet Kieu

IZA DP No. 13952 DECEMBER 2020 Network Sorting and Labor Market Outcomes: Evidence from the Chaotic Dispersal of the Viet Kieu* Immigrants’ social networks exert considerable influence over their labor market opportunities and yet the pre-sorting of co-nationals by ability and across space, endures as a key challenge for empiricists attempting to establish causal network effects. To surmount this issue, we leverage the chaotic dispersal of Vietnamese refugees across the U.S. in 1975, which was demonstrably exogenous in both initial network size and quality, in tandem with an absence of pre-existing networks of co-nationals, to causally identify the effects of network size and network quality on refugees’: occupational outcomes, skill intensity and skill upgrading. Our administrative data provide refugee’s precise initial locations and pre-placement characteristics in Vietnam, which we uniquely employ as additional controls, as well as longitudinal information about their locations and occupations six years hence. We construct instruments from the initial quasi-random refugee allocations of network size and quality and leverage refugees’ geo-locations to insulate our results from the Reflection Problem. Overall, network quality is a far more important determinant of refugees’ labor market outcomes when compared to network size, one interpretation of which is that the type of referrals network members receive are more important than the overall number of referrals. Blue-collar networks: increase the probability of refugees’ working in blue-collar jobs, draw additional workers into more manual and less complex intensive employment and serve to up-skill individuals along the manual skill dimension. Given the protracted circumstances under which the Viet Kieu entered the U.S., the composition of their networks played a pivotal role in their ultimate success. JEL Classification: F22, J61
Male Violence Against Women: An Exploratory Study of Its Manifestations, Causes, and Discrepancies over Jordanian Women under Corona Pandemic

Objectives: The study aimed to uncover the percentages of battered women in Jordan, its causes, forms, and relationship to several social factors. Methodology: The study was conducted on a random, targeted sample of (1308) women based on social sample survey method and electronic questionnaire tool for data collection, the Descriptive statistical method and chi-square test were used to examine statistically significant differences. Results: The study found that the percentage of battered women reached 17.1% during Corona pandemic in 2020, and the increase in men's violence against women during this period of was in large and medium degrees according to the sample. Husbands were the most practicing violence against their wives at 37.5%, followed by fathers against daughters at 28.6%, and brothers against sisters at 26.8%. The causes of male violence are due to social factors represented by male domination culture, interference by family and relatives, and economic factors represented by the high cost of living, low household income and poverty. The most common forms of violence are verbal, physical, and psychological. The study found that women in southern Jordan, who are poorer and less educated, and who live in a large family, are the most abused. The study recommended activating family and community service and reform offices, solving problems of deteriorating economic situation and individual poverty, and enlightening community awareness of the dangers of domestic violence.
Music Source Separation Using ASPP Based on Coupled U-Net Model

Noise has established itself as one of the factors that interfere with modern human life, and various noise canceling techniques have been studied to prevent noise. While the old era's noise-canceling technique focused on the physical soundproofing technique, multiple studies have been conducted on the active noise canceling technique that removes only the activated noise in the current era. Active noise canceling (ANC) or digital noise-canceling technology is based on the sound source separation method. This leads to sound source separation technology, which refers to the technology to separate individual sound signals from mixture sounds. Most of the source separation technologies focus on improving speech, not noise reduction. This technology makes it possible to obtain desired sound information more accurately and further improves noise-canceling technology by eliminating unwanted sound information. To provide deeper capability and more enhanced sound separation than the existing structure, we are focused on coupled U-Net model and Atrous spatial pyramid pooling technique (ASPP). This paper presents the music source separation method that combined Coupled U-Net structure with Atrous spatial pyramid pooling technique. To prove the proposed source separation method, we compared GNSDR, GSIR, and GSAR using MIR-1K, a data set that can evaluate the performance of the music source separation. Performance results show that the proposed source separation method overcame other methods' disadvantages and strengthened the feature map.
Detecting Suicide Risk Through Twitter

Mental illness is one of the main causes of illness worldwide. Currently, it is estimated that about 300 million people suffer from depression according to the World Health Organization (WHO). In this context, this work deals with the construction of a platform that allows to detect the risk of suicide using data from Twitter. This platform combines external emotional processing systems, clustering techniques and a system based on machine learning that facilitate the automatic classification of the information obtained. The entire process is articulated around a multidisciplinary team of professionals in Health Sciences and Information Technology, generating as a result a useful prototype for suicide prevention
Exploring educational attainment by immigrant background

This paper aims to show the variation in 2015 PISA reading scores in six OECD countries based on immigrant generation. OLS regression models for each country are used to consider self-selection into migration by parents and their human capital and ultimately predict reading scores of their descendants compared to ancestral natives. Particular attention is paid to national origins of immigrants in Australia and Austria to determine whether segmented assimilation is apparent. Additionally, there is in depth analysis of the 2.5 generation and how this group operates compared to both the natives and the mono-national second generation. Moreover, comparison between the parental composition of this 2.5 generation, native fathers versus native mothers is undertaken. Results indicate that there is a gap between immigrant children and natives in non-Anglo-speaking countries. Positive self-selection, linguistics and stringent migration policies offer an explanation as to why these positive and negative gaps appear across the countries. Segmented assimilation is also identified with diverging trajectories based on different origin groups in both Austria and Australia. Within the 2.5 generation native fathers are more important than native mothers and this combination offers some form of buffering to avoid second generation disadvantage in certain destinations.
Coping Skills Of The Female Softball Players At Different Levels Of Performance

The purpose of present study was to compare the coping skills among the inter-college, inter-university and national level female softball players. A total three hundred and eighty five (N=385) softball players (87 inter-college level, 193 inter-university level and 105 national level) were selected as subjects from various colleges, universities and states. The average age of players was 19.98±1.86 years. Coping skills of the softball players was assessed with the help of Athletic Coping Skills Inventory-28 (ACSI-28), constructed by Smith et al. (1995). The results of One-way Analysis of Variance (ANOVA) revealed significant differences on sub-variables of coping skill i.e. coping with adversity (p<0.05), coachability (p<0.05), concentration (p<0.05), confidence and achievement motivation (p<0.05), goal setting and mental preparation (p<0.05), peaking under pressure (p<0.05) and freedom from worry (p<0.05) among the inter-college, inter-university and national level softball players. The post-hoc analysis showed that inter-college level softball players had significantly lower mean values on most of the sub-variables than inter-university and national level softball players. Statistical significantly differences were reported in overall mean score of coping skills (p<0.05) among the players of different performance level. The post-hoc analysis revealed that inter-university and national level players were found to have significantly higher mean value on coping skills (p<0.05) as compared to intercollege level players. It is concluded that the players belonging to higher performance level reported higher scores on coping skills and its sub-variables.