Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of word reading and oral language skills in reading comprehension in Syrian refugee children

Abstract Canada has resettled more than 57,000 Syrian refugees since 2015 (Government of Canada, 2017). However, little is known about refugee children’s language and literacy development. The present study evaluated Syrian refugee children’s performance on language and literacy measures in English and Arabic, and examined whether the simple view of reading model is applicable in both of their languages. Participants consisted of 115 Syrian refugee children 6–13 years of age. They received a battery of language and literacy measures including word reading, vocabulary, oral narratives, and reading comprehension in both English and Arabic. Compared to the normative samples, refugee children performed poorly on English standardized measures. They also demonstrated difficulties in Arabic, as more than half of the children were not able to read in the language. Despite the relatively low performance, there was evidence to support the simple view of reading model in both languages. In addition, oral language skills played a larger role in English reading comprehension in the older group than the younger group. This age-group comparison was not carried out in Arabic due to reduced sample size. Theoretical and practical implications of the findings are discussed.
WHAT FACTORS PREVENT INDEPENDENT DIRECTORS FROM PROTECTING INTERESTS OF SHAREHOLDERS IN CASES OF CONFLICT OF INTEREST?

One way to mitigate agency conflicts between shareholders and managers in publicly traded companies with dispersed ownership is supplementation of independent outside directors to the board, who can monitor the top executives and prevent them from malfeasances. However, in conflict of interest situations, independent board members are not always enabled to oppose senior managers, particularly CEOs, who put their personal interests above those of shareholders. In such cases, the efforts of independent directors to monitor managers will be unsuccessful, which, in turn, may result in the latter receiving unjustified personal benefits at the expense of shareholders. Active participation of independent directors in resolving situations where interests of these groups do not align ensures the mitigation of tension and the proper functioning of the company. Thus, the establishment of cases where independent outsiders cannot fulfill their basic duties of shielding shareholders appears to be rather a significant issue.
An improved two-stream CNN method for abnormal behavior detection

In recent years, abnormal behavior detection has become an active research field in computer vision and image processing. Several methods based on traditional two-stream CNN or 3D-CNN have been proposed and successfully applied in abnormal behavior detection. However, the abnormal behavior data is rarely less in the real scenario, in other words, the data is imbalanced, which may lead to overfitting problem and affect the final results. In this paper, an improved abnormal behavior detection method based on a two-stream CNN method was proposed to address the problem mentioned above. In the proposed method, DenseNet is adopted to extract both spatial and temporal features, and focal loss is employed to alleviate the influence of imbalanced data. The experiment results show that the proposed method provides a good performance in the real-world scenario.
Temporal Aggregation and Long Memory for Asset Price Volatility

The effects of temporal aggregation and choice of sampling frequency are of great interest in modeling the dynamics of asset price volatility. We show how the squared low-frequency returns can be expressed in terms of the temporal aggregation of a high-frequency series. Based on the theory of temporal aggregation, we provide the link between the spectral density function of the squared low-frequency returns and that of the squared high-frequency returns. Furthermore, we analyze the properties of the spectral density function of realized volatility series, constructed from squared returns with different frequencies under temporal aggregation. Our theoretical results allow us to explain some findings reported recently and uncover new features of volatility in financial market indices. The theoretical findings are illustrated via the analysis of both low-frequency daily Standard and Poor’s 500 (S&P 500) returns from 1928 to 2011 and high-frequency 1-min S&P 500 returns from 1986 to 2007.
PHYTOCHEMICAL SCREENING AND BACTERICIDAL POTENCY OF JUSTICIA ADHATODA AGAINST CLINICAL PATHOGENS

Objective: Increasing prevalence of bacterial resistance has made an important public health issue in the modern world. Nowadays, many research efforts have been directed toward the development of drugs from medicinal plants for the treatment of diseases caused by microorganisms. Hence, the present attempt has been made to evaluate the bactericidal potency of traditional medicinal plant, Justicia adhatoda against selected clinical pathogens. Methods: Leaf extracts of J. adhatoda were subjected to standard phytochemical analyses by qualitative color tests to confirm the presence of active Phytoconstituents. Bactericidal potency of the leaf extracts of J. adhatoda was assessed by growth inhibition zone against Bacillus cereus, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Staphylococcus aureus. Sensitivity of the tested bacterial strains was also checked with standard reference antibiotic vancomycin. Results: Qualitative phytochemical analyses revealed the presence of alkaloids, terpenoids, flavonoids, tannins, steroids, anthraquinones, saponins, resins, glycosides, and phenols. Fourier-transform infrared spectrum confirmed the presence of phytochemicals belong to various functional groups such as alkenes, alkanes, aliphatic and aromatic amines, aldehydes, esters, alcohols, and phenols. Results on bactericidal efficacy demonstrated that solvents used in the present experiment showed higher activity in the order ethanol >ethyl acetate >chloroform >diethyl ether >methanol >acetone >water. Leaf extracts of J. adhatoda showed the higher activity for different clinical pathogens in the order of P. aeruginosa >B. cereus >E. coli >K. pneumoniae >S. aureus. Among the tested bacterial pathogens, B. cereus exhibited maximum susceptibility (18.33±0.94 mm) toward ethyl acetate extract. The maximum activity index (2.67±0.42) and fold area increase (6.31±2.24) were also observed against B. cereus for ethyl acetate extract. Conclusion: The present study revealed that J. adhatoda has broad spectrum of antibacterial activity and a potential source of antibacterial agents that could be useful for the control of infectious diseases.
Research on Missile Intelligent Penetration Based on Deep Reinforcement Learning

Penetration capability is one of the most critical measures for assessing the effectiveness of missile systems. In the face of an increasingly sophisticated antimissile system, this article explores the use of artificial intelligence to achieve missile intelligent penetration. The applicability of the deep reinforcement learning method to the missile intelligent penetration problem is elaborated. The Actor-Critic deep reinforcement learning algorithm is studied. The deep reinforcement learning environment model for the missile penetration scenario is developed based on the generic combat effectiveness simulation system, WESS. A multi-sample collaborative training method is designed. On this basis, a missile intelligent penetration training system framework based on WESS and deep reinforcement learning is proposed to guide the missile penetration intelligence learning.
Damage localization method for building structures based on the interrelation of dynamic displacement measurements using convolutional neural network

This study presents a damage localization method for building structures using dynamic displacement responses based on a convolutional neural network (CNN). The proposed method is based on the interrelation of dynamic displacement response measured from a building in a healthy state. Based on the interrelation constructed by CNN in advance, damaged stories are localized by investigating the discrepancy of dynamic responses between healthy and damaged states. Hence, this allows identification of the location of damage without the use of structural response labeled with damage information in the CNN training stage. Specifically, to construct the CNN presenting the interrelation of structural response of the building under the healthy state, dynamic displacement response is utilized in the both input and output of CNN. Then, when the building is suspected to be damaged, the displacement response measured from the building is used as an input data in the previously trained CNN. Based on the discrepancy between the output obtained by inputting damaged state response into CNN and the response in a healthy state, the location of damage in the building is identified. To express this discrepancy, indicators for damage localization are newly defined in this study, which can be calculated by healthy and damaged state responses with the trained CNN. Through the investigation of the distribution of those indicators extracted from multiple stories of the structure, the location of damage in building structures is identified. We validated the proposed method for identifying damage locations through a numerical study and an experimental study.
Transverse momentum broadening of high-energy partons from 3D lattice EQCD simulations

Heavy-ion collision experiments and related theoretical efforts of understanding the strong interaction constitute key endeavors in the modern pursuit of understanding nature. A central field of comparison for experiment and theory are the interactions of high-energy particles with a strongly interacting medium. The transverse momentum distribution of a high-energy particle broadens in crossing that very medium, quantified by the transverse collision kernel, C(b ⊥ ). It receives dominant infrared corrections which cannot be tackled perturbatively, even at weak coupling. In the present thesis, we calculated this contribution on the lattice in Electrostatic QCD as proposed by Caron-Huot and pioneered by Panero, Rummukainen, and Schafer. In order to put us into the position of extrapolating the discretization errors away, we developed a procedure to entirely remove discretization errors linear in the lattice spacing. Our data, provided at four different temperatures, render the common approximations of C(b ⊥ ) redundant.
Life and Death of Selfish Genes: Comparative Genomics Reveals the Dynamic Evolution of Cytoplasmic Incompatibility

Abstract Cytoplasmic incompatibility is a selfish reproductive manipulation induced by the endosymbiont Wolbachia in arthropods. In males Wolbachia modifies sperm, leading to embryonic mortality in crosses with Wolbachia-free females. In females, Wolbachia rescues the cross and allows development to proceed normally. This provides a reproductive advantage to infected females, allowing the maternally transmitted symbiont to spread rapidly through host populations. We identified homologs of the genes underlying this phenotype, cifA and cifB, in 52 of 71 new and published Wolbachia genome sequences. They are strongly associated with cytoplasmic incompatibility. There are up to seven copies of the genes in each genome, and phylogenetic analysis shows that Wolbachia frequently acquires new copies due to pervasive horizontal transfer between strains. In many cases, the genes have subsequently acquired loss-of-function mutations to become pseudogenes. As predicted by theory, this tends to occur first in cifB, whose sole function is to modify sperm, and then in cifA, which is required to rescue the cross in females. Although cif genes recombine, recombination is largely restricted to closely related homologs. This is predicted under a model of coevolution between sperm modification and embryonic rescue, where recombination between distantly related pairs of genes would create a self-incompatible strain. Together, these patterns of gene gain, loss, and recombination support evolutionary models of cytoplasmic incompatibility.
GUI Test Case Prioritization using Social Network Analysis

Graphical User Interfaces (GUIs) has become popular in today software products due to its user-friendly merit. However, GUI tests are costly and suffer explosive test cases. Moreover, software evolution causes the GUIs inevitably changed resulting in harder testing. Regression test has been widely used for GUIs software testing. It is considered to be the most expensive phase in the software testing process, though. Among several techniques, test case prioritization would help identify the reduced test cases in the regression testing suite, still maintain the same criteria as the original number of test cases in order to solve the limitation of time and cost in this phase. In this paper, we propose using network centrality from social network analysis to prioritize the test cases and compare each parameter to find a suitable parameter for GUI test case prioritization.
Factores de capital humano que impactan el capital social: estudio de hoteles del sur de Tamaulipas

Since the last century, there has been increasing interest in social capital and human capital. At least 3 out of the 17 sustainable development goals (SDGs) of the United Nations (UN) contemplate capital human as an important factor of social capital. However, this relationship between human capital factors as developers of social capital has not been sufficiently studied in Mexico, including Tamaulipas. The objective of this research was to determine if the variables of education, training, motivation and organizational environment have an impact on the social capital of small and medium sized enterprises. 82 questionnaires were administered to owners, managers or managers of 3 and 4 star hotels affiliated to the Association of Hotels and Motels of Southern Tamaulipas. A multiple linear regression analysis was performed to determine the correlations between the constructs. The results indicated that there is a relationship between the factors of human capital and social capital. A strong link between social capital andtraining was established.
Klasifikasi Pelanggan Deposito Potensial menggunakan Ensembel Least Square Support Vector Machine

Abstract The vast amount of data in the banking industry is very difficult to analyze manually to obtain useful information in order to determine a policy. Therefore, the use of data mining is expected to contribute in processing the data. Various methods have been widely used to classify data, one of which is the support vector machine method. This study aims to classify customers who have the potential to subscribe to deposits in the bank marketing dataset. The focus of this study proposes the development of the support vector machine method, which is the least square support vector machine method, then ensemble using boosting. The data to be processed is the bank marketing dataset. The results show that the proposed method that is ensemble least square support vector machine is better than other methods with a percentage of accuracy level of 95.15%, sensitivity 92.93%, specialization of 97.61%. The average total classification results is 95.23%. The vast amount of data in the banking industry is very difficult to analyze manually to obtain useful information in order to determine a policy. Therefore, the use of data mining is expected to contribute in processing the data. Various methods have been widely used to classify data, one of which is the support vector machine method. This study aims to classify customers who have the potential to subscribe to deposits in the bank marketing dataset. The focus of this study proposes the development of the support vector machine method, which is the least square support vector machine method, then ensemble using boosting. The data to be processed is the bank marketing dataset. The results show that the proposed method that is ensemble least square support vector machine is better than other methods with a percentage of accuracy level of 95.15%, sensitivity 92.93%, specialization of 97.61%. The average total classification results is 95.23%. Keyword: SVM, LS-SVM, Ensemble, AdaBoost, Bank Marketing. Journal of System and Computer Engineering (JSCE) ISSN (online): 2723-1240 Vol. 1 No. 1 Juli 2020
The Academic Mobility of Students from Kazakhstan to Japan: Problems and Prospects

Background: Despite the internationalization of higher education (IoHE) in Kazakhstan and it being among the top 15 countries sending students abroad, the level of student mobility between Kazakhstan and Japan and factors influencing it have not been well analyzed. Aim: The purpose of this study was to explore outgoing student mobility from Kazakhstan to Japan and the reasons underlining this situation. Methods: A descriptive study, involving a literature review and an analysis of data from the two largest universities in Kazakhstan together with those from the Bolashak international scholarship scheme, was performed. Results: The analysis suggested that outgoing student mobility from Kazakhstan to Japan remains at a stable but low level. The mobility of students is mainly a matter of the initiative of universities or the students themselves. The main reasons are the dependence of the policy of the IoHE on the foreign policy of Kazakhstan; the low level of pull factors for students such as affordability, recommendations from acquaintances, or lack of awareness about the country; and absence of a supportive legislative basis for the development of cooperation on higher education between the two countries. Conclusion: Given the areas of common interest economically and politically between Kazakhstan and Japan, there is potential for significantly more student mobility than there is at present.
Estimating the Overhead of Flow Control in Hierarchical Software-Defined Networks

Software-defined networks of a large dimension have a complex and branched structure; they are inherent in them by continuous expansion and dynamic characteristics, which significantly limits the application of known methods for their modelling and optimization. Consequently, the problems of the analysis of next-generation networks at the system level and the study of their new features, mainly due to large size and complex geometry (topology), are relevant. The tasks of constructing optimal structures of complex networks are solved by means of the theory of hierarchical systems, which is widely used to describe network structures, in particular, flow control networks. The work is devoted to the load balancing in SDN depending on their topological hierarchical structure and type of traffic (vertical, horizontal, and hybrid).
A Biomechanical Study of Various Fixation Strategies for the Treatment of Clavicle Fractures Using Three-Dimensional Upper-Body Musculoskeletal Finite Element Models

Plate or nail fixations have been applied to the repair of clavicle fractures. However, it is quite difficult to fairly evaluate the different clavicle fixation techniques owing to variations in the bone anatomy, bone quality, and fracture pattern. The purpose of this study was to investigate the biomechanical performances of different fixation techniques applied to a clavicle fracture using the finite element method. A simplified single-clavicle model and a complete human upper-body skeleton model were developed in this study. Three types of plate fixations, namely, superior clavicle plate, anterior clavicle plate, and clavicle anatomic spiral fixations, and one nail fixation, a titanium elastic nail fixation, were investigated and compared. The plate fixation techniques have a better fixation stability compared to the nail fixation technique. However, the nail fixation technique shows lower bone stress and can reduce the risk of a peri-implant fracture compared to the plate fixation techniques. Increasing the number of locking screws for the clavicle plate system can reduce the implant stress. Insertion of the bone plate into the anterior site of the clavicle or a multi-plane fixation is recommended to achieve the required biomechanical performance. A plate fixation revealed a relatively better fixation stability, and a nail fixation showed a lower risk of a peri-implant fracture.
Using Graph Convolutional Networks Skeleton-Based Pedestrian Intention Estimation Models for Trajectory Prediction

In autonomous driving scenarios, pedestrian trajectory prediction is an important research direction. Based on the spatio-temporal graph convolutional neural network, we propose a new pedestrian trajectory prediction algorithm. The new algorithm constructs a series of new models around pedestrian intention estimation. The construction of the estimation algorithm considers the following aspects: the contextual information of pedestrians and the surrounding environment, the “pedestrian ego-vehicle” interaction combined with the vehicle speed estimation, the pedestrian’s own skeletal structure information and body language estimation, which includes head joints and the relative structural relationship of the torso joints, including whether it is out of the same plane, is rotated, and so on. Skeleton information feature extraction and construction adopts the method of graph convolutional neural network to structure pedestrians into joints in the form of graphs in non-Euclidean space, and further adopts spatial temporal graph convolutional network for feature extraction and learning. The new method is named a “head-torso”-based spatial temporal graph convolutional network (HT-STGCN). On the dataset PID, the novel method achieves substantial improvements over mainstream methods. Experimental results show that combining HT-STGCN with observed action can improve trajectory prediction.
Whither mammalian ecology?

The critical agenda for mammalian ecologists over this century is to obtain a synthetic and predictive understanding of the factors that limit the distribution and abundance of mammals on Earth. During the last 100 years, a start has been made on this agenda, but only a start. Most mammal species have been described, but there still are tropical areas of undisclosed species richness. We have been measuring changes in distribution and abundance of many common mammals during the last century, and this monitoring agenda has become more critical as climate change has accelerated and habitat destruction has increased with human population growth. There are a small number of factors that can limit the distribution and abundance of mammals: weather, predation, food supplies, disease, and social behavior. Weather limits distribution and abundance mostly in an indirect manner by affecting food supplies, disease, and predation in the short term and habitat composition and structure in the longer term. A good starting point for all studies of mammals is to define them within a well-structured trophic web, and then quantify the major linkages within that web. We still are far from having data on enough model systems to develop a complete theory and understanding of how food webs are structured and constrained as climate shifts and humans disturb habitats. We have many of the bits and pieces for some of our major ecosystems but a poor understanding of the links and the resilience of our mammalian communities to changes in trophic webs driven by climate change and human disturbances.