Miracle-dz/UniFAR-FTUs huggingface dataset:
DISTRIBUTIONAL AND PHYLOGENETIC STUDIES ON INDIAN FOSSIL MAMMALS . IV THE PHYLOGENY OF THE INDIAN SUIDAE AND THE ORIGIN OF THE HIPPOPOTAMIDAE

During the middle and later portions of the Tertiary period, India was a great center for the adaptive radiation of the Suidae. The fossil remains of pigs are numerous in the Siwalik deposits of the northern Punjab, and they represent a variety of types indicative of the phylogenetic plasticity of the suid group. Here, in one locality, in a series of deposits ranging in age from Upper Miocene to Lower Pleistocene, may be found almost the complete phylogenetic history of the Suidae, from the most primitive, undifferentiated types to the very advanced, highly specialized genera. The following remarks will constitute a brief review of the Indian fossil Suidae, as found in the Siwalik Series, with especial emphasis on their relation to the general phylogenetic history of the group.
A Comparative Study on the Mother's Attachment, Self-concept, Daily Stress, Depression of Children from Multicultural and Typical Korean Families.

This research examined the psychological characteristics of children, such as children's attachment to their mothers, self-concept, daily stress, and depression, in both multicultural and typical Korean families. Elementary school children in 3rd to 6th grade were surveyed in Chunnam province. 158 cases of multicultural families and 181 cases of typical Korean families were analyzed by the methods of Cronbach's a coefficient and independent t-test using SPSS program. Children from multicultural families had less attachment to their mothers and self respect than those from typical Korean families. Regarding daily stress, children from multicultural families highly recognize the economic and physical environments, whereas children from typical Korean families highly recognize the academic achievements and the relationships with their teachers. Children from multicultural families were highly prone to depression compared to the children from typical Korean families.
Linear and nonlinear relations between psychosocial job characteristics, subjective outcomes, and sickness absence : baseline results from SMASH

This study investigates the demand-control-support (DCS) model by (a) using a more focused measure of job control, (b) testing for interactive and nonlinear relationships, and (c) further extending the model to the prediction of an objective outcome measure (i.e., company-administrated sickness absence). Hypotheses were tested in a heterogeneous sample of 1,739 employees from a 3-year prospective cohort study called SMASH (Study on Musculoskelatal Disorders, Absenteeism, Stress, and Health). Baseline results showed that a linear additive model was superior for job satisfaction, psychosomatic health complaints, and sickness absence, whereas a curvilinear model was superior for emotional effects. Second, it seems sensible to pay more attention to curvilinear relationships in future research. Finally, the DCS model was not supported using a more objective outcome measure.
Investigating the Representation of Multiple Intelligences Theory in TPSOL Textbooks.

As the main applicable language resources in classrooms, textbooks provide the primary source of information, and they serve the foundation of instruction and transfer the curriculum objectives. Therefore, teachers should be conscious in choosing the right textbooks and activities for their students. The purpose of this study is to investigate one series of commonly used TPSOL textbooks in light of Multiple Intelligences Theory. Three Textbooks of Farsi Biyamuzim series were analyzed using a MI checklist developed by the present researchers based on the review of the related literature and frameworks of the theory. Careful analysis of the activities revealed that textbooks mostly catered for verbal/linguistic, visual/spatial, logical/mathematical, and interpersonal intelligences. The least dominant intelligence were intrapersonal, musical, and naturalist intelligence types and no example of the bodily/kinesthetic intelligence was observed in the analyzed textbooks' activities. In addition, the results of the study did not show any significant effect of proficiency level on application of intelligence types in textbooks. The findings of the study suggest the administrators and material developers to consider the necessity of applying various intelligence types in designing TPSOL textbooks.
The Use of Collaborative Strategic Reading to Teach Reading

The objective of this research is to investigate the types of grammatical errors made by the sixth semester English department students of Syiah Kuala University in writing. The method used in this research was quantitative in nature. The samples of this research were 20 English Department Students of Syiah Kuala University using writing test on the descriptive text. The data were analysed that focus on omission, addition, misformation, and misordering. The result found that the most common type of errors made by the students is misformation (47 errors), followed by omission (43 errors), addition (6 errors), misordering (5 errors). So, it can be concluded that students still made errors in their writing. Hence, this research result can give information for the teachers or readers about the types of grammatical error committed by the students. Thus, through the error analysis, the lecturers should give more detailed grammar teaching materials to the students.
Feedback Provided Within Structured Reading Programs: A Systematic Review

Abstract Feedback can have a positive effect on student performance in the classroom, however not all feedback is equal, and some types of feedback can actually worsen performance. Little is known about whether existing evidence-based reading programs provide the feedback that has been shown to enhance student performance. The purpose of this study was to systematically review five evidence-based early reading program manuals to explore the types of feedback provided to students. A systematic coding system was developed and applied to describe the feedback provided in light of existing research on the effects of feedback. Although each program involves provision of a substantial amount of feedback to students, areas for improvement in characteristics of the feedback provided were noted. Suggestions for improvement include eliminating general and vague publicly provided praise statements, increasing the provision of private, individualized feedback that communicates specific information about task performance, and feedback that includes comparison to standards or previous performances rather than to other peers. Directions for future research and additional implications for program developers and teachers are provided.
Identities for generalized balancing numbers

Abstract: We study properties of generalized balancing numbers. We start with some basic identities. Thereafter, we focus on connections to generalized Fibonacci numbers. Using generating functions we prove fundamental relations between these two sequences. Many interesting examples involving balancing, Lucas-balancing, Fibonacci, and Lucas numbers are obtained as special cases of our relations.
A framework for adaptive MaxEnt modeling within distributed sensors and decision fusion for robust target detection/recognition

The Maximum Entropy (MaxEnt) information theoretic model parametric framework was introduced in a prior paper for distributed decision fusion (DDF) without knowledge of prior probabilities of local decisions. The paper demonstrated the effectiveness of the MaxEnt fusion center by achieving the best, realistic detection performance with respect to published results of either the Bayesian formulation or the Neyman-Pearson criterion. This paper represents the framework of an extension of MaxEnt DDF, called E-MaxEnt using: individual sensor MaxEnt classifiers for targets classification/recognition, and by fusing local classifier decisions. Specifically, in E-MaxEnt each sensor has a front-end pre-processing system for both signal detection and to process unique target attributes extracted for example from observed target imagery, which attributes are stored for reference/learning/comparison in the sensors MaxEnt classifiers. Based on the degree of match, each sensor generates local binary decisions that are sent to a MaxEnt fusion center, in the usual parallel architecture. No assumptions are made about knowing any local decision rules. The sensors are taking simultaneous (synchronized) measurements with overlapping FOV overages. It should be noted that the above description is not meant to address the “needle-in-haystack” problem, but rather address finding the presence, viz., classify/recognize a previously seen “known” target in areas where previously seen targets most likely are, along with other targets. At the time of writing, the data sets to test the algorithm were not available, but front-end image processing and MaxEnt classifiers were implemented. It is hoped that someone could provide the necessary data sets so the efficacy of the method could be demonstrated and compared with alternative approaches.
Deep representation learning for keypoint localization

Keypoint localization aims to locate points of interest from the input image. This technique has become an important tool for many computer vision tasks such as ﬁne-grained visual categorization, object detection, and pose estimation. Tremendous eﬀort, therefore, has been devoted to improving the performance of keypoint localization. However, most of the proposed methods supervise keypoint detectors using a conﬁdence map generated from ground-truth keypoint locations. Furthermore, the maximum achievable localization accuracy diﬀers from keypoint to keypoint, because it is determined by the underlying keypoint structures. Thus the keypoint detector often fails to detect ambiguous keypoints if trained with strict supervision, that is, permitting only a small localization error. Training with looser supervision could help detect the ambiguous keypoints, but this comes at a cost to localization accuracy for those keypoints with distinctive appearances. In this thesis, we propose hierarchically supervised nets (HSNs), a method that imposes hierarchical supervision within deep convolutional neural networks (CNNs) for keypoint localization. To achieve this, we ﬁrstly propose a fully convolutional Inception network with several branches of varying depths to obtain hierarchical feature representations. Then, we build a coarse part detector on top of each branch of features and a ﬁne part detector which takes features from all the branches as the input.
Third-party Views of Incarceration: Justice, Desistance, and Offender Reintegration

One potential reason for punishing criminal offenders is so that third parties will become willing to socially reintegrate the offenders after their punishments have been completed. Another reason is simply to satisfy third-party desires that appropriate punishments be issued. In an experimental study, conducted with undergraduate students in South Africa, it was found that increasing the length of incarceration had a causal effect on increasing both reintegration willingness and punishment appropriateness, but the effect on reintegration willingness was substantially smaller. Also, two intermediary punishment goals were tested as mediators: the perceived likelihood of criminal desistance, and the perceived attainment of punitive justice. For the relationship between length of incarceration and reintegration willingness, desistance acted as a mediator but punitive justice did not. The findings indicate that, in order to increase reintegration willingness, policies need to increase the perceived likelihood of desistance rather than the perceived attainment of punitive justice.
Relationship between Social Support and Work-Family Conflict: A Case Study of Women Employees in a Malaysian Higher Education Institution

Work-family conflict (WFC) is a relatively less studied construct in the Eastern world and even sparser in the academic literature regarding working women in Malaysia. This paper is one of the few studies to examine social support mechanism used to alleviate such conflicts. The study examined the relationship between social support from work domain (supervisor support) and family domain (family support) addressing both directions of the conflict, work-to-family (W-to-FC) and family-to-work conflict (F-to-WC). A total of 278 women employees (146 academics and 132 non-academics) at a Malaysian Technical University participated in the study. The well-established instrument in work family literature was used to collect data via a survey. The results indicated a moderate level but relatively high mean value of W-to-FC than F-to-WC reported by women. Contradictory to a large body of work-family literature, the Pearson correlation analysis showed that work-family specific supervisor support is not related to either directions of work-family conflict (WFC) for Malaysian women. Social support provided by family however, was found to be statistically and negatively related to both directions of conflict. Findings from this study contribute to the understanding of the role of informal workplace social support by supervisor and family support with WFC in Malaysian educational institutions specifically in TVET. The results showed that women employees do experience WFC and supervisor work-family support is not enough to mitigate these conflicts. Hence, to produce women role models and retain them in the workplace, TVET higher learning institutions such as universities need to look into other workplace social support (like, family friendly policies, maternal leave, onsite childcare) to reduce the conflict for women employees.
Determinants of dividend payout decisions – the case of publicly quoted food industry enterprises operating in emerging markets

Abstract The paper examines the factors influencing dividend payout decisions. Our analysis is based on unbalanced panel data with 799 observations of companies from 15 countries over a period of 14 years. The study develops eight research hypotheses and uses a modelling approach based on the random effects panel probit model. An important conclusion reached in our study is that a company’s financial situation in preceding year influences the dividend payout decision. In addition, the key significant determinants of dividend payout decision in the period covered by our study include free cash flow, growth, liquidity, profitability and size. These important research results are confirmed by other studies in the field. They are therefore essential for determining dividend policies. Individual effects across investigated enterprises also played an important role in the dividend policy.
Detecting file:// and exposed component vulnerabilities in Android apps

In only a few years, smartphones have already become indispensable tools for many people to manage their daily lives. However, our privacy and security are constantly threatened by mobile malwares and vulnerable mobile apps. Detecting these malwares and uncovering vulnerable apps is therefore one of the most pressing problems confronting the security research community. This thesis considers two main security problems in Android platform, the most popular mobile operating system to date. First, we identify four types of attacks in Android browsers, collectively known as FileCross that exploits the vulnerable file:// interfaces to obtain user’s private files, such as cookies, bookmarks, and browsing histories. We design an automated system to dynamically test 115 browser apps collected from Google Play and find that 64 of them being vulnerable to the attacks. They include the popular Firefox, Baidu and Maxthon browsers, and the more application-specific ones, including UC Browser HD for tablet users, Wikipedia Browser, and Kids Safe Browser. A detailed analysis of these browsers further shows that 26 browsers (23%) expose their browsing interfaces unintentionally. In response to our reports, the developers concerned promptly patched their browsers by forbidding file:// access to private file zones, disabling JavaScript execution in file:// URLs, or even blocking external file:// URLs. We employ the same system to validate the ten patches received from the developers and find one still failing to block the vulnerability. The second problem is related to the fundamental feature of Android—the componentbased communication—in which apps can utilize other apps’ exported components for flexible coding and data sharing. In return for this convenience, the exported components, if not well designed, will run into serious security risks. In this study, we consider a general class of vulnerabilities occurred in exported components, named exposed component vulnerability (ECV), which exposes privileged capabilities or private resources to other unauthorized apps.
Differentiated Instruction: Accommodating the Needs of All Learners

The purpose of this qualitative research study is to understand how elementary school teachers differentiate their instruction to meet their students’ learning needs. This study also seeks to uncover teachers’ conceptualizations of differentiation, the challenges they experience with its implementation into their practice and their response to them. Literature on differentiation suggests that teachers are hesitant to implement differentiation into their practice and that few are able to accommodate their students’ learning needs due to their limited understanding of differentiation and lack of training. Consequently, the main research question guiding this study is: How do elementary school teachers differentiate their instruction to meet the learning needs of their students and what outcomes do they observe? Data collected through semi-structured interviews with two elementary school teachers working in the Greater Toronto Area, suggests that teachers in this study have a thorough understanding of differentiation. It also suggests that they are adept at accommodating their students’ learning needs through differentiation and that they recognize the importance of differentiation to student learning. These findings indicate that teachers’ experiences differentiating their instruction and their conceptualizations thereof, have not yet been adequately explored within the literature on differentiation, and that further research may help to gain insight into the complexity of teachers’ experiences with the latter.
A MACHINE LEARNING-BASED APPROACH TO ACCELERATING COMPUTATIONAL DESIGN SYNTHESIS

A modification-based framework for computational design synthesis augmented by machine learning is presented. The framework allows a wide range of engineering design problems to be addressed via a machine learning based search algorithm with minimal required adaptation of the search heuristics. Search is accomplished via two agents, a ‘data modeller’ and a ‘modification advisor’, that work together to guide a generate-and-test-oriented search with suggested actions based on past observation of the search. A proposed implementation of the search algorithm is discussed and the results of its application to two design examples are presented. Implications of the method for engineering design are discussed.
Data Mining: Practical Machine Learning Tools and Techniques, 3/E

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization
Computational methods in semantics

Meaning Representation, or AMR (Banarescu et al., 2013), is a more recent formalism for representing the meaning of linguistic structures as directed graphs. The last few years have seen a rise in AMR-related work, including a corpus of AMR-annotated text (Banarescu et al., 2013), several approaches to generating AMRs from running text (Vanderwende et al., 2015; Peng et al., 2015; Pust et al., 2015), and various applications to computational semantics (Pan et al., 2015; Liu et al., 2015). Figure 2.8: AMR representation of The boy wants to go (Banarescu et al., 2013, p.179) Nodes of AMR graphs represent concepts of two basic types: they are either English words, or framesets from PropBank (Palmer et al., 2005), used to abstract away from English syntax. PropBank framesets are essentially English verbs (or verb-particle con-
Multimodal optimization by using hybrid of artificial bee colony algorithm and BFGS algorithm

Optimization has become one of the important fields in Mathematics. Many problems in engineering and science can be formulated into optimization problems. They maybe have many local optima. The optimization problem with many local optima, known as multimodal optimization problem, is how to find the global solution. Several metaheuristic methods have been proposed to solve multimodal optimization problems such as Particle Swarm Optimization (PSO), Genetics Algorithm (GA), Artificial Bee Colony (ABC) algorithm, etc. The performance of the ABC algorithm is better than or similar to those of other population-based algorithms with the advantage of employing a fewer control parameters. The ABC algorithm also has the advantages of strong robustness, fast convergence and high flexibility. However, it has the disadvantages premature convergence in the later search period. The accuracy of the optimal value cannot meet the requirements sometimes. Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is a good iterative method for finding a local optimum. Compared with other local optimization methods, the BFGS algorithm is better. Based on the advantages of the ABC algorithm and the BFGS algorithm, this paper proposes a hybrid of the artificial bee colony algorithm and the BFGS algorithm to solve the multimodal optimization problem. The first step is that the ABC algorithm is run to find a point. In the second step is that the point obtained by the first step is used as an initial point of BFGS algorithm. The results show that the hybrid method can overcome from the basic ABC algorithm problems for almost all test function. However, if the shape of function is flat, the proposed method cannot work well.