Miracle-dz/UniFAR-FTUs huggingface dataset:
A study on conceptual associations of colors in preschool children

In this study, the conceptual associations of colors in preschool children were examined with an interdisciplinary perspective. Designed as a preliminary review, this study provides insights and suggestions about how conceptual associations of colors can be used for developing products and services for kids and improving the effectiveness of learning activities in education. This study was designed as descriptive survey because it describes an existing situation. This research’s working group was chosen through a purposive sampling method. The study also includes interpreted components. Two-stage interviews were made with 204 children aged between 60 and 72 months in pre-school education in Ankara with active participation of their form teachers, and the data were collected using the context analysis technique. The study found that children show dominant preference for certain colors in connection with certain concepts and they made consistent spectrum preference for certain concepts. These preferences indicate that the children aged between 60 and 72 months are able to make associations between concepts and colors and attribute meanings to colors in the background, with important hints for the use of colors in designing products and planning learning activities for children.
Fracture behaviour of ultra-high performance concrete

In this work, an attempt has been made to develop and investigate mechanical and the fracture behaviour of ultra-high performance concrete (UHPC). Geometrically similar notched beam specimens of different sizes made up of ultra-high performance fibre reinforced concrete (UHPFRC) have been considered for the experimental investigation. Centre point bending tests have been performed on beam specimens in crack mouth opening displacement control manner for investigating the fracture properties. Various fracture properties such as, fracture toughness, fracture energy, brittleness number etc. have been determined on the basis of non-linear fracture mechanics theory. The results of UHPC concrete have been compared with that of normal strength concrete available in the literature. It has been observed that addition of fibres increases the energy absorption capacity to a great extent for all size of specimens of ultra-high performance concrete as compared to normal concrete. Moreover, the brittleness number has been found to decrease due to the addition of fibers thereby enhancing the ductility. A decrease in nominal strength of UHPC based concrete with the increase in specimen size has been observed in similar fashion as normal strength concrete. However, the sensitivity to the size effect of ultra-high performance concrete is effectively reduced compared with normal concrete.
Performance of newly employed trainee accountants in Gauteng, South Africa, versus the skills expectations of employers: How big is the gap?

The objective of this article is to determine whether the professional skills demonstrated by newly employed first-year trainee accountants (i.e. their performance) meet the expectations of audit managers and, if they do not, to determine the size of the expectation–performance gap. A questionnaire was used to determine the expectations and the perceptions of the actual capabilities of trainee accountants among South African audit managers at large audit firms in Gauteng. The findings revealed that newly employed first-year trainee accountants did not meet audit managers’ expectations, with their expectations always exceeding their perceptions of performance. The professional skill with the largest expectation–performance gap was the ability to take responsibility for one’s own development (37.9%) and that with the smallest gap was the ability to display honesty and integrity (9.4%).
Efficient Transfer Feature Learning and Its Applications on Social Media

of the Dissertation Efficient Transfer Feature Learning and Its Applications on Social Media by Ming Shao Doctor of Philosophy in Computer Engineering Northeastern University, April 2016 Principle Adviser Name: Yun Fu, Adviser In the era of social media, more and more social characteristics are conveyed by multimedia, i.e., images, videos, audios, and webpages with rich media information. With the emergence and popularity of the Internet, collecting multimedia data cannot be much easier than today. However, tons of data available on-line are lack of organization and their tags or labels are sparse or loosely organized for certain problems we are interested in. The basic question is how to learn discriminant features or representations given very limited or poor training data for social media analytics. In this thesis, we focus on the popular social media data such as, face, object, digital number images, and study the problems of social media analytics in two lines: (1) developing efficient and effective machine learning tools given limited or poor training data by considering the structure of the data from different domains, (2) applying existing or developed machine learning tools to novel social media problems, e.g., kinship verification, family photo understanding. These two lines are detailed as followings: For knowledge-based machine learning algorithms, label or tag is critical in training the discriminative model. However, labeling data is not an easy task because these data are either too costly to obtain or too expensive to hand-label. For that reason, researchers use labeled, yet relevant, data from different databases to facilitate learning process. This is exactly transfer learning that studies how to transfer the knowledge gained from an existing and well-established data (source) to a new problem (target). To this end, we propose two novel methods to align the structure of the source and target data in the learned subspace to mitigate the domain divergence, which provide robust, scalable, and discriminant features for learning tasks. The first method utilizes a low-rank regularizer to guide the discriminant subspace learning. The second method manages to take advantage of the unlabeled source data for efficient large-scale transfer learning. Transfer learning approaches above are appropriate for social media analytics problems such as kinship verification. A critical observation is that faces of parents captured while they were
On Cohesion and coherence in Written Texts

Halliday put forward the theory of cohesion and coherence in 1976 and hereafter it has attracted much attention of many linguists and also become a contentious topic in the field of textual linguistics. This paper, from the angle of formal written texts, attempts to study the concept and function of cohesion and its relation to coherence. While pointing out the different cohesive features in both written and spoken texts, it stresses the importance of cohesion and coherence in students English writing.
Multilayer perceptron training with multiobjective memetic optimization

Nieminen, Paavo Multilayer Perceptron Training with Multiobjective Memetic Optimization Jyväskylä: University of Jyväskylä, 2016, 151 p. (Jyväskylä Studies in Computing ISSN 1456-5390; 247) ISBN 978-951-39-6823-6 (nid.) ISBN 978-951-39-6824-3 (PDF) Finnish summary Diss. Machine learning tasks usually come with several mutually conflicting objectives. One example is the simplicity of the learning device contrasted with the accuracy of its performance after learning. Another common example is the trade-off that must often be made between the rate of false positive and false negative predictions in diagnostic applications. For computer programs that learn from data, these objectives are formulated as mathematical functions, each of which describes one facet of the desired learning outcome. Even functions that intend to optimize the same facet may behave in a subtly different and mutually conflicting way, depending on the task and the dataset being examined. Multiobjective optimization methods developed for simultaneous optimization of such multiple objectives found their way to machine learning a few decades ago. This dissertation discusses the past and current uses of multiobjective optimization in supervised learning, especially in training a multilayer perceptron (MLP) artificial neural network for object classification. A literature overview of multiobjective MLP training is presented, supported by a semi-automatic survey using a software tool created partly by the author. Based on the literature, key goals and algorithmic elements are identified and applied to create a new framework for training MLPs consistent with an implementation used earlier for industrial projects using single-objective methods. Simulated datasets are used to illustrate the functionality of the created training algorithm, and how memetic Pareto-based multiobjective learning can be used for MLP classifier training. Emphasis is put on formulating useful representations and objective functions for
Combining Markov Chain Monte Carlo Approaches and Dynamic Modeling

This chapter contains sections titled: Model Calibration, Bayesian Framework: Prior and Posterior, Example and Notation, What Is Markov Chain Monte Carlo? Motivations for Markov Chain-Driven Sampling Approaches, Computational Concerns, Software Framework, Running the MCMC Analysis, The Impact of Empirical Data Availability on Posteriors, Pragmatics, Conclusion, Notes, References
Exploring the Influence of Demographic Characteristics of Malaysian Citizens Towards Tax Evasion

The higher number of tax evasion cases and increase in the statistics of unpaid tax in Malaysia shows that Malaysian citizens still rationalize that the act of tax evasion as tolerable and acceptable. This study examines the linkages between citizens’ of Malaysia attitudes toward tax evasion with their demographic profile. This survey was undertaken in Malaysia, involving a representative sample of 173 of citizens.The scope of this study is limited to the salary earning citizens and retirees only. The salary earning and retirees are chosen as they are the potential taxpayers that may pay tax in future and thus their opinions seem reliable. This study found that Malaysian citizens find it acceptable to evade tax on all sizes of income regardless whether it is “a small part”, “a large part”, or “all part” of their income. From the demographic profile factor, it was found that only age and nature of employment have a significant difference on the attitudes towards tax evasion. On the other hand, the respondents’ gender, level of income and level of education seem to bear no significant difference to their attitudes towards tax evasion. In relation to age, this study found out that older people of above 55 are more opposed to tax evasion compared to citizens in the range of age 25 to 34 and 45 to 54. Such findings are expected as older people tend to become more compliant as they respect the law and the government compared to young people (Ross and McGee, 2011). Secondly, from this study, it appears that retirees are more opposed to tax evasion and there have a two-way tied for the least opposed to tax evasion which is self-employed taxpayers and employers. Retirees might be the ones opposed the most to tax evasion due to their age. Older people tend to respect the authority and law, hence are more averse. Self-employed people might be the ones who are the least opposed to tax evasion that could be due to availability of opportunities to evade tax is larger compared to other groups. In this study, it is also found that employers are the one who are the least oppose to tax evasion. Perhaps, employers earning high income may think that the tax system would reap all their hard work. Besides, employers may perceive that there are too many red tapes that might be a hassle for the employers to comply, thus they tend to evade tax. This study has provided useful insights to facilitate tax authorities in imposing actions and strategies regarding tax evasion.
Understanding How Undergraduate Students Formalise Their Written Argumentations in Mathematics

All students who take an undergraduate university course in mathematics will confront the task of having to present a written argument to someone else. This could be one or more of their colleagues, or teaching staff who are also likely to be the markers of their course assignments, tutorials, tests and examinations. During lectures and tutorials, the lecturer and tutor may provide justifications for various aspects of mathematics presented – which might include elementary proofs, or give examples of how various theorems or known results contribute to solving a range of problems. Students will very likely be exposed to modes of argumentation that encourage certain habits of mind and speech, and at the same time, foster a disposition suited to engaging successfully with mathematics. These combine to shape the first experiences students have with argumentation in mathematics. Usually, by their second year, many students will have been introduced to formal mathematics through linear algebra, differential and integral calculus, or courses that are specifically designed to teach logic, proof and formal thinking in mathematics. Certainly, by their third year, reading and writing formal arguments of proof is an established part of the student’s life-world. However, literature from mathematics education shows that proof is a difficult mathematical concept for students to master. Underlying this research, is an assertion that argumentation plays an important role in developing the thinking, reasoning and communicative skills required to produce formal mathematical arguments. One way to investigate this is to see how students adapt their written argumentations from ones that give a personal conviction to ones aimed at convincing an other. This research applied Tall’s model of Three Worlds of Mathematics, Toulmin’s layout of an argument, and Habermas’ three dimensions of rationality to create a framework that was used to examine the argumentations of students, investigate how they formalised them, and describe some obstacles that may have prevented them from doing so. Students who were taking a first year mathematics course, and a smaller number taking a second year mathematics course participated in this research by completing tasks that were designed to promote the production of written argumentations. The argumentation of four students were tracked over three years in an attempt to capture, and explain, general changes that occurred over a longer term. These were analysed using the research framework developed in this study. The results of this study suggest that the first year mathematics students had the readiness
Linking Organizational and Field Level Analyses: The Diffusion of Corporate Environmental Practice

This paper examines the diffusion of corporate environmental practice in the context of field level dynamics. It builds a conceptual model that makes links between: (1) the complex constituency of the institutional field driving environmental concerns, (2) the multiple cultural frames that emerge from that constituency, and (3) the corresponding structural and cultural routines that become enacted within firms. It offers contributions for research in the domains of both environmental practice and institutional theory. For environmental practice, this paper attends to the genesis and diffusion of environmental practice that go beyond individual organization level. For institutional theory, this paper moves beyond the traditional focus on isomorphism and elaborates the processes of field-level dynamics. It shows how field level heterogeneity can occur by depicting inertia, traditionally a phenomenon attributed to the field, as the result of individual organization level dynamics that resist change. The paper concludes with ideas for future research.
Alertness Monitoring System for Vehicle Drivers using Physiological Signals

The present chapter deals with the development of a robust real-time embedded system which can detect the level of drowsiness in automotive and locomotive drivers based on ocular images and speech signals of the driver. The system has been cross-validated using Electroencephalogram (EEG) as well as Psychomotor response tests. A ratio based on eyelid closure rates called PERcentage of eyelid CLOSure (PERCLOS) using Principal Component Analysis (PCA) and Support Vector Machine (SVM) is employed to determine the state of drowsiness. Besides, the voiced-to-unvoiced speech ratio has also been used. Source localization and synchronization of EEG signals have been employed for detection of various brain stages during various stages of fatigue and cross-validating the algorithms based in image and speech data. The synchronization has been represented in terms of a complex network and the parameters of the network have been used to trace the change in fatigue of sleep-deprived subjects. In addition, subjective feedback has also been obtained.
Ontology-based Automated Metadata Generation Considering Semantic Ambiguity

There has been an increasing necessity of Semantic Web-based metadata that helps computers efficiently understand and manage an information increased with the growth of Internet. However, it seems inevitable to face some semantically ambiguous information when metadata is generated. Therefore, we need a solution to this problem. This paper proposes a new method for automated metadata generation with the help of a concept of class, in which some ambiguous words imbedded in information such as documents are semantically more related to others, by using probability model of consequent words. We considers ambiguities among defined concepts in ontology and uses the Hidden Markov Model to be aware of part of a named entity. First of all, we constrict a Markov Models a better understanding of the named entity of each class defined in ontology. Next, we generate the appropriate context from a text to understand the meaning of a semantically ambiguous word and solve the problem of ambiguities during generating metadata by searching the optimized the Markov Model corresponding to the sequence of words included in the context. We experiment with seven semantically ambiguous words that are extracted from computer science thesis. The experimental result demonstrates successful performance, the accuracy improved by about 18%, compared with SemTag, which has been known as an effective application for assigning a specific meaning to an ambiguous word based on its context.
Designing an Elementary School Uniform with Functions of Fit, Comfort, and Road Safety

Abstract The aim of this study was to propose and verify a new design of school uniforms for Chinese elementary school students in a functional design process, with attention to size fit, road safety, and wearing comfort. According to feedback from a questionnaire concerning the quality of current school uniforms, the most common complaints were unsafe fabric, improper size, outdated style, monotonous color, poor quality, and need for frequent purchase. Thus, a functional design for elementary school uniform was proposed, including the use of natural and environmentally friendly materials, creative design, attachments of reflective, and nocturnal materials, and attention to aesthetic style. A prototype was developed to verify these new concepts. A unique design with adjustable sizes was validated with wear trials, showing that one uniform design fit six wearers of different heights and body shapes, which could extend the service time of the school uniforms from one year to three years. It was also found that the reflective and nocturnal materials on the newly developed prototype significantly enhanced the visibility of clothing in dark environments, which could contribute to the road safety. The research findings provided some new concepts and a basic methodology for functional designs of elementary school uniforms.
Performance of Weeder in Mechanically Transplanted Rice Cultivation

Field performance of low land weeder was evaluated in mechanically transplanted rice fieldat Bahirbagh and Provakordi representing the silty loam soil under Gopalganj district of Bangladesh during the non-irrigated wet season (Aman) 2016. Twenty-one-day-old seedlings were transplanted by walk behind type 4-rows mechanical rice transplanter (DP480) at a pre-set spacing of 300 × 170 mm. Randomized Complete Block (RCB) design with three replications was applied with weeding practices of BRRI weeder (BW) followed by (fb) one hand weeding (HW), BRRI power weeder (BPW) fb one HW, two HW, pyrazosulfuron-ethyl (PSE) fb one HW, weedy check, weed free and mulching fb two HW (farmers’ practice) in two locations. The common weed species were observed in experimental sites. Weeding efficiency (WE) of BPW and BW was 67 and 44, respectively. Field capacity of BPW and BW was obtained 0.07 and 0.03 ha hr-1, respectively. Operator’s skill influenced the performance of weeder. During operation, BPW damaged 14-15% tillers in both the locations. However, damaged plants were revived after few days. The labour requirement in BW fb one HW, BPW fb one HW, PSE fb one HW and two HW were 380, 362, 243 and 616 man-hr ha-1 respectively. Except weedy check, weed management practices showed identical grain yield in both the locations. The BCR was accounted in PSE fb one HW (1.35), BW fb one HW (1.21), BPW fb one HW (1.20) whereas the lowest BCR was observed in weedy (0.83) and two HW (1.00). It can be concluded that pyrazosulfuron-ethyl, BRRI power weeder followed by one hand weeding and BRRI weeder followed by one hand weeding appeared as the cost effective weed control methods in mechanically transplanted Aman rice production. Bangladesh Rice j. 2018, 22(1): 25-34
Working memory in L2 character processing: The case of learning to read Chinese

This study examines the influence of individual working memory (WM) on learning to read L2 Chinese in a character learning experiment. Two research questions are addressed. First, how do visual features of characters affect learning to read and WM involvement? Second, which domain of WM plays a more important role in learning to read Chinese? Seventy American college students enrolled in beginning/intermediate Chinese classes participated in a character learning experiment, a visuospatial and verbal WM task, and a character knowledge measure. In the character learning experiment, participants learned to read 18 unfamiliar simple Chinese characters divided into three levels of visual distinctiveness: distinctive, normal, and similar. In the distinctive set, one stroke of each character was artificially enhanced to make the characters visually distinctive. The normal set consisted of normal characters, and the similar set consisted of character pairs whose members were visually similar. Results revealed that participants with higher levels of individual WM were better able to learn to read simple Chinese characters. Participants who had higher visuospatial WM spans were better able to learn the visually enhanced characters, while those with higher verbal WM capacities performed better in learning the regular Chinese characters belonging to the normal and the similar sets. The results suggest that learning ordinary Chinese characters is essentially a linguistic task for L2 character learners, which inevitably involves verbal WM. Visuospatial and verbal WM in Chinese beginning literacy acquisition and the domain-specificity/-generality of WM are discussed.