Miracle-dz/UniFAR-FTUs huggingface dataset:
Extra-Curricular Activities and Teacher Attitude to Girl-Child Education and Psycho-Emotional Preparedness

Abstract The study examined how teacher attitude and variation in extracurricular programmes affect the Girl-Child emotional preparedness. The descriptive survey design following the multi-stage sampling technique was employed to pick participants from the thirty-six states and the Federal Capital City (Abuja), Nigeria for the study. Using the stratified sampling method, the simple random technique was utilized to select one thousand, seven hundred and sixteen (1,716) primary school teachers with age range of between 18 and 57 years (x = 39.64, SD = 11.3). The Teachers’ Perception of Pupils Attitude to Schooling Scale (TPPAS), the School Extracurricular Activities (SEA) and the Feeling Perceptual Scale (FPS) were the research instruments used in the study. The simple descriptive statistics of relative frequency counts was utilized for analysis. The findings showed among others, that the primary school teachers’ perception of girls’ attitude, as compared to the boy-child, in going to school was negative. It showed that 73.2% of the teachers affirmed that the girl-child is not interested in going to school while 79.9% of the pupil who are girls said that they are frightened to go to school sometimes due to their teachers’ treat of beating them. Moreover, while over 90.0% of the schools had extracurricular facilities for the boy-child, the facilities for the girl-child are either not available and, or are minimally provided. The findings showed that the education of the girl-child, as compared to that of the boy-child is at variance due to their negative perception. It further showed that the attitude of teachers is not supportive to facilitate the emotional development of particularly the girl-child. The study recommended that Governmental policies should be designed to provide the girl-childfriendly school facilities that can advance the benefits of extracurricular activities for the learners.
An efficient parallel critical path algorithm

The problem of identifying one of the longest sensitizable paths in a circuit is called criti- cal path problem. Several critical path algorithms have been proposed in the last few years. However, due to the long computation time required to produce accu- rate results these algorithms may not be able to gen- erate any result for large designs with many long false paths unless the accuracy of the results is compromised. Parallel processing seems to be an appropriate way to speed up the required computation. In this paper we studied the parallel algorithms for critical path prob- lem. We first present a sensitization criterion. Based on this sensitization criterion the DT-algorithm, which is a variation of the D-algorithm with stable time range of signals also taken into consideration, is developed. It is especially suitable to determine the sensitizability of a given path in a parallel processing environment. We also discussed a parallel implementation of the criti- cal path algorithm on a shared-memory multiproces- sor. The experimental results show that a reasonable speed-up can be obtained.
Diagnosis of Glaucoma through Semivariance Fractal Dimension Method

The present paper explores a state of art for to detection of glaucoma in human eye through cup-disk segmentation & fractal dimension using image analysis. Author extracted fractal dimension feature from glaucomatous eyes for the portion of segmented cup-disk through semi variance algorithm. In the first step authors adopted Chan-vese algorithm to extract the region of interest (ROI) for which fractal dimension is determined. The strong findings of the present paper are 1. Semivariance method found to be more efficient. 2. Fractal dimension feature could be used as a diagnostics parameter for detection of glaucoma 3. The visualization of segmentation reveals that decrease in area of signifies attacking glaucoma neuroretinal rim by glaucoma 4. Fractal Dimension (FD) found to be in the range of 1.50-1.690 for the glaucoma affected eye. 5. FD found to be in the range of 1.70 to 1.99 for non glaucoma neuroretinal rim. 6. sill point obtained for glaucoma neuroretinal rim for smaller lag distance (k=10) whereas for non glaucoma neuroretinal rim larger lag distance (k=30).
Hierarchical Statistical Learning of Generic Parts of Object Structure

With the growing interest in object categorization various methods have emerged that perform well in this challenging task, yet are inherently limited to only a moderate number of object classes. In pursuit of a more general categorization system this paper proposes a way to overcome the computational complexity encompassing the enormous number of different object categories by exploiting the statistical properties of the highly structured visual world. Our approach proposes a hierarchical acquisition of generic parts of object structure, varying from simple to more complex ones, which stem from the favorable statistics of natural images. The parts recovered in the individual layers of the hierarchy can be used in a top-down manner resulting in a robust statistical engine that could be efficiently used within many of the current categorization systems. The proposed approach has been applied to large image datasets yielding important statistical insights into the generic parts of object structure.
Active learning via neighborhood reconstruction

In many real world scenarios, active learning methods are used to select the most informative points for labeling to reduce the expensive human action. One direction for active learning is selecting the most representative points, ie. , selecting the points that other points can be approximated by linear combination of the selected points. However, these methods fails to consider the local geometrical information of the data space. In this paper, we propose a novel framework named Active Learning via Neighborhood Reconstruction (ALNR) by taking into account the locality information directly during the selection. Specifically, for the linear reconstruction of target point, the nearer neighbors should have a greater effect and the selected points distant from the target point should be penalized severely. We further develop an efficient two-stage iterative procedure to solve the final optimization problem. Our empirical study shows encouraging results of the proposed algorithms in comparison to other state-of-the-art active learning algorithms on both synthetic and real visual data sets.
Restraining the Genuine Homo Economicus: Why the Economy Cannot Be Divorced from its Governance

The Homo economicus of traditional economics is far from being completely self-interested, rational, or as individualistic as he is purported to be; he will haggle to death over price but will not take what he wants by force. Implicitly, he is assumed to behave ruthlessly within a welldefined bubble of sainthood. Based on a simple model, I first examine what occurs when this assumption is relaxed and genuine, amoral Homo economici interact. Productivity can be inversely related to compensation; a longer shadow of the future can intensify conflict; and, more competition among providers of protection reduces welfare. The patently inefficient outcomes that follow call for restraining self-interest, for finding ways to govern markets. I then review some of the different ways of creating restraints, from the traditional social contract, to the hierarchical domination of kings and lords, to modern forms of governance. Checks and balances, wider representation, the bureaucratic form of organization, and other ingredients of modern governance can partly be thought of as providing restraints to the dark side of self-interest. Though highly imperfect, these restraints are better than the alternative, which typically involves autocratic, amateurish, and corrupt rule. Then, thinking of most problems in terms of a first-best economic model is practically and scientifically misguided.
Fear of Cancer Recurrence Among Survivors of Adult Cancers

Purpose: Fear of Cancer Recurrence (FCR) is a common concern for which cancer survivors want professional help to cope. Understanding the prevalence, predictors and mediators of FCR is important to facilitate the identification of those at-risk for clinically-significant FCR in order to expediently refer them into appropriate interventions. To clarify the empirical gaps which would be useful in this regard, the objectives of this study were: 1) to assess the prevalence of FCR; 2) to examine the relationships among predictors of FCR; and 3) examine the relationships among mediators of FCR. Methods: Survivors attending a cancer survivorship clinic were invited to participate in this cross-sectional, mixed mode survey study. Participants completed standardized assessments of FCR, Self-Esteem, Personality, Generalized Expectancies, Illness Representations, and Coping Styles, in addition to a demographic form. Clinical and treatment information was extracted from hospital charts. SPSS was used to conduct descriptive statistics to address objective 1, and Mplus was used to conduct a structural equation modeling analysis to examine predictors and mediators
Joint POS Tagging and Text Normalization for Informal Text

Text normalization and part-of-speech (POS) tagging for social media data have been investigated recently, however, prior work has treated them separately. In this paper, we propose a joint Viterbi decoding process to determine each token's POS tag and non-standard token's correct form at the same time. In order to evaluate our approach, we create two new data sets with POS tag labels and non-standard tokens' correct forms. This is the first data set with such annotation. The experiment results demonstrate the effect of non-standard words on POS tagging, and also show that our proposed methods perform better than the state-of-theart systems in both POS tagging and normalization.
C-V Characterization and Analysis of Temperature and Channel Thickness Effects on Threshold Voltage of Ultra-thin SOI MOSFET by Self-Consistent Model

The threshold voltage and capacitance voltage characteristics of ultra-thin Silicon-on-Insulator MOSFET are greatly influenced by the thickness and doping concentration of the silicon film. In this work, the capacitance voltage characteristics and threshold voltage of the device have been analyzed with quantum mechanical effects using the Self-Consistent model. Reduction of channel thickness and adding doping impurities cause an increase in the threshold voltage. Moreover, the temperature effects cause a significant amount of threshold voltage shift. The temperature dependence of threshold voltage has also been observed with SelfConsistent approach which are well supported from experimental performance of practical devices. Keywords—C-V characteristics, Self-Consistent Analysis, Siliconon-Insulator, Ultra-thin film.
Investigating Critical Organizational Factors Toward Sustainability Index: Insights from the Taiwanese Electronics Industry

To improve sustainable practices and attract investors, companies in emerging markets have increasingly embraced strategies for inclusion in rapidly expanding sustainability indices. However, most early studies on socially responsible investment or sustainability investment have only focused on exploring the relationship between corporate sustainability and firm value. Moreover, little has been done to explore the practices of emerging market companies for engaging with a sustainability index. To address this research gap, we employed the decision-making trial and evaluation laboratory (DEMATEL) method to identify critical factors that influence the inclusion of emerging market companies in the Dow Jones Sustainability Index (DJSI). Five critical factors and best practices were identified based on the analysis of seven Taiwanese electronics companies that have been listed in the DJSI for several consecutive years. Our results provide insights on the critical factors and best practices that reinforce the sustainable practices of emerging market companies for inclusion in the DJSI. This study also contributes to the literature by investigating the engagement of emerging market companies with the DJSI.
Join-semilattices with two-dimensional congruence amalgamation

We say that a 〈∨, 0〉-semilattice S is conditionally co-Brouwerian if (1) for all nonempty subsets X and Y of S such that X ≤ Y (i.e. x ≤ y for all 〈x, y〉 ∈ X × Y ), there exists z ∈ S such that X ≤ z ≤ Y , and (2) for every subset Z of S and all a, b ∈ S, if a ≤ b ∨ z for all z ∈ Z, then there exists c ∈ S such that a ≤ b ∨ c and c ≤ Z. By restricting this definition to subsets X, Y , and Z of less than κ elements, for an infinite cardinal κ, we obtain the definition of a conditionally κ-co-Brouwerian 〈∨, 0〉-semilattice. We prove that for every conditionally co-Brouwerian lattice S and every partial lattice P , every 〈∨, 0〉-homomorphism φ : Conc P → S can be lifted to a lattice homomorphism f : P → L for some relatively complemented lattice L. Here, Conc P denotes the 〈∨, 0〉semilattice of compact congruences of P . We also prove a two-dimensional version of this result, and we establish partial converses of our results and various of their consequences in terms of congruence lattice representation problems. Among these consequences, for every infinite regular cardinal κ and every conditionally κ-co-Brouwerian S of size κ, there exists a relatively complemented lattice L with zero such that Conc L ∼= S.
A Mass Assignment Approach to Granular Association Rules for Multiple Taxonomies

The use of hierarchical taxonomies to organise information (or sets of objects) is a common approach for the semantic web and elsewhere, and is based on progressively finer granulations of objects. In many cases, seemingly crisp granulation disguises the fact that categories are based on loosely defined concepts which are better modelled by allowing graded membership. A related problem arises when different taxonomies are used, with different structures, as the integration process may also lead to fuzzy categories. Care is needed when information systems use fuzzy sets to model graded membership in categories - the fuzzy sets are not disjunctive possibility distributions, but must be interpreted conjunctively. We clarify this distinction and show how an extended mass assignment framework can be used to extract relations between fuzzy categories. These relations are association rules and are useful when integrating multiple information sources categorised according to different hierarchies. Our association rules do not suffer from problems associated with use of fuzzy cardinalities. An example of discovering associated film genres is given.
CNTRICS imaging biomarkers selection: Working memory.

The sixth meeting of the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) consortium was focused on selecting promising imaging biomarker measures for each of the cognitive constructs selected in the first CNTRICS meeting. In the domain of working memory (WM), the 2 constructs of interest were "goal maintenance" and "interference control. " CNTRICS received 7 task nominations for goal maintenance and 3 task nominations for interference control. For goal maintenance, the breakout group for WM recommended the AX Continuous Performance Test/Dot Pattern Expectancy (DPX) and the Switching Stroop task for translation and further development for use in clinical trial contexts in schizophrenia research. Notably, these same 2 paradigms were recommended for "rule generation and selection" in executive control, a highly related construct. For interference control, the breakout group recommended the Suppress Task and the Sternberg Item Recognition Paradigm for translation for use in clinical trials. This manuscript describes the ways in which each of these tasks met the criteria used by the breakout group to recommend tasks for further development. In addition, the group revisited the construct of WM capacity. Since the initial CNTRICS meeting, a growing body of work has emerged on the neurobiological substrates of WM capacity, making measure of this construct ready for translation. The group suggested a promising imaging biomarker measure for capacity, a version of the change detection task that measures delay activity over posterior parietal and occipital cortex.
Choosing Text Description Language Features for Training and Testing Adaptive Resonance Theory: A Case Study for Botanical Classification

The practical methodologies of stable pattern classification using artificial intelligence as advisory tools are researched here according to studies in the flowering plant genera Lithops N. E. Br. (Aizoaceae). In this paper, the use of a neural network model with the adaptive resonance theory is a practical generation of groups as a classifier for botanical taxa. In order to provide comparisons for this study of effective classification performance, the study here in the succulent plant genus Lithops involved the classification of 87 records that comprise about 35 species. It is demonstrated that the proposed system using artificial neural networks technique with statistical property of grouping method can achieve a classification rate of 85.71% separated records into 35 groups referred to the traditional plant taxonomic groups.
Static Program Analysis of Multi-Applet JavaCard Applications

Java Card provides a framework of classes and interfaces that hides the details of the underlying smart card interface and makes it possible to load and run on the same card several applets, from different application providers with complex trust relationships. This fact opens prospects for new business applications, but the card issuer has to secure absence of malicious or faulty card applets. He has to be able to check that (i) applets do not cause illicit method invocations that violate temporal restrictions of inter-applet communication, (ii) applets protect themselves from unwanted information flow to third parties and (iii) it is not possible for an unhandled Java Card API exception to leave an applet in an unpredictable state that is potentially dangerous for the application’s security. We explore recent advances in theory and tool support of static program analysis and we present an approach for automatic verification of smart card applications that by definition are security critical.
Combined Reliability Testing: An approach to assure reliability under complex loading conditions

Reliability tests, which combine different loading types, come into the focus of research activities. One reason can be given by their ability to enable test acceleration. Based on an understanding of failure mechanisms, tests can be developed, which indicate robustness of power devices with respect to loads like mechanical loads (vibration, bending), moisture, temperature and temperature cycling. On the other hand the real life use conditions of modern electronic modules are characterized by a combination of different loading types, which occur in a sequential and parallel manner. Hence robustness validation methods which consider combined loads are needed. Our results on solder interconnect and polymer-silicon interface reliability are assembled in this publication. They show that competing and interacting failure mechanisms occur. Depending on the ratio of loading types different mechanisms dominate the degradation process. Therefore it is concluded that a combined load test is not in general relevant for qualification of complete modules or systems. To cover for the observed effects, a development and application of lifetime models of the potential failure mechanisms and their interaction is needed. The approach is exemplified on solder interconnects under vibration and thermal cycling loads, but can be transferred to other coupled failure mechanisms.
Study of junction temperature effect on electrical power of light-emitting diode (LED) devices

Junction temperature affects photometric and electrical performance of LED devices. In this paper, junction temperature effect on electrical power is investigated. The theoretical results and measurement results indicate the consumed electrical power of an LED device not only relates to injection current, but also depends on junction temperature. A mathematic model of electrical power of LED devices, considering the combined effect of junction temperature and injection current, is proposed. The proposed electrical power model has been verified with experimental measurements. It is helpful for LED performance evaluation.
Non-orthogonal 2.5D PEEC for power integrity analysis

Power distribution network (PDN) of a multilayered PCB is designed to supply low noise and stable power to ICs. Reduced voltage levels, increased current requirements make it challenging to attain the desired PDN impedance profile. It is therefore necessary to have multiple design iterations for optimal performance of the PDN. 3D full-wave electromagnetic solvers like the Partial Element Equivalent Circuit (PEEC) method are time constrained and therefore ill-suited for early stage design. On the other hand, 2.5D tools have lower time and memory requirements and are reasonably accurate for planar power-ground structures. For example, Multilayered Finite Difference Method (MFDM) is a 2.5D formulation suitable for PDN analysis. However, present MFDM techniques are based on orthogonal meshes, such that power-ground planes with irregular shapes and holes require unnecessarily fine mesh at the boundary for a suitable staircase approximation. In this paper, a non-orthogonal 2.5D PEEC formulation is proposed to alleviate this problem. Numerical results using quadrilateral meshes demonstrate good accuracy as compared to 3D full-wave formulation for planar geometries.
An investigation on speaker vector-based speaker identification under noisy conditions

This paper presents the speaker identification method based on a speaker vector under noisy conditions. The aim of this work is to improve the performance of the speaker identification under noisy conditions. The identification system is based on the method of anchor models. In this system, the location of each speaker is represented by the speaker vector which consists of the set of the likelihood between a target utterance and the anchor models. Since the acoustic model of the target is not needed, speaker identification can be performed with a very short reference speech (5.5 sec average). In order to achieve the improvement, the structure of anchor models was investigated. Evaluations were performed on 8 or 30-speaker identification task in Japanese. The results showed that a speaker identification rate of 70.98\% has been obtained by using phonetic-class structured GMMs (pcs_GMMs) as anchor models under noisy conditions.
Flagging nationalism: Some thoughts on the nature of provocation

This paper considers the media frenzy and public outcry that surrounded the publication of findings of a relationship between the flying of Australian flags on cars for Australia Day and racist attitudes. While the findings did not surprise many sociologists, they were apparently highly contentious for portions of the Australian public. The role of the academic in fomenting public debate on national identity and the use of national symbols, and their responsibility in relation to calls for a ‘public sociology’, are discussed, using the lens of the concept of provocation.
Quasar Structure and Cosmological Feedback

Feedback from quasars and AGNs is being invoked frequently in several cosmological settings. Currently, order of magnitude, or more, uncertainties in the structure of both the wind and the 'obscuring torus' make predictions highly uncertain. To make testable models of this 'cosmological feedback' it is essential to understand the detailed structure of AGNs sufficiently well to predict their properties for the whole quasar population, at all redshifts. Progress in both areas is rapid, and I describe the near-term prospects for reducing these uncertainties for 'slow' (non-relativistic) AGN winds and the obscuring torus.