Miracle-dz/UniFAR-FTUs huggingface dataset:
Training School Personnel to Facilitate a Family Intervention to Prevent Conduct Problems

This study evaluates school personnel perceptions, knowledge, and behaviors before and after a 36-hr training program designed to prepare early childhood school personnel for implementation of an after-school family preventive intervention for conduct problems. Participants were 40 female school personnel (22 professionals and 18 paraprofessionals). Research Findings: Participation and satisfaction with the training program were high. Before training, school personnel responded correctly to 53% to 66% of knowledge questions and indicated that they would be “somewhat comfortable to comfortable” in facilitating the after-school groups with families. Before training, professionals had greater knowledge than paraprofessionals; there was no difference in initial comfort level by professional status. Trainees made substantial gains in knowledge related to cognitive–behavioral strategies for preschoolers, program philosophy, and group facilitation skills, responding correctly to 69% to 77% of questions. These large effects on knowledge were not moderated by professional status. There were no significant changes in comfort level. Gains in knowledge in cognitive–behavioral strategies generalized over time (5 months) but not across contexts (into the classroom). Practice or Policy: This study provides preliminary evidence for the feasibility and potential efficacy of a training program to prepare early childhood school personnel to implement an after-school family preventive intervention for conduct problems.
QAF: Frame Semantics-based Question Interpretation

Natural language questions are interpreted to a sequence of patterns to be matched with instances of patterns in a knowledge base (KB) for answering. A natural language (NL) question answering (QA) system utilizes meaningful patterns matching the syntac-tic/lexical features between the NL questions and KB. In the most of KBs, there are only binary relations in triple form to represent relation between two entities or entity and a value using the domain specific ontology. However, the binary relation representation is not enough to cover complex information in questions, and the ontology vocabulary sometimes does not cover the lexical meaning in questions. Complex meaning needs a knowledge representation to link the binary relation-type triples in KB. In this paper, we propose a frame semantics-based semantic parsing approach as KB-independent question pre-processing. We will propose requirements of question interpretation in the KBQA perspective, and a query form representation based on our proposed format QAF (Ques-tion Answering with the Frame Semantics), which is supposed to cover the requirements. In QAF, frame semantics roles as a model to represent complex information in questions and to disambiguate the lexical meaning in questions to match with the ontology vocabu-lary. Our system takes a question as an input and outputs QAF-query by the process which assigns semantic information in the question to its corresponding frame semantic structure using the semantic parsing rules.
Quality initiatives in the cardiac catheterization and interventional laboratories

DOI: 10.3760/cma.j.issn.0366-6999.20133085 Department of Medicine, Cardiovascular Section, VA Medical Center and University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA (Latif F) Indiana University School of Medicine, Community Healthcare System, St Mary Medical Center, Hobart, Indiana, USA (Nguyen T) Division of Cardiology, University of Florida, USA (Bass TA) Correspondence to: Dr. Faisal Latif, Department of Medicine, Cardiovascular Section, VA Medical Center and University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA (Tel: 405-323-9115. Fax: 405-456-1576. Email: Faisal-latif@ouhsc. edu) Perspective
Distributed LQR Design for Identical Dynamically Decoupled Systems

We consider a set of identical decoupled dynamical systems and a control problem where the performance index couples the behavior of the systems. The coupling is described through a communication graph where each system is a node and the control action at each node is only function of its state and the states of its neighbors. A distributed control design method is presented which requires the solution of a single linear quadratic regulator (LQR) problem. The size of the LQR problem is equal to the maximum vertex degree of the communication graph plus one. The design procedure proposed in this paper illustrates how stability of the large-scale system is related to the robustness of local controllers and the spectrum of a matrix representing the desired sparsity pattern of the distributed controller design problem.
Robust Dirichlet Process mixtures

Non-parametric Dirichlet Process mixture (DPM) approaches for density estimation and clustering allow for automatic model selection. In this paper, we aim to develop robust DPM algorithm for clustering datasets with scatter objects, or outliers. In the developed mean-field variational inference algorithms, the auxiliary posterior distributions are factorized in a tree-structured form. In the experiments, we first show the advantage of the tree-structured factorization over the commonly-used full factorization. Then the performances of the robust DPM is evaluated using controlled experiment settings. Finally, the developed robust DPM is applied to biology datasets.
Structure-Function Analysis of Rotavirus NSP2 Octamer by Using a Novel Complementation System

ABSTRACT Viral inclusion bodies, or viroplasms, that form in rotavirus-infected cells direct replication and packaging of the segmented double-stranded RNA (dsRNA) genome. NSP2, one of two rotavirus proteins needed for viroplasm assembly, possesses NTPase, RNA-binding, and helix-unwinding activities. NSP2 of the rotavirus group causing endemic infantile diarrhea (group A) was shown to self-assemble into large doughnut-shaped octamers with circumferential grooves and deep clefts containing nucleotide-binding histidine triad (HIT)-like motifs. Here, we demonstrate that NSP2 of group C rotavirus, a group that fails to reassort with group A viruses, retains the unique architecture of the group A octamer but differs in surface charge distribution. By using an NSP2-dependent complementation system, we show that the HIT-dependent NTPase activity of NSP2 is necessary for dsRNA synthesis, but not for viroplasm formation. The complementation system also showed that despite the retention of the octamer structure and the HIT-like fold, group C NSP2 failed to rescue replication and viroplasm formation in NSP2-deficient cells infected with group A rotavirus. The distinct differences in the surface charges on the Bristol and SA11 NSP2 octamers suggest that charge complementarity of the viroplasm-forming proteins guides the specificity of viroplasm formation and, possibly, reassortment restriction between rotavirus groups.
Harmonics Elimination in Multilevel Inverter with Two Unequal Voltage Batteries

Elimination of harmonics in a multilevel inverter with two unequal dc sources is considered. That is, for a given fundamental output voltage, our aim is to find the switching angles, that produce the fundamental while not generating specifically chosen harmonics. A procedure is given to find all sets of switching angles for which the fundamental is produced while lower order harmonics are eliminated. This is done by first converting the transcendental equations that specify the elimination of the harmonics into an equivalent set of polynomial equations. Then, using the mathematical theory of resultants, solution to this equivalent problem is found.
Festivals, Cooperative Stakeholders and the Role of the Media: A Case Analysis of Newspaper Media

Abstract Image, brand narrative and stakeholder collaboration each represent pivotal paradigms in the analysis, evaluation and formation of good management practice for festivals. The role of the media as significant intermediary offers a core measurement instrument linking these paradigms. This exploratory work applies a two‐stage empirical study to investigate and posit a methodological procedure for this instrument. A principal component analysis of data relating to the scales of significance given by festival visitors and festival directors, respectively, to the socio‐cultural effects of festivals indicates that both the local media and national media are strong elements in the emerging factors. A further media framing methodology is provided to assess variations in the role of newspapers (a medium identified as particularly significant in the decision making process of festival goers) in converging agendas which may influence and vary the public perception of the socio‐cultural influences of festivals. The authors conclude that these agendas are affiliated and can be measured with reference to the factors that emerged in the principal component analysis.
Spatial simulation of forest using Bayesian state-space models and remotely sensed data

Utilizing spatial properties of forest attributes may provide increased accuracy in forestry remote sensing applications, compared to common non-spatial methods. Spatial models are often complex and their inference are usually difficult, though. Such problems may be addressed using Bayesian models, estimated using the computer-intensive Markov-Chain Monte Carlo (MCMC) stochastic simulation methods. This article presents a Bayesian statespace model of forest attributes using field measurements and remote sensing data. The model is defined on a spatial lattice where each lattice cell corresponds to the spatial extent of one raster cell measurement in the remote sensing data. As prior distribution function, the Conditional Autoregressive model (CAR) is utilized since it is well defined for simulation using the Gibbs sampler. The parameters of the CAR were estimated using a variogram model. Inference is provided by the MCMC method Gibbs sampler, a method which allows inference of very complex models. That is, estimation is made by simulating from the posterior distribution (conditional to the available field measurements and remote sensing data). A casestudy is presented where the model is applied to produce a 5917 ha large raster map of forest stem volume using field measurements and Landsat 5 TM data in northern Sweden. This corresponds to estimation of a parameter vector of size exceeding 360 000. The mapping accuracy was assessed using sampled field plots and field measured forest stands, not utilized in the model. Simulations made using Gibbs Sampler did converge to reasonable realizations of the forest, in spite of the very large size of the estimated parameter vector. The general mapping accuracy was low, though, 76.1% root mean square error (RMSE), in per cent of the mean, for raster cell (25 by 25m) predictions, and 60.5% RMSE for stand (0.5 – 22.1 ha) predictions. The methodology shows substantial potential although further development of the model would clearly be beneficial.
Classification of Large Biomedical Data Using ANNs Based on BFGS Method

Artificial neural networks have been widely used for knowledge extraction from biomedical datasets and constitute an important role in bio-data exploration and analysis.In this work, we proposed a new curvilinear algorithm for training large neural networks which is based on the analysis of the eigenstructure of the memoryless BFGS matrices. The proposed method preserves the strong convergence properties provided by the quasi-Newton direction while simultaneously it exploits the nonconvexity of the error surface through the computation of the negative curvature direction without using any storage and matrix factorization.Moreover, for improving the generalization capability of trained ANNs, we explore the incorporation of several dimensionality reduction techniques as a pre-processing step.
Uncoupling of Expression of an Intronic MicroRNA and Its Myosin Host Gene by Exon Skipping

ABSTRACT The ancient MYH7b gene, expressed in striated muscle and brain, encodes a sarcomeric myosin and the intronic microRNA miR-499. We find that skipping of an exon introduces a premature termination codon in the transcript that downregulates MYH7b protein production without affecting microRNA expression. Among other genes, endogenous miR-499 targets the 3′ untranslated region of the transcription factor Sox6, which in turn acts as a repressor of MYH7b transcriptional activity. Thus, concerted transcription and alternative splicing uncouple the level of expression of MYH7b and miR-499 when their coexpression is not required.
A hybrid framework for enhancing correlation to solve cold-start problem in recommender systems

The online shopping is becoming a trend in the age of digital technology. By using intelligent recommendations, the online shops or online retailers directly approach and meet customers' demand easier than the physical stores. However, the online shopping still has its drawbacks, among a variety of diverse product types, sizes and design, customers need to browse and filter from a wide range of sub-categories to find the suitable products. That is why the justice system that collects customer information and products to make appropriate suggestions for each user is raised encouraged using on the commercial website. The purpose of this work aims at proposing a hybrid framework for enhancing correlation to solve cold-start problem in recommender systems. Experiments are performed using MovieLens dataset to make a realistic methodology.
Towards Adaptive Dashboards for Learning Analytic - An Approach for Conceptual Design and Implementation

Designing Learning Analytic (LA) dashboards can be a challenging and complex task when dealing withabundant data generated from heterogeneous sources with various uses. On top of that, each dashboard isdesigned in accordance with the user's needs and their observational objectives. Therefore, understanding thecontext of LA and its users is compulsory as it is part of the dashboard design approach.Our research effort starts with an exploratory study of different contextual elements that could help us definewhat an adaptive dashboard is and how it fulfills the user's needs. To do so, we have conducted a needsassessment to characterize the user profiles, their activities, their visualization preferences and objectives whenusing a dedicated dashboard. In this paper, we introduce a conceptual model, which will be used to generate a variety of LA dashboards. Our main goal is to provide users with adaptive dashboards, generated accordinglyto their context of use while satisfying the users' requirements. We also discussed the implementation processof our first prototype as well as further improvements.
Decentralized Autonomous Navigation Strategies for Multi-Robot Search and Rescue

In this report, we try to improve the performance of existing approaches for search operations in multi-robot context. We propose three novel algorithms that are using a triangular grid pattern, i.e., robots certainly go through the vertices of a triangular grid during the search procedure. The main advantage of using a triangular grid pattern is that it is asymptotically optimal in terms of the minimum number of robots required for the complete coverage of an arbitrary bounded area. We use a new topological map which is made and shared by robots during the search operation. We consider an area that is unknown to the robots a priori with an arbitrary shape, containing some obstacles. Unlike many current heuristic algorithms, we give mathematically proofs of convergence of the algorithms. The computer simulation results for the proposed algorithms are presented using a simulator of real robots and environment. We evaluate the performance of the algorithms via experiments with real robots. We compare the performance of our own algorithms with three existing algorithms from other researchers. The results demonstrate the merits of our proposed solution. A further study on formation building with obstacle avoidance for a team of mobile robots is presented in this report. We propose a decentralized formation building with obstacle avoidance algorithm for a group of mobile robots to move in a defined geometric configuration. Furthermore, we consider a more complicated formation problem with a group of anonymous robots; these robots are not aware of their position in the final configuration and need to reach a consensus during the formation process. We propose a randomized algorithm for the anonymous robots that achieves the convergence to a desired configuration with probability 1. We also propose a novel obstacle avoidance rule, used in the formation building algorithm.
Optimization of Anti-Spam Systems with Multiobjective Evolutionary Algorithms

In this paper anti-spam filtering is presented as a cumbersome service, as opposed to a software product perspective. The huge human effort for setting up, adaptation, maintenance, and tuning of filters for spam detection in anti-spam systems is explained. Choosing the best importance scores for the spam filters is essential for the accuracy of any rules based anti-spam system, and is also one of the biggest challenges in this research area. Optimal filters score settings for Apache SpamAssassin project the most widely adopted anti-spam open-source software is addressed. In addition to a survey done on single/multi-objective optimization research in this area, we also present a study for filters score setting using multiobjective optimization based on two most representative evolutionary algorithms, NSGA II and SPEA2. Problem description, simulation and results analysis is done for SpamAssassin public mail corpus which is widely used for benchmarking purposes.
A study of pronunciation verification in a speech therapy application

Techniques are presented for detecting phoneme level mispronunciations in utterances obtained from a population of impaired children speakers. The intended application of these approaches is to use the resulting confidence measures to provide feedback to patients concerning the quality of pronunciations in utterances arising within interactive speech therapy sessions. The pronunciation verification scenario involves presenting utterances of known words to a phonetic decoder and generating confusion networks from the resulting phone lattices. Confidence measures are derived from the posterior probabilities obtained from the confusion networks. Phoneme level mispronunciation detection performance was significantly improved with respect to a baseline system by optimizing acoustic models and pronunciation models in the phonetic decoder and applying a nonlinear mapping to the confusion network posteriors.
The Project of Rationalization: A Critique and Reappraisal of Neo-Institutionalism in Organization Studies

This article critically approaches various neo-institutional accounts of the process of formal organizing. While acknowledging the importance of the overall orientation marked by neo-institutional studies, the article identifies several crucial aspects that have escaped the attention of neo-institutional research. In particular, it criticizes the inability of neo-institutionalism to provide an account of the means linking situated forms of organizing with wider instrumental beliefs and practices, in terms other than adaptivist, diffusionist. Such a limitation is partly a consequence of unwillingness of neo-institutionalism to focus on and analyze the very architecture of the rationalized patterns and relationships which neo-institutionalists claim to be diffusing across organizational populations and fields. Drawing on several sources, the article develops a framework that seeks to outline the conceptual means for decomposing the carriers of rationalized patterns, models and techniques and showing the distinctive ways in which they implicate the building blocks of formal organizing.
Validity of Self‐Reported Waist and Hip Circumferences in Men and Women

Recent epidemiologic evidence indicates an association between fat distribution and many diseases. To assess the validity of circumference measurements obtained by self-report, the authors analyzed data from 123 men aged 4C75 years and 140 women aged 41–65 years, drawn from two large ongoing prospective studies. On mailed questionnaires, subjects were asked to measure and record their weight and waist and hip circumferences. These data were compared with standardized measurements taken approximately six months apart by technicians who visited participants at their homes. Crude Pearson correlations between self-reported waist circumferences and the average of two technician-measured waist circumferences were 0.95 for men and 0.89 for women. Similar correlations for hip measurements were 0.88 for men and 0.84 for women, and for waist-to-hip ratios, 0.69 for men and 0.70 for women. After adjusting for age and body mass index (kg/m2), correlations for waist-to-hip ratios were 0.55 for men and 0.58 for women. Correlations became stronger after correcting for random within-person variability from daily or seasonal fluctuations. Self-reported and measured weights were highly correlated: 0.97 for men and 0.97 for women. Self-reported waist, hip, and weight measurements appear reasonably valid. The moderate degree of measurement error for the ratio of self-reported waist and hip circumferences, however, implies that previously reported associations based on self-report of these measures may have been appreciably underestimated. (Epidemiology 1990; 1:46&473)
Realization of partial discharge signals in transformer oils utilizing advanced computational techniques

The measurement of acoustic and electrical signals for the partial discharge (PD) activity due to the presence of metallic particles within transformer oil are utilized for the characterization of the incipient hazards. The utilization of phase resolved, in addition, to time resolved partial discharge signals is undertaken to extract numerous features using statistical and frequency analyzers. The extracted features are down scaled to pinpoint the effective attributes that render an intelligent classification means useful for determining contaminating particle type and dimensions. This is accomplished by utilizing feature selection wrapper models undertaking the sequential floating forward selection (SFFS) and particle swarm optimization as alternative search strategies. Support vector machines (SVM) is finally used for the classification of contaminating particles identity. A comprehensive comparison between various selection techniques of the best feature vector for the most efficient classification is tackled based on size of selected feature vector, processing time and success of classification. Results of this study can be integrated into a smart automatable tool based on recent data mining techniques that would provide an efficient and prompt identification for the nature of incipient hazards due to lack in insulation integrity.
Multiple Roles, Balance Between Work and Leisure, and Satisfaction with Level of Living

The purpose of this study was to investigate the relationship among multiple role involvement, perceived discrepancy in the balance between work and leisure, and satisfaction with the level of living for 513farm women. A path analysis included role involvement, discrepancy, and satisfaction as dependent variables and self-esteem, locus of control, age, and education as independent variables. Self-esteem, locus of control, and age predicted role involvement; locus of control, age, and role involvement predicted discrepancy; role involvement, discrepancy, self-esteem, and locus of control predicted satisfaction. Age and self-esteem had an indirect effect on discrepancy mediated through role involvement. Self-esteem, locus of control, age, and role involvement had an indirect effect on satisfaction mediated through discrepancy.