Miracle-dz/UniFAR-FTUs huggingface dataset:
Observing collaborative problem‐solving processes and outcomes

This study examined the problem of assessing group process in a collaborative problem-solving situation. Students in seven collaborative groups worked on a two-part math and logic problem—first individually, then in groups, and finally, individually again. Groups engaging in behaviors that facilitated collaboration obtained higher group and individual accuracy scores on a challenging problem set. High-achieving students were influential in group problem-solving outcomes. Group scores did not reflect the individual achievement of low-achieving students. Examining collaborative group process and outcomes offers a new direction in functional and contextualized assessment for school psychologists. © 2001 John Wiley & Sons, Inc.
Virtual fixtures: Perceptual tools for telerobotic manipulation

In order to enhance operator performance and understanding within remote environments, most research and development of telepresence systems has been directed towards improving the fidelity of the link between operator and environment. Although higher fidelity interfaces are important to the advancement of a telepresence system, the beneficial effects of corrupting the link between operator and remote environment by introducing abstract perceptual information into the interface called virtual fixtures are described. <<ETX>>
An Overview of Distance Metric Learning

In our previous comprehensive survey [41], we have categori zed the disparate issues in distance metric learning. Within each of the four cat egories, we have summarized existing work, disclosed their essential connection s, strengths and weaknesses. The first category is supervised distance metric learning, w hich contains supervised global distance metric learning, local adaptive supervise d distance metric learning, Neighborhood Component Analysis (NCA) [13], and Relevant C omponents Analysis (RCA) [1]. The second category is unsupervised distance metric learning, covering linear (Principal Component Analysis (PCA) [14], Mul tidimensional Scaling (MDS) [5]) and nonlinear embedding methods (ISOMAP [35], Lo cally Linear Embedding (LLE) [30], and Laplacian Eigenamp (LE) [2]). We fur ther unify these algorithms into a common framework based on the embedding comp utation. The third category, which is maximum margin based distance metric lea rning approaches, includes the large margin nearest neighbor based distance met ric learning methods and semi-definite Programming (SDP) methods to solve the kernel ized margin maximization problem. And the fourth category discussing kernel met hods towards learning distance metrics, covers kernel alignment [28] and its SDP a pproaches [26], and also the extension work of learning the idealized kernel [25]. In addition to this survey [41], here we provide a complete an d updated summarization of the related work on both unsupervised distance metri c learning and supervised distance metric learning, including the most recent work in the area of distance metric learning. Many unsupervised distance metric learning algorithms are ess ntially for the purpose of unsupervised dimensionality reduction, i.e. learn ing a low-dimensional embedding of the original feature space. This group of methods can be divided into nonlinear and linear methods. The well known algorithms for nonlinear unsupervised dimensionality reduction are ISOMAP [35], Locally Linear Embedding ( LLE) [30], and Laplacian Eigenamp (LE) [2]. ISOMAP seeks the subspace that best p r erves the geodesic distances between any two data points, while LLE and LE focus n the preservation of local neighbor structure. An improved and stable version of LLE is achieved in [45], by introducing multiple linearly independent local weight ve ctors for each neighborhood. Among the linear methods, Principal Component Analysis (PC A) [14] finds the subspace that best preserves the variance of the data; Multidim ensional Scaling (MDS) [5] finds the low-rank projection that best preserves the interpoint distance given by the pairwise distance matrix; Independent components analysi s (ICA) [4] seeks a linear transformation to coordinates in which the data are maximal ly statistically indepen-
Learning User Profiles from Tagging Data and Leveraging them for Personal(ized) Information Access

Due to the high popularity of social bookmarking systems, a large amount of metadata is available. Aggregating the metadata belonging to one user results in an user profile similar to those often used in Information Filtering. This paper shows how to create user profiles from tagging data. We present the Add-A-Tag algorithm for profile construction which takes account of the structural and temporal nature of tagging data. In addition, we explore ways of leveraging these user profiles. There are two main insights gained. Firstly, as we experienced in a small-scale user study, simply being able to view aggregated information about past tagging behavior was considered useful. Secondly, the user profile can be used to guide the user’s navigation, that is, to provide the user with personalized access to information resources.
Aquisição fonológica de crianças de classe sócio econômica alta

PURPOSE: to investigate phonological acquisition in population of children with normal development. METHODS: the participants of this study were 240 children of both genders, between 3 and 8-year old. Data were analyzed with respect to age and gender. RESULTS: the results allow to conclude that at the age of 3 the phonemes /p/, /b/, /t/, /d/, /k/, /g/, /m/, and /n/ are already acquired and stabilized in the phonological system of the children. Also the phonemes /f/, /v/, /s/, /ʃ/, /z/, /ʒ/, /ʎ/ and /ɲ/ and the affricates [tʃ]e[dʒ] are already acquired at that age although we found a great production variability between children. The acquisition of the phoneme /ɾ/ occurs initially in simple onset (age 4) and afterwards in complex onset (age 5), the acquisition of the phoneme /l/ in simple onset occurs at age 3 and in complex onset at age 4 and the phoneme /R/ in simple onset is acquired at age of 3 and in final position at the age of 4. CONCLUSION: as for the total number of phonemes acquired, it can be concluded that from the age of 3 many children have a complete phonetic inventory, there is a great variability among children. As far as PCC and PCC-R is concerned, the mean percentage of correct consonants increases significantly and gradually while age increases. With regard to gender, not a single significant difference was found for any of the analyses performed in the current study.
The Mediating Role of Intrinsic Motivation on the Relationship between Developmental Feedback and Employee Job Performance

Although previous researchers have paid significant attention to the effect of feedback on employee behavioral outcomes, few have specifically examined the influence of developmental feedback on employee job performance. In this study we examined the influences of intrinsic motivation and social exchange on the relationship between developmental feedback and job performance. Participants were 202 supervisor-subordinate dyads from 12 enterprises in China. We found that developmental feedback had a significant positive effect on both intrinsic motivation and job performance. In addition, intrinsic motivation had a significant positive relationship with job performance but it also had a partial mediating effect on the relationship between developmental feedback and job performance. Managerial implications and directions for further research are discussed.
Socialism against markets? A critique of two recent proposals

The debate over the feasibility of socialism has lasted for much of the twentieth century. This essay evaluates two important recent proposals for systems of socialist planning in which markets are given merely a marginal role. Beforehand, some of the key theoretical issues are placed in the context of the ‘socialist calculation debate’ that lasted from the 1920s to the 1940s. It is argued that the Austrian critics of centralist socialism rightly focused on questions of information and knowledge, and similar considerations also undermine the two recent proposals under review. However, a full appreciation of the role of learning in a knowledge-intensive economy also challenges some of the presuppositions of market individualism.
Incremental Non-Projective Dependency Parsing

An open issue in data-driven dependency parsing is how to handle non-projective dependencies, which seem to be required by linguistically adequate representations, but which pose problems in parsing with respect to both accuracy and efficiency. Using data from five different languages, we evaluate an incremental deterministic parser that derives non-projective dependency structures in O(n 2 ) time, supported by SVM classifiers for predicting the next parser action. The experiments show that unrestricted non-projective parsing gives a significant improvement in accuracy, compared to a strictly projective baseline, with up to 35% error reduction, leading to state-of-the-art results for the given data sets. Moreover, by restricting the class of permissible structures to limited degrees of non-projectivity, the parsing time can be reduced by up to 50% without a significant decrease in accuracy.
Shrinking Number of Clusters by Multi-Dimensional Scaling.

Clustering is to divide given data and then, automatically find out the meanings hidden in the data. It analyzes data, which are difficult for people to check in detail, and then, makes several clusters consisting of data with similar characteristics. Clustering, which is used in various fields, is automatically done without human interference, but the number of clusters should be decided by men in advance. The number of clusters is a very important element because the result of clustering can be different, depending on the number of clusters. Therefore, this paper proposed a method of deciding the number of clusters, which is projecting the center of a cluster on the two-dimensional plane by use of Multi-Dimensional Scaling, and then, combining the clusters. As a result of experimenting this method with real data, it was found that clustering performance became better. in-depth analysis by laying up the documents with similar characteristics, not by accurately separating all the documents. Classification needs no more worrisome because the subjects to be classified are clear and the number of classifications is same as that of the subjects to be classified. Clustering allows a user to set up the number of clusters and its result has its own meaning. However, from the viewpoint of general users, it may be felt inconvenient that the result of clustering depends on the number of clusters and that the best optimal number of clusters can be obtained after users test with various numbers of clusters. Accordingly, a method to automatically decide the number of clusters is necessary, but there is still a lack of the researches compared with the researches of the clustering algorithm. People want to get a result in a very short time in information retrieval system. Besides the clustering performance is superior, it will be inconvenient system if the clustering takes too long time. Shrinking the number of clusters is aim to enhance the clustering performance, but if it takes too long time, it will be meaningless task after all. Therefore, this paper studied the method of automatically deciding the number of clusters without users' repetitive tests and without giving great influence on the clustering time. This paper is composed of: descriptions of the existing techniques of deciding the number of clusters (chapter 2); explanation of how to reduce the number of clusters (chapter 3); usefulness of the method proposed in chapter 3 through analysis of test results (chapter 4); and suggestions for future studies (chapter 5).
Productive Hepatitis C Virus Infection of Stem Cell-Derived Hepatocytes Reveals a Critical Transition to Viral Permissiveness during Differentiation

Primary human hepatocytes isolated from patient biopsies represent the most physiologically relevant cell culture model for hepatitis C virus (HCV) infection, but these primary cells are not readily accessible, display individual variability, and are largely refractory to genetic manipulation. Hepatocyte-like cells differentiated from pluripotent stem cells provide an attractive alternative as they not only overcome these shortcomings but can also provide an unlimited source of noncancer cells for both research and cell therapy. Despite its promise, the permissiveness to HCV infection of differentiated human hepatocyte-like cells (DHHs) has not been explored. Here we report a novel infection model based on DHHs derived from human embryonic (hESCs) and induced pluripotent stem cells (iPSCs). DHHs generated in chemically defined media under feeder-free conditions were subjected to infection by both HCV derived in cell culture (HCVcc) and patient-derived virus (HCVser). Pluripotent stem cells and definitive endoderm were not permissive for HCV infection whereas hepatic progenitor cells were persistently infected and secreted infectious particles into culture medium. Permissiveness to infection was correlated with induction of the liver-specific microRNA-122 and modulation of cellular factors that affect HCV replication. RNA interference directed toward essential cellular cofactors in stem cells resulted in HCV-resistant hepatocyte-like cells after differentiation. The ability to infect cultured cells directly with HCV patient serum, to study defined stages of viral permissiveness, and to produce genetically modified cells with desired phenotypes all have broad significance for host-pathogen interactions and cell therapy.
Reconsidering the Relationship Between Student Employment and Academic Outcomes

Prior observers have consistently found a negative relationship between school performance and the intensity of high school students' employment. However, few observers have thought carefully enough about exactly why student employment and schooling outcomes are related. Most have used a zero-sum model of students' resource allocation to explain this relationship. The author identifies conceptual problems with the zero-sum model and proposes a social psychological primary orientation model to explain this association. He then uses new data collected in a pilot study on adolescent employment that allows him to weight the merits of these competing models. In an exploratory analysis, the author finds that these data are more consistent with the primary orientation model than with the zero-sum perspective.
MOTIVATING FACTORS IN CHILD LEARNING

There are many problems of motivation with young children which need to be investigated. There are few studies on the influence of incentives on young children and the motivating factors in the experiments on learning in young children give few data for evaluation of incentives and rewards. The present experiment was run as the second series in an experiment by Johnson.2 It was planned to test the influence of an intrinsic reward plus an unpleasant incentive on the speed of learning to open a simple problem box.
The association of diet with quality of life, disability, and relapse rate in an international sample of people with multiple sclerosis

Abstract Objectives To explore the association between dietary factors including fat, fruit and vegetable intake, dairy and meat consumption, and health-related quality of life (HRQOL), disability and relapse rate in a large international sample of people with multiple sclerosis (MS). Methods Participants with MS were recruited to the study via Web 2.0 platforms and completed a comprehensive survey measuring demographic and clinical characteristics, HRQOL, disability, relapse rate, and the Diet Habits Questionnaire (DHQ). Results Of 2469 participants with confirmed MS, 2087 (84.5%) provided complete data on their dietary habits (DHQ total score). Multivariate regression models demonstrated that every 10-point increase on the DHQ total score was associated with nearly a six-point and five-point increase in physical and mental HRQOL, respectively, and 30.0% reduced likelihood of a higher level of disability. After controlling for age and gender, and the other dietary covariates, ‘healthy’ consumption of fruit and vegetables and dietary fat predicted better quality of life and less likelihood of higher disability when compared to respondents with a ‘poor’ diet. For those with relapsing–remitting MS, the DHQ total significantly predicted a lower relapse rate and reduced odds of increasing disease activity, but the model fit was poor and the predicted change only marginal. Discussion This study supports significant associations of healthy dietary habits with better physical and mental HRQOL and a lower level of disability. Further research is urgently required to explore these associations including randomized controlled trials of dietary modification for people with MS.
Bounded rationality: The adaptive toolbox

In a complex and uncertain world, humans and animals make decisions under the constraints of limited knowledge, resources, and time. Yet models of rational decision making in economics, cognitive science, biology, and other fields largely ignore these real constraints and instead assume agents with perfect information and unlimited time. About forty years ago, Herbert Simon challenged this view with his notion of "bounded rationality. " Today, bounded rationality has become a fashionable term used for disparate views of reasoning. This book promotes bounded rationality as the key to understanding how real people make decisions. Using the concept of an "adaptive toolbox," a repertoire of fast and frugal rules for decision making under uncertainty, it attempts to impose more order and coherence on the idea of bounded rationality. The contributors view bounded rationality neither as optimization under constraints nor as the study of people's reasoning fallacies. The strategies in the adaptive toolbox dispense with optimization and, for the most part, with calculations of probabilities and utilities. The book extends the concept of bounded rationality from cognitive tools to emotions; it analyzes social norms, imitation, and other cultural tools as rational strategies; and it shows how smart heuristics can exploit the structure of environments.
Public Support for the Death Penalty

Over the last decade public support for capital punishment has increased. Yet few empirical studies have attempted theoretically to explain this occurrence. Culling rather diverse literature, the seeds of three plausible explanations have emerged. They have been identified as the Rational, the Systems, and the Traditional Values models. Data for the project were derived from survey questionnaire responses of 1149 of 1872 persons (63 percent response rate), via a 4-wave mail survey. The sample was drawn from all available telephone directories in Washington State; both adult male and females were surveyed. The data offered minimal support for either the Rational or Systems models, and provided better support for the Traditional Values explanation. Because the Traditional Values model was the best supported, it was then subjected to a more systematic analysis by focusing on three of its salient dimensions. These included sexual restraint, orderliness, and discipline. Finally, policy implications of the data were addressed, particularly in light of recent United States Supreme Court decisions.
Scalable coordinated uplink processing in cloud radio access networks

Featured by centralized processing and cloud based infrastructure, Cloud Radio Access Network (C-RAN) is a promising solution to achieve an unprecedented system capacity in future wireless cellular networks. The huge capacity gain mainly comes from the centralized and coordinated signal processing at the cloud server. However, full-scale coordination in a large-scale C-RAN requires the processing of very large channel matrices, leading to high computational complexity and channel estimation overhead. To resolve this challenge, we show in this paper that the channel matrices can be greatly sparsified without substantially compromising the system capacity. Through rigorous analysis, we derive a simple threshold-based channel matrix sparsification approach. Based on this approach, for reasonably large networks, the non-zero entries in the channel matrix can be reduced to a very low percentage (say 0.13% ~ 2%) by compromising only 5% of SINR. This means each RRH only needs to obtain the CSI of a small number of closest users, resulting in a significant reduction in the channel estimation overhead. On the other hand, the high sparsity of the channel matrix allows us to design detection algorithms that are scalable in the sense that the average computational complexity per user does not grow with the network size.
Visual methodology: previously, now and in the future

This special issue of IJRME reflects the fact that visual methodology in educational research is at an important turning point. There has been a rapid expansion in practice within educational research, but we feel that it is now necessary to reflect critically on the ways in which visual methods are used: the collection of data, approaches to analysis and dissemination, the questions visual data are being used to address and those they are not; and how research identified as ‘visual’ is located in the wider field and how it contributes to the wider aspirations of research for learning and teaching. This special issue aims to facilitate dialogue about visual methodology in educational research and to explore how researchers in the field are conceptualizing quality in visual methods. We invited contributors to investigate issues of trustworthiness, rigour, reliability, validity and credibility and how these can be approached to give visual methods equivalent status alongside other education research practices. Overall, the purpose of this issue is not only to exemplify good practice in analysis and representation of visual data, but also to focus on emerging theoretical debates about the role of visual methods in the creation and translation of knowledge and their relevance for education. In answer to the question of ‘why now? ’, it is worth noting that although the use of visual artefacts as data has a longer history in some social sciences (Banks 2001), the visual had tended until relatively recently to be overlooked in education (Prosser 1998; Rose 2001), but is now frequently a concern of researchers. That there has been this rapid expansion in research practice suggests that this special issue is timely. Furthermore, while some disciplines have tended to concentrate either on the visual data themselves (Rose 2001) or alternatively to mainly use visual information to elicit further data (Harper 2002), education has tended to borrow from a diversity of disciplines in terms of methods, intentions and uses of the visual. This is probably related to the more pragmatic, practice-based understandings that many educational researchers have of their field and, in fact, many of the popular methods within education research can be traced back to techniques developed in practice in education or elsewhere. So, for example, draw and write techniques were initially developed to elicit understandings of concepts such as risk by public health professionals (Whetton and McWhirter 1998) while enabling children to make photographs remains a particularly popular method in research in public health and community psychology (Darbyshire, Macdougall, and Schiller 2005; Croghan et al. 2008), as well as in education. Certainly, our experiences as researchers has consisted in initially making use of visual methods because visual data are common in schools and visual artefacts are often successful in mediating conversations with participants of all ages. With backgrounds in teaching,