Miracle-dz/UniFAR-FTUs huggingface dataset:
Future Primary Teachers Beliefs, Understandings and Intentions of to Teach STEM

The development of integrated skills and knowledge in science, technology, engineering, and mathematics (STEM) are necessary in order to deal with challenging complex situations and should be developed from primary school. It is expected that early experiences can influence and foster a deep and ongoing interest in STEM. In order to provide these early experiences in their future classrooms, preservice teachers need subject matter knowledge, pedagogical content knowledge and expertise to innovate and deal with STEM in their own future classrooms This research focused on the beliefs and understandings preservice primary teachers (n=119) have about teaching and to what extent they are prepared to teach STEM subjects in primary schools. A questionnaire based on the position paper on STEM issued by the Australian Office of the Chief Scientist (Prinsley & Johnston, 2015) and guided by the theory of reasoned action was used as the basis of this study. The data was analysed qualitatively and quantitatively. The results suggest the preservice teachers in this study believed there should be STEM in the curriculum, but they were not confident in their ability to teach STEM without more professional preparation and development.
Gender Matching and Competitiveness: Experimental Evidence

This paper experimentally investigates if and how people's competitiveness depends on their own gender and on the gender of people with whom they interact. Participants are given information about the gender of the co-participant they are matched with, they then choose between a tournament or a piece rate payment scheme, and finally perform a real task. As already observed in the literature, we find that significantly more men than women choose the tournament. The gender of the co-participant directly influences men's choices (men compete less against other men than against women), but only when the gender information is made sufficiently salient. A higher predicted competitiveness of women induces more competition. Giving stronger tournament incentives, or allowing the participants to choose the gender of their co-participant, increases women's willingness to compete, but does not close the gender gap in competitiveness.
The C‐terminus of the metabotropic glutamate receptor 1b regulates dimerization of the receptor

The Group C G protein‐coupled receptors include the metabotropic glutamate receptors (mGluRs), the GABAB receptor, the calcium sensor and several taste receptors, most of which are obligate dimers, indeed recent work has shown that dimerization is necessary for the activation of these receptors. Consequently factors that regulate their ability to homo‐ or heterodimerize are important. The Group 1 mGluRs include mGluR1 and mGluR5 both of which have splice variants with altered C‐termini. In this study, we show that mGluR1b is a dimer and that it does not efficiently heterodimerize with mGluR1a, unlike the two splice variants of mGluR5 that can heterodimerize. Mutation of a positively charged motif (RRKK) at the C‐terminus of the mGluR1b tail permits mGluR1b to heterodimerize with mGluR1a. Co‐expression of mGluR1a and mGluR1b in COS‐7 cells results in the accumulation of mGluR1b in intracellular inclusions that do not contain mGluR1a. This behaviour is mimicked by a chimera of the lymphocyte antigen CD2 with the C‐terminus of mGluR1b (pCD1b) and depends on the presence of the RRKK motif. These accumulations are immunoreactive for endoplasmic reticulum (ER) markers, but not Golgi and ERGIC markers. This segregation of mGluR1b from other ER proteins may contribute to its failure to dimerize with mGluR1a.
Learning to teach sport education: investigating a pre-service teacher’s knowledge development

ABSTRACT The pre-service teacher (PST) learning process has been claimed to include multiple and complex forms of learning because various areas of knowledge growth occur at the same time. In the Sport Education (SE) literature, there has been a noticeable dearth of research regarding how PSTs learn, interpret and deliver the model. While several studies report PSTs having experienced SE prior to the formal study being carried out, to our knowledge, only one study has followed PSTs through a series of learning experiences. In this study, we used the three-level model of learning as a framework to investigate a PST’s continuing process of learning to teach SE as part of a PETE program and while teaching during the school placement component of the PETE program. The study was guided by the question, ‘How does a PST’s knowledge of teaching and learning SE develop? ’ This study reports on one physical education PST learning to teach SE. The learning experience was composed of four PETE courses (two content courses and two school placements) divided into five phases. Data collection employed five semi-structured interviews, coursework and a focus group. Data were analyzed using a hybrid approach of inductive and deductive theme development. Results revealed that the PST progressively developed conscious awareness and understanding about teaching and learning SE. The comprehensive learning experience made the PST develop understanding of teaching and learning SE that reflected knowledge on an abstract level. Studying the relationships between SE concepts, while connecting them with knowledge from various PETE courses, the theoretical foundation of SE became accessible. We encourage physical education teacher educators to allow for a continuing growth of understanding where PSTs develop knowledge through various SE learning and teaching experiences tailored around their needs and concerns.
Trade protection and tax evasion: Evidence from Kenya, Mauritius, and Nigeria

We examine the effect of trade protection rates on evasion in three African countries Kenya, Mauritius, and Nigeria. In capturing the effect of trade protection on tariff evasion, we use a much improved measure of trade protection (MAcMap-HS6 2001 and 2004). For two of these countries, this dataset allows the novelty of using variation in trade protection across product, time, and trading partners leading to significantly refined estimates of evasion elasticity relative to existing studies on tariff evasion. We find a robust evidence for positive elasticity of evasion with respect to tariffs in Kenya and Nigeria with relatively weaker evidence for Mauritius. Our results match the rankings of countries in institutional quality. Greater responsiveness of evasion to the level of tariffs is established in Nigeria (comparatively weak institutional quality) vis-à-vis Kenya, and in Kenya vis-à-vis Mauritius (comparatively good institutional quality). This pattern is preserved even when focusing on same set of trading partners and same set of imported products for the three countries. This result is robust to controlling for protection on related products (that creates incentives/ opportunities for evasion) and also for degree of differentiation of the product and some other characteristics.
The Use of Decision Criteria in Selecting Information Systems/Technology Investments

In a competitive environment, selecting and effectively pursuing the right information systems/technology (IST) investments can be a key factor in sustaining corporate viability and prosperity. This study examines the criteria used by 80 organizations in allocating strategic IST resources. Senior executives were asked to indicate which of 15 criteria they use in deciding among competing projects. They also identified how frequently the criteria are used and ranked them by importance. The results indicate that criteria such as the support of explicit business objectives and response to competitive systems are now important in selecting IST investments. Although financial criteria are used by most organizations, the extent of analysis and application appears to leave room for improvement.
Federal Reserve Bank of San Francisco Working Paper Series Timeless Perspective Policymaking: When Is Discretion Superior? Timeless Perspective Policymaking: When Is Discretion Superior? Federal Reserve Bank of San Francisco

The views in this paper are solely the responsibility of the authors and should not be interpreted as reflecting the views of the Federal Reserve Bank of San Francisco or the Board of Governors of the Federal Reserve System. Abstract In this paper I show that discretionary policymaking can be superior to timeless perspective policymaking and identify model features that make this outcome more likely. Developing a measure of conditional loss that treats the auxiliary state variables that characterize the timeless perspective equilibrium appropriately, I use a New Keynesian DSGE model to show that discretion can dominate timeless perspective policymaking when the Phillips curve is relatively ‡at, due, perhaps, to …rm-speci…c capital (or labor) and/or Kimball (1995) aggregation in combination with nominal price rigidity. These results suggest that studies applying the timeless perspective might also usefully compare its performance to discretion, paying careful attention to how policy performance is evaluated.
Mining object-oriented design models for detecting identical design structures

The object-oriented design is the most popular design methodology of the last twenty-five years. Several design patterns and principles are defined to improve the design quality of object-oriented software systems. In addition, designers can use unique design motifs which are particular for the specific application domain. Another common habit is cloning and modifying some parts of the software while creating new modules. Therefore, object-oriented programs can include many identical design structures. This work proposes a sub-graph mining based approach to detect identical design structures in object-oriented systems. By identifying and analyzing these structures, we can obtain useful information about the design, such as commonly-used design patterns, most frequent design defects, domain-specific patterns, and design clones, which may help developers to improve their knowledge about the software architecture. Furthermore, problematic parts of frequent identical design structures are the appropriate refactoring opportunities because they affect multiple areas of the architecture. Experiments with several open-source projects show that we can successfully find many identical design structures in each project. We observe that usually most of the identical structures are an implementation of common design patterns; however we also detect various anti-patterns, domain-specific patterns, and design-level clones.
Knowledge-based recommender systems

1. Introduction Recommender systems provide advice to users about items they might wish to purchase or examine. Recommendations made by such systems can help users navigate through large information spaces of product descriptions, news articles or other items. As on-line information and e-commerce burgeon, recommender systems are an increasingly important tool. A recent survey of recommender systems is found in The most well known type of recommender system is the collaborative-or social-filtering type. These systems aggregate data about customers' purchasing habits or preferences, and make recommendations to other users based on similarity in overall purchasing patterns. For example, in the Ringo music recommender system (Shardanand & Maes, 1995), users express their musical preferences by rating various artists and albums, and get suggestions of groups and recordings that others with similar preferences also liked. Content-based recommender systems are classifier systems derived from machine learning research. For example, the NewsDude news filtering system is a recommender system that suggests news stories the user might like to read (Billsus & Pazzani, 1999). These systems use supervised machine learning to induce a classifier that can discriminate between items likely to be of interest to the user and those likely to be uninteresting. A third type of recommender system is one that uses knowledge about users and products to pursue a knowledge-based approach to generating a recommendation, reasoning about what products meet the user's requirements. The PersonalLogic recom-mender system offers a dialog that effectively walks the user down a discrimination tree of product features.
Retrieval of Spoken Documents: First Experiences

We report on our rst experiences in dealing with the retrieval of spoken documents. While lacking the tools and the know-how for performing speech recognition on the spoken documents, we tried to use in the best possible way our knowledge of probabilistic indexing and retrieval of tex-tual documents. The techniques we used and the results we obtained are encouraging, motivating our future involvement in other further experimentation in this new area of research. Supported by a \Marie Curie" Research Fellowship from the European Community.
Modeling Design Flaw Evolution Using Complex Systems

By modelling a software project as a complex system, its internal structure can be analyzed in order to asses its design quality. As a software system is being developed, the quality of its internal structure is evolving too, not always for the better. Flaws in the internal structure are usually indicators of code that is hard to understand, maintain and, in many cases, they are pointers of accumulated technical debt. While there are already methods and tools that enable design flaw detection, they only look at a snapshot of the code, they do not analyze how the design flaw evolved over time. We propose an approach which enhances design flaw detection with history information, in order to: (i) find patterns in the evolution of a design flaw, which could then be used to predict future activity, (ii) improve detection by eliminating false negatives, (iii) improve the recommendation system to provide better refactoring advices and a better ranking of design flaws, in order to address the most critical first.
Revenue maximization for telecommunications company with social viral marketing

Viral marketing, a marketing strategy that leverages the influence power in intimate relationship, has become more prevalent due to the popularity of online social networking services in recent years. Consumers are more likely to make a purchase based on social media referrals. Since marketing through social media and traditional channels may target on different audiences, how to maximize the revenue of a telecommunications company by employing different advertising ways and selecting initial users for advertisements is a critical problem. Therefore, in this paper, we formulate a new research problem, namely Cost-Aware Multi-wAy Influence maXimization (CAMAIX) to address the need mentioned above. We design a 1/2-approximation algorithm with various pruning and budget allocation strategies to solve CAMAIX efficiently. We conduct extensive experiments on a large-scale real dataset from a telecommunications company. The results show that our proposed algorithm outperforms the baseline algorithms in both solution quality and efficiency.
An investigation of clinical outcome prediction from integrative genomic profiles in ovarian cancer

Integrative clinical outcome prediction models that combines gene expression and methylation profiles are investigated in this paper in order to reveal genomic features and models that bear important prognostic value. The models all include the integration and feature selection steps. In the integration step, a method to combine gene expression and methylation profiles is introduced. In the feature selection step, several approaches were investigated including the supervised principal component and the elastic net method to identify genes, whose expression or associate CpG methylation contribute to the clinical outcome. A set of 87 ovarian cancer patients was used in this study to evaluate the proposed methods. The test results showed that the integrative methods improved the prediction performance over those based on gene expression alone.
Where Can We Really Find the First Stars’ Remnants Today?

A number of recent numerical investigations concluded that the remnants of rare structures formed at very high redshift, such as the very first stars and bright redshift z ≈ 6 QSOs, are preferentially located at the center of the most massive galaxy clusters at redshift z = 0. In this paper we readdress this question using a combination of cosmological simulations of structure formation and extended Press-Schechter formalism, and we show that the typical remnants of Population III stars are instead more likely to be found in a group environment, that is, in dark matter halos of mass ≲2 × 1013 h−1 M☉. Similarly, the descendants of the brightest z ≈ 6 QSOs are expected to be in medium-sized clusters (mass of a few 1014 h−1 M☉), rather than in the most massive superclusters (M > 1015 h−1 M☉) found within the typical 1 Gpc3 cosmic volume where a bright z ≈ 6 QSO lives. The origin of past claims that the most massive clusters preferentially host these remnants is rooted in the numerical method used to initialize their numerical simulations: Only a small region of the cosmological volume of interest was simulated with sufficient resolution to identify low-mass halos at early times, and this region was chosen to host the most massive halo in the cosmological volume at late times. The conclusion that the earliest structures formed in the entire cosmological volume evolve into the most massive halo at late times was thus arrived at by construction. We demonstrate that, to the contrary, the first structures to form in a cosmological region evolve into relatively typical objects at later times. We propose alternative numerical methods for simulating the earliest structures in cosmological volumes.
Rapid prototyping of semantic applications in smart spaces with a visual rule language

One of the major limitations of Ambient Intelligent systems today is the lack of semantic models in human behavior and the environment, so that the system can recognize the specific activity being performed by the users and act accordingly. In this context, we address the general problem of knowledge representation in Smart Spaces. In order to monitor and act over human behavior in intelligent environments, we design a sufficiently simple and flexible visual language to be managed by non-expert users, thus facilitating the programming of the environment. The prototype of the visual language serves to represent rules about human behavior to provide the Smart Space with more usability. These rules can be mapped into SPARQL queries and rule subscriptions. In addition, we add support to represent imprecise and fuzzy knowledge. The proposed general-domain language can help managing resource allocation, assisting people with special needs, in remote monitoring and other domains.
Dendritic targeting of short and long 3′ UTR BDNF mRNA is regulated by BDNF or NT-3 and distinct sets of RNA-binding proteins

Sorting of mRNAs in neuronal dendrites relies upon inducible transport mechanisms whose molecular bases are poorly understood. We investigated here the mechanism of inducible dendritic targeting of rat brain-derived neurotrophic factor (BDNF) mRNAs as a paradigmatic example. BDNF encodes multiple mRNAs with either short or long 3′ UTR, both hypothesized to harbor inducible dendritic targeting signals. However, the mechanisms of sorting of the two 3′ UTR isoforms are controversial. We found that dendritic localization of BDNF mRNAs with short 3′ UTR was induced by depolarization and NT3 in vitro or by seizures in vivo and required CPEB-1, -2 and ELAV-2, -4. Dendritic targeting of long 3′ UTR was induced by activity or BDNF and required CPEB-1 and the relief of soma-retention signals mediated by ELAV-1, -3, -4, and FXR proteins. Thus, long and short 3′ UTRs, by using different sets of RNA-binding proteins provide a mechanism of selective targeting in response to different stimuli which may underlay distinct roles of BDNF variants in neuronal development and plasticity.
Exploiting Structure, Annotation, and Ontological Knowledge for Automatic Classification of XML Data

This paper investigates how to automatically classify non-schematic XML data into a user-defined topic directory. The main focus is on constructing appropriate feature spaces on which a classifier operates. In addition to the usual text-based term frequency vectors, we study XML twigs and tag paths as extended features that can be combined with text term occurrences in XML elements. Moreover, we show how to leverage ontological background information, more specifically, the WordNet thesaurus, for the construction of more expressive feature spaces. For efficiency our implementation computes features incrementally and caches ontology entries. Our experiments demonstrate the improved accuracy of automatic classification based on the enhanced feature spaces.
Ethology, Sociobiology, and Evolutionary Psychology

In the years leading up to the Second World War the ethologists Konrad Lorenz and Nikolaas Tinbergen, created the tradition of rigorous, Darwinian research on animal behavior that developed into modern behavioral ecology. At first glance, research on specifically human behavior seems to exhibit greater discontinuity that research on animal behavior in general. The 'human ethology' of the 1960s appears to have been replaced in the early 1970s by a new approach called ‘sociobiology’. Sociobiology in its turn appears to have been replaced by an approach calling itself Evolutionary Psychology. Closer examination, however, reveals a great deal of continuity between these schools. At present, whilst Evolutionary Psychology is the most visible form of evolutionary psychology, empirical and theoretical research on the evolution of mind and behavior is marked by a diversity of ideas and approaches and it is far from clear which direction(s) the field will take in future.
Critical Management Perspectives on Information Systems

Foreword As I write this foreword, the news reaches us of the death of Heinz Klein, a founding father of the critical approach to information systems and contributor to this volume. The number of projects his death leaves us to complete is testimony not only to the generous way he worked with so many colleagues throughout the world, but also to his belief in extending and challenging the critical approach. This book is a further step in the process of extending, challenging and developing the critical approach to information systems. It takes seriously the need to acknowledge the historical roots of the subject in the socio-technical and critical management approaches and the contribution of work on Habermas and Foucault. However, it also takes seriously the call of Heinz Klein to diversify the approach, to bring in new voices and new ideas, and to revisit and extend older ideas. This book addresses these points in a number of important ways. Stahl’s attention to the ethical dimension of critical IS reminds us that ethics is at the heart of Habermas’s work, yet it is often overlooked as a subject of enquiry, both in mainstream IS and in critical IS. Semiotics and ethnography are two areas hitherto minimally addressed in critical IS. They receive welcome attention in this book, adding to the push for the exploration of new approaches. Critical IS research has, at times, been criticised for its lack of engagement with empirical issues. Thus, the chapters by Oliver and Romm on ERP systems and Doolin on healthcare systems are all the more welcome, especially as these are empirical areas subject to much discussion within mainstream IS. Two final chapters provide useful reflection. It is fitting that Heinz’s chapter closes the book. However, this does not mean that he has the last word and that his work is over. Rather, it serves as a reminder that his legacy is to leave us with questions to answer and work to do in the critical tradition which he did so much to form and shape. Professor Alison Adam Director of the Information Systems, Organisations and Society Research Centre University of Salford, U.K.