Miracle-dz/UniFAR-FTUs huggingface dataset:
International Teachers’ Judgment of Gifted Mathematics Student Characteristics

Abstract Teachers play a key role in the identification and training of talented mathematicians, and their attitudes are important in improving math instruction for gifted students. We surveyed secondary mathematics teachers from South Korea, Turkey, and the United States. These teachers completed a survey instrument called the Teachers’ Judgments of Gifted Mathematics Student Characteristics (TJGMSC) that measured how important they believed 40 behaviors were with regards to students’ mathematics giftedness. They also evaluated different strategies for teaching mathematics. The more years teachers taught mathematics, the more likely they were to report that students’ computational skills, students’ ability to relate mathematics to everyday life, and students’ ability to generate multiple and unique solutions to problems were indicators of mathematical talent. The opposite was true for the highest level of mathematics taught. The higher the grade level of mathematics teachers taught, the less they valued each of these. Teachers with advanced degrees were less impressed with computation skills. Teachers from South Korea, whose students score near the top on international mathematics exams, were less likely to view mathematical talent as innate. They saw mathematics as an abstract subject in which students who were having difficulty should be given time in class to practice by themselves. They were less likely to regard mathematics as a practical topic or a formal way of representing the world. They were also less likely to use a variety of representations (pictures, concrete objects, and symbols) when teaching mathematics.
Stochastic Consensus Seeking With Noisy and Directed Inter-Agent Communication: Fixed and Randomly Varying Topologies

We consider consensus seeking of networked agents on directed graphs where each agent has only noisy measurements of its neighbors' states. Stochastic approximation type algorithms are employed so that the individual states converge both in mean square and almost surely to the same limit. We further generalize the algorithm to networks with random link failures and prove convergence results.
Expanding Window Random Linear Codes for data partitioned H.264 video transmission over DVB-H network

Rateless codes can be advantageously used to provide Application Layer Forward Error Correction (AL-FEC) with a distinct advantage that an infinite number of packets can be generated on the fly from the source packets. For heterogeneous users and/or variable channel conditions, significant adaptation features and improvement in the video quality can be achieved by partitioning the video data into different priority classes. Such partitioned data can be unequally protected using appropriate FEC scheme based on its contribution to video reconstruction. Expanding Window Random Linear Codes (EW RLC) are a simple unequal error protection fountain coding scheme which can adapt to the prioritized data transmission. In this paper, EW RLC are proposed for broadcasting the H.264/Advanced video coding partitioned with the data partitioning feature. The results show viability of the EW RLC for multimedia broadcast applications to suit different data rates and channel conditions.
Accurate Reconstruction and Suppression for Azimuth Ambiguities in Spaceborne Stripmap SAR Images

In this letter, an accurate mathematical model for azimuth ambiguity in stripmap synthetic aperture radar (SAR) images is first constructed, with an azimuth ambiguity factor (AAF) defined as the residual amplitude and phase terms of ambiguities. Next, a novel framework for reconstructing and suppressing azimuth ambiguity is proposed based on the analysis of the AAF. In this framework, azimuth ambiguities are accurately reconstructed by applying reconstruction filters in the range Doppler and 2-D frequency domain, and then, the reconstructed signal is used for suppressing azimuth ambiguities. Moreover, the proposed framework does not depend on the statistical characteristics of a SAR image and is capable of reducing the space-variant ambiguities. As verified by both simulated data and real TerraSAR-X data, the proposed method is capable of suppressing azimuth ambiguities in SAR images.
Experimental framework for mobility anchor point selection scheme in hierarchical mobile IPv6

Hierarchical Mobile IPv6 (HMIPv6) was designed to support IP micro-mobility management in the next generation Internet Protocol (IPv6) and the Next Generation Networks (NGN) framework. The general idea behind this protocol is the usage of Mobility Anchor Point (MAP) located at any level router of network to support hierarchical mobility management and seamless handover. Further, the distance MAP selection in HMIPv6 causes MAP overloaded and increase frequent binding update as the network grows. Therefore, to address the issue in designing MAP selection scheme, we propose an enhance distance scheme with a dynamic load control mechanism (DMS-DLC). From the experimental results we obtain that the proposed scheme gives better distribution in MAP load and increase handover speed. In addition a new proposed research framework was established that uses the four stages model.
Observing Young Children's Rough-and-Tumble Play

THIS RESEARCH INVESTIGATED THE rough-and-tumble play of 17 five-year-old children in two early childhood settings. The study resulted in an increased understanding of the forms of rough-and-tumble play displayed by young children. This study demonstrates that both boys and girls are engaged in a variety of rough-and-tumble play behaviours. Twenty-seven distinct rough-and-tumble play behaviours were exhibited during the observation period. They included eight components identified as rough-and-tumble play in previous research and also 19 behaviours not previously identified as elements of such. These elements indicate pre-operational forms of rough-and-tumble play. The results of this study have implications for the understanding of child development. It suggests that rough-and-tumble play evolves as children age; that children move into more complex play behaviours as they mature.
Statistical Learning Analysis in Neuroscience: Aiming for Transparency

Encouraged by a rise of reciprocal interest between the machine learning and neuroscience communities, several recent studies have demonstrated the explanatory power of statistical learning techniques for the analysis of neural data. In order to facilitate a wider adoption of these methods, neuroscientific research needs to ensure a maximum of transparency to allow for comprehensive evaluation of the employed procedures. We argue that such transparency requires “neuroscience-aware” technology for the performance of multivariate pattern analyses of neural data that can be documented in a comprehensive, yet comprehensible way. Recently, we introduced PyMVPA, a specialized Python framework for machine learning based data analysis that addresses this demand. Here, we review its features and applicability to various neural data modalities.
Diversity management for all? An empirical analysis of diversity management outcomes across groups

Purpose – The purpose of this paper is to examine if and how diversity management outcomes differ across non-native and native Dutch groups within public sector organizations. The effects of diversity management on the extent employees feel their organizational environment is inclusive and on their attitudes and behaviour, are expected to be dependent on how diversity management is perceived by non-native and native Dutch employees in the organization. Drawing on social exchange theory, the authors expect that employees who positively value diversity management practices will reciprocate through showing attitudes and behaviours that are valued by the organization. Since social exchange refers to a social relationship between the employee and the organization that goes beyond the formal contract alone, the authors analyse affective commitment and organizational citizenship behaviour (OCB) as employee outcomes. Design/methodology/approach – The authors analyse data from a quantitative survey of a sample of .
A test method for delay faults in NoC interconnects

Network-on-Chip (NoC) delay faults can only be detected when NoC works at normal operating speed, so it is very hard to test such faults. In this paper, we propose a method for at-speed testing of delay faults in NoC interconnects. Based on the NoC delay fault model, we first formulate the problems of the NoC delay faults. Then, we propose a delay test method for NoC data transfer through handshaking and add an extra hardware respectively on the transmitter and the receiver as test circuits, achieving the automatic testing of the delay faults. Finally, the analysis shows that the proposed method guarantees that all the good chips will not be detected as faulty, avoiding the waste of chip resources.
Biological, Adoptive, and Mixed Families

ABSTRACT An abundance of research has focused on the many challenges faced by families that have children with special needs. While special needs has been defined and operationalized in a number of ways, virtually all measures are linked to additional familial challenges. Few, if any, studies to date have examined the topic of the special needs child from the perspective of differing family types (biological, adoptive or mixed). Considering the increasing number of parents choosing to adopt internationally, the existing literature also lacks a systematic examination of the differences prevalent between families who have chosen to adopt internationally. The present study was based on two central predictions: (1) parental and marital satisfaction would be adversely impacted by the presence of a special needs child, and (2) the impact of the special needs child would differ depending on what family type was examined. Participants included 91 mothers who provided data on 231 children. A family questionnaire was administered to participants across the United States and from two foreign countries. Parental satisfaction was found to be lower in adoptive families with a special needs child, but the overall satisfaction rating was still relatively high.
Development of Phosphorothioate DNA and DNA Thioaptamers

Nucleic acid aptamers are short RNA- or DNA-based affinity reagents typically selected from combinatorial libraries to bind to a specific target such as a protein, a small molecule, whole cells or even animals. Aptamers have utility in the development of diagnostic, imaging and therapeutic applications due to their size, physico-chemical nature and ease of synthesis and modification to suit the application. A variety of oligonucleotide modifications have been used to enhance the stability of aptamers from nuclease degradation in vivo. The non-bridging oxygen atoms of the phosphodiester backbones of RNA and DNA aptamers can be substituted with one or two sulfur atoms, resulting in thioaptamers with phosphorothioate or phosphorodithioate linkages, respectively. Such thioaptamers are known to have increased binding affinity towards their target, as well as enhanced resistance to nuclease degradation. In this review, we discuss the development of phosphorothioate chemistry and thioaptamers, with a brief review of selection methods.
A Comparative Analysis of Software Reliability Growth Models using Defects Data of Closed and Open Source Software

The purpose of this study is to compare the fitting (goodness of fit) and prediction capability of eight Software Reliability Growth Models (SRGM) using fifty different failure Data sets. These data sets contain defect data collected from system test phase, operational phase (field defects) and Open Source Software (OSS) projects. The failure data are modelled by eight SRGM (Musa Okumoto, Inflection S-Shaped, Goel Okumoto, Delayed S-Shaped, Logistic, Gompertz, Yamada Exponential, and Generalized Goel Model). These models are chosen due to their prevalence among many software reliability models. The results can be summarized as follows: Fitting capability: Musa Okumoto fits all data sets, but all models fit all the OSS datasets. : Prediction capability: Musa Okumoto, Inflection S-Shaped and Goel Okumoto are the best predictors for industrial data sets, Gompertz and Yamada are the best predictors for OSS data sets. : Fitting and prediction capability: Musa Okumoto and Inflection are the best performers on industrial datasets. However this happens only on slightly more than 50% of the datasets. Gompertz and Inflection are the best performers for all OSS datasets.
High Value-Added Biomolecules from Beach Waste of Marine Origin-Screening for Potential Candidates among Seagrass of the Cymodoceaceae Family

Method: Six species of seagrass belonging to the genera Syringodium, Cymodocea and Halodule were collected from their native habitat. The L-chiro-inositol content of the crude aqueous extracts prepared from different batches of Cymodocea nodosa flotsam was measured by quantitative H-NMR spectroscopy. High concentrations were found with values ranging from 22.2 to 35.0 mg.g plant dw. The presence of L-chiro-inositol in the tropical species C. rotundata, C. serrulata, Syringodium isoetifolium, and Halodule pinifolia was also characterized by qualitative NMR. The chicoric acid content of crude aqueous methanolic extracts prepared from C. rotundata, C. serrulata, S. isoetifolium, and Halodule pinifolia was determined by quantitative HPLC-DAD. The values found ranged from 0.39 to 6.15 mg.g dry weight. Chicoric acid was unambiguously identified as the major phenolic in S. isoetifolium, and Halodule pinifolia, while it was found in mixture with flavonol derivatives in C. rotundata and C. serrulata. Flavonol derivatives are also of interest for their bioactivity.
Quantification of colocalization and cross‐talk based on spectral angles

Common methods for quantification of colocalization in fluorescence microscopy typically require cross‐talk free images or images where cross‐talk has been eliminated by image processing, as they are based on intensity thresholding. Quantification of colocalization includes not only calculating a global measure of the degree of colocalization within an image, but also a classification of each image pixel as showing colocalized signals or not. In this paper, we present a novel, automated method for quantification of colocalization and classification of image pixels. The method, referred to as SpecDec, is based on an algorithm for spectral decomposition of multispectral data borrowed from the field of remote sensing. Pixels are classified based on hue rather than intensity. The hue distribution is presented as a histogram created by a series of steps that compensate for the quantization noise always present in digital image data, and classification rules are thereafter based on the shape of the angle histogram. Detection of colocalized signals is thus only dependent on the hue, making it possible to classify also low‐intensity objects, and decoupling image segmentation from detection of colocalization. Cross‐talk will show up as shifts of the peaks of the histogram, and thus a shift of the classification rules, making the method essentially insensitive to cross‐talk. The method can also be used to quantify and compensate for cross‐talk, independent of the microscope hardware.
Masked non-negative matrix factorization for eire detection using weakly labeled data

Acoustic monitoring of bird species is an increasingly important field in signal processing. Many available bird sound datasets do not contain exact timestamp of the bird call but have a coarse weak label instead. Traditional Non-negative Matrix Factorization (NMF) models are not well designed to deal with weakly labeled data. In this paper we propose a novel Masked Non-negative Matrix Factorization (Masked NMF) approach for bird detection using weakly labeled data. During dictionary extraction we introduce a binary mask on the activation matrix. In that way we are able to control which parts of dictionary are used to reconstruct the training data. We compare our method with conventional NMF approaches and current state of the art methods. The proposed method outperforms the NMF baseline and offers a parsimonious model for bird detection on weakly labeled data. Moreover, to our knowledge, the proposed Masked NMF achieved the best result among non-deep learning methods on a test dataset used for the recent Bird Audio Detection Challenge.
Spatial Point Process Modeling of Vehicles in Large and Small Cities

The uncertainty in the locations of vehicles on streets induced by vehicles passing and queueing make the spatial modeling of vehicles a difﬁcult task. To analyze the performance of vehicle-to-vehicle (V2V) communication for vehicular ad hoc networks (VANETs), accurate spatial modeling is of great im-portance. In this paper, we concentrate on spatial point process modeling for random vehicle locations in large and small cities, performing empirical experiments with real location data of mobile taxi trajectories recorded by the global positioning system (GPS) in Beijing city of China and Porto city of Portugal. We ﬁnd that the empirical probability mass functions (PMFs) of the number of taxis in test sets in different regions of Beijing or in Porto all follow a negative binomial (NB) distribution. Based on the above, we show that the Log Gaussian Cox Process (LGCP) model, whose empirical PMF nicely ﬁts the NB distribution, accurately characterizes diverse spatial point patterns of random vehicle location in both large and small cities. This is veriﬁed by the minimum contrast method. The LGCP model can be applied to analyze performance metrics (i.e., connectivity, coverage, and capacity) and optimize the practical deployments of VANETs.
Chain-computerisation as a research methodology: The fruits of six years of Chain Landscape Research at Utrecht University

Dit artikel is beschikbaar in het Engels en het Nederlands Met het emeritaat van prof. Jan Grijpink doet zich een natuurlijk moment voor om de balans op te maken van het Ketenlandschapsonderzoek dat sinds 2005 onder zijn leiding aan de Universiteit Utrecht heeft gelopen. Centraal in dit onderzoek staat het uitvoeren van ketenanalyses, aan de hand van het leerstuk Keteninformatisering. De hierbij gebruikte profielen kunnen zowel beschrijvend als toetsend worden gebruikt. In de afgelopen zes jaar zijn ongeveer 25 maatschappelijke ketens onder de loep genomen. Generieke, terugkerende resultaten hebben betrekking op het dominante ketenprobleem, niveauvergissingen en identiteit(sfraude). Het Ketenlandschapsonderzoek gaat verder in dispuutsvorm en dat is hard nodig gezien de bijdrage die Keteninformatisering kan leveren aan de beantwoording van grootschalige informatievraagstukken.
Sequence generation error (SGE) minimization based deep neural networks training for text-to-speech synthesis

Feed-forward deep neural networks (DNNs) based text-to-speech (TTS) synthesis, which employs a multi-layered structure to exploit the statistical correlations between rich contextual information and the corresponding acoustic features, has been shown to outperform a decision tree based, GMM-HMM counterpart. However, the DNN-based TTS training has not taken the whole sequence, i.e., sentence, into account in optimization, hence results in some intrinsic inconsistency between training and testing. In this paper we propose a “sequence generation error” (SGE) minimization criterion for DNN-based TTS training. By incorporating the whole sequence parameter generation directly into the training process, the mismatch between training and testing is eliminated and the original constraints between the static and dynamic features are naturally embedded in the optimization process. Experimental results performed on a speech database of 5 hours show that DNN-based TTS trained with this new SGE minimization criterion can further improve the DNN baseline performance, particularly, in subjective listening tests.
Semantic Web Machine Reading with FRED

A machine reader is a tool able to transform natural language text to formal structured knowledge so as the latter can be interpreted by machines, according to a shared semantics. FRED is a machine reader for the semantic web: its output is a RDF/OWL graph, whose design is based on frame semantics. Nevertheless, FRED’s graph are domain and task independent making the tool suitable to be used as a semantic middleware for domainor task-specific applications. To serve this purpose, it is available both as REST service and as Python library. This paper provides details about FRED’s capabilities, design issues, implementation and evaluation.
Measuring metaphoricity

This paper presents the ﬁrst computationally-derived scalar measurement of metaphoricity. Each input sentence is given a value between 0 and 1 which represents how metaphoric that sentence is. This measure achieves a correlation of 0.450 (Pearson’s R, p < 0.01) with an experimental measure of metaphoricity involving human participants. While far from perfect, this scalar measure of metaphoricity allows different thresholds for metaphoricity so that metaphor identiﬁcation can be ﬁtted for speciﬁc tasks and datasets. When reduced to a binary classiﬁcation evaluation using the VU Amsterdam Metaphor Corpus, the system achieves an F-Measure of 0.608, slightly lower than the comparable binary classiﬁcation system’s 0.638 and competitive with existing approaches.
Influence of lecturers’ verbal and non-verbal immediacy behaviour on perceived affective and cognitive learning in a multicultural context.

The intention of the present study was to determine whether (1) the immediacy behaviourof lecturers whose home language is Afrikaans or English contributes positively to theaffective and cognitive learning of learners whose home language is Afrikaans, Englishor one of the African languages; and (2) whether the form of immediacy behaviourdisplayed by English or Afrikaans lecturers functions differently for learners whose homelanguage is one of the African languages in relation to those whose home language isAfrikaans or English. Data was collected by means of a questionnaire that had separatesections on immediacy behaviour and learning. Positive correlations between theimmediacy behaviour of the lecturers and the affective and cognitive learning of thelearners were observed for the whole test group. The immediacy behaviour displayed bythe lecturers functions differently for learners whose home language is one of the Africanlanguages than for those whose home language is Afrikaans or English. Considering thesefindings, it is imperative that instructional communication in today’s South Africa beincreasingly characterized by a culture-centred approach.
Photoreceiver module using an InP HEMT transimpedance amplifier for over 40 gb/s

We developed a photoreceiver module for over 40 Gb/s that uses two ultrahigh- speed device technologies: an InP HEMT transimpedance amplifier (TIA) and a uni-traveling-carrier photodiode (UTC-PD). The TIA was designed to have a wide dynamic range by using cascade HEMT topology for the output buffer. We found that reducing the standing wave at the PD-TIA interface by decreasing the change of arg(S/sub 11/) of the TIA within the required frequency region is important for increasing the bandwidth of the module. We obtained a minimum sensitivity of -7.6 dBm and a dynamic range of 11 dB for 43-Gb/s nonreturn-to-zero optical input signal. Error-free operation of the module was confirmed at a data rate of 50 Gb/s.