Miracle-dz/UniFAR-FTUs huggingface dataset:
Native and Non-Native Speakers in Online and Face-to-Face Discussions: Leveling the Playing Field

This study investigated the role of native language in the context of online versus face-to-face learning environments. Findings from a mixed-methods analysis revealed that native language was a factor in distinguishing among the learning opportunities in these two classes. Data for the online course were 10 archived asynchronous discussions on its threaded bulletin board. Data for the face-to-face course were verbatim transcripts of audio-taped recordings made during 10 of the 15 class sessions. Non-native speakers were more passive in the face-to-face classroom leaving the native speakers to assume leadership characteristics. In the online environments, non-native speakers and native speakers participated more equally in the discussion. Findings suggested that difficulties experienced by non-native speakers during impromptu face-to-face discussions may be alleviated given more opportunities and time for reflective articulation.
On multistep prediction error methods for time series models

Multistep prediction error methods for linear time series models are considered from both a theoretical and a practical standpoint. The emphasis is on autoregressive moving-average (ARMA) models for which a multistep prediction error estimation method (PEM) is developed. The results of a Monte Carlo simulation study aimed at establishing the possible merits of the multistep PEM are presented.
Syntactic locality and tree adjoining grammar: grammatical, acquisition and processing perspectives

It has been widely recognized that the relations human grammar exploits are sensitive to constraints on structural locality. Indeed, much research in generative syntax has focused on the precise characterization of the locality conditions that grammatical processes respect. In this dissertation, I propose that locality reflects the underlying formal system with which grammars are represented. In particular, I argue that the formalism of Tree Adjoining Grammar (TAG) is the appropriate meta-language for grammatical principles. TAG provides a mechanism for composing phrase structure representations from small structural domains, and in so doing, restricts the class of possible grammatical principles to those expressible over these domains. Under this view, the existence of locality conditions is not directly stipulated, but instead follows from the representational machinery which the formal grammar makes available. I consider evidence from three domains of linguistic inquiry which provide convergent support for this view. I first address the problem of constructing a grammatical theory in the principles and parameters framework in the context of the TAG formalism. I develop a substantive theory of the atomic objects of TAG, elementary trees, and argue for a Condition on Elementary Tree Minimality (CETM) which restricts the domain of an elementary tree. The CETM combined with TAG versions of the Projection Principle and the Empty Category Principle yields elegant analyses of a range of constructions including raising, copular sentences, wh-movement and gerunds. Next, I turn to the domain of parsing and demonstrate how a TAG-based theory of grammar resolves the apparently incompatible demands of grammatical transparency and computational efficiency. The model I develop operates incrementally by processing in elementary tree-sized chunks, as determined by the CETM. This view of incrementality is supported by psycholinguistic evidence concerning the closure points of syntactic processing. Finally, in the domain of acquisition, I argue that the assumption that children cannot perform the more complex operation of TAG, adjoining, explains relative difficulties in the acquisition of seemingly disparate constructions including wh-movement, control, raising, and relative clauses. This represents a new kind of explanation for the time course of acquisition through properties of formal and computational complexity.
The Relation between Size Distortion and Shooting Conditions for Stereoscopic Images

Research on shooting methods of 3-D program production for natural and fatigue-free 3-D images is under way. Toed-in and parallel camera configurations are available for shooting 3-D programs. The former is usually used because shooting and viewing conditions can be easily set to get a desired 3-D design. It has been shown, however, that this camera configuration brings about the inconsistency between depth information by lens perspective and by binocular parallax, and that this inconsistency leads to the size distortion called the puppet theater effect. This effect describes the tendency of objects in 3-D images to look unnaturally small. On the contrary, the parallel camera configuration does not cause such inconsistency under the specific shooting and viewing conditions called the orthostereoscopic condition. The possibilities of representation in 3-D images using this method are discussed in this paper.
A comparison of hand and computer-linked records

A study was conducted to develop and evaluate the efficacy of computerized record linkage. A random sample of Dade County Public School (DCPS) records (n = 3,213) was manually linked to Florida birth certificate records, using a split-screen display, to determine the relative utility of several identifiers. Of the 2,990 eligible records, 2,849 were linked, a success rate of 95%. A computer algorithm was then developed to automate the linkage procedure. The accuracy of electronically linked record pairs was assessed using the manual matches as the "gold standard. " Overall, the computer program was able to correctly identify 97% of the manual linkages. This study demonstrated the feasibility of computerized record linkage methodology as a viable alternative when studying the longterm psychoeducational outcome of children.
‘Mister D.J., Cheer Me Up!’: Musical and Textual Features for Automatic Mood Classification

Abstract Mass consumption of large collections of digital music asks for efficient and intuitive ways of organization. In this article, a system is presented which recognizes the evoked music mood on the basis of a wide variety of features, closely sticking to real world conditions. A two-dimensional mood model is discussed in which moods resemble binary values for arousal and valence and an easy and thus user-friendly method is presented through which a fuzzy seven-class mood cluster is deducted. The songs of the ‘Twenty Years of MTV Europe Most Wanted’ music database consisting of recorded pop music tracks serve for evaluation of three groups of features: firstly, traditional features such as rhythm and tonal features, zero crossing rate, cepstral, and MPEG-7 Low Level Descriptors for audio content are extracted. Secondly, lyrics, chord sequences, and genre data are obtained from on-line sources. Thirdly, from all these, the high-level features, musical mode, and as a novel feature, the suited ballroom dance style, are created automatically. The features selected are data-driven, and Support Vector Machines are used for classification. Prediction accuracies of 77.4% for arousal and 72.9% for valence as well as 71.8% (including neighbours) for the seven-class cluster model are obtained pre-serving realism in terms of non-prototypical music selection and feature extraction throughout.
Integrated approach for surface and volumetric segmentation of range images using biquadrics and superquadrics

The problem of part definition, description, and decomposition is central to the shape recognition systems. We present an integrated framework for segmenting dense range data of complex 3-D scenes into their constituent parts in terms of surface (bi-quadrics) and volumetric (superquadrics) primitives, without a priori domain knowledge or stored models. Surface segmentation is performed by a novel local-to-global iterative regression approach of searching for the best piecewise description of the data in terms of bi-quadric models. Region adjacency information, surface discontinuities, and global shape properties are extracted and used to guide the volumetric segmentation. Superquadric models are recovered by a global-to- local residual-driven procedure, which recursively segments the scene to derive the part- structure. A set of acceptance criteria provide the objective evaluation of intermediate descriptions, and decide whether to terminate the procedure, or selectively refine the segmentation, or generate negative volume description. Superquadric and bi-quadric models are recovered in parallel to incorporate the best of the coarse-to-fine and fine-to-coarse segmentation strategies. The control module generates hypotheses about superquadric models at clusters of underestimated data and performs controlled extrapolation of part-models by shrinking the global model. We present results on real range images of scenes of varying complexity, including objects with occluding parts, and scenes where surface segmentation is not sufficient to guide the volumetric segmentation. We conclude by discussing the applications of our approach in data reduction, 3-D object recognition, geometric modeling, automatic model generation, object manipulation, qualitative vision, and active vision.
The Demands and Requirements of Computer Programming: A Literature Review

This review discusses recent literature on computer programming. It focuses on psychological studies of programming and selected issues related to instruction in programming. The purpose is to inform computer educators about the nature of the cognitive processes involved in programming, and the potential benefits to be gained from learning programming. Concerns relevant to pre-college students of programming are emphasized. The literature reviewed includes empirical studies of programming, rational analyses of the programming task by computer scientists, and expository essays by educators and psychologists. The literature is presented to illuminate three major issues: 1) what are the cognitive demands of programming and what are the possible cognitive outcomes? 2) how does instruction influence learning? 3) who benefits, and in what ways, from instruction?
Archives and Records Management Education and Training: what can Africa learn from Europe and North America?

While education and training of archives and records professionals within Sub Saharan Africa has a history of about five decades, elsewhere professional development has a history lasting several centuries. In Europe, archivists have been in existence since the 17th century while in North America, while the history is just about one century old, within that time a lot of innovation has been evident. This article provides a summary of developments in the two continents, drawing lessons that could be useful in reinvigorating discussion within the African continent.
ICT-Related Intangibles and Organizational Innovation: Indicators for Improving Connectedness and Flexibility

Conceptual models communicate the important aspects of a problem domain to stakeholders. The quality of the models is highly dependent on the usability of the modelling method used. This paper presents a survey conducted on a method that integrates the use of a semi-formal notation, namely the Unified Modelling Language (UML) and a formal notation, namely B. The survey assessed the usability of the method by using grounded theory, the Cognitive Dimensions of Notations (CD) framework and several criteria suggested by the International Organization for Standardization (ISO). Ten participants responded to the survey. The results suggest that the method is accessible to users when the principles and roles of each notation are obvious and well understood, and when there is strong support from the environment. Based on the findings, a usability profile based on CD for designing a method that integrates semi-formal and formal notations is proposed.
SOMWeb : Supporting a Distributed Clinical Community of Practice Using Semantic Web Technologies

This thesis concerns supporting the collaboration and knowledge sharing of distributed clinicians of oral medicine, a sub-discipline of dentistry. The Swedish Oral Medicine Network (SOMNet) holds monthly telephone conferences where a group of clinicians discuss interesting and difficult cases, which distinguishes it from one-to-one teleconsultations. SOMNet can be seen as a distributed community of practice, that is, a group of people sharing a concern and who interact regularly to extend their individual and collective expertise. Related to this, several topics need further investigation: How can geographically distributed clinical collaboration be characterized? What is appropriate functionality for a Web-based system supporting such collaborations? What are the impacts of such systems on collaboration? Further, Semantic Web technologies, such as the Web Ontology Language (OWL), have been proposed as a means of enhancing knowledge sharing. What are benefits and limitations of using these technologies to encode domain knowledge in oral medicine and to support clinical collaboration, and what practical issues face developers? The developed system, SOMWeb, focuses on functionality for meetings and structured cases, and has been regularly used for three years. Interviews, observations, a questionnaire, system log analysis, and case analysis were used to study SOMNet's collaboration and identify system impacts. The documentation of the forms of collaboration in SOMNet can serve as a model for other groups of clinicians wishing to establish a distributed collaboration. SOMNet's meetings provide a necessary rhythm for the community and the cases give context to the clinicians' learning which point toward that the centrality of meetings and cases in a tool will benefit collaboration. Impacts on SOMNet's collaboration include enabling the participation of a wider range of clinics. Factors influencing this are the more accessible submission process as well as the increased tangibility of the collaboration. The thesis also provides recommendations for developers of systems supporting clinical collaboration and knowledge sharing. The use of OWL in examination descriptions has enabled reasoning over cases in the system to provide improved case browsing. At the same time, limitations were found in using OWL for examination templates. Based on the lessons learned in this development, the thesis provides recommendations for using Semantic Web technologies, which can be of value for other developers and to guide future research.
Training data optimization for ANNs using genetic algorithms to enhance MPPT efficiency of a stand-alone PV system

Maximum power point tracking (MPPT) algorithms are used to force photovoltaic (PV) modules to operate at their maximum power points for all environmental conditions. In artificial neural network (ANN)-based algorithms, the maximum power points are acquired by designing ANN models for PV modules. However, the parameters of PV modules are not always provided by the manufacturer and cannot be obtained readily by the user. Experimental measurements implemented in the overall PV system may be used to obtain the ANN dataset. One drawback of this method is that the generalization ability of the neural network usually degrades and some data reducing the effectiveness of the network may exist. A genetic algorithm can be used to automatically select the important data among all the inputs, resulting in a smaller and more effective dataset. In our study, a genetic algorithm is used to improve the MPPT efficiency of a PV system with induction motor drive by optimizing the input dataset for an ANN model of PV modules. A variable frequency volts-per-Hertz (V/f ) control method is applied for speed control of the induction motor, and a space-vector pulse-width modulation (SV-PWM) method is used to operate a 3-phase inverter. Both simulation and experimental results are presented to demonstrate the validation of the method.
Extending the suite of bioclim variables: a proposed registry system and case study using principal components analysis

The so‐called BIOCLIM variables have played a central role in the advancement of ecologicalmodellingmethods for correlative species distribution modelling. We propose to establish a register of the BIOCLIMvariables, allowing for the extension of the suite of described, readily available covariate data tosupport bioclimatic modelling. The registry will provide a simple means for researchers to add usefuldata sets in a transparent, documented manner. As a case study, we introduce and describe the set offivemost significant principal components of the first 35 BIOCLIM variables (Bio01 to Bio35) and proposethey be assigned as Bio36 to Bio40.Together, these five PCA variables capture more than 90%of thevariability in the full suite of 35 BIOCLIM variables. The PCA variables may provide a means to explore the climaticlimits of poorly known species, with a reduced risk of overfitting the models. The BIOCLIM variable registry should facilitate the expansion of the suite of variables commonly used in species distribution modelling.
A variable transimpedance preamplifier for use in wide dynamic range optical receivers

A preamplifier with an automatic gain control (AGC) function based on a new circuit configuration suitable for monolithic integration is proposed as an approach for realizing optical receivers with wide dynamic ranges. This new preamplifier, intended for transmission systems operating above 100 Mb/s, is designed for fabrication using 3-/spl mu/m Si-bipolar IC technology. The fabricated IC exhibits a bandwidth of more than 220 MHz and an equivalent input noise current of about 3 pA//spl radic/Hz at a maximum transimpedance of 18 k/spl Omega/. To examine the AGC capability of the new preamplifier IC, a 140-Mb/s transmission experiment was carried out using a laser diode (LD) transmitter and a p-i-n receiver with its gain controlled by the new preamplifier. An optical dynamic range of 21.5 dB was achieved and thus it should be possible to realize optical receivers with wide dynamic ranges using this preamplifier.
Four-part Harmonization Using Bayesian Networks: Pros and Cons of Introducing Chord Nodes

We explore Bayesian network models that generate four-part harmonies according to the melody of a soprano voice. The probabilistic models that most studies have proposed already contain nodes or states that represent chord symbols. Chord symbols, however, may not be suitable as a node of probabilistic models because they are ambiguous; chords with different voicings are represented as the same symbol. In this paper, we develop Bayesian networks that do and do not include chord nodes and compare the harmonies generated by them. Experimental results show that the non-chord model generates harmonies in which each voice has smoother note motions than the chord model.
Informal learning on YouTube: exploring digital literacy in independent online learning

The focus of this paper is a project conducted in 2011, exploring the use of YouTube in the classroom. The project conducted a number of focus groups for which highlighted a number of issues surrounding independent informal learning environments. The questions posed by this research are concerned with what constitutes learning in these spaces; how valid this is perceived to be by the students and how they engage with materials in this space. A question also posed was how cognisant the students are of their learning in these spaces and how they perceive the efficacy of the materials to support and enhance their learning. The research uncovered how the students interacted with each other in these informal spaces and the role that YouTube video content plays in community formation and supporting informal peer learning. The nature of informal learning spaces being that their focus being not solely of education, but also of entertainment leads to a variation in quality, reliability and suitability of content. The research also explored the students' digital literacy, uncovering the strategies used to first navigate in these spaces and then critically engage, analyse and assess materials that they may find.
Ion chemistry in space

We review the gas-phase chemistry in extraterrestrial space that is driven by reactions with atomic and molecular ions. Ions are ubiquitous in space and are potentially responsible for the formation of increasingly complex interstellar molecules. Until recently, positively charged atoms and molecules were the only ions known in space; however, this situation has changed with the discovery of various molecular anions. This review covers not only the observation, distribution and reactions of ions in space, but also laboratory-based experimental and theoretical methods for studying these ions. Recent results from space-based instruments, such as those on the Cassini–Huygens space mission and the Herschel Space Observatory, are highlighted.
A combinatorial approach to modeling imperfect coverage

A new algorithm combines a coverage model with a combinatorial model to compute system unreliability. Its advantage is that for a class of computer systems, it is simpler than current algorithms. The method applies to combinatorial models which can generate cutsets for the system. This set of cutsets is augmented with cutsets representing the uncovered failures of the system. The resulting set is manipulated by combining standard multi-state and sum-of-disjoint products solution techniques. It is possible to compute the exact unreliability of the system using this algorithm. If the size of the system and the time required for the analysis become prohibitive, however, the solution can be truncated and bounds on the system unreliability computed. The authors' algorithm is important because it adapts standard combinatorial solution techniques to a problem that was previously thought to require a Markov solution. The ability to model a fault-tolerant computer system completely within a combinatorial model allows results to be calculated more quickly and accurately, and thus to impact system design. This new technology is easily integrated into existing design/analysis methodologies. Coverage provides a more accurate picture of system behavior, and gives more faith in reliability estimates. >
Channel characterisation for spread spectrum satellite communications

The paper examines the characteristics of satellite propagation channels for spread spectrum communications. Based on measurement campaigns at L-band, the signal bandwidth is determined when the channel becomes frequency-selective. A wideband channel model for land mobile satellite (LMS) services is presented which characterises the time-varying transmission channel between a satellite and a mobile user terminal. The parameters of the model are the results of fitting procedures applied to the measured data. The parameters are given for various environments and elevation angles. For satellite diversity, the correlation of two independent channels is determined. This channel property is applicable to wideband and narrowband channel models.