Miracle-dz/UniFAR-FTUs huggingface dataset:
The oral-literate connection : perspectives on L2 speaking, writing, and other media interactions

How does speaking affect writing in a second language? Research suggests that connecting speaking and writing in the second language classroom can support and enhance students' development of these skills. This volume presents the most current insights from research and theory on the numerous ways in which second language speaking and writing can interact and influence each other.Contributors to this collection not only report on recent research and theoretical developments but also on state-of-the-art pedagogical practices linking oracy and literacy.Although research and theory are informed by practice throughout the text's four parts, the thematic organization proceeds from more theoretical and research-oriented issues to more classroom-based perspectives. A wide array of theoretical and classroom-oriented topics is offered, ranging from sociocultural and second-language acquisition theory to classroom concordancing programs.
An Attribute Selection For Severity Level Determination According To The Support Vector Machine Classification Result

Determination of bug severity level is needed in fixing bug. Actually, in bug-tracking system, there is around 14 attributes used for defining a bug. But, all this time we do not know which attributes are highly influential for this. In this research, a new model of severity type classification using Infogain method for Bugzilla is proposed. As for the classsification process, we use Support Vector Machine, because this method is suitable in handling a massive data records. In this research, 8 bug attributes and 17.746 record of bug reports are involved. From the result of the experiment, we recommend five attributes which can be used effectively in classifying the severity types with a minimal value of infogain 0,33 which is component, qa_contact, summary, cc_list and product. The combination of those 5 attributes resulting in 99,83% accuracy of severity types classification. Keywords- Bug Tracking System; Severity Level Classification; TF-IDF; Infogain; SVM.
Introduction to Computing Using Python: An Application Development Focus

Perkovic's Introduction to Programming Using Python provides an imperative-first introduction to Python focusing on computer applications and the process of developing them. The text helps develop computational thinking skills by covering patterns of how problems can be broken down and constructively solved to produce an algorithmic solution. The approach is hands-on and problem oriented. The book also introduces a subset of the Python language early on to help write small functions. Chapters include an introduction to problem solving techniques and classical algorithms, problem-solving and programming and ways to apply core skills to application development.
AN IMPROVED METHOD FOR ASSESSING THE IMPACT OF MANAGEMENT JOURNALS

Despite their limitations, journal rankings and impact data often form the basis of evaluations of research performance. Recent advances in citation analysis may facilitate the creation of journal rankings with broader coverage, particularly in terms of international coverage. This study explores three sources of citation data: Web of Science, Scopus and Google Scholar. It was found that Web of Science indexes approximately one-fifth of all academic management titles and includes fewer citations than Google Scholar. Six rankings of management journals were combined to produce a list of 57 titles. Overall, the analysis of citation data in this study suggests that the gindex provides an improved mechanism for measuring the impact of journals.
Radio di Kawasan Perbatasan Indonesia dalam Centering the Margin

Kawasan perbatasan Indonesia banyak mengalami blank spot layanan informasi sehingga siaran yang menghubungkan warganegara dan pemerintah tidak tersampaikan dengan baik. Padahal, keberadaan media di perbatasan sangat strategis sebagai penyedia informasi yang merefl eksikan dinamika lokal, mengartikulasikan kepentingan daerah sehingga dapat didengar oleh pusat. Harapannya, artikulasi tersebut dapat memberi warna pada dinamika sosial, politik, ekonomi, dan budaya di tanah air. Tulisan ini mengeksplorasi bagaimana radio di wilayah perbatasan memberikan kontribusi dalam peran centering the margin, yakni membawa aspirasi di perbatasan guna â€œmemusatkan yang pinggirâ€.
Comparability of Computer and Paper-and-Pencil Scores for Two CLEP General Examinations

This report describes two studies that investigated the comparability of scores from paper-and-pencil and computer-administered versions of the College-Level Examination Program (CLEP) General Examinations in Mathematics and English Composition. The first study used a prototype computer-administered version of each examination. Based on the results of the first study and feedback from the study participants, several modifications were made to these prototype versions. A second study was then conducted using the modified computer versions. Both studies used a single-group counterbalanced equating design. Data for the Mathematics Examination were collected at Southwest Texas State University, and data for the English Composition Examination were collected at Utah State University. The results of Study 1 suggest that, despite efforts to design computer versions of the CLEP Mathematics and English Composition General Examinations that were administratively similar to the paper-and-pencil examinations (i.e., allowed item review and answer changing and were comparably timed), mode-of-administration effects (i.e., changes in average scores as a function of the mode of test delivery) were found. The results of Study 2 suggest that the modifications made to the computer versions eliminated the mode-of-administration effects for the English Composition Examination but not for the Mathematics Examination. The results of both studies underscore the need to determine empirically (rather than to just assume) the equivalence of computer and paper versions of an examination.
An efficient routing protocol with no acknowledgement algorithm in ad-hoc wireless sensor network

A sensor sends data to another node or cluster head which, normally, will send a message back to ensure whether or not the data can be sent and received especially in the transmission phase that will dissipate much energy. Therefore, this paper proposes a novel routing protocol with no acknowledgement algorithm called NACK in the Ad-hoc wireless sensor networks.Conditions for sensed data directly sent without any acknowledgement may be failed so that this paper also considers as nodes failed to send the data back, the base station will be taken over for the network. However, simulation results reveal the network lifetime can be extended and the performance is much better. Key-Words: Ad-hoc wireless sensor networks, Routing protocol, No acknowledgement, Energy efficiency, Network lifetime.
A survey of computational methods for pricing Asian options

In this thesis, we investigate two numerical methods to price nancial options. We look at two types of options, namely European options and Asian options. The numerical methods we use are the nite di erence method and numerical inversion of the Laplace transform. We apply nite di erence methods to partial di erential equations with both uniform and non-uniform spatial grids. The Laplace inversion method we use is due to Talbot. It is based on the midpoint-type approximation of the Bromwich integral on a deformed contour. When applied to Asian options, we have the problem of computing the hypergeometric function of the rst kind. We propose a new method for numerically calculating the hypergeometric function. This method too is based on using Talbot contours. Throughout the thesis, we use the Black-Scholes equation as our benchmark problem.
Sexual function and ways of coping in patients with multiple sclerosis and their partners

Abstract The sexual and marital life of people with multiple sclerosis (MS) and their partners is frequently affected by the disease. One hundred and sixteen MS sufferers (72 females, 44 males) and their partners were questioned about their sexual and marital satisfaction; specific sexual difficulties caused by MS; and ways of coping with sexual problems. Demographic data, impact and acceptance of MS, cognitive functioning and mood state were also measured. Results showed that both male and female patients had sex lives that were greatly affected by their disability. Problems included indirect physical changes (numbness, spasms and fatigue); direct sexual dysfunctions (impotence, vaginismus and anorgasmia); concerns about future changes (incontinence, fertility); and sexuality-related changes (priorities, expectations and communication with partner). Men had higher levels of sexual dysfunction and talked to their doctors more frequently compared with women. Three percent of women and 25% of men had been t.
Computational Time-Accurate Body Movement: Methodology, Validation, and Application

Abstract : A computational fluid dynamics (CFD) procedure for computing time-accurate body motion has been developed. The procedure can be used for bodies usmg a predefined motion, or for body motion resulting from loads. The chimera moving overset-mesh methodology is used in either case. The procedure is validated by comparing predictions of a pitching airfoil and a generic store trajectory to measured data. The procedure is then applied to a multiple body launch from a simulated Triple Ejection Rack (TER) and used to estimate the effect of mutual interference on a trajectory.
Learning Object Representations for Visual Object Class Recognition

This talk discussed our object-class recognition method that won the classification contest of the Pascal VOC Challenge 2007. We submitted two recognition methods sharing the same underlying image representations defined by a choice of image sampler, local descriptor and global spatial grid. The submitted methods also share the classifier, which is a one-against-rest non-linear Support Vector Machine with chi-square kernel. The methods differ in the way they combine multiple representations (channels). The first method is based on the approach of Zhang et al., where the final similarity measure is the sum of per-channel similarities. The second method employs a genetic algorithm, which is used to determine (on per-class basis) the parameters of the generalized RBF kernel incorporating all the channels, i.e., to estimate the importance of each sampling/description/spatial method for the recognition and to optimize the required level of generalization. Both methods showed superior performance compared to other state-of-the-art submissions.
A THEORETICAL PREDICTION OF WORK-TRIP PATTERNS

A NEW THEORY FOR PREDICTING TRAVEL PATTERNS IN URBAN AREAS IS PRESENTED. GIVEN THE DISTRIBUTION OF RESIDENCES OF WORKERS AND THE LOCATIONS OF ALL JOBS IN BOUNDED REGION, THE PROPOSED METHOD CAN BE USED TO DEVELOP THEORETICAL WORK-TRIP PATTERNS THAT APPROACH THE ACTUAL PATTERNS CLOSELY ENOUGH TO BE USABLE. THIS METHOD IS LESS COSTLY THAN A COMPREHENSIVE O-D SURVEY. FURTHER RESEARCH MAY LEAD TO THEORIES FOR PREDICTING SHOPPING-TRIP, PERSONAL-BUSINESS-TRIP, AND RECREATION-TRIP PATTERNS.
Bayesian latent variable models with applications

The massive increases in computational power that have occurred over the last two decades have contributed to the increasing prevalence of Bayesian reasoning in statistics. The often intractable integrals required as part of the Bayesian approach to inference can be approximated or estimated using intensive sampling or optimisation routines. This has extended the realm of applications beyond simple models for which fully analytic solutions are possible. Latent variable models are ideally suited to this approach as it provides a principled method for resolving one of the more difficult issues associated with this class of models, the question of the appropriate number of latent variables. This thesis explores the use of latent variable models in a number of different settings employing Bayesian methods for inference. The first strand of this research focusses on the use of a latent variable model to perform simultaneous clustering and latent structure analysis of multivariate data. In this setting the latent variables are of key interest providing information on the number of sub-populations within a heterogeneous data set and also the differences in latent structure that define them. In the second strand latent variable models are used as a tool to study relational or network data. The analysis of this type of data, which describes the interconnections between different entities or nodes, is complicated due to the dependencies between nodes induced by these connections. The conditional independence assumptions of the latent variable framework provide a means of taking these dependencies into account, the nodes are independent conditioned on an associated latent variable. This allows us to perform model based clustering of a network making inference on the number of clusters. Finally the latent variable representation of the network, which captures the structure of the network in a different form, can be studied as part of a latent variable framework for detecting differences
On the Implementation of Interactive Dynamics

A new project for the enhancement of undergraduate engineering courses via the use of computers inthe classroom is being developed at Penn State University. This project involves the introduction ofsimulation, experiment, and teamwork in courses traditionally containing neither. We brieflydescribe our approach and detail some of the important issues and hurdles that we have encounteredduring its implementation. It is our purpose that this information will assist other educators inimplementing the Interactive Mechanics concept or learning environments like it.
A FAST METHOD OF EVALUATION FOR THE TRANSLATING AND PULSATING GREEN'S FUNCTION

The computation of the unsteady (harmonic) Green's function with forward speed involves numerical difficulties and requires a high computational time. A new fast and precise method of calculation is presented, valid regardless of the values for frequency and forward speed. The integrations over the complex exponential integral use a Runge-Kutta fourth order scheme with a stepsize control. Calculations are compared with other methods, and the linearized free surface boundary condition is satisfied with good accuracy. Examples of free surface elevations above a submerged pulsating and translating source are presented.
Code Smells in the Mobile Applications Domain

The mobile applications market is growing rapidly, over 85 billion mobile applications have been downloaded. Smartphone sales are already bigger than computer sales and this might become the first year in which over one billion smart phones will be sold. Regardless of these statistics there is not a lot of research to be found on the subject. In this thesis one of the observations done in a recent study on mobile applications is reproduced. We also look for code smells (patterns in source code that are associated with bad design and bad programming practices) in a number of commercial and open source applications. The results of this analysis is used to determine if certain code smells have a higher likelihood to appear in mobile application source code.
Algorithmic Aspects of Graph Connectivity

Algorithmic Aspects of Graph Connectivity is the first comprehensive book on this central notion in graph and network theory, emphasizing its algorithmic aspects. Because of its wide applications in the fields of communication, transportation, and production, graph connectivity has made tremendous algorithmic progress under the influence of the theory of complexity and algorithms in modern computer science. The book contains various definitions of connectivity, including edge-connectivity and vertex-connectivity, and their ramifications, as well as related topics such as flows and cuts. The authors comprehensively discuss new concepts and algorithms that allow for quicker and more efficient computing, such as maximum adjacency ordering of vertices. Covering both basic definitions and advanced topics, this book can be used as a textbook in graduate courses in mathematical sciences, such as discrete mathematics, combinatorics, and operations research, and as a reference book for specialists in discrete mathematics and its applications.
Impact of Brand Loyalty in Assessing Purchase Intentions of a Customer: A Study of Automobile Industry in South Asian Perspective

In this modern age, brands are more concentrating on consumers’ quality of life style as these reflect their status symbol or social class through the company’s products. The current study is aimed to check the effect of brand name, awareness, and price and brand quality on the brand loyalty of the product of automobiles, after that brand loyalty effect is examined on the product purchase intention specifically in South Asian context. For checking the effect of the variables brand name, brand awareness, brand price and brand quality were considered as independent variables and brand loyalty was considered as a dependent variable. The study was correlational as it shows the relationship of different dependent and independent variables. This study has been conducted through a survey design. The population was considered as the citizens of Bahawalpur, one of the oldest stateS in South Asian region and for the data collection random sampling technique was used. A total number of 300 questionnaires were distributed randomly to the users of automobile in the Bahawalpur out of which 287 responses were received after deducting the omission and errors. Different hypotheses were tested in this research for relationships of variables, out of which five were accepted and the one which was about the effect of brand price on brand loyalty was rejected. The collected data were analyzed after using different statistical techniques and it shows that brand awareness, name, quality has a direct and positive impact on the brand loyalty of the product and on the other hand there is also a positive effect of brand loyalty on the purchase intention of the product.
Efficient runtime support for cluster-based distributed shared memory multiprocessors

Distributed shared memory (DSM) systems provide a shared memory programming paradigm on top of a physically distributed network of computers. The DSM system removes the necessity for programmers to move data explicitly between processors. The principle challenge in the development of an efficient DSM system lies in reducing the amount of communication necessary to maintain coherence to an absolute minimum. This thesis presents Brazos, a DSM system for use in an environment of symmetric multiprocessor (SMP) personal computers that are networked together by industry-standard 100 Mbps FastEthernet. Brazos is distinguished by its use of application-level multithreading, selective multicast, adaptive runtime mechanisms, and a unique performance history mechanism. Through the detailed analysis of twelve scientific programs, we show that Brazos outperforms the current state-of-the-art software DSM system by an average of 83%, and outperforms a version of the same DSM system that has been altered to take advantage of SMP personal computers by an average of 32%. Our results indicate that networks of commodity personal computers using available PC networks and operating systems can perform comparably on a wide variety of scientific applications to more traditional networks of high-end engineering workstations.
A Journey into the Heart of Workaholism: empirical findings from several multi-sample studies

La adiccion al trabajo es un concepto que con el tiempo ha generado cada vez mayor interes e investigacion. A traves de la realizacion de 5 estudios empiricos, en esta tesis se profundiza todavia mas en su estudio. Concretamente se estudia cualitativamente la adiccion al trabajo, proponiendo un modelo heuristico en el que constan diversos antecedentes y consecuentes de la misma. Asi mismo se determina que la adiccion al trabajo y el engagement (vinculacion con el trabajo en espanol), son dos estados mentales diferentes relacionados con el ambito laboral (el primero meramente negativo, el segundo totalmente positivo). Ademas, se presenta el perfil de personalidad que caracteriza a los trabajadores adictos al trabajo y a los trabajadores "engaged", y se expone la relacion que este dano psicosocial tiene con el bienestar (i.e., felicidad y salud percibida), la aufoeficacia laboral (relacion sorprendentemente positiva) y con otras variables como la satisfaccion laboral (negativa), la sobrecarga de trabajo (positiva) y el conflicto trabajo-familia (positiva). Ademas al final de la tesis se plantean diversas preguntas de investigacion que todavia quedan por responder para que investigacion futura pueda continuar con el estudio del concepto. En su conjunto con esta tesis se ha avanzado en la comprension de algunos de los factores clave para la aparicion y desarrollo de la adiccion al trabajo.
Toward a Model of Attention and the Development of Automatic Processing.

Abstract : A model for the development of automatic processing is briefly described. The model is a quasi-neural model in which information processing is done through the transmission of vectors between visual, lexical, semantic, and motor processing units. Controlled processing involves gating of the output power of vectors to perform matches and to release response vectors. As subjects practice consistent tasks, associative learning enables an input vector to evoke an output vector and priority learning determines the power with which a vector is transmitted. Automatic processing involves a cascade of vector transmissions in which the output power of each transmission is determined by priority learning. The transition from controlled to automatic processing takes place in four phases. Empirical illustrations of the transition are described. (Author).
Deep learning and face recognition: the state of the art

Deep Neural Networks (DNNs) have established themselves as a dominant technique in machine learning. DNNs have been top performers on a wide variety of tasks including image classification, speech recognition, and face recognition.1-3 Convolutional neural networks (CNNs) have been used in nearly all of the top performing methods on the Labeled Faces in the Wild (LFW) dataset.3-6 In this talk and accompanying paper, I attempt to provide a review and summary of the deep learning techniques used in the state-of-the-art. In addition, I highlight the need for both larger and more challenging public datasets to benchmark these systems. Despite the ability of DNNs and autoencoders to perform unsupervised feature learning, modern facial recognition pipelines still require domain specific engineering in the form of re-alignment. For example, in Facebook's recent DeepFace paper, a 3D "frontalization" step lies at the beginning of the pipeline. This step creates a 3D face model for the incoming image and then uses a series of affine transformations of the fiducial points to "frontalize" the image. This step enables the DeepFace system to use a neural network architecture with locally connected layers without weight sharing as opposed to standard convolutional layers.6 Deep learning techniques combined with large datasets have allowed research groups to surpass human level performance on the LFW dataset.3, 5 The high accuracy (99.63% for FaceNet at the time of publishing) and utilization of outside data (hundreds of millions of images in the case of Google's FaceNet) suggest that current face verification benchmarks such as LFW may not be challenging enough, nor provide enough data, for current techniques.3, 5 There exist a variety of organizations with mobile photo sharing applications that would be capable of releasing a very large scale and highly diverse dataset of facial images captured on mobile devices. Such an "ImageNet for Face Recognition" would likely receive a warm welcome from researchers and practitioners alike.