Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating Statistical Power and Required Sample Sizes for Organizational Research Using Multilevel Modeling

The use of multilevel modeling to investigate organizational phenomena is rapidly increasing. Unfortunately, little advice is readily available for organizational researchers attempting to determine statistical power when using multilevel models or when determining sample sizes for each level that will maximize statistical power. This article presents an introduction to statistical power in multilevel models. The unique factors influencing power in multilevel models and calculations for estimating power for simple fixed effects, variance components, and cross-level interactions are presented. The results of simulation studies and the existing general rules of thumb are discussed, and the available power analysis software is reviewed.
An empirical study of task type and communication medium in GDSS

The authors investigate the effects of task type and communication medium on group decision outcome in GDSS (group decision support system)-supported meetings. The two task types used are an intellective task and a preference task. The communication medium is varied by using a face-to-face setting and a dispersed setting. The group decision outcome variables of interest are consensus change, decision satisfaction, and decision scheme satisfaction. With the intellective task, no significant differences were found between face-to-face groups and dispersed groups for the group decision outcome variables. With the preference task, face-to-face groups achieved higher consensus change and experienced higher decision satisfaction and higher decision scheme satisfaction than dispersed groups. From the consensus and satisfaction perspectives, groups with a communication medium which is too lean for their task seem to experience more difficulties than groups with a communication medium which is too rich for their task. <<ETX>>
AR Based on ORB Feature and KLT Tracking

Currently Augmented Reality (AR) simply based on natural features is not efficient. In this paper, we propose a novel simple method of AR registration using Oriented FAST and Rotated BRIEF (ORB) features and Kanade-Lucas-Tracker (KLT) tracking algorithm. First we extract ORB natural features from a reference image and the first video frame. Select the matching points using appropriate Hamming distance and Random Sample Consensus (RANSAC) algorithm. Then we track the matched ORB features of the next video frames with an updating KLT strategy. Finally we calculate and determine the camera pose using the matched feature point sets. Our experiments show that, in the cases of different scales, different angles, some changes in ambient light, complex backgrounds or part of the reference image blocked, our AR system performs well to track and position accurately. And the processing speed of our system approximately meets the requirement of real-time system.
Using ANT ideas in the managing of systemic action research

Little guidance has been given to the management of action research and this paper suggests that ideas from the actor-network theory (ANT) may be helpful. Although ANT has been widely used as a tool for interpretation and description of the interplay of the social and technical aspects of organizations, this paper presents a different use of the ideas. It advocates a proactive use, to bring about changes in a situation rather than merely observing and describing that situation disinterestedly from the outside. The case is described of a long-term action research study carried out within a corporate bank, where the ideas of ANT were employed in this way, as a device for understanding the turbulent social and political context of the research, to plan and manage the intervention process, and where ideas such as networks of interest, translation of interests and black boxes were used to practical effect. Copyright © 2007 John Wiley & Sons, Ltd.
On Principal Hessian Directions for Data Visualization and Dimension Reduction: Another Application of Stein's Lemma

O n P r i n c i p a l Hessian D i r e c t i o n s f o r Data V i s u a l i z a t i o n and D i m e n s i o n R e d u c t i o n : A n o t h e r A p p l i c a t i o n o f Stein's L e m m a Author(s): Ker-Chau L i S o u r c e : Journal of the American Statistical Association, V o l . 87, N o . 4 2 0 ( D e c , 1992), p p . 1025¬ P u b l i s h e d b y : A m e r i c a n Statistical A s s o c i a t i o n S t a b l e U R L : http://www.jstor.org/stable/2290640 Accessed: 18/05/2011 Y o u r use of the J S T O R archive indicates your acceptance of J S T O R ' s Terms and Conditions of U s e , available at http://www.jstor.org/page/info/about/policies/terms.jsp. J S T O R ' s Terms and Conditions of U s e provides, i n part, that unless y o u have obtained prior permission, y o u may not download an entire issue of a journal or multiple copies of articles, and y o u may use content i n the J S T O R archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at http://www.jstor.org/action/showPublisher? publisherCode=astata. E a c h copy of any part of a J S T O R transmission must contain the same copyright notice that appears on the screen or printed page of such transmission. J S T O R is a not-for-profit service that helps scholars, researchers, and students discover, use, and b u i l d upon a wide range of content i n a trusted digital archive. W e use information technology and tools to increase productivity and facilitate new forms of scholarship. F o r more information about J S T O R , please contact support@jstor.org. H1 iPv! American Statistical Association is collaborating with J S T O R to digitize, preserve and extend access to Journal of the American Statistical Association. m S T O R http://www.jstor.org
EEG based bci using visual imagery task for robot control

The aim of this study is to detect the brain activation on scalp by Electroencephalogram (EEG) task–based for brain computer interface (BCI) using wirelessly control robot. EEG was measured in 8 normal subjects for control and task conditions. The objective is to determine one scalp location which will give signals that can be used to control the wireless robot using BCI and EEG, using non invasive and without subject training. In control condition subjects were ask to relax but in task condition, subjects were asked to imagine a star rotating clockwise at position 45 degrees direction pointed by the wireless robot where at this angle the target is located. At position 0 and 90 degree angle subjects were asked to relax since there is no target on that direction. Using EEG spectral power analysis and normalization, the optimum location for this task has been detected at position F8 which is in frontal cortex area and the rhythm happened at alpha frequency band. At this position, the signals from the brain should be able to drive the robot to the required direction by giving correct and accurate signals to robot moving towards target.
Design of high-speed, wide-band MOS oscillators for monolithic phase-locked loop applications

Methods are presented for the realization of MOS oscillators with multidecade tuning range and GHz top speed. The theory yields transistor-only circuits with robust, digital-like structure. These are relaxation networks capable of high-frequency operation due to the use of parasitic timing capacitors, simplified feedback networks, and submicron-channel transistors. Optimum design solutions exist for both NMOS and CMOS, the latter having inherent advantages such as parasitic 'masking' properties. The digital-and-voltage-controlled oscillators developed are intended for complex monolithic PLL applications. <<ETX>>
SPICE simulation of electro-thermal effects in new-generation multicellular VDMOS transistors

In this paper a novel SPICE-based simulation tool is presented, suitable to describe the steady-state electro-thermal behavior of new-generation multicellular VDMOS transistors for low-voltage applications. Compared to similar approaches, the proposed one presents substantial improvements: (a) a new electrothermal sub-circuit representation for the elementary transistor is used, which provides an accurate prediction of device characteristics over the temperature range [300 K-400 K] also in quasi-saturation operating mode; (b) the thermal network accounts for both self-heating and thermal interaction effects, is based solely on layout data and is automatically generated; (c) non linear thermal phenomena and layout effects are taken into account.
Tag, You’re It: Enhancing Access to Graphic Novels

Current users of academic libraries are avid readers of graphic novels. These thought-provoking materials are used for leisure reading, in instruction, and for research purposes. Libraries need to take care in providing access to these resources. This study analyzed the cataloging practices and social tagging of a specific list of graphic novel titles in the academic libraries of the Association of Research Libraries (ARL). Results found that of the 668 graphic novel records 68 (10.17 percent) used the genre heading “graphic novel” and 99 (14.8 percent) were tagged. This limited access could be improved by using social tagging and genre heading.
Comparative analysis of the acquired clinical skills assessment of undergraduate medical students in a problem-based learning and traditional curricula

The article describes the results from the comparative analysis of the acquired clinical skills’ assessment of undergraduate medical students in an innovative problem-based learning (PBL) and traditional curriculum. Attention is paid on the fact that even partially introduced in part of the early phases of the curriculum the PBL format of education has its impact on the future clinical functioning of the medical students. The PBL students performed better in comparison with their peers from the traditional track in regard to: the complete history taking as gathering data for the family history, available contacts with acute infectious diseases and known allergies to foods, drugs and other substances; and the use of more laboratory and other tests for building a diagnostic plan. ISSN: 1804-0527 (online) 1804-0519 (print) PP. 120-122
Developing Online Language Teaching: Research-Based Pedagogies and Reflective Practices

When moving towards teaching online, teachers are confronted every day with issues such as online moderation, establishing social presence online, transitioning learners to online environments, giving feedback online. This book supports language teaching professionals and researchers who are keen to engage in online teaching and learning. It integrates theory and practice from a research-informed teaching perspective and helps teachers in formal and informal settings to become confident users of online tools. The authors of the 11 chapters draw on a wide range of experience that will aid readers for independent self-training, pre-service teacher training courses, and for in-service staff development. The book also offers inspiration and guidance to researchers starting in the field who will benefit from the succinct overviews of research done in the area of online language teacher training.
A growing parallel self-organizing map for unsupervised learning

SOM approximates a high dimensional unknown input distribution with lower dimensional neural network structure to model the topology of the input space as closely as possible. We present a SOM that processes the whole input in parallel and organizes itself over time. This way, networks can be developed that do not reorganize their structure from scratch every time a new set of input vectors is presented but rather adjust their internal architecture in accordance with previous mappings.
Holistic modelling for human-autonomous system interaction

For complex systems that embed automation, but also rely on human interaction for guidance and contingency management, holistic models are needed that provide for an understanding of the individual human and computer elements, and address the critical interactions of such complex systems. Discrete event simulation (DES) models and system dynamics (SD) models are two different approaches that can be used to address these requirements. Both modelling approaches can support the designers of future autonomous vehicle (AV) systems by simulating the impact of alternate designs on vehicle, operator, and system performance. However, the DES modelling approach is likely best suited for using probabilistic distributions to accurately model an operator who is a serial processor of discrete tasks, as well as an environment with randomly occurring events. The SD modelling approach is better suited for modelling continuous performance feedback that is temporally dependent and is affected by qualitative variables such as trust.
Using interdocument similarity information in document retrieval systems

The first part of this paper reports a comparative study of the document classifications produced by the use of the single linkage, complete linkage, group average, and Ward clustering methods. Studies of cluster membership and of the effectiveness of cluster searches support previous findings that suggest that the single linkage classifications are rather different from those produced by the other three methods. These latter methods all produce large numbers of small clusters containing just pairs of documents. This finding motivates the work reported in the second part of the paper, which considers the use of clusters consisting of a document together with that document with which it is most similar. A comparison of the use of such clusters with conventional best match searches using seven document test collections suggests that the two types of search are of comparable effectiveness, but they retrieve noticeably different sets of relevant documents. © 1986 John Wiley & Sons, Inc.
ORIGAMI: A Novel and Effective Approach for Mining Representative Orthogonal Graph Patterns

In this paper, we introduce the concept of α-orthogonal patterns to mine a representative set of graph patterns. Intuitively, two graph patterns are α-orthogonal if their similarity is bounded above by α. Each α-orthogonal pattern is also a representative for those patterns that are at least β similar to it. Given user defined α, β ∈ [0, 1], the goal is to mine an α-orthogonal, β-representative set that minimizes the set of unrepresented patterns. We present ORIGAMI, an effective algorithm for mining the set of representative orthogonal patterns. ORIGAMI first uses a randomized algorithm to randomly traverse the pattern space, seeking previously unexplored regions, to return a set of maximal patterns. ORIGAMI then extracts an α-orthogonal, β-representative set from the mined maximal patterns. We show the effectiveness of our algorithm on a number of real and synthetic datasets. In particular, we show that our method is able to extract high-quality patterns even in cases where existing enumerative graph mining methods fail to do so. Copyright © 2008 Wiley Periodicals, Inc., A Wiley Company Statistical Analy Data Mining 1: 000-000, 2008 The first two authors contributed equally for this research.
Prediction of Oestrus in Dairy Cows: An Application of Machine Learning to Skewed Data

The Dairy industry requires accurate detection of oestrus(heat) in dairy cows to maximise output of the animals. Traditionally this is a process dependant on human observation and interpretation of the various signs of heat. Many areas of the dairy industry can be automated, however the detection of oestrus is an area that still requires human experts. This thesis investigates the application of Machine Learning classification techniques, on dairy cow milking data provided by the Livestock Improvement Corporation, to predict oestrus. The usefulness of various ensemble learning algorithms such as Bagging and Boosting are explored as well as specific skewed data techniques. An empirical study into the effectiveness of classifiers designed to target skewed data is included as a significant part of the investigation. Roughly Balanced Bagging and the novel Under Bagging classifiers are explored in considerable detail and found to perform quite favourably over the SMOTE technique for the datasets selected. This study uses non-dairy, commonplace, Machine Learning datasets; many of which are found in the UCI Machine Learning Repository.
Agreement phenomena in Sign Language of the Netherlands

For some years, linguists have tried to account for the occurrence and interpretation of null arguments by relating them to (rich) agreement (among others Taraldsen 1980; Chomsky 1981; Rizzi 1982). However, numerous problems were encountered. Some languages (such as German) appear not to allow null arguments, even though they have rich agreement systems. Other languages (such as Chinese) do allow null arguments, but have no agreement at all. For the latter type of languages, alternative analyses have been suggested, in which, not agreement, but a null topic recovers the content of the null argument (Huang 1984; Raposo 1986). Signed languages complicate matters even more due to the fact that they allow null arguments with all verbs but, although they have a rich agreement system, only a subset of verbs can have agreement marking. Lillo-Martin (1986; 1991) suggests a combined recovery strategy for the content of null arguments in American Sign Language (ASL): agreement and null topics. Since then, new facts have come forward, which we will address here and which we will relate to the matter of the recovery of null arguments. We discuss the agreement system and verb types of signed languages with special reference to Sign Language of the Netherlands (NGT). In contrast to previous analyses we argue that the w features person and number do not play a role in the verb agreement system and the pronominal system of signed languages. We claim that the only two relevant types of w features in signed
Interacting with computers by voice: automatic speech recognition and synthesis

This paper examines how people communicate with computers using speech. Automatic speech recognition (ASR) transforms speech into text, while automatic speech synthesis [or text-to-speech (TTS)] performs the reverse task. ASR has been largely developed based on speech coding theory, while simulating certain spectral analyses performed by the ear. Typically, a Fourier transform is employed, but following the auditory Bark scale and simplifying the spectral representation with a decorrelation into cepstral coefficients. Current ASR provides good accuracy and performance on limited practical tasks, but exploits only the most rudimentary knowledge about human production and perception phenomena. The popular mathematical model called the hidden Markov model (HMM) is examined; first-order HMMs are efficient but ignore long-range correlations in actual speech. Common language models use a time window of three successive words in their syntactic-semantic analysis. Speech synthesis is the automatic generation of a speech waveform, typically from an input text. As with ASR, TTS starts from a database of information previously established by analysis of much training data, both speech and text. Previously analyzed speech is stored in small units in the database, for concatenation in the proper sequence at runtime. TTS systems first perform text processing, including "letter-to-sound" conversion, to generate the phonetic transcription. Intonation must be properly specified to approximate the naturalness of human speech. Modern synthesizers using large databases of stored spectral patterns or waveforms output highly intelligible synthetic speech, but naturalness remains to be improved.
Citation Analysis and Authorship Patterns of Two Linguistics Journals

This article analyzes the sources cited in articles published in two linguistics journals, Applied Linguistics and Journal of Linguistics, from 2001 to 2010. A retrospective descriptive study was conducted using bibliometric indicators, such as types of cited sources, timeliness of cited sources, authorship patterns, rank lists of the most frequently cited journals and authors, degree of research collaboration, and yearly distribution of articles in the journals. A total of 884 papers were reviewed. The papers analyzed provided a total of 15,283 bibliographic references, with an average of 17.2 citations per paper. References to books and monographs dominated, making up 53.3 percent of citations, while references to journals accounted for 35.9 percent. More than 70 percent of the cited sources exceeded ten years old. Findings also revealed that sole authorship dominated reports on linguistics research. Single-authored sources constituted 63.2 percent of the cited works, while 36.8 percent had multiple authors. The degree of collaboration in linguistics research was very low, with joint research projects making up only 0.37 percent of the articles. Twenty-three most frequently cited journals were identified, with Applied Linguistics and Linguistic Inquiry ranking first and second, respectively. Similarly, twenty-eight frequently cited authors were identified, with Noam Chomsky ranking first. This study would be useful for linguistics research and for collection development of libraries, particularly academic libraries that need to support researchers in linguistics.
Algorithmic Prediction of Inter-song Similarity in Western Popular Music

Abstract We investigate a method for automatic extraction of inter-song similarity for songs selected from several genres of Western popular music. The specific purpose of this approach is to evaluate the predictive power of different feature extraction sets based on human perception of music similarity and to develop an algorithm able to reproduce and predict human ratings. The algorithm is a linear model that was trained and tested using perceptual data. We use publicly available algorithms to extract acoustic feature values from 78 songs used in a previous perceptual experiment. Feature value differences between songs are used in a multivariate linear regression calculation to find the optimal weighting coefficients for the feature values to best approximate the human similarity perception data. We use two evaluation methods: metrical and ordinal. We use a bootstrapping approach by randomly separating the experimental data into training and testing sets. We compare the performance of this model against the G1C model by Pampalk, winner of the MIREX 2006 competition on music similarity prediction. Both models produce a rather low performance on the metrical evaluation. However, on the ordinal evaluation, the linear regression model shows encouraging results (significantly outperforming the G1C algorithm): in the triadic comparison task, it can correctly predict 52.3 ± 0.5% of the most similar pairs, while the estimated theoretical maximum, based on participant consistency on the most similar pair rankings is 78 ± 8%. In a comparison of feature sets, we found the MIR toolbox to produce the best performance.
Supervised learning algorithms for visual object categorization

This thesis presents novel techniques for image recognition systems for better understanding image content. More specifically, it looks at the algorithmic aspects and experimental verification to demonstrate the capability of the proposed algorithms. These techniques aim to improve the three major components that are part of current state-of-the-art image recognition systems. This thesis offers four algorithms implementing different strategies to effectively classify images into correct categories automatically. A set of images from all categories is selected, labeled and next become learning data to learn models to categorize other images. To show the effectiveness of the proposed algorithms, all approaches were validated on several standard datasets namely PASCAL 2006 and 2007, Caltech-101 and Corel. Each proposed algorithm is explained in detail in separate chapters in this thesis.