Miracle-dz/UniFAR-FTUs huggingface dataset:
An Empirical Evaluation of Data Flow Testing of Java Classes

This paper tackles the problem of structural integration testing of stateful classes. Previous work on structural testing of objectoriented software exploits data flow analysis to derive test requirements for class testing and defines contextual def-use associations to characterize inter-method relations. Non-contextual data flow testing of classes works well for unit testing, but not for integration testing, since it misses definitions and uses when properly encapsulated. Contextual data flow analysis approaches investigated so far either do not focus on state dependent behavior, or have limited applicability due to high complexity. This paper proposes an efficient structural technique based on contextual data flow analysis to test state-dependent behavior of classes that aggregate other classes as part of their state.
Machine learning - a probabilistic perspective

All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher. Machine learning : a probabilistic perspective / Kevin P. Murphy. p. cm. — (Adaptive computation and machine learning series) Includes bibliographical references and index. Contents Preface xxvii 1 Introduction 1 1.1 Machine learning: what and why? 1 1.1.1 Types of machine learning 2 1.2 Supervised learning 3 1.2. 1 Classification 3 1.2. 2 Regression 8 1.3 Unsupervised learning 9 1.3. 1 Discovering clusters 10 1.3. 2 Discovering latent factors 11 1.3. 3 Discovering graph structure 13 1.3. 4 Matrix completion 14 1.4 Some basic concepts in machine learning 16 1.4. 1 Parametric vs non-parametric models 16 1.4. 2 A simple non-parametric classifier: K-nearest neighbors 16 1.4. 3 The curse of dimensionality 18 1.4. 4 Parametric models for classification and regression 19 1.4. 5
Goal Format in Small-Sided Soccer Games: Technical Actions and Offensive Scenarios of Prepubescent Players

The aim of this study was to investigate the influence of the number of goal-posts and the positioning of goal-posts used within small-sided games on the frequency of technical actions and offensive scenarios performed by prepubescent players within soccer. The participants were eight male prepubescent soccer players (12.1 ± 0.5 years). The participants were video recorded for 20 min playing four different formats of 4v4 small-sided games: (1) standard two goal game; (2) four goal game, one goal in each corner; (3) two goal game with goal-posts positioned 9.14 m/10 yd infield, scoring only through the back of the goal; (4) four goal-game, one goal positioned 9.14 m/10 yd infield in each corner, scoring through either the front or back of each goal. Chi-squared tests of independence were utilized to statistically explore the impact of the different small-sided game formats. There were significant associations (p < 0.05) observed between the different small-sided game formats and the frequency of turns, dribbles, shots, goals and overlaps performed. For example, players performed more turns in small-sided game format two and more shots during small-sided game format four. It is suggested coaches should consider using a variation of the number and positioning of goal-posts in small-sided games as an effective training tool in the development of prepubescent soccer players. This will enable coaches to vary the focus of sessions, and develop specific technical and tactical actions within a situation similar to that of real match-play.
VT: An Expert Elevator Designer That Uses Knowledge-Based Backtracking

VT (vertical transportation) is an expert system for handling the design of elevator systems that is currently in use at Westinghouse Elevator Company. Although VT tries to postpone each decision in creating a design until all information that constrains the decision is known, for many decisions this postponement is not possible. In these cases, VT uses the strategy of constructing a plausible approximation and successively refining it. VT uses domain-specific knowledge to guide its backtracking search for successful refinements. The VT architecture provides the basis for a knowledge representation that is used by SALT, an automated knowledge-acquisition tool. SALT was used to build VT and provides an analysis of VT's knowledge base to assess its potential for convergence on a solution.
On the two weights problem for the Hilbert transform

In this paper, we prove sufficient conditions on pairs of weights (u,v) (scalar, matrix or operator valued) so that the Hilbert transform H f(x) = p.v. [f(y) / x - y] dy, is bounded from L2(u) to L2(v).
Distinguishing Phrase-Final and Phrase-Medial High Tone on Finally Stressed Words in Turkish

The goal of this paper is to investigate the nature of the high tones realized on finally stressed words in Turkish. Following Ipek & Jun’s [1] AM model of intonational phonology of Turkish, it was hypothesized that the high tone realized on the last syllable of a phrase (i.e., Intermediate Phrase (ip)) is realized differently from that of a phrase-medial prosodic word (PW), reflecting the prosodic hierarchy. Acoustic data show that an ip-final High tone shows larger f0 rise than a PW-final High tone, and the ip-final syllable is longer than the PW-final syllable. Furthermore, the degree of coarticulation is weaker across an ip boundary than a PW boundary. These findings support the prosodic structure and tonal categories proposed in Ipek & Jun’s [1] model of Turkish intonation.
Age Differences in the Vulnerability to Nicotine Addiction: Evidence from a Rat Model of Adolescent Nicotine Taking

II ACKNOWLEDGEMENTS . IV TABLE OF CONTENTS . V LIST OF PUBLICATIONS . X SUMMARY OF ABBREVIATIONS . XI LIST OF TABLES . XII LIST OF FIGURES . XIII STATEMENT OF RESEARCH PROBLEM . 1 CHAPTER
First results from a rotational Raman scattering cloud algorithm applied to the Suomi National Polar-orbiting Partnership (NPP) Ozone Mapping and Profiler Suite (OMPS) Nadir Mapper

Abstract. This paper reports initial results from an Ozone Mapping and Profiler Suite (OMPS) Nadir Mapper cloud pressure and cloud fraction algorithm. The OMPS cloud products are intended for use in OMPS ozone or other trace-gas algorithms. We developed the OMPS cloud products using a heritage algorithm developed for the Ozone Monitoring Instrument (OMI) on NASA's Aura satellite. The cloud pressure algorithm utilizes the filling-in of ultraviolet solar Fraunhofer lines by rotational Raman scattering. The OMPS cloud products are evaluated by comparison with OMI cloud products that have been compared in turn with other collocated satellite data including cloud optical thickness profiles derived from a combination of measurements from the CloudSat radar and MODerate-resolution Imaging Spectroradiometer (MODIS). We find that the probability density functions (PDFs) of effective cloud fraction retrieved from OMPS and OMI measurements are very similar. The PDFs of the OMPS and OMI cloud pressures are comparable. However, OMPS retrieves somewhat higher pressures on average. The current NASA total ozone retrieval algorithm makes use of a monthly gridded cloud pressure climatology developed from OMI. This climatology captures much of the variability associated with the relevant cloud pressures. However, the use of actual cloud pressures retrieved with OMPS in place of the OMI climatology changes OMPS total column ozone estimates locally (presumably in the correct direction) only in areas with large differences between climatological and actual cloud pressures. The ozone differences can be up to 5% in such areas.
Teaching HCI to make it come alive

We review how to teach effectively in higher education (covering both the literature and the author’s own opinions), with particular reference to HCI and using real life-and-death examples, based on simple medical design issues. Students are motivated because even elementary HCI knowledge empowers them to make a real and significant difference in the world. “When I see how much education can be reformed, I have hope that society may be reformed. ” Gottfried Wilhelm Leibniz “The main part of intellectual education is not the acquisition of facts but learning how to make facts live. ” Oliver Wendell Holmes, Jr.
Luffa cylindrica - An emerging cash crop

Sponge-gourds, the fruit of Luffa cylindrica, are widely used throughout the world. It is an annual climbing crop which produces fruit containing fibrous vascular system. It is a summer season vegetable. At the moment, from the results of studies carried out on this wonder crop, it encourages the commercial cultivation of the crop because of its increasing economic importance. The objective of this review is to show the potentialities of this crop that is virtually found around the globe. Areas such as agriculture, medicine, science, engineering and biotechnology will be discussed. Recent major advances and discoveries will be considered.
Modulation of taxonomic (versus thematic) similarity judgments and product choices by inducing local and global processing

ABSTRACT Perceived similarity is influenced by both taxonomic and thematic relations. Assessing taxonomic relations requires comparing individual features of objects whereas assessing thematic relations requires exploring how objects functionally interact. These processes appear to relate to different thinking styles: abstract thinking and a global focus may be required to explore functional interactions whereas attention to detail and a local focus may be required to compare specific features. In four experiments we explored this idea by assessing whether a preference for taxonomic or thematic relations could be created by inducing a local or global perceptual processing style. Experiments 1–3 primed processing style via a perceptual task and used a choice task to examine preference for taxonomic (versus thematic) relations. Experiment 4 induced processing style and examined the effect on similarity ratings for pairs of taxonomic and thematically related items. In all cases processing style influenced preference for taxonomic/thematic relations.
Categorization Approach to Video Scene Classification Using Keypoint Features

Scene classification based on local keypoint features has emerged as a promising research direction. Each image is represented by a “bag of visual words” as high-dimensional, vector-quantized keypoint features, which is analogous to the “bag of words” representation of text documents. Based on such representation, we take a fully text categorization approach to the scene classification problem, with an emphasis on comparatively evaluating various implementation choices related to this visual-word representation, including vocabulary size, feature weighting and normalization, feature selection, etc. We intend to practical insights as to the optimal representation choices that maximize the classification performance. Scene classification experiments based on a large video corpus lead to many important observations: 1) using our approach, carefully engineered visual-word features achieve comparable performance to that of traditional color/texture features, and their combination significantly enhances the performance; 2) the visual word distribution in the video corpus bears many similarities yet important differences to the word distribution in a text corpus; 3) a vocabulary much larger than the ones currently used is preferred; 4) frequent visual words are not “stop words” but more informative than rare words; 5) binary features are as effective as tf or tf-idf features, and normalization always hurts the classification performance; 6) feature selection can reduce the vocabulary size by half without loss of performance; 7) spatial information is much more useful with a small vocabulary than with a large one.
Meaning-Making in Emerging Adults’ Narratives About the 2008 U.S. Presidential Election

The study aimed to explore narratives of the 2008 U.S. Presidential Election to describe the ways emerging adults constructed the meaning of the self when narrating what the historical event meant. Using life story theory, place, power, and voice from feminist theory and a model of individual development that connects self to history, I argue that the historical election provided emerging adults with a socioculturally mediated context to construct the meaning of the self in the world. The narrative analysis revealed that the meaning emerging adults constructed reflected developmental processes such as individuating from parents and connecting to a collective identity as well as developing a political consciousness. Also, some emerging adults constructed personal meaning by positioning the self in relation to master narratives about breaking racial barriers and voting privileges. The findings extend and compliment current understanding of meaning-making construction within sociocultural and historical contexts as well as the formation of political interests.
Hyper-Rectangle Selection and Distribution Algorithm for Parallel Adaptive Numerical Integration

In this paper we consider parallel numerical integration algorithms for multi-dimensional integrals. A new hyper-rectangle selection strategy is proposed for the implementation of globally adaptive parallel quadrature algorithms. The well known master-slave parallel algorithm prototype is used for the realization of the algorithm. Numerical results on the SP2 computer and on a cluster of workstations are reported. A test problem where the integrand function has a strong corner singularity is investigated. A modified parallel integration algorithm is proposed in which a list of subproblems is distributed among slave processors.
Exploring vibrotactile feedback on the body and foot for the purpose of pedestrian navigation

In this paper, we present an evaluation of vibrotactile onbody feedback for the purpose of pedestrian navigation. For this specific task, many researchers already provide different approaches such as vibrating belts, wristbands or shoes. Still, there are issues left that have to be considered, such as which body position is most suitable, what kind of vibration patterns are easy to interpret, and how applicable are vibrotactile feedback systems in real scenarios. To find answers, we reconstructed prototypes commonly found in literature and continued to further evaluate different foot-related designs. On the one hand, we learned that vibrotactile feedback at the foot reduces visual attention and thus also potentially reduces stress. However, on the other hand, we found that urban space can be very diverse, and ambiguous and therefore a vibrotactile system cannot completely replace common path finding systems for pedestrians. Rather, we envision such a system to be applied complementary as an assistive technology.
Bulbar Conjunctival and Tenon's Layer Thickness Measurement using Optical Coherence Tomography

ABSTRACT Purpose: Observations made during glaucoma filtering surgery (trabeculectomy) suggest variability in the thickness of the bulbar conjunctiva and Tenon's layers between individuals. We propose that this could infuence the final bleb morphology and function. We designed a pilot study to assess this using optical coherence tomography (OCT) to measure bulbar conjunctival and Tenon's layer thickness. Materials and methods: A total of 67 eyes of 48 individuals were scanned using an optovue Mode RT100 version 2.0 OCT machine. Cross-line CAM-L scans were taken and the com bined bulbar conjunctival and Tenon's layer thickness was measured 3 mm above the superior limbus. Conjunctival and Tenon's layers appeared as a hyper-refective section as opposed to the hypo refective underlying sclera. Measurements were taken using the inbuilt review software. Results: The age ranged from 23 to 91 years. There were 20 mal e s and 28 females. The mean conjunctival and Tenon's layer thick ness was 393 ± 67 microns (mean ± SD) ranging from 194 to 573 microns. Conclusion: Optical coherence tomography conjunctival and Tenon's layer thickness measurements appear to vary significantly between individuals. We postulate that this could infuence the final bleb morphology and may predict the risk of bleb encapsulation and failure or thin avascular blebs. Further assessment could establish cut-offs on which patients should receive intraoperative antimetabolites and/or Tenon's layer excision. How to cite this article: Howlett J, Vahdani K, Rossiter J. Bulbar Conjunctival and Tenon's Layer Thickness Measurement using Optical Coherence Tomography. J Curr Glaucoma Pract 2014;8(2):63-66.
Evidence for Widespread Genomic Methylation in the Migratory Locust, Locusta migratoria (Orthoptera: Acrididae)

The importance of DNA methylation in mammalian and plant systems is well established. In recent years there has been renewed interest in DNA methylation in insects. Accumulating evidence, both from mammals and insects, points towards an emerging role for DNA methylation in the regulation of phenotypic plasticity. The migratory locust (Locusta migratoria) is a model organism for the study of phenotypic plasticity. Despite this, there is little information available about the degree to which the genome is methylated in this species and genes encoding methylation machinery have not been previously identified. We therefore undertook an initial investigation to establish the presence of a functional DNA methylation system in L. migratoria. We found that the migratory locust possesses genes that putatively encode methylation machinery (DNA methyltransferases and a methyl-binding domain protein) and exhibits genomic methylation, some of which appears to be localised to repetitive regions of the genome. We have also identified a distinct group of genes within the L. migratoria genome that appear to have been historically methylated and show some possible functional differentiation. These results will facilitate more detailed research into the functional significance of DNA methylation in locusts.
A Formal Statistical Approach to Collaborative Filtering

Grouping people into clusters based on the items they have purchased allows accurate recommendations of new items for purchase: If you and I have liked many of the same movies, then I will probably enjoy other movies that you like. Recommending items based on similarity of interest (a.k.a. collaborative ltering) is attractive for many domains: books, CDs, movies, etc., but does not always work well. Because data are always sparse { any given person has seen only a small fraction of all movies { much more accurate predictions can be made by grouping people into clusters with similar taste in movies and grouping movies into clusters which tend to be liked by the same people. Finding optimal clusters is tricky because the movie groups should be used to help determine the people groups and visa versa. We present a formal statistical model of collaborative ltering, and compare di erent algorithms for estimating the model parameters including variations of K-means clustering and Gibbs Sampling. This formal model is easily extended to handle clustering of objects with multiple attributes.
Stock Direction Forecasting Techniques: An Empirical Study Combining Machine Learning System with Market Indicators in the Indian Context

Stock price movement prediction has been one of the most challenging issues in finance since the time immemorial. Many researchers in past have carried out extensive studies with the intention of investigating the approaches that uncover the hidden information in stock market data. As a result of which, Artificial Intelligence and data mining techniques have come to the forefront because of their ability to map non-linear data. The study encapsulates market indicators with AI techniques to generate useful extracts to improve decisions under conditions of uncertainty. Three approaches (fundamental model, technical indicators model and hybrid model) have been tested using the standalone and integrated machine learning algorithms viz. SVM, ANN, GA-SVM, and GA-ANN and the results of all the three approaches have been compared in the four above mentioned methods. The core objective of this paper is to identify an approach from the above mentioned algorithms that best predicts the Indian stocks price movement. It is observed from the results that the use of GA significantly increases the accuracy of ANN and that the use of technical analysis with SVM and ANN is well suited for Indian stocks and can help investors and traders maximize their quarterly profits.
Using Unmanned Aerial Vehicles (UAV) for High-Resolution Reconstruction of Topography: The Structure from Motion Approach on Coastal Environments

The availability of high-resolution Digital Surface Models of coastal environments is of increasing interest for scientists involved in the study of the coastal system processes. Among the range of terrestrial and aerial methods available to produce such a dataset, this study tests the utility of the Structure from Motion (SfM) approach to low-altitude aerial imageries collected by Unmanned Aerial Vehicle (UAV). The SfM image-based approach was selected whilst searching for a rapid, inexpensive, and highly automated method, able to produce 3D information from unstructured aerial images. In particular, it was used to generate a dense point cloud and successively a high-resolution Digital Surface Models (DSM) of a beach dune system in Marina di Ravenna (Italy). The quality of the elevation dataset produced by the UAV-SfM was initially evaluated by comparison with point cloud generated by a Terrestrial Laser Scanning (TLS) surveys. Such a comparison served to highlight an average difference in the vertical values of 0.05 m (RMS = 0.19 m). However, although the points cloud comparison is the best approach to investigate the absolute or relative correspondence between UAV and TLS methods, the assessment of geomorphic features is usually based on multi-temporal surfaces analysis, where an interpolation process is required. DSMs were therefore generated from UAV and TLS points clouds and vertical absolute accuracies assessed by comparison with a Global Navigation Satellite System (GNSS) survey. The vertical comparison of UAV and TLS DSMs with respect to GNSS measurements pointed out an average distance at cm-level (RMS = 0.011 m). The successive point by point direct comparison between UAV and TLS elevations show a very small average distance, 0.015 m, with RMS = 0.220 m. Larger values are encountered in areas where sudden changes in topography are present. The UAV-based approach was demonstrated to be a straightforward one and accuracy of the vertical dataset was comparable with results obtained by TLS technology.