Miracle-dz/UniFAR-FTUs huggingface dataset:
A pilot study on the rhythmic properties of English produced by Japanese learners before and after a five- month study abroad period

The present study was designed as a pilot study to investigate whether and how the rhythmic properties of sentences produced by adult Japanese learners of English (JS, N=7) changed before and after a 5-month study abroad period. The participants read and recited a set of 15 English sentences which had different rhythmic patterns. Some of the sentences had an alternation of stressed and unstressed syllables in content and function words, and others included a succession of stressed syllables in content words (i.e., a stress clash). The rhythm index called “normalized pairwise variability index of vocalic intervals (nPVI-V)” was used to analyze the changes in the rhythmic properties of the sentences. The results showed that nPVI-V increased to approximate that of the native speakers of English (NS, N=8), although such improvement was observed only in JS with a higher proficiency level. NS also evaluated the degree of accentedness in terms of the rhythmic properties among JSʼs productions. It was found that, commensurate with the results on nPVI-V, the evaluation scores increased to varying degrees before and after the study abroad period. The two measures, however, were found to be significantly, but only weakly correlated (r=.386). Another aim of the study was to evaluate the validity of nPVI-V as a measure of the rhythmic properties in L2 learners. The results suggested that nPVI may have a number of limitations, including difficulty with estimating the native level of nPVI-V, and with controlling for the prosodic, phonological, and phonemic properties in the speech materials used for a comparison between JS and NS. Teaching implications were discussed.
Bayesian Network for Image Classification

Under the conditional independence assumption among local features , naive bayes nearest neighbor classifier (NBNN) computes the direct classification results without any learning or training phase. It outperforms all the previous works in non-parametric classifiers. However, among the local features, there are strong dependences. This assumption, which is obviously against the compositionality of objects, weaken the performance of NBNN. Therefore, we propose a novel Bayesian Network for image classification, which consider the dependence among the local features. In order to utilize the dependences to improve the classification results, we further define the relationship between the high-level and lowlevel features, by which we optimize the Bayesian Network with relations between high-level and low-level features. By testing our method against previous works in the dataset of Caltech101, our optimized method achieves up to 20% relative accuracy improvement over prior methods in the similar time consuming with NBNN (less than 0.001s difference).
Enhancement and Quality Assessment of Multi-Spectral Image Using Different Fusion Methods

Image Fusion is a process of combining the relevant information from a set of images into a single image, where the resultant fused image will be more informative and complete than any of the input images. Image fusion techniques can improve the quality and increase the application of these data. This paper presents three types of fusion method principle component analysis (PCA), wavelet and P+XS to fuse multispectral and panchromatic land sat image with (30 and 15 ) meter spatial resolution respectively. Many criteria such as (ERGAS, RMSE, RASE, SC) have been applied to evaluate the results.
Inference in Moment Inequality Models That Is Robust to Spurious Precision under Model Misspecification

Standard tests and conﬁdence sets in the moment inequality literature are not robust to model misspeciﬁcation in the sense that they exhibit spurious precision when the identiﬁed set is empty. This paper introduces tests and conﬁdence sets that provide correct asymptotic inference for a pseudo-true parameter in such scenarios, and hence, do not suﬀer from spurious precision.
Study on Spectrum Sensing Techniques

Cognitive radio is a recent emerging technology for effective use of spectrum. To provide newer opportunities in advanced wireless communication system. Spectrum sensing and security are the key issues of Cognitive Radio (CR). The main aim of Cognitive Radio (CR) is to detect unused or idle frequencies of cellular spectrum, not used by Primary User (PU’s). Practically PU’s frequencies can be idle or busy. The goal is to investigate spectrum sensing predictive methods. These methods find idle or busy states of frequencies at discrete interval of time periods. The decision is made to assign these free frequencies to Secondary Users (SU’s). For this we use Machine Learning (ML) techniques to improve its performance, usability and efficiency. A machine learning system includes pre-processing, feature extraction and decision making classifier. This paper lists recent research activities and co-operative spectrum sensing. Identification of CR terminals is done by predictive spectrum sensing methods. Machine learning algorithm are used for classification.
Configurable and Sound Static Analysis of JavaScript: Techniques and Applications

Author(s): Kashyap, Vineeth | Advisor(s): Hardekopf, Ben | Abstract: JavaScript is widespread. Web developers use JavaScript to enrich user experience via dynamic content ranging from scripts to enhance a web page's appearance, to full-blown web applications, to extending the functionality of web browsers in the form of browser addons. Desktop developers use JavaScript, e.g., for OS applications in Windows 8. JavaScript's growing prominence means that secure, correct, and fast JavaScript code is becoming ever more critical. Static analysis traditionally plays a large role in providing these characteristics: it can be used for security auditing, error-checking, debugging, optimization, and program refactoring, among other uses. Thus, a sound, precise static analysis platform for JavaScript can be of enormous advantage.In this thesis, we present our work on creating a sound, precise, configurable and fast static analysis for JavaScript called JSAI that we have made openly available to the research community. JSAI is both a practical tool for JavaScript static analysis and also a research platform for experimenting with static analysis techniques. JSAI showcases a number of novel techniques to soundly compute a combination of type inference, pointer analysis, control-flow analysis, string analysis, and integer and boolean constant propagation for JavaScript programs. It also provides a unique method for modularly configuring analysis precision that is based on fundamental new insights into the theory of static analysis. We describe precision-increasing techniques for the analysis using type refinement; and performance-increasing techniques for the analysis based on parallelization of JSAI. As an example use-case for JSAI, we discuss a novel security analysis for JavaScript-based browser addon vetting.
Continuous-Time Latent Markov Factor Analysis for Exploring Measurement Model Changes Across Time

Abstract. Drawing valid inferences about daily or long-term dynamics of psychological constructs (e.g., depression) requires the measurement model (indicating which constructs are measured by which items) to be invariant within persons over time. However, it might be affected by time- or situation-specific artifacts (e.g., response styles) or substantive changes in item interpretation. To efficiently evaluate longitudinal measurement invariance, and violations thereof, we proposed Latent Markov factor analysis (LMFA), which clusters observations based on their measurement model into separate states, indicating which measures are validly comparable. LMFA is, however, tailored to “discrete-time” data, where measurement intervals are equal, which is often not the case in longitudinal data. In this paper, we extend LMFA to accommodate unequally spaced intervals. The so-called “continuous-time” (CT) approach considers the measurements as snapshots of continuously evolving processes. A simulation study compares CT-LMFA parameter estimation to its discrete-time counterpart and a depression data application shows the advantages of CT-LMFA.
The Implementation and Evaluation of Several CT Image Segmentation Algorithms

Interactive segmentation, region growing segmentation and thresholding segmentation are studied, which are used widely in image segmentation. These algorithms are applied in some experiments to deal with CT image and the satisfactory results are gotten. The results show that among these algorithms, thresholding segmentation does best in CT image segmentation; in dealing with relatively small images, satisfactory results can be got by using region growing segmentation; although more time will be needed in using interactive segmentation which is the most complex, it dose fairly well with images whose edges are relatively smooth. The evaluation of the three algorithms provides a scientific basis of the application of these algorithms.
Regional drought frequency analysis using L-moments and adjusted charged system search

The parameter estimation of statistical distributions is important for regional frequency analysis (RFA). The accuracy of different parts of RFA such as estimating the regional quantiles of the selected statistical distribution, determining the heterogeneity measure, and choosing the best distribution based on the Monte Carlo simulation, may be influenced by using the different values of regional parameters. To fulfill this aim, in the present study, a new model is developed for regional drought frequency analysis. This model utilizes the L-moments approach and the adjusted charged system search as an advanced meta-heuristic algorithm, in which some modifications on the equations of the algorithm are performed to improve its standard variant. The verification of the regional parameters estimated by the new methodology yields accurate results compared to other models. Furthermore, this study illustrates the usefulness of the robust discordancy measure against the classic one. For this purpose, different values of the subset factors (α) are utilized in the robust discordancy measure, and finally, the best value of subset factor is found equal to 0.8, which can accurately recognize discordant sites within the region.
Frame Wise Video Editing based on Audio-Visual Continuity

In this paper, we describe a method for freely changing the length of a video clip, leaving its content almost unchanged, by removing video frames considering both audio and video transitions. In a video clip that contains many video frames, there are less important frames that only extend the length of the clip. Taking the continuity of audio and video frames into account, the method enables us to change the length of a video clip by removing or inserting frames that do not significantly affect the content. Our method can be used to change the length of a clip without changing the playback speed. Subjective experimental results demonstrate the effectiveness of our method in preserving the video content. We also present a video authoring application using the proposed method.
Methods for Building Network Models of Neural Circuits

Methods for Building Network Models of Neural Circuits Brian DePasquale Artificial recurrent neural networks (RNNs) are powerful models for understanding and modeling dynamic computation in neural circuits. As such, RNNs that have been constructed to perform tasks analogous to typical behaviors studied in systems neuroscience are useful tools for understanding the biophysical mechanisms that mediate those behaviors. There has been significant progress in recent years developing gradient-based learning methods to construct RNNs. However, the majority of this progress has been restricted to network models that transmit information through continuous state variables since these methods require the input-output function of individual neuronal units to be differentiable. Overwhelmingly, biological neurons transmit information by discrete action potentials. Spiking model neurons are not differentiable and thus gradient-based methods for training neural networks cannot be applied to them. This work focuses on the development of supervised learning methods for RNNs that do not require the computation of derivatives. Because the methods we develop do not rely on the differentiability of the neural units, we can use them to construct realistic RNNs of spiking model neurons that perform a variety of benchmark tasks, and also to build networks trained directly from experimental data. Surprisingly, spiking networks trained with these non-gradient methods do not require significantly more neural units to perform tasks than their continuous-variable model counterparts. The crux of the method draws a direct correspondence between the dynamical variables of more abstract continuous-variable RNNs and spiking network models. The relationship between these two commonly used model classes has historically been unclear and, by resolving many of these issues, we offer a perspective on the appropriate use and interpretation of continuous-variable models as they relate to understanding network computation in biological neural circuits. Although the main advantage of these methods is their ability to construct realistic spiking network models, they can equally well be applied to continuous-variable network models. An example is the construction of continuous-variable RNNs that perform tasks for which they provide performance and computational cost competitive with those of traditional methods that compute derivatives and outperform previous non-gradient-based network training approaches. Collectively, this thesis presents efficient methods for constructing realistic neural network models that can be used to understand computation in biological neural networks and provides a unified perspective on how the dynamic quantities in these models relate to each other and to quantities that can be observed and extracted from experimental recordings of neurons.
ULTRAWIDE BAND POWER DIVIDER BASED ON SUBSTRATE INTEGRATED WAVEGUIDE ( SIW ) FOR S-BAND APPLICATIONS

ABSTARCT This paper proposes a novel design of power divider based on SIW with extra via. Simulation and measurement are done to investigate the characteristic of the power divider. Characteristic dependence to the power divider structure parameters are investigated, such as: diameter of via, thickness of substrate, and feeding transition. The power divider is designed by using substrate with high constant dielectric (ε = 6.15). The design obtain minimum structure of 6495 mm which is reduced the size up to 80% comparing with the previous design. The measurement result is satisfied to requirement parameters target of return loss (S11) less than 14.3 dB, insertion loss (S21 and S31) more than 4.3 dB with at frequency range of 2-3.5 GHz of fractional band width at 54.5%.
Property values and water quality: A nationwide meta-analysis and the implications for benefit transfer

We conduct a meta-analysis using a comprehensive review of studies that examine the effects of water quality improvements on waterfront and non-waterfront housing values. Rather than conducting the meta-analysis using dollar values, this study estimates mean elasticity responses. We identify 36 studies that result in 656 unique observations. Mean property price elasticities with respect to numerous water quality measures are calculated (e.g., chlorophyll-a, fecal coliform, nitrogen, and phosphorous) for purposes of value transfer. In the context of water clarity, function transfers can be performed. We estimate numerous meta-regressions, and compare transfer performance across models using an out-of-sample transfer error exercise. The results suggest value transfers often perform just as well as more complicated function transfers. In our context, however, a simple function transfer that accounts for baseline water clarity performs best. We discuss the implications of these results for benefit transfer, and outline key limitations in the literature.
Fabrication of Cyanoacrylate Hollow Particles using Microbubble Generation Technique

This paper describes the fabrication of hollow particles using microbubble generation technique. Conventional hollow particle generation techniques relate to several problems such as long synthesis time and complicated process. In this study, we succeeded in the fabrication of hollow particles having a diameter of <5 μm by simply blowing cyanoacrylate vapor into water in the form of microbubbles. However, in order to apply the hollow particles for medical applications, it is required to decreasing the diameter. Therefore, in the form of microbubbles and examined the effect of pH and gas supply on the fabricated particles. As a result, hollow particles fabricated in pH 3 condition were much smaller than those fabricated in pH 7 condition, and the use of poorly-soluble gas increased the ratio of hollow particles to solid particles. The CA particles prepared in this study would possibly have excellent thermal and sound insulation properties owing to the hollow structure. Furthermore, though further downsizing of hollow particles are required, CA particles can find the application for drug delivery to the targeted sites.
Pitch Pipe: An Automatic Low-pass Filter Calibration Technique for Pointing Tasks

A BSTRACT Practitioners use low-pass ﬁlters to improve the quality of noisy input device signals whose optimal parameters depend on application-speciﬁc precision and latency requirements, as well as situational human and environmental factors. Two common calibration approaches are to learn optimal ﬁlter parameters from training data, or interactively tune via trial and error until satisfaction ensues, both having major drawbacks. We propose a novel, automatic custom calibration technique for pointing tasks called Pitch Pipe that in three straightforward steps is able to determine appropriate parameters for a given ﬁlter, and is therefore suitable for deployment into unknown environments. Speciﬁcally, we estimate noise and user speed, and then select those parameters that best meet system requirements. In a widely deployed Fitts’ task user study, we show that Pitch Pipe-tuned ﬁlters perform on par with their manually calibrated counterparts, demonstrating that one may use our automatic approach for custom calibration.
Aggregated Demand Increase Line Overload Transmission Network Distribution Network Remotely Turning ON / OFF Devices Automatic Generation Increase

Power grids have just recently been shown to be vulnerable to MAnipulation of Demand (MAD) attacks using high-wattage IoT devices. In this paper, we introduce two forms of defenses against line failures caused by these attacks: (1) we develop two algorithms named SAFE and IMMUNE for finding efficient operating points for generators during the normal operation of the grid such that no lines are overloaded instantly after any potential MAD attacks, and (2) assuming lines can temporarily tolerate overloads, we develop efficient methods to verify in advance if such overloads can quickly be cleared by changing the operating points of the generators after any attacks. We then define the novel notion of αD-robustness for a grid indicating that line overloads can either be prevented or cleared after any attacks based on the two forms of introduced defenses if an adversary can increase/decrease the demands by at most α fraction. We demonstrate that practical upper and lower bounds on the maximum α for which a grid is αD-robust can be found efficiently in polynomial time. Finally, we evaluate the performance of the developed algorithms and methods on realistic power grid test cases.
Environmental Grain , Organism Fitness , and Type Fitness

Natural selection is the result of organisms’ interactions with their environment, but environments vary in space and time, sometimes in extreme ways. Such variation is generally thought to play an important role in evolution by natural selection, maintaining genetic variation within and between populations, increasing the chance of speciation, selecting for plasticity of responses to the environment, and selecting for behaviors such as habitat selection and niche construction. Are there different roles that environmental variation plays in natural selection? When biologists make choices about how to divide up an environment for the sake of modeling or empirical research, are there any constraints on these choices? Since diverse evolutionary models relativize fitnesses to component environments within a larger environment, it would be useful to understand when such practices capture real aspects of evolutionary processes, and when they count as mere modeling conveniences. In this paper, I try to provide a general framework for thinking about how fitness and natural selection depend on environmental variation. I’ll give an account of how the roles of environmental conditions in natural selection differ depending the probability of being experienced repeatedly by organisms, and how environmental conditions combine probabilistically to help determine fitness. My view has implications for what fitness is, and suggests that some authors have misconceived its nature.
AN EXPLORATORY INVESTIGATION INTO INFLUENCE TACTICS USED BY A NEW PRODUCT DEVELOPMENT PROJECT MANAGER

The strategic importance of NPD and the important role that marketers play in managing that process in Fast Moving Consumer Good (FMCG) organisations provides the impetus for this research paper. This exploratory in-depth case study examined the use of influence tactics used by a marketer managing a NPD project. The full range of soft influence tactics was used by the Project Manager, producing very similar findings on the widespread use of soft influence tactics reported in the existing literature. However, it would appear that the marketer managing the NPD process did not use the full range of hard influence tactics. A possible explanation for this is the corporate culture and wider Australian business culture within which the case study organisation operates in. The paper also identifies a range of future research directions stemming from this exploratory project.
Learning to Rank in Theory and Practice: From Gradient Boosting to Neural Networks and Unbiased Learning

This tutorial aims to weave together diverse strands of modern Learning to Rank (LtR) research, and present them in a unified full-day tutorial. First, we will introduce the fundamentals of LtR, and an overview of its various sub-fields. Then, we will discuss some recent advances in gradient boosting methods such as LambdaMART by focusing on their efficiency/effectiveness trade-offs and optimizations. Subsequently, we will then present TF-Ranking, a new open source TensorFlow package for neural LtR models, and how it can be used for modeling sparse textual features. Finally, we will conclude the tutorial by covering unbiased LtR -- a new research field aiming at learning from biased implicit user feedback. The tutorial will consist of three two-hour sessions, each focusing on one of the topics described above. It will provide a mix of theoretical and hands-on sessions, and should benefit both academics interested in learning more about the current state-of-the-art in LtR, as well as practitioners who want to use LtR techniques in their applications.
Introducing ALIAS

ALIAS is a Python library for constructing, manipulating, storing, visualising, and converting argumentation structues. It is available with full source code under a copyleft license and aims to become a Swiss Army Knife for working with arguments in a variety of end-user, researcher, pedagogical, and developer contexts.