Miracle-dz/UniFAR-FTUs huggingface dataset:
The R Package metaLik for Likelihood Inference in Meta-Analysis

Meta-analysis is a statistical method for combining information from different studies about the same issue of interest. Meta-analysis is widely diffuse in medical investigation and more recently it received a growing interest also in social disciplines. Typical applications involve a small number of studies, thus making ordinary inferential methods based on first-order asymptotics unreliable. More accurate results can be obtained by exploiting the theory of higher-order asymptotics. This paper describes the metaLik package which provides an R implementation of higher-order likelihood methods in meta-analysis. The extension to meta-regression is included. Two real data examples are used to illustrate the capabilities of the package.
DATA MINING AND ITS APPLICATIONS FOR MODELLING RAINFALL EXTREMES

ABSTRACT Data mining is a new powerful technology which helps in extracting hidden predictive information (future trends and behaviours) from large databases and thus facilitating decision makers to make proactive, knowledge-driven decisions. In this paper, a brief overview of various data mining functionalities, and an extensive review of the works done on temporal data mining are discussed. Of the two frameworks of temporal data mining, one that of frequent episodes is discussed in detail by explicating the various algorithms developed so far. Also, a case study using one of the algorithms, Minimal Occurrences With Constraints And Time Lags (MOWCATL), for extracting the rules to explain the spatial and temporal variation for extreme events in India is discussed and the results are shown.
Comparison Of Feature Extraction Algorithms For Gender Classification From Face Images

Gender Classification is the hot research topic from last two decades but still a gap exist between the requirements and actual performances. This gap lies due to the variation in pose, expression and illumination condition etc. Gender classification of face images is the process of identification of gender by their facial images. In this paper we compared the performance of two feature extraction algorithm i.e. Local binary pattern (LBP) and Histogram of oriented gradient (HOG) in order to determine the more efficient approach for gender classification from face images. Haar Cascade Classifier is used for the face detection from an image. Histogram equalization normalization technique is used for normalizing illumination effects. Support vector machine (SVM) is used as a classifier for gender classification. We implement gender classification system architecture using OpenCv 2.4. 2. Indian face database (IFD) is used for the experiment . Experimental results on Indian face database show that HOG is more efficient approach for gender classification and improves gender recognition rate upto 95.56%. KeywordsGender classification, Haar cascade classifier, Histogram of oriented gradient, Local binary pattern, Support vector machine.
Coordination Assistance for Mixed Human and Computational Agents

In many application areas (such as concurrent engineering, software development, hospital scheduling, manufacturing scheduling, and military planning), individuals are responsible for an agenda of tasks and face choices about the best way to locally handle each task, in what order to do tasks, and when to do them. Such decisions are often hard to make because of {\em coordination problems}: individual tasks are related to the tasks of others in complex ways, and there are many sources of uncertainty (no one has a complete view of the task structure at arbitrary levels of detail, the situation may be changing dynamically, and no one is entirely sure of the outcomes of all of their actions). The focus of this paper is the development of support tools for distributed, cooperative work by groups (collaborative teams) of human and computational agents. We will discuss the design of a set of distributed autonomous computer programs (``agents'''') that assist people in coordinating their activities by helping them to manage their agendas. Our approach can also supply coordination and scheduling facilities to computational domain agents (such as autonomous design checkers or databases). We briefly describe an initial implementation of these ideas using only computational agents and abstract task environments.
An Efficient ARM Technique for Information Retrieval in Data Mining

Association rule mining is the one of the most important technique of the data mining. Its aim is to extract interesting correlations, frequent patterns and association among set of items in the transaction database. In association rule mining (ARM), there are several algorithms. FP growth is the classical and most efficient algorithm. In this paper, author considers data (Supermarket data) and tries to obtain the result using Weka data mining tool. Association rule algorithms are used to find out the best combination of different attributes in any data. Here author consider three association rule algorithms: Apriori Association Rule, FP-Growth association rule and Tertius Association Rule. Author compares the result of these three algorithms and presents the result. According to the result obtained using data mining tool author find that FP growth Association algorithm performs better than the Apriori association rule and Tertius Association Rule algorithms.
Modelling the Flow Inherent in Speech Representations

This paper presents two new methods for modelling the flow inherent in speech: flow-based prediction (FBP)and acoustic flow interpolation (AFI). These are presented as extensions of the form of prediction implied in calculating the delta and delta-delta coefficients often used in automatic speech recognition.All these methods are presented as special cases of a general vector linear prediction model, but it is shown that the new techniques, which make the flow of features within the data explicit, are significantly better at modelling spectrogram-like data. Several speech representations, using both parametric and non-parametric analyses, are discussed both in terms of their ability to represent speech accurately and of their appropriateness to these flow-based models. AFI and FBP error coefficients, for both male and female speakers, are measured and compared with the delta and delta-delta coefficients. Wherever possible, the parameters and methods used to produce the representations have been chosen to be directly comparable with one another.
skip a beat : studying cardiac rhythm in human pluripotent stem cell-derived cardiomyocytes

Inherited arrhythmogenic diseases account for a large number of sudden cardiac death cases. Advances in genetic medicine have enabled the identification of mutations responsible for these syndromes. Recently, we showed that a TECRLc.331+1G>A mutation is associated with arrhythmias and high risk of sudden cardiac death. Patient-specific human induced pluripotent stem cells (hiPSCs) carrying the homozygous TECRLc.331+1G>A mutation were differentiated to cardiomyocytes (CMs) which revealed abnormal electrophysiological and calcium handling properties compared with control CMs. Here, we investigated the molecular and functional differences between TECRL mutant CMs. Global gene expression analysis identified perturbation of genes associated with functions in endoplasmic reticulum (ER) and mitochondria. Increased expression of a spliced variant of XBP1 suggested ER stress in TECRL mutant CMs. On the other hand, functional analysis using fluorescent dyes revealed increased mitochondrial mass and polarity. Measuring metabolic parameters by Seahorse flux assay demonstrated decreased oxygen consumption rate (OCR) and increased glycolytic rate (GR) in TECRL mutant CMs ultimately resulting in reduced ATP turnover. These results suggest a role for TECRL in cellular metabolism and further studies are necessary to understand how mutations in this gene cause cardiac arrhythmias.
Do Stored Reserves and Endogenous Hormones in Overwintering Twigs Determine Flower Bud Differentiation of Summer Blooming Plant – Styrax tonkinensis?

Styrax tonkinensis (Pierre) Craib ex Hartwich is one kind of important tree species with a combination of oil, medicine and ornamental values. With the aim of understanding the effects of stored reserves and endogenous hormones on flower bud differentiation, thirty 4a S. tonkinensis trees were used for experimental analysis. The overwintering twigs in two consecutive years were collected to measure the content of stored reserves and endogenous hormones to build the relationship with the flower bud differentiation of S. tonkinensis. The results showed that soluble sugar and soluble protein could help flowering, while starch was not conducive to the flowering. ABA contributed to flowering, however, GA could not promote flowering. IAA had no significant effect on flowering. The number of flowers in two consecutive years had a significant positive correlation with the content of soluble sugar, soluble protein and ABA, and had a negative correlation with the content of starch, GA and IAA. Our experimental results could provide a basis for controlling flowering and further study on S. tonkinensis. © 2019 Friends Science Publishers
MEASURING THE ADOPTION OF UBIQUITOUS TECHNOLOGY TOWARDS ONLINE PURCHASE INTENTION IN HOTEL INDUSTRY

In the digital era, online purchase platform in the hotel industry has become a vital channel for the organization to market and distribute their product and services to consumers. This support by Tourism Malaysia Integrated Promotion Plan 2018-2020 agenda in the tourism industry as it is addressed in the 11th Malaysian Plan (2016-2020), which expected for optimizing the use of the latest information technology in order to promote the local tourism. Survey data was collected from 150 respondents were analyzed using Statistical Package for the Social Science (SPSS 23.0). The findings were obtained based on adapted the Unified Theory of Acceptance and Use of Technology (UTAUT) model, namely, performance expectancy, effort expectancy, social influence, facilitating conditions and online purchase intention. This research also revealed that the effort expectancy, social influence, and facilitating conditions has a significant relationship with online purchase intention. However, there is no salient relationship between performance expectancy and online purchase intention in the hotel industry. The results of this study can be useful to understand how the adoption of technology will affect the consumer behavior in perform the online purchase in the hotel industry.
Chapter 1 . 1 LOTUS JAPONICUS AS A MODEL SYSTEM

Lotus japonicus was proposed as a model legume twelve years ago, because of a number of characteristics making this system very amenable for legume research. These characteristics include small plant, large and abundant flowers, easy hand pollination, high seed production, short generation time, easy cultivation, amenable to plant transformation and regeneration from tissue culture. At present, a set of genetic resources and tools has rapidly become available, including ecotypes, mutant lines, genetic maps, RIL lines, transformation procedures, EST sequences, and a whole genome-sequencing project. In these twelve years, research on L. japonicus has greatly contributed to the understanding of both symbiotic processes, i.e. with Rhizobium and mycorrhiza, making possible the cloning of several key genes involved in both symbioses. Now Lotus is regarded as one of the most useful plants for legume study and researchers who have interests in nodulation and other aspects of legume biology use it worldwide. In this introductory chapter we deal with the most general aspects and possibilities of the biology of L. japonicus, plant growth conditions, culture media, nitrogen supply and symbiotic partners; plant tissue culture; genetic transformation and regeneration of transgenic plants, contribution to the understanding of symbiotic processes and the role of this model plant for other research topics and exploiting microsynteny.
Actions, beliefs and intentions in multi-action utterances

Multi-action utterances convey critical information about agents' beliefs and intentions with respect to the actions they talk about or perform. Two such utterances may, for example, describe the same actions while the speakers of these utterances hold beliefs about these actions that are diametrically opposed. Hence, for a language interpretation system to understand multi-action utterances, it must be able (1) to determine the actions that are described and the ways in which they are related, and (2) to draw appropriate inferences about the agents' mental states with respect to these actions and action relations. This thesis investigates the semantics of two particular multi-action constructions: utterances with means clauses and utterances with rationale clauses. These classes of utterances are of interest not only as exemplars of multi-action utterances, but also because of the subtle differences in information that can be felicitously inferred from their use. Their meaning is shown to depend on the beliefs and intentions of the speaker and agents whose actions are being described as well as on the actions themselves. Thus, the thesis demonstrates (a) that consideration of mental states cannot be reserved to pragmatics and (b) that other aspects of natural language interpretation besides the interpretation of mental state verbs or plan recognition may provide information about mental states. To account for this aspect of natural language interpretation, this thesis presents a theory of logical form, a theory of action and action relations, an axiomatization of belief and intention, and interpretation rules for means clauses and rationale clauses. Together these different pieces constitute an interpretation model that meets the requirements specified in (1) and (2) above and that predicts the set of beliefs and intentions shown to be characteristic of utterances with means clauses and rationale clauses. This model has been implemented in the MAUI system (Multi-Action Utterance Interpreter), which accepts natural language sentences from a user, computes their logical form, and answers questions about the beliefs and intentions of the speaker and actor regarding the actions and action relations described.
Secondary wavelet feature decoupling (SWFD) and its use in detecting patient respiration from the photoplethysmogram

We describe a method for the identification of time-frequency features associated with patient respiration in the wavelet decomposition of the photoplethysmogram where the respiration features are masked by other signal components with similar spectral content. In the novel methodology a secondary transform is performed on a signal derived from the original wavelet decomposition in the region of the pulse band. The method has wide application to many other problematic signals.
Comparison of Social Media in English and Russian During Emergencies and Mass Convergence Events

Twitter is used for spreading information during crisis events. In this paper, we ﬁrst retrieve event-related information posted in English and Russian during six disasters and sports events that received wide media coverage in both languages, using an adaptive information ﬁltering method for automating the collection of about 100 000 messages. We then compare the contents of these messages in terms of 17 informational and linguistic features using a diﬀerence in diﬀerences approach. Our results suggest that posts in each language are focused on diﬀerent types of information. For instance, almost 50% of the popular people mentioned in these messages appear exclusively in either the English messages or the Russian messages, but not both. Our results also suggest diﬀerences in the adoption of platform mechanics during crises between Russian-speaking and English-speaking users. This has important implications for data collection during crises, which is almost always focused on a single language.
Excel Statistics: A Quick Guide

Ideal for those already familiar with basic Excel features, this updated Third Edition of Neil J. Salkinds Excel Statistics: A Quick Guide shows readers how to utilize Microsoft Excels functions and Analysis ToolPak to answer simple and complex questions about data. Part I explores 35 Excel functions, while Part II contains 20 Analysis ToolPak tools. To make it easy to see what each function or tool looks like when applied, at-a-glance two-page spreads describe each function and its use with corresponding screenshots. In addition, actual data files used in the examples are readily available online at an open-access Student Study Site.
A Registration Algorithm for the Identification of Individual Parrots Based on the Patterns of Filing Ridges on the Internal Surface of Their Upper Bill Tip

Ridges on the internal side of the upper bill tip (i.e., “filing ridges”) of parrots and some cockatoos form patterns that are characteristic of genera and, therefore, can be used for the classification of parrots and cockatoos. These patterns are also individually variable and, thus, could be used to fingerprint individual parrots. The objectives of our project were to evaluate the degree of individual variability and to develop a method and algorithm for registering and comparing the patterns of ridges by using a rigid registration technique. Within the datasets, we have been successful in identifying each unidentified dataset using this method. For 22 of the 27 unidentified datasets, we found a degree of magnitude (i.e., the total error taken as the sum of distances between corresponding points) to be on the scale of 101 or greater between the match with the correct dataset and the match with all other datasets of the database via a squared residual error assessment. For the remaining datasets, instances occurred in which the difference was smaller for two birds, but the correct bird was identified, nonetheless, while accurately ruling out over 90% of the incorrect bird matches.
Fuzzy Image Processing and Applications with MATLAB

In contrast to classical image analysis methods that employ "crisp" mathematics, fuzzy set techniques provide an elegant foundation and a set of rich methodologies for diverse image-processing tasks. However, a solid understanding of fuzzy processing requires a firm grasp of essential principles and background knowledge. Fuzzy Image Processing and Applications with MATLAB presents the integral science and essential mathematics behind this exciting and dynamic branch of image processing, which is becoming increasingly important to applications in areas such as remote sensing, medical imaging, and video surveillance, to name a few. Many texts cover the use of crisp sets, but this book stands apart by exploring the explosion of interest and significant growth in fuzzy set image processing. The distinguished authors clearly lay out theoretical concepts and applications of fuzzy set theory and their impact on areas such as enhancement, segmentation, filtering, edge detection, content-based image retrieval, pattern recognition, and clustering. They describe all components of fuzzy, detailing preprocessing, threshold detection, and match-based segmentation. Minimize Processing Errors Using Dynamic Fuzzy Set Theory This book serves as a primer on MATLAB and demonstrates how to implement it in fuzzy image processing methods. It illustrates how the code can be used to improve calculations that help prevent or deal with imprecisionwhether it is in the grey level of the image, geometry of an object, definition of an objects edges or boundaries, or in knowledge representation, object recognition, or image interpretation. The text addresses these considerations by applying fuzzy set theory to image thresholding, segmentation, edge detection, enhancement, clustering, color retrieval, clustering in pattern recognition, and other image processing operations. Highlighting key ideas, the authors present the experimental results of their own new fuzzy approaches and those suggested by different authors, offering data and insights that will be useful to teachers, scientists, and engineers, among others.
On the deformation field of bent crystals for channeling experiments

Anticlastic deformation (AD) is an established mechanical property of isotropic solid bodies, which has been recently used to steer particle beams through channeling in Silicon (Si) crystals. An analysis of AD in an anisotropic material such as Si has been worked out with particular emphasis to the cases used in channeling experiments. Both a theoretical model and finite element simulations were developed and compared to experimental data achieved by optical profilometry on bent Si crystals. A quantifier of the extent of anticlastic deformation, namely the ratio between primary and secondary curvature radii, has been found to be orientation dependent and determined analytically. The realistic case of Si crystal bending by a mechanical holder has been studied for applications. Crystals and the mechanical holder have been realized at the Sensors and Semiconductors Laboratory of the University of Ferrara.
Demand-Side Management Policy : Implementation and Success

The policy side of demand-side management (DSM) has been under-researched in the academic literature with the majority of studies focussing on technological trials, utility programmes and modelling analyses on the technical potential of DSM. The research contributes to filling this knowledge gap by concentrating on the evaluation of DSM policies that have been implemented around the world since the energy crises of the 1970s. The meta-evaluation aims to determine the mechanisms behind the success, failure and transferability of DSM policies by undertaking a global systematic review (realist synthesis) of policies implemented in all sectors (residential, commercial and industrial, but excluding transport and agriculture). The paper focuses on one part of the research project – the global implementation and success of DSM policies. Broad categories of DSM policy include those that are market-based (e.g. demand response tariffs or market transformations), regulatory (e.g. utility obligations or performance standards), financial (e.g. grants or tax incentives), information-based (e.g. educational or training programmes), and voluntary (e.g. voluntary industrial agreements). The systematic review synthesises 119 highquality documents (covering 690 evaluations) that have evaluated specific DSM policies, which were obtained from 35 academic, industrial and governmental databases. The results show that 35 countries and 61 states (including provinces and regions) across six continents have implemented DSM policies with at least one high-quality evaluation. 22 success factors were identified and were analysed using a proposed method for examining policy success involving frequency and weighting. The most frequently discussed factors across evaluations were: regulatory frameworks, comprehensive evaluation, industry engagement, and legislative support. However, the most highly weighted factors were: regulatory frameworks, political support, clear timeframes, stable budgets, and clear definition of roles. Overall, regulatory frameworks was considered to be the most important factor across all DSM policies. The key success factors for each DSM policy category are given.