Miracle-dz/UniFAR-FTUs huggingface dataset:
Dictionary form in decoding, encoding and retention: Further insights

Abstract The aim of the paper is to investigate the role of dictionary form (paper versus electronic) in language reception, production and retention. The body of existing research does not give a clear answer as to which dictionary medium benefits users more. Divergent findings from many studies into the topic might stem from differences in research methodology (including the various tasks, participants and dictionaries used by different authors). Even a series of studies conducted by one researcher (Dziemianko, 2010, 2011, 2012b) leads to contradictory conclusions, possibly because of the use of paper and electronic versions of existing dictionaries, and the resulting problem with isolating dictionary form as a factor. To be able to argue with confidence that the results obtained follow from different dictionary formats, rather than presentation issues, research methodology should be improved. To successfully generalize about the significance of the medium for decoding, encoding and learning, the current study replicates previous research, but the presentation of lexicographic data on paper and on screen is now balanced, and the paper/electronic opposition is operationalized more appropriately. A real online dictionary and its paper-based counterpart composed of printouts of screen displays were used in the experiment in which the meaning of English nouns and phrases was explained, and collocations were completed with missing prepositions. A delayed post-test checked the retention of the meanings and collocations. The results indicate that dictionary medium does not play a statistically significant role in reception and production, but it considerably affects retention.
Real Analysis Mathematical Knowledge for Teaching: An Investigation.

The goal of this research was to investigate the relationship between real analysis content and high school mathematics teaching so that we can ultimately better prepare our teachers to teach high school mathematics. Specifically, I investigated the following research questions. (1) What connections between real analysis and high school mathematics content do teachers make when solving tasks? (2) What real analysis content is potentially used by mathematics teachers during the instructional process?
Blind recognition of analog modulation schemes for software defined radio

BLIND RECOGNITION OF ANALOG MODULATION SCHEMES FOR SOFTWARE DEFINED RADIO by Haifeng Xiao With the emergence of software defined radios (SDRs), an adaptive receiver is needed that can configure various parameters, such as carrier frequency, bandwidth, symbol timing, and signal to noise ratio (SNR), and automatically identify modulation schemes. In this dissertation research, several fundamental SDR tasks for analog modulations are investigated, since analog radios are often used by civil government agencies and some unconventional military forces. Hence, the detection and recognition of "old technology" analog modulations remain an important task both for civil and military electronic support systems and for notional cognitive radios. In this dissertation, a Cyclostationarity-Based Decision Tree classifier is developed to separate between analog modulations and digital modulations, and classify signals into several subsets of modulation types. In order to further recognize the specific modulation type of analog signals, more effort and work are, however, needed. For this purpose, two general methods for automatic modulation classification (AMC), featurebased method and likelihood-based method, are investigated in this dissertation for analog modulation schemes. For feature-based method, a multi-class SVM-based AMC classifier is developed. After training, the developed classifier can achieve high classification accuracy in a wide range of SNR. While the likelihood-based methods for digital modulation types have been well developed, it is noted that the likelihood-based methods for analog modulation types are seldom explored in the literature. Average-
U-Net Based Convolutional Neural Network for Skeleton Extraction

Skeletonization is a process aimed to extract a line-like object shape representation, skeleton, which is of great interest for optical character recognition, shape-based object matching, recognition, biomedical image analysis, etc. Existing methods for skeleton extraction are typically based on topological, morphological or distance transform and are known to be sensitive to the noise on the boundary and require post-processing procedure for redundant branches pruning. In this work, we introduce U-net based approach for direct skeleton extraction of the object within Pixel SkelNetOn - CVPR 2019 challenge, inspired by CNNs success in skeleton extraction from real images task. The main idea of our approach is to consistently edit a skeleton mask by feature propagation through different scale layers. It opposes final skeleton generation from different scale object shape representations as occurs in approaches with deep supervision for skeleton extraction from the real image. Our U-net based model showed ~0.75 F1-score on the validation set and the ensemble of eight identical models, trained on different data subsets, got 0.7846 F1-score on the test data.
Experiencing the Food Safety Regime: Compliance, Market Access, and Coping Strategies of Indian Fishery Secto

This paper has attempted to capture the impact of food safety standards on seafood exports from India. The research issues contemplated in the study are: (i) quantifying the trade effects on fishery exports from India due to the evolving stringency of international food safety standards, (ii) structural changes of export dynamics in the standards regime, and (iii) analysing the implications of standards-related compliance on participation of actors in the export value chain. A unidirectional gravity model (for the period 1998–2013) has been used, wherein, India's fishery exports to 34 countries has been modelled by taking food safety standard as the key independent variable. The structural changes in export dynamics are captured through the constant market share analysis and export market diversification analysis. To understand the forms of coordination in the seafood export value chain, snowball sampling technique has been adopted to select the key informants of the value chain. The study has revealed that the regulations are trade restrictive, and the standards regime is causing a major restructuring of the existing value chain. It is inevitable that India should elevate the national system for testing, certification and laboratory accreditation so as to be at par with the prevailing international trade regulatory safety parameters.
Dynamic Assessment Approach in Language Teaching: A Review

Several forms of assessment have been implemented in order to satisfy the need of evaluating students’ development in the teaching and learning process; the most commonly used is Static Assessment (SA), better known as traditional assessment. However, there is an alternative form to assess students’ learning, called Dynamic Assessment (DA), which is based on Vygotsky’s sociocultural theory. This study presents an overview of Dynamic Assessment and its application in language teaching, especially in the improvement of reading and writing skills. As a main result, the literature reveals that the implementation of DA in writing and reading skills is effective and it works as a functional complement to traditional assessment, but some factors such as context, number of hours, teachers’ competences and training have to be considered to ensure the effecti-veness of this approach to assessment. stage, (b) idea generation and structuring stage, and (c) macro-revising stage in which each stage is designed in three steps as the pre-task, mediation, and the post-task. In this study, DA mediation was presented to the learners in the form of either dialogues between teacher and students or mediational tools (e.g., guidelines, reading materials, samples, etc.), and nothing was carried out regarding assessing the participants’ performance. In other words, Xiaoxiao and Yan focused only on the scaffolding aspect of DA during the instruction of the writing process, and no assessment was performed with regard to the participants’ ZPD. The findings of their study indicated that teaching in a dialogic manner was useful in enhancing participants’ learning interest and improving the writing competence.
Using Multi-Criteria Decision Making Methods to Make Logistics Decisions in Sports Clubs

Sports have evolved into a more professional and industrial sector in the last decades and the usage of analytical decision making methods in sports clubs has gained importance than ever. Decision makers in sports clubs have to make many decisions about their logistics activities. In sports clubs, decision making of transportation of a team to the games is a complex problem that needs to be solved carefully. In this study, a Multi-Criteria Decision Making method has been proposed to solve the transportation problem of sports clubs. This problem has not been addressed for sports clubs in the literature. In this study, a two-step method is proposed to solve the problem. Analytic Hierarchy Process (AHP) is used to determine the criteria weights and ELECTRE is used to order transportation alternatives from the best to the worst. To test the proposed method, a real-life case study is presented. In this real problem, the best transportation option among various alternatives such as outsourcing or buying large, medium or small sized buses for the senior team of a volleyball branch of a Turkish sports club is chosen. The criteria are determined as cost, comfort level, time and prestige. The case study revealed that the decision makers in the club should revise their logistics decisions as the best decision is to buy a large sized bus instead of outsourcing buses per game basis.
On the extreme rainfall events during the southwest monsoon season in northeast regions of the Indian subcontinent

Hydrological disasters are recurrent over the northeast Indian region (NEI) due to heavy downpours within a short span of time. Therefore, the characteristics of rainfall extremes during the southwest monsoon over the NEI, their evolution and dissipation features, are discussed using rainfall data from the Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 and the Climate Prediction Center for 16 years from 2000 to 2015. Circulation features during the lead–lag of extreme events are also examined using the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis products. Thirty‐two extreme events such that rainfall of the day exceeds the 90th percentile were identified. During the extreme events, heavy rainfall is located mainly in the NEI without much spread in the aerial domain. The highest number of extreme rain events is found in July, followed by June and August. Trend analysis of the rainfall indicates a significant decrease in low rainfall events and this decreasing trend is compensated by the significant increasing trend in very heavy (>95 percentile) to extremely heavy (>99 percentile) rainfall events. The average life of the extreme rainfall events is found to be about 3 days during which the NEI receives more than 100 mm/day; however, on individual examination, this ranges from 2 to 5 days. A strengthening of the southerly component of lower level wind from the Bay of Bengal and an updraft due to convergence at 850 hPa over the region favours the rainfall peak. The sea level pressure anomaly gradient and vertical velocity show a sudden development of conditions favourable for a heavy downpour over the northeast region.
Valve and Steam Trap Component Recognition Using Machine Vision in an Industrial Application

In this work an autonomous identification/recognition system capable of classifying valve and steam trap components in an industrial painting application was implemented, using vision-based supervised pattern recognition. The proposed vision system has the main objective of being a foundation for a solution to be installed in the manufacturing facilities of a company specialized in steam equipment, in order to complement the modernization and automation of the process. The process would rely on robots instead of human beings, using specific programs which would be called depending on a prior product identification result. The first step corresponds to the creation of a dataset with a group of the best-selling/most produced products, grabbing frames from a image acquisition scenario similar to the one possibly built in the industrial environment. The following step consists in pre-processing, where image processing techniques are introduced to threshold and treat the images as well as removing the claw that holds the products in position for painting. At this point the image contains a blob that exclusively represents the products. The following step consists in the implementation of two feature extraction methods. Firstly blob features based on shape and overall geometric characteristics, followed by a HOG implementation. Both feature extraction techniques are then used on the post-processing images and are trained on a discriminative and generative classifier, respectively a multiclass Support Vector Machine (SVM) and Naive Bayes classifier (NBC). In terms of classification results, the SVM proved to be the best solution in terms of performance, speed, and robustness, outclassing the NBC. Regarding the choice between blob features or HOG features, it was concluded that the blob features would do a better job in describing the objects, showing results with 100% precision for all possible threshold values, and recalls equally high for thresholds below 0.65− 0.70.
Improved Algorithm for Load Flow Analysis of Radial Distribution System

Objectives: In this paper an improved algorithm for load flow analysis of Radial Distribution Systems (RDS) is presented. The algorithm incorporates probabilistic variations of line and load data along with composite loads. Methodology: The algorithm uses Simple Backward and Forward Sweep based method for solving RDS and has further been modified to solve distribution systems with Distributed Generation (DG). Findings: This Improved algorithm for load flow analysis is tested on standard RDS and considerable promising results have been delivered for the system having Distributed Generator for improving the voltage profile and reduce the losses. Novelty/Improvement: The results obtained are exemplary that confirms usefulness of the algorithm and can have significant impact on future planning and operation purposes of unbalanced RDS.
A denotational theory of synchronous communicating systems

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. A denotational theory of synchronous communicating systems Albert Benveniste, Paul Le Guernic
CORPORATE ENTREPRENEURSHIP IN ORGANISATIONAL LIFE-CYCLE Jurga Duobiene

The paper deals with the development of corporate entrepreneurship in different stages of organisational life-cycle. The research presents a model for the evaluation of corporate entrepreneurship and systemises relevant theoretical and empirical research in the field of entrepreneurship and corporate entrepreneurship. Moreover, it describes the development of corporate entrepreneurship in the entire organisational life-cycle since most of researchers who discuss the topics of corporate entrepreneurship and organisational life-cycle, focus on the establishment and growth of organisations. Thus, there are only some research works that analyse corporate entrepreneurship in further stages of organisational life-cycle. The theoretical background of the research is based on Schumpeter's (1942) approach to entrepreneurship, transformed into organisational level and developed by Pinchot and Pinchot (1994), and Miller and Friesen's (1984) five stage life-cycle model. The research methodology derives from the quantitative approach that was applied in 77 Lithuanian organisations. The research model and questionnaires are developed and adapted from Wickham's (2004) model of entrepreneurship by means of updating three characteristics of entrepreneurship, i.e. innovations, strategic objectives and potential for growth, to the relevant and appropriate for quantitative research content of corporate entrepreneurship. The stages of organisational life-cycle are tested according the methodology of Lester et al. (2003). The research results disclose differences of the development of corporate entrepreneurship during organisational life-cycle and might draw the attention of practitioners to the value of innovations for successful organisational development stages, i.e. from maturity to renewal rather than that of decline. Moreover, the findings of the research demonstrate that the strategic objectives for the renewal stage are less entrepreneurial than the potential for growth. This, in turn, as a possibility could be further explored by organisations. The type of the article: Empirical study.
A systematic review on the validity and reliability of tape measurement method in leg length discrepancy

Background: Leg length discrepancy measurement is an essential part of musculoskeletal clinical assessment, and tape measurement is a common clinical method. This study aimed to systematically review the results of the findings of studies on validity and reliability of the tape measurement method and the quality of reporting the literature on this topic. Methods: A search was performed in PubMed, EBSCO, Science Direct, Web of Knowledge, Scopus, Embase, and Google Scholar using selected keywords from inception to December 2017.This systematic review was based on the PRISMA guideline. After a systematic selection process, the quality of the included studies was assessed independently by 2 reviewers using the Brink and Louw Scale for quality assessment. Results: A total of 11 studies were finally considered for this systematic review. Two studies were about the validity of (a measurement tool) studies and 4 were reliability analysis only. Validity and reliability analyses were simultaneously applied to 5 studies. Also, 9 out of 11 studies were deemed to be of high quality based on Brink and Louw Scale. Studies showed high (ICC=0.7) to very high (ICC=0.9) levels of interrater and intrarater reliability. The validity of the methods ranged from low to very high depending on subjects. Conclusion: Tape measurement method has acceptable reliability and validity in healthy people, but it does not have acceptable validity in measuring obese people and patients with musculoskeletal disorders. Thus, using a suitable method for LLD leg length discrepancy measurement seems to be necessary for obese and individuals with leg length discrepancy.
Developing and Testing the Team Flow Monitor (TFM)

Abstract Research has shown that the psychological experience of flow delivers great benefits in all aspects of life, including work settings. But, flow is typically studied at the individual level, even though work often comprises complex tasks performed in teams. Therefore, the focus of this study is on team flow, defined as a shared experience of flow during the execution of interdependent personal tasks in the interest of the team, originating from an optimized team dynamic and typified by seven prerequisites and four characteristics. We developed and tested the Team Flow Monitor as an instrument to assess team flow and related outcomes. The empirical findings of 110 teams support the operationalization of team flow as a second-order model that consists of two factors. Moreover, team flow related positively to individual and team outcomes. These findings suggest that team flow can serve as an important indicator in the management of work teams.
Bitrate Reduction Techniques for Low-Complexity Surveillance Video Coding

High resolution surveillance video cameras are invaluable resources for effective crime prevention and forensic investigations. However, increasing communication bandwidth requirements of high definition surveillance videos are severely limiting the number of cameras that can be deployed. Higher bitrate also increases operating expenses due to higher data communication and storage costs. Hence, it is essential to develop low complexity algorithms which reduce data rate of the compressed video stream without affecting the image fidelity. In this thesis, a computer vision aided H.264 surveillance video encoder and four associated algorithms are proposed to reduce the bitrate and computational complexity. The proposed techniques are (I) Speeded up foreground segmentation (II) Skip decision (III) Reference frame selection and (IV) Face Region-of-Interest (ROI) coding. In the first part of the thesis, a modification to the adaptive Gaussian Mixture Model (GMM) based foreground segmentation algorithm is proposed to reduce computational complexity. This is achieved by replacing expensive floating point computations with low cost integer operations. To maintain accuracy, we compute periodic floating point updates for the GMM weight parameter using the value of an integer counter. Experiments show speedups in the range of 1.33 1.44 on standard video datasets where a large fraction of pixels are multimodal. In the second part, we propose a skip decision technique that uses a spatial sampler to sample pixels. The sampled pixels are segmented using the speeded up GMM algorithm.
Pediatric Sleep Stage Classification Using Multi-Domain Hybrid Neural Networks

Sleep staging is an important part of clinical neurology. However, it is still performed manually by technical experts and is labor-intensive and time-consuming. To overcome these obstacles in the manual sleep staging process, a large number of machine learning-based classifiers with hand-engineered features have been proposed. Additionally, combinations of a deep neural network (DNN) have been recently highlighted as the state-of-the-art classifiers in view of their effectiveness for automatic sleep staging. In spite of the existence of a large number of these types of classifiers, to-this-date, no prior DNN-based approach has attempted sleep-stage classification using pediatric electroencephalographic (EEG) signals. In this paper, we propose a novel end-to-end classifier based on a multi-domain hybrid neural network (HNN-multi) approach consisting of a convolutional neural network and bidirectional long short-term memory for automatic sleep staging with pediatric scalp EEG recordings. To find effective temporal, spatial, and domain-specific conditions, we investigated noticeable changes in the classification performance corresponding to: 1) the length of input signals; 2) the number of channels; and 3) the types of input signals in the time and frequency domains. Our HNN-based classifier yielded the best performance metrics using 30-s time series in combination with an instantaneous frequency using a 19-channel, three-stage classification, with overall accuracy, F1 score, and Cohen’s Kappa, equal to 92.21%, 0.90, and 0.88, respectively. We suggest that an effective combination of temporal and spatial time-domain clues with time-varying frequency domain information plays a pivotal role in pediatric, automatic sleep staging. Sufficiently reasonable performance of our HNN-based approach coping with the highly complicated pediatric EEG signatures hopefully sheds light on the clinical feasibility of the DNN-based automatic sleep staging for pediatric neurology.