Miracle-dz/UniFAR-FTUs huggingface dataset:
Processing collocations: Do native speakers and second language learners simultaneously access prefabricated patterns and each single word?

The purpose of this study was to examine whether native English speakers and Japanese ESL (English as a Second Language) learners concurrently access prefabricated patterns as well as each word in these patterns when collocation meanings are retrieved. Previous studies have tended to focus on either holistic or analytic solo processing; however, there is the further possibility that single-word knowledge is simultaneously activated when prefabricated patterns are accessed. With this possibility, this study examined the possible parallel processing of collocations. Two experiments were conducted with 30 native English speakers and 30 advanced Japanese ESL learners. It was found that (1) the native speakers (NSs) generally processed collocations in parallel, but some collocations were solely processed; and (2) ESL learners generally used sole processing and tended to use fewer prefabricated patterns than the NSs. From these findings, it was suggested that the ESL learners processed collocations differently from NSs even after the knowledge of collocations had been acquired. This provides some evidence that dealing with simultaneous processes is challenging for ESL learners.
Forex trading prediction using linear regression line, artificial neural network and dynamic time warping algorithms

Forex prediction has become a challenging task in the Forex market since the late 1970s due to uncertainty movement of exchange rates.In this paper, we utilised linear regression equation to analyse the historical data and discover the trends patterns in Forex.These trends patterns are modeled and learned by Artificial Neural Network algorithm, and Dynamic Time Warping algorithm is used to predict the near future trends.Our experiment result shows a satisfactory result using the proposed approach.
Medical Simulation: "See one, do one, teach one…just not on my Mom" Part One: Why simulation should be a priority.

This manuscript is part-one of a three-part series on Medical Simulation. Part-one will address the “why” of Simulation, namely, why Medical Simulation offers novel opportunities to improve education, continuing-competency, and patient safety. Part-two will focus on the “how” of simulation, namely, how to design, implement, and maintain a viable program. Part-three will cover the “what”, namely what the future directions are likely to be, what sort of programs are currently available, and what evidence supports their implementation.
DIGITAL TOOLS USED FOR THE DEVELOPMENT OF COMPUTATIONAL THINKING IN PRIMARY EDUCATION: A TEN YEAR SYSTEMATIC LITERATURE REVIEW

This study presents a 10-year (2006-2016) systematic literature review related to the digital tools which have been used into K-6 education in order to develop Computational Thinking (CT) skills which are of great interest in the research community over the last years. In fact, many attempts have been conducted by various researchers for the development of CT skills in all educational levels using diverse ways. One of these ways is through the use of various digital tools which have been used in students of all ages and across different subjects. Thus, the aim of this paper is to review the CT-literature of the last decade related to primary education with the intention to: (a) identify the digital tools which have been used for the development of CT skills, (b) describe their characteristics and their potentials as educational tools in terms of CT-skill development, (c) demonstrate the ways these tools have been integrated in diverse educational contexts. The analysis of the data shows thatfor students’ CT skills’ cultivation: (a) twenty-six different digital tools have been used by primary school students, (b) most of these tools are graphical environments which use visual programming languages which were utilized to involve students in learning programming, construct games, develop simulations/models, and program robots, and (c) the most common educational use of these digital tools was for learning programming and in a less extend for game programming and game activities, for collaboration and pair programming, as well as for simulation, modeling and robotic education. It is hoped that, the systematic review that is presented in this study would help other researchers and primary education level educators to devise new digital tools which could be used to develop students’ CT skills.
Optimal constrained power scheduling in Electricity Market

-An optimal scheduling of units in an electric spot market presents in this paper. Unit commitment is a non-linear and complex combinatorial optimization problem which is difficult to be solved for large-scale power systems so this study addresses a linear expression of the problem. Proposed approach is a mixed-integer linear programming to minimize the total energy dispatch cost in 24 hours of a day. A system as the same structure as Iranian power market is used to demonstrate the linear expression of the problem. Simulation results compared with another approach. The results shows the applicability of the proposed method.
Dynamic assessment of women pelvic floor function by using a fiber Bragg grating sensor system

We present a novel sensing system consisting of an intravaginal probe and an optoelectronic measurement unit, which allows an easy, comfortable and quantitative dynamic evaluation of women pelvic floor muscle strength. The sensing probe is based on a silicone cylinder that transduces radial muscle pressure into axial load applied to a fiber Bragg grating strain sensor. The performance of a first sensor probe prototype with temperature referentiation and of the autonomous, portable optoelectronic measurement unit with data logging capabilities and graphical user interface is disclosed. The presented results refer to an ongoing collaboration work between researchers from the Medical, Optoelectronics and Mechanical areas, directed to the development of equipment that can assist in medical practice and help in the research of primary mechanisms responsible for several pelvic floor disorders, in particular urogenital prolapses.
Towards a Smart Car Seat Design for Drowsiness Detection Based on Pressure Distribution of the Driver’s Body

—Driver fatigue is a serious problem causing thousands of road accidents each year. The major challenge in the ﬁeld of accident avoidance systems is the development of technologies for detecting or preventing drowsiness at the wheel. In this paper, we present a novel approach for fatigue estimation based on the design of an intelligent seat able to anticipate driver fatigue through analysis of driver’s body pressure distribution.
Diversity in Level 1 GEES Assessment: moving from less of more to more of less

Abstract This article reports on an analysis of assessment items in a Level 1 Earth Science and Climatology module at Liverpool John Moores University (LJMU). It examines the effect of increasing the diversity of assessment methods in the module and increasing the number of summative assessment items from 4 to 8. The effect of doubling the number of assessment items on student performance is examined. Regression analysis highlights differences in how well the marks in the 8 assessment items predict the students’ final module total. The analysis highlights how students ranked overall in the top quartile performed better in the so called ‘deep learning’ assessments (field reports, weather analysis and use of a wiki) whereas students in the lowest quartile performed better in so called ‘shallow learning’ assessments (on-line multiple choice test and the formal written examination). Individual student’s ‘assessment profiles’ are examined and strengths (high class ranking) compared with weaknesses (low class ranking). The reasons for the differences are explored and discussed in the light of students’ motivations, recently introduced ‘graduate skills mapping’ in the University and research by assessment experts.
Application and Flight Testing of an Adaptive Autopilot on Precision Guided Munitions

This paper presents the application of an adaptive control methodology to an existing linear gain-scheduled production control laws of two precision guided munitions and its subsequent evaluation. The adaptive control software is operated in parallel with the existing autopilot and outputs from the adaptive control software are treated as increments to the control surface deflections commanded by a baseline autopilot. A major advantage of this methodology is that it provides the benefit of adaptation without requiring modification of the baseline autopilot software. An overview of issues related to the implementation and testing of the adaptive control algorithm are provided along with results from flight testing.
Plan Bouquets : An Exploratory Approach to Robust Query Processing

Over the last four decades, relational database systems, with their mathematical basis in firstorder logic, have provided a congenial and efficient environment to handle enterprise data during its entire life cycle of generation, storage, maintenance and processing. An organic reason for their pervasive popularity is intrinsic support for declarative user queries, wherein the user only specifies the end objectives, and the system takes on the responsibility of identifying the most efficient means, called “plans”, to achieve these objectives. A crucial input to generating efficient query execution plans are the compile-time estimates of the data volumes that are output by the operators implementing the algebraic predicates present in the query. These volume estimates are typically computed using the “selectivities” of the predicates. Unfortunately, a pervasive problem encountered in practice is that these selectivities often differ significantly from the values actually encountered during query execution, leading to poor plan choices and grossly inflated response times. The database research community has spent considerable efforts to address the above challenge, which is of immediate relevance to currently operational systems. The proposed techniques include: (a) Improving estimation accuracy through novel statistical models, sampling and execution-feedback mechanisms; (b) Identifying execution plans that are relatively robust to such errors; and (c) Dynamically changing plans at run-time if estimation errors are detected during the execution of the originally chosen plan. While this rich body of literature features several innovative formulations, the prior techniques all suffer from a systemic limitation – the
Language structure and language acquisition: grammatical categorization using phonological and distributional information

This thesis addresses the question of how words are grouped according to their grammatical categories during language acquisition. Over the past 20. years a general consensus has developed that distributional and phonological cues are important cues that language learners utilize in the grammatical categorization process (e.g., Kelly, 1992; Redington, Chater, & Finch, 1998). The combination ofthese cues was investigated with artificial language learning experiments, which combined two categories of. phonologically coherent words with co-occurring distributional cues, and corpus analysis techniques. Experiments I to 4 indicated that both phonological and distribution cues are necessary for the categorization of high and low frequency words. Additionally, these experiments indicated that distributional information alone was sufficient to categorize high frequency words, but that phonological cues were necessary for low frequency words. It was also found that succeeding bigram distribution cues induced more grammatical categorization than the preceding bigram cues. This is explained by the Rescorla-'\yagner (1972) model of associative learning; associations were stronger between the category words and succeeding cues as a single succeeding cue followed all category words. Associations were weaker with preceding cues as numerous category words followed the preceding cues. Experiments 5, 6 and 7 also found that the effectiveD~ss of the distributional cues was influenced by prior linguistic experience, resulting in higher learning with distributional cues which were phonologically consistent with distributional cues found in the participants' native language (English). This thesis also investigated the debate as to what type of distributional cue is most useful in the categorization process, with some researchers advocating trigram cues (Mintz, 2002) while others advocate bigram cues (Monaghan & Christiansen, 2004; Valian & Coulson, 1988). The results of a corpus analysis and two experiments provided evidence that trigram cues (aXb) are very effective at categorization, but preliminary evidence suggests that this categorization may simply be due to the combined influence of the beginning and ending bigrams (aX and Xb). Overall, this thesis indicates that phonological and distributional cues are key to grammatical categorization, which occurs through associative learning principles; grammatical categorization progresses . faster with succeeding cues; and bigram distribution cues may be the initial source of distributional information in the grammatical categorization process.
HiPeR : Hierarchical progressive exact retrieval in multi dimensional spaces

In this article, we are interested in accelerating similarity search in high dimensional vector spaces. The presented approach, called HiPeR, is based on a hierarchy of sub- spaces and indexes: it performs nearest neighbors search across spaces of different dimensions, by beginning with the lowest dimensions up to the highest ones, with the aim of minimizing the effects of the curse of dimensionality. HiPeR significantly accelerates exact retrieval even with the best indexes, and also allows for progressive retrieval, i.e. the possibility to provide results to the user progressively with refinements until satisfaction. Scanning the hierarchy can be done according to several strategies. We propose and evaluate two heuristics: the first one supposes an a priori knowledge on the data-set distribution, while the second chooses the most interesting levels at run time. HiPeR is evaluated for range queries on 3 real data-sets varying from 500,000 vectors to 4 millions.
Shortest Processing Time First and Hadoop

Big data has revealed itself as a powerful tool for many sectors ranging from science to business. Distributed data-parallel computing is then common nowadays: using a large number of computing and storage resources makes possible data processing of a yet unknown scale. But to develop large-scale distributed big data processing, one have to tackle many challenges. One of the most complex is scheduling. As it is known to be an optimal online scheduling policy when it comes to minimize the average flowtime, Shortest Processing Time First (SPT) is a classic scheduling policy used in many systems. We then decided to integrate this policy into Hadoop, a framework for big data processing, and realize an implementation prototype. This paper describes this integration, as well as tests results obtained on our testbed.
Artifacts Removal in JPEG Decompression via learned dictionary and Total variation regularization

JPEG Compression scheme is a widely used form of compression technique which provides good compressed result. However, the standard JPEG decompression result usually contains some artifacts, such as Blocking artifacts, Gibbs artifacts, Staircase artifacts, etc. Compression artifacts are a noticeable distortion of media caused by lossy compression technique. This artifact usually occurs when the compression ratio is high. This paper proposes a novel strategy for reducing artifacts with the help of a two step algorithm. First step is dictionary learning and second step is solving total variation regularization for decompressed images. Primal duel algorithm is used to solve total variation decompression model more effectively. The resultant image is enhanced with JPEG dithering technique. Numerical experiments are performed to demonstrate that the proposed method outperforms DicTv method (Dictionary Total variation) in the measure of PSNR (peak of signal to noise ratio) and, SSIM metric (structural similarity). Keywords— DCT; Total variation; JPEG Compression; Quantization.
The Need for Convergence of BIM and 3D Imaging in the Open World

̶ Two types of technologies that are rapidly (exponentially) impacting the Architecture, Engineering, Construction, and Facilities Management (AEC/FM) industry are: Building Information Modelling (BIM) and 3D imaging technologies. The latter is currently mainly represented by laser scanning, but is being rapidly complemented with photogrammetry (both terrestrial and aerial), ground-penetrating radar (GPR), as well as their integration with other technologies such as infrared imaging, GPS, and RFID. While these two types of technologies individually offer significant performance improvements in all parts of the sector, it is their integration that promises to provide the most benefits. Integrated, the two types of technologies would form a cyber-physical system that would deliver bi-directional interoperability, allowing seamless coordination between the virtual worlds captured in the BIM model and the real world construction. Yet, it is interesting to note there are very few software solutions that enable the joint visualisation and manipulation of 3D imaging and BIM data/information. Furthermore, none of those solutions are based on the combined use of open standards and tools, thereby limiting the extent to which researchers can develop and test new solutions to increase the value of both BIM and 3D imaging technologies. This clearly demonstrates the need for an open source software platform (based on open source standards and tools) supporting the management and processing of BIM and 3D imaging data, particularly point clouds, for facility lifecycle management. The paper first builds a case for the need for such a solution, identifying critical Virtual-to-Real, Real-to-Virtual and Virtual-vs-Real applications. A novel open, web-based core software platform is then proposed that utilises the open standards IFC and E57 for real-time bi-directional interoperability between BIM models and 3D imaging data (especially point clouds). Finally, it is revealed how such a system can be developed and grown over time from industry contributions through an open source initiative.
Meffil: efficient normalisation and analysis of very large DNA methylation samples

Background Technological advances in high throughput DNA methylation microarrays have allowed dramatic growth of a new branch of epigenetic epidemiology. DNA methylation datasets are growing ever larger in terms of the number of samples profiled, the extent of genome coverage, and the number of studies being meta-analysed. Novel computational solutions are required to efficiently handle these data. Methods We have developed meffil, an R package designed to quality control, normalize and perform epigenome-wide association studies (EWAS) efficiently on large samples of Illumina Infinium HumanMethylation450 and MethylationEPIC BeadChip microarrays. We tested meffil by applying it to 6000 450k microarrays generated from blood collected for two different datasets, Accessible Resource for Integrative Epigenomic Studies (ARIES) and The Genetics of Overweight Young Adults (GOYA) study. Results A complete reimplementation of functional normalization minimizes computational memory requirements to 5% of that required by other R packages, without increasing running time. Incorporating fixed and random effects alongside functional normalization, and automated estimation of functional normalisation parameters reduces technical variation in DNA methylation levels, thus reducing false positive associations and improving power. We also demonstrate that the ability to normalize datasets distributed across physically different locations without sharing any biologically-based individual-level data may reduce heterogeneity in meta-analyses of epigenome-wide association studies. However, we show that when batch is perfectly confounded with cases and controls functional normalization is unable to prevent spurious associations. Conclusions meffil is available online (https://github.com/perishky/meffil/) along with tutorials covering typical use cases.
A Scientometric Map of Global Educational Research

Educational research covers a diverse area of topics that presents integration challenges. We used a bibliographic analysis of the Scopus database to describe educational research and present a global map that consists of 18 research clusters that are connected by distinct sets of references. Clusters differ in their core references and how much they focus on educational research. Some clusters share references in a highly connected way and focus directly on teaching and learning (e.g. Learning cluster) while other clusters share references more loosely and focus on topics that lie at the interaction of education and other related fields (e.g. Sociology of Education cluster). As such, clusters represent subfields of educational research that are differentially organized around different research topics and incorporate shared knowledge bases embodied in shared references. Students, researchers, and practitioners can use our results to explore the collectively built knowledge bases of educational research.
Java RMI

From the Publisher: Java RMI contains a wealth of experience in designing and implementing Java's Remote Method Invocation. If you're a novice reader, you will quickly be brought up to speed on why RMI is such a powerful yet easy to use tool for distributed programming, while experts can gain valuable experience for constructing their own enterprise and distributed systems. With Java RMI, you'll learn tips and tricks for making your RMI code excel. The book also provides strategies for working with serialization, threading, the RMI registry, sockets and socket factories, activation, dynamic class downloading, HTTP tunneling, distributed garbage collection, JNDI, and CORBA. In short, a treasure trove of valuable RMI knowledge packed into one book.
Large scale visualization of virtual movie sets for performance capture actors

MARIA SHCHERBAN: Large scale visualization of virtual movie sets for performance capture actors Tampere University of Technology Master of Science thesis, 68 pages January 2016 Master’s Degree Programme in Information Technology Major: Mathematics Examiners: Professor Atanas Gotchev, Professor Ossi Nykänen
FURTHER RESULTS ON THE DIRAC DELTA APPROXIMATION AND THE MOMENT GENERATING FUNCTION TECHNIQUES FOR ERROR PROBABILITY ANALYSIS IN FADING CHANNELS

In this article, we employ two distinct methods to derive simple closed-form approximations for the statistical expectations of the positive integer powers of Gaussian probability integral [ ( )] p E Q γ β γ Ω with respect to its fading signal-to-noise ratio (SNR) γ random variable. In the first approach, we utilize the shifting property of Dirac delta function on three tight bounds/approximations for Q (.) to circumvent the need for integration. In the second method, tight exponential-type approximations for Q (.) are exploited to simplify the resulting integral in terms of only the weighted sum of moment generating function (MGF) of γ. These results are of significant interest in the development of analytically tractable and simple closedform approximations for the average bit/symbol/block error rate performance metrics of digital communications over fading channels. Numerical results reveal that the approximations based on the MGF method are much more versatile and can achieve better accuracy compared to the approximations derived via the asymptotic Dirac delta technique for a wide range of digital modulations schemes and wireless fading environments.