Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Critical Thinking Skills of College Students through RMS Model for Learning Basic Concepts in Science.

The purposes of this study were to: 1) Examine the effect of RMS learning model towards critical thinking skills. 2) Examine the effect of different academic abilities against critical thinking skills. 3) Examine the effect of the interaction between RMS learning model and different academic abilities against critical thinking skills. The research method used was quasi experimental with 2x2 factorial pretest posttest non equivalent group design.The critical thinking skills instrument used was essay test Asia-Pacific Forum on Science Learning and Teaching, Volume 17, Issue 1, Article 12 (Jun., 2016) Ahmad MUHLISIN, Herawati SUSILO, Mohamad AMIN and Fatchur ROHMAN Improving critical thinking skills of college students through RMS model for learning basic concepts in science Copyright (C) 2016 EdUHK APFSLT. Volume 17, Issue 1, Article 12 (Jun., 2016). All Rights Reserved. with the high level of reliability is 0.712. The data analysis used descriptive analysis and ANACOVA statistical analysis. The results showed that RMS learning model effectively improved students’ critical thinking skills and was able to align students critical thinking skills in different academic abilities. The impact of RMS learning model is higher 55,6% than conventional model on critical thinking skills. The basic concepts of science and biology lecturer were recommended to apply RMS learning model to apply them in high schools, middle schools, and elementary schools.
An Improved Scheme of Privacy Preserving Based on Lagrange Interpolation in Cloud Storage

The increasing maturity of cloud computing has led many companies to store critical information in the cloud. The privacy preserving in the cloud storage still remains major concern because the management of the data might not be fully trustworthy. Privacy preserving brings in concern for data confidentiality. In this paper, the work pays more attention to the issues involved in the data confidentiality and service availability. It proposes an improved scheme of privacy preserving based on Lagrange interpolation which uses multi-clouds instead of single cloud service provider. Compared with its previous data hiding scheme based on Lagrange interpolation algorithm and Multiclouds, this improved scheme only uses Lagrange interpolation to ensure both data confidentiality and service availability . The experiment was done through deploying this system in four clouds. It shows that the improved scheme outperforms the existing multi-clouds researches in terms of the cost of storage space and performance in the situation that data confidentiality and service availability are ensured. Keywords-multi-clouds; cloud storage; privacy preserving; data confidentiality; Lagrange interpolation
Digital Signal Processing: A Breadth-First Approach

The subject of Digital Signal Processing (DSP) is enormously complex, involving many concepts, probabilities, and signal processing that are woven together in an intricate manner. To cope with this scope and complexity, many DSP texts are often organized around the numerical examples of a communication system. With such organization, readers can see through the complexity of DSP, they learn about the distinct concepts and protocols in one part of the communication system while seeing the big picture of how all parts fit together. From a pedagogical perspective, our personal experience has been that such approach indeed works well. Based on the authors extensive experience in teaching and research, Digital Signal Processing: A Breadth-First Approach is written with the reader in mind. The book is intended for a course on digital signal processing, for seniors and undergraduate students. The subject has high popularity in the field of electrical and computer engineering, and the authors consider all the needs and tools used in analysis and design of discrete time systems for signal processing. Key features of the book include: The extensive use of MATLAB based examples to illustrate how to solve signal processing problems. The textbook includes a wealth of problems, with solutions Worked-out examples have been included to explain new and difficult concepts, which help to expose the reader to real-life signal processing problems The inclusion of FIR and IIR filter design further enrich the contents.
Fast Document Summarization using Locality Sensitive Hashing and Memory Access Efficient Node Ranking

Text modeling and sentence selection are the fundamental steps of a typical extractive document summarization algorithm. The common text modeling method connects a pair of sentences based on their similarities. Even thought it can effectively represent the sentence similarity graph of given document(s) its big drawback is a large time complexity of $O(n^2)$, where n represents the number of sentences. The quadratic time complexity makes it impractical for large documents. In this paper we propose the fast approximation algorithms for the text modeling and the sentence selection. Our text modeling algorithm reduces the time complexity to near-linear time by rapidly finding the most similar sentences to form the sentences similarity graph. In doing so we utilized Locality-Sensitive Hashing, a fast algorithm for the approximate nearest neighbor search. For the sentence selection step we propose a simple memory-access-efficient node ranking method based on the idea of scanning sequentially only the neighborhood arrays. Experimentally, we show that sacrificing a rather small percentage of recall and precision in the quality of the produced summary can reduce the quadratic to sub-linear time complexity. We see the big potential of proposed method in text summarization for mobile devices and big text data summarization for internet of things on cloud. In our experiments, beside evaluating the presented method on the standard general and query multi-document summarization tasks, we also tested it on few alternative summarization tasks including general and query, timeline, and comparative summarization.
Extensions to Dietz theory and behavior of gravity tongues in slightly tilted reservoirs

Investigation of accuracy implications in the modeling of unstable gravity tongues in slightly tilted, anisotropic reservoirs. An extented form of Dietz's equation is examined and its range of validity established by reference to high-resolution simulation. The theory is also used to investigate the onset of viscous fingering with a new viscous-to-gravity number.
Cognition and the Brain: The Philosophy and Neuroscience Movement

Part I. Date and Theory in Neuroscience: 1. Localization in the brain and other illusions Valerie Gray Hardcastle and C. Matthew Stewart 2. Neurophenomenology Evan Thompson, Antoine Lutz and Diego Cosmelli 3. Out of the mouth of autistics Victoria McGeer Part II. Neural Representation: 4. Moving beyond the metaphors Chris Eliasmith 5. Brain time and phenomenological time Rick Crush 6. The puzzle of temporal experience Sean Kelly Part III. Visuomotor Transformation: 7. Grasping and perceiving objects Pierre Jacob 8. Action-oriented representation Pete Mandik Part IV. Colour Vision: 9 Chimerical colours Paul Churchland 10. Opponent processing, linear models, and the verticality of colour perception Zoltan Jakob Part V. Consciousness: 11. A neurofunctional theory of consciousness Jesse Prinz 12. Making consciousness safe for neuroscience Andrew Brook.
International Conference on Computational Modeling and Security ( CMS 2016 ) An Analysis of Leaf Chlorophyll Measurement Method using Chlorophyll Meter and Image Processing Technique

A regular and periodic monitoring of crop health is essential in any cultivation. An important parameter which act as indices of crop health is the leaf chlorophyll measurement. In the Asian part of the world, Betel vine (Piper betle L., family Piperaceae) ranks second to coffee and tea in terms of daily consumption. Therefore, these important and highly productive crash crop is selected for the purpose of study. The experiment was conducted in an established pan betel vine crop field (Pan boroj). A small review of the popular method of leaf chlorophyll measurement is done and some of the drawbacks of the existing methods are reported. The review point out a need for fast and precise leaf chlorophyll measurement technique. Thus an image processing technique based on trichromatic colors i.e., red green and blue (RGB) model is proposed. For the purpose of analysis of the proposed model, the model outcome was compared with atLEAF+ chlorophyll meter reading. And a regression analysis was performed the result of regression analysis proof that there is a strong correlation between proposed image processing technique and chlorophyll meter reading. Thus, it appears that the proposed image processing technique of leaf chlorophyll measurement will be a good alternative for measuring leaf chlorophyll rapidly and with ease. © 2015 The Authors. Published by Elsevier B.V. Peer-review under responsibility of organizing committee of the 2016 International Conference on Computational Modeling and Security (CMS 2016).
Learning vector representations for sentences: The recursive deep learning approach

According to the principle of compositionality, the meaning of a sentence is computed from the meaning of its parts and the way they are syntactically combined. Unfortunately, unlike formal semantics, distributional semantics has no elegant compositional mechanisms like function application of lambda calculus. Searching for vectorial composition functions is therefore a challenge, which is still unsolved. Thanks to the rapid rise of deep learning, the idea of using neural networks as composition functions came back, resulting in the recursive neural network (RNN) model and a bundle of its extensions. With (near) state-of-the-art performances on a wide range of tasks such as syntactic parsing and sentiment analysis, this approach has become a promising solution to the challenge. Following this trend, this dissertation aims at extending the RNN model. In one direction, conventional neural networks are replaced by more expressive ones such as Long short term memory units for tree structures. This improves the performances on sentence classification tasks such as sentiment analysis. In another direction, information is allowed to flow in a parse tree not only bottom-up but also top-down such that both the content and context of a constituent can be recursively encoded in vectors. This extends the range of tasks that can be performed, including top-down parsing and semantic role labeling.
The Effect of Multimedia Dictionary-Based Software on the Vocabulary Learning of Persian EFL Learners

Vocabulary learning has been one of the important issues in the English Language Teaching. A Variety of ways has been employed to teach lexical items of a language. The current study demonstrated the use of multimedia instructional technology for effective vocabulary teaching. To this end, Oxford Quick Placement test was administered to 50 female and male students studying at a private institute in Iran, Rafsanjan. Thirty pre-intermediate participants were selected. Thirty target words were selected to be taught to four groups of mixed gender via different methods. The experimental groups were provided with multimedia dictionary software. The control groups were assigned to use traditional teaching materials. Data were collected over the course of six sessions via a pre /posttest, and a delayed posttest to measure the participants' long term retention of vocabulary. The findings of ANOVA tests revealed that there was a significant difference between the experimental and control groups. Females in comparison to males showed some preferences to some designed tasks. Additionally, there are some pedagogical implications for language educators and material designers. They can use the tasks used in multimedia software and expand their experience of teaching with new technological devices to improve learning and teaching vocabulary items of a language.
Clinical Assessment of the Hand - A Review of Joint Angle Measures

Measurement of range of motion (ROM) at a joint is regarded by many clinicians as objective and is therefore frequently used as an assessment tool. There are many methods of measurements available to the clinician and the researcher, but information about their reliability may be less accessible. Some techniques of measurement, such as goniometry, have been studied frequently, whereas other popular techniques have little or no published evidence to support their use. The aim of this paper is to summarise the results from the literature surrounding methods of measurement, particularly those concerned with hand assessment.
Modeling count time series following generalized linear models

Count time series are found in many different applications, e.g. from medicine, finance or industry, and have received increasing attention in the last two decades. The class of count time series following generalized linear models is very flexible and can describe serial correlation in a parsimonious way. The conditional mean of the observed process is linked to its past values, to past observations and to potential covariate effects. In this thesis we give a comprehensive formulation of this model class. We consider models with the identity and with the logarithmic link function. The conditional distribution can be Poisson or Negative Binomial. An important special case of this class is the so-called INGARCH model and its log-linear extension. A key contribution of this thesis is the R package tscount which provides likelihood-based estimation methods for analysis and modeling of count time series based on generalized linear models. The package includes methods for model fitting and assessment, prediction and intervention analysis. This thesis summarizes the theoretical background of these methods. It gives details on the implementation of the package and provides simulation results for models which have not been studied theoretically before. The usage of the package is illustrated by two data examples. Additionally, we provide a review of R packages which can be used for count time series analysis. A detailed comparison of tscount to those packages demonstrates that tscount is an important contribution which extends and complements existing software. A thematic focus of this thesis is the treatment of all kinds of unusual effects influencing the ordinary pattern of the data. This includes structural changes and different forms of outliers one is faced with in many time series. Our first study on this topic is concerned with retrospective detection of such changes. We analyze different approaches for modeling such intervention effects in count time series based on INGARCH models. Other authors treated a model where an intervention affects the non-observable underlying mean process at the time point of its occurrence and additionally the whole process thereafter via its dynamics. As an alternative, we consider a model where an intervention directly affects
The Effect of Reading-To-Write Approach on EFL Undergraduate Essay Writing

This paper attempts to investigate the effect of pre-writing reading on the undergraduate English as a foreign language (EFL) learners’ performance in academic writing. Specifically, the main aim of the study is to examine whether the employment of ‘reading-to-write approach’ improves the university learners’ essay writing at home situation. It also tries to check if the suggested approach makes any difference in the target group’s writing. Control group and experimental group research design was used to examine the difference between the two groups and to determine if the suggested approach could make any significance statistically. The two groups sat for pretest and post-test in cause and effect essay writing. To compare the performance of the two groups in the post-test, an Independent Sample T-test was used. The results of the post-test indicate that there is a noteworthy improvement of the experimental group’s essay writing. In addition, the study used grading rubric as criteria to assess the population’s performance in the major components of academic essay writing. The study findings show that the learners’ literacy is a major concern for the students as evident in the lack of reasonable ideas in their writing. Additionally, the paper is a genuine call to foster reading habits that would eventually develop academic writing.
Morphology and Glass Transition of Thin Polylactic Acid Films

The aim of this study is to highlight the effect of the morphology of polylactic acid (PLA) thin films on the glass transition in this confined geometry. For that purpose, poly(L-lactic acid) and poly(D-lactic acid) polymer films were prepared on different surfaces such as mica and silicon surfaces by using two different solvents. The films exhibit different morphologies starting from individual macromolecular chains at very low concentration to homogeneous films at higher concentrations. Globular to elongated rodlike structures of the PLA macromolecules were observed according to the polarity of the surface and the solvent. The Tg of PLA enantiomeric film, determined by adhesion force using atomic force microscopy (AFM) as a function of temperature, was confirmed by ellipsometric measurements. Surprisingly, this value is independent of the morphology of thin enantiomeric film. A balance between the attractive surface effects and a decrease of the crystallinity of PLA on the mobility in the amorphous region will be discussed.
Bars, drugs and football thugs: Alcohol, cocaine use and violence in the night time economy among English football firms

This article draws on ethnographic fieldwork, the aim of which was to explore the functionality of cocaine (used in conjunction with alcohol and on its own) in the subcultural milieu of the English football firm. The study was originally concerned with the use of violence associated with cocaine use among football fans on match days but, like much ethnography, the research evolved beyond its original remit to include the extension of football firm violence within the night time economy (NTE). The study is unique in giving a voice to this group of individuals and permitting them to be active interpreters of their own world. It included 20 interviews with members of football firms who habitually took part in violent exchanges and found that concurrent use of cocaine and alcohol fulfilled three main functions: the facilitation of extreme violence; the acquisition of ‘time out’; and the construction of a (hyper-)masculine identity. These functions were not confined to the subcultural context of the football firm, but had also become an integral component of their mainstream leisure pursuits within the NTE. The findings from this explorative study also contribute answers to the under researched question of whether those ‘who are violent in the NTE are also violent in other contexts’ (Finney, 2004: 5).
THE DEVELOPMENT AND APPLICATION OF DYNAMIC MODELS FOR PREDICTING TRANSIT ARRIVAL TIMES

DEVELOPMENT AND APPLICATION OF DYNAMIC MODELS FOR PREDICTING TRANSIT ARRIVAL TIMES by Yuqing Ding Stochastic variations in traffic conditions and ridership often have a negative impact in transit operations resulting in the deterioration of schedule/headway adherence and lengthening of passenger wait times. Providing accurate information on transit vehicle arrival times is critical to reduce the negative impacts on transit users. In this study, models for dynamically predicting transit arrival times in urban settings are developed, including a basic model, a Kalman filtering model, link-based and stop-based artificial neural networks (ANNs) and Neural/Dynamic (ND) models. The reliability of these models is assessed by enhancing the microscopic simulation program CORSIM which can calculate bus dwell and passenger wait times based on time-dependent passenger demands and vehicle inter-departure times (headways) at stops. The proposed prediction models are integrated with the enhanced CORSIM individually to predict bus arrival times while simulating the operations of a bus transit route in New Jersey. The reliability analysis of prediction results demonstrates that ANNs are superior to the basic and Kalman filtering models. The stop-based ANN generally predicts more accurately than the link-based ANN. By integrating an ANN (either linkbased or stop-based) with the Kalman filtering algorithm, two ND models (NDL and NDS) are developed to decrease prediction error. The results show that the performance of the ND models is fairly close. The NDS model performs better than the NDL model when stop-spacing is relatively long and the number of intersections between a pair of stops is relatively large. In the study, an application of the proposed prediction models to a real-time headway control model is also explored and experimented through simulating a high frequency light rail transit route. The results show that with the accurate prediction of vehicle arrival information from the proposed models, the regularity of headways between any pair of consecutive operating vehicles is improved, while the average passenger wait times at stops are reduced significantly. THE DEVELOPMENT AND APPLICATION OF DYNAMIC MODELS FOR PREDICTING TRANSIT ARRIVAL TIMES
AN INVESTIGATION OF ENGLISH TEACHING STRATEGIES IN ENHANCING STUDENTS’ VOCABULARY IMPLEMENTED BY A PRE-SERVICE ENGLISH TEACHER

The need to teach English language appropriately in particular is a challenge for all the teachers in Indonesia. Today, it has become obligatory for the teachers to rethink and renovate their teaching strategies with the changing times. Since there has been a continuous transformation in the teaching methods and techniques all over the world in every subject, methods and techniques for teaching vocabulary need desirable innovation to fulfill students’ need of vocabulary learning. This research was aimed to investigate the strategies used by a pre-service teacher to improve students ’ vocabulary and students’ responses toward the teacher’s strategies . A qualitative approach with case study design with a pre-service English teacher was used and 35 students in one junior high school participated in this study. The data were collected through observations, interview, and questionnaire. The results showed that the teacher used varieties of techniques in numerous methods as his teaching strategies, such as Contextual Teaching and Learning with neighborhood walk, Silent way with pictures and crosswords puzzle, and Total Physical Response with gamification, to enhance students’ vocabulary . The srategies are proven to be appropriate to be implemented in the classroom and leads to the vocabulary collection improvement for most students in the classroom .
Parallel Implementation of Multi-channel Time Domain Clutter Suppression Algorithm for Passive Radar

Cancellation of clutter and multi-path is one of the key steps in passive radar target information extraction. Extensive Cancellation Algorithm Batches (ECA-B) is an effective time-domain clutter suppression algorithm, but with high time and space complexity, and even higher with multi-channel (or multi-beam) data processing. Combining high memory throughput and tremendous computational horsepower of GPU graphics processor, this paper proposes a multi-channel ECA-B algorithm which is suitable for parallel implementation on GPUs. Firstly, the principle of multi-channel ECA-B algorithm is derived, avoiding the redundancy of processing each channel singly. Then an iterative calculation method is presented for reducing the biggest time-consuming calculation of the correlation matrix, so that time and space complexity are both reduced to 1/K (K is clutter's degree of freedom) of the conventional method. Finally, the full GPU parallel implementation of the algorithm is given. The simulation and experimental results verify the accuracy and effectiveness of the proposed algorithm.