Reviewerly/exHarmony huggingface dataset:
Implementing backward design to improve students’ academic performance in EFL classes

This study is based on implementing backward design to improve students’ academic assessment, so public high school students can take part of meaningful contexts where they show what they learned at the end of a class term. Taking in consideration, backward design is a great tool to be applied for public high schools where most of the classes are textbooks centered, provoking students’ frustration due to the lack of English knowledge from schools. There is a high rate of students failing courses because students are placed in English levels where they do not belong to or they come from other institutions where they did not learn English. Backward design lets teachers be creative and use skills and methodologies according to students’ needs and be very reflexive in the way of designing it. In this study, pairedT_Test is applied to compare scores from the same level, but using different ways of testing. In conclusion, the experimental group (G2), in which backward design was applied, got better results than the controlled group (G1), who did not apply it. In this case the independent variable which mean and scores are high. Results show backward design is very meaningful not only for students, but also for teachers. It is true that running appropriate action plans, selecting the best resources and conducting a final assessment through a performance task, gave the opportunity for students to gain more confidence on the language and apply it in a real context.
A Quality of Experience Management Framework for Mobile Users

Voice transmission is no longer the main usage of mobile phones. Data transmissions, in particular Internet access, are very common actions that we might perform with these devices. However, the spectacular growth of the mobile data demand in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mn mathvariant="normal">5</mml:mn></mml:mrow></mml:math>G mobile communication systems leads to a reduction of the resources assigned to each device. Therefore, to avoid situations in which the Quality of Experience (QoE) would be negatively affected, an automated system for degradation detection of video streaming is proposed. This approach is named QoE Management for Mobile Users ( QoEMU ). QoEMU is composed of several modules to perform a real-time analysis of the network traffic, select a mitigation action according to the information of the traffic and to some predefined policies, and apply these actions. In order to perform such tasks, the best Key Performance Indicators (KPIs) for a given set of video traces are selected. A QoE Model is trained to define a global QoE for the set of traces. When an alert regarding degradation in the quality appears, a proper mitigation plan is activated to mitigate this situation. The performance of QoEMU has been evaluated over a degradation situation experiments with different video users.
A Path Analysis of Typical Intellectual Engagement, Learning Style and Preference for Assessment

One of the most significant issues in the process of teaching and learning is to consider students' individual differences.The aim of this study was to investigate the interrelationships among Iranian EFL Learners' Typical Intellectual Engagement, Learning Style and Preference for Assessment Methods.The sample included 200 English learners majoring in TEFL (Teaching English as a Foreign Language) from 3 different universities in Mashhad, Iran.To this end, three questionnaires; Typical Intellectual Engagement questionnaire, learning style inventory, and preference for assessment methods questionnaire were administered.The interrelationships among all variables were examined using path analysis.The model was fitted after some modifications.Findings revealed that Typical Intellectual Engagement was a positive and significant predictor of all four sub-factors of EFL learners' learning styles except Activist.Also, all four subfactors of EFL learners' learning styles except pragmatist were significant predictors of preference for assessment methods.Finally, the fit model showed that Typical Intellectual Engagement was a positive significant predictor of preferred assessment methods (r=.24, .22,p<.05).This study has several implications for teachers, learners, and syllabus designers.
Context-Aware Modeling for Augmented Reality Display Behaviour

Current surgical augmented reality (AR) systems typically employ an on-demand display behavior, where the surgeon can toggle the AR on or off using a switch. The need to be able to turn the AR off is in part due to the obstructing nature of AR overlays, potentially hiding important information from the surgeon in order to provide see-through vision. This on-demand paradigm is inefficient as the surgeon is always in one of two sub-optimal states: either they do not benefit at all from the image guidance (AR off) or the field of view is partially obstructed (AR on). Additionally, frequent toggling between the two views during the operation can be disruptive for the surgeon. This letter presents a novel approach to automatically adapt the AR display view based on the context of the surgical scene. Using gaze tracking in conjunction with information from the surgical instruments and the registered anatomy, a multi Gaussian process model can be trained to infer the desired AR display view at any point during the procedure. Furthermore, a new AR display view is introduced in this model, taking advantage of the context information to only display a partial view of the AR when relevant. To validate the presented approach, a detailed simulation of a neurosurgical tumor contour marking task is designed. A study conducted with 15 participants demonstrates the usefulness of the proposed approach, showing a statistically significant mean reduction of 48% in the average time necessary for the detection of simulated bleeding, as well as statistically significant improvements in total task time.
Data Science and AI-Based Optimization in Scientific Programming

is special issue gives the opportunity to know recent advances in the application of intelligent techniques to data-based optimization problems in scientific programming. Artificial intelligence is today supported for different powerful data science and optimization techniques. For instance, data science commonly relies on AI algorithms to efficiently solve classification, regression, and clustering problems. is fact is particularly interesting nowadays, when big data area gathers strength supplying huge amounts of data from many heterogeneous sources. On the other hand, complex optimization problems that cannot be tackled via traditional mathematical programming techniques are commonly solved with AI-based optimization approaches such as the metaheuristics. ese approaches provide optimal solutions avoiding consumption of many computational resources. Data science and AI-based optimization have also largely been used to solve problems related to scientific programming. Various examples are reported by the literature on task assignment in distributed/parallel systems, knowledge discovery, large-scale data mining, high-performance computing, big data, distributed/parallel search, text analysis/process/classification, and optimization for manufacturing, scheduling, and civil and financial engineering , among others. In this sense, this area provides a wide set of research lines and applications that deserves to be explored. is special issue presents nine original, high-quality articles, clearly focused on theoretical and practical aspects of the interaction between artificial intelligence and data science in scientific programming, including cutting-edge topics about optimization, machine learning, recommender systems, metaheuristics, classification, recognition, and real-world application cases. e first article in this special issue is entitled Opti-mizing the Borrowing Limit and Interest Rate in P2P System: From Borrowers' Perspective by Z. Li et al. is article shows a good example of how artificial intelligence algorithms can optimize some parameters involved in problems characterized by data flows. e work elaborates on the advantages of using a three-layer BP neural network algorithm to predict the borrowing limit and interest rate when individuals take advantage of P2P online service to borrow money. is approach provides a novel focus from borrowers to predict and optimize the borrowing limit and interest rate given the limited information. In addition, both parameters are optimized by means of an algorithmic proposal where the neural network and a genetic algorithm work together to solve both single-target and double-target programming optimization problems. e proposal is tested on real-world data to check its goodness as a high-accuracy prediction method. e second article is entitled Leveraging
Quantum-error-correction-assisted quantum metrology without entanglement

In this article we study the role that quantum resources play in quantum-error-correction-assisted quantum metrology (QECQM) schemes. We show that there exist classes of such problems where entanglement is not necessary to retrieve noise-free evolution and Heisenberg scaling in the long time limit. Over short timescales, noise-free evolution is also possible even without any form of quantum correlations. In particular, for qubit probes, we show that whenever noise-free quantum metrology is possible via QECQM, entanglement-free schemes over long timescales and correlation-free schemes over short timescales are always possible.
Listening to Improvisation

Is there something peculiar in our appreciation of improvised music? How does knowing that the music we are listening to is improvised affect our experience? As a first step in answering these questions, I have conducted an experiment in which an audio recording of the very same piece of music – a saxophone/clarinet freely improvised duet – was presented to 16 listeners, either as an improvisation ("IMPRO" condition), or as the live performance of a composition for saxophone and clarinet ("COMPO" condition). Listeners were encouraged both to reflect on their listening experience and to describe in their own words the music they heard. First, evaluative judgments were strongly different in the two listening conditions: listeners approached the piece with specific sets of values in mind, by relying on different features or different kinds of criteria (aesthetic ones in the COMPO condition vs ethical ones in the IMPRO condition) to ground their appreciative judgments. Second, and maybe more importantly, listening experiences were quite different in the two conditions: in the COMPO condition, the piece was more commonly experienced as a sonic product, with listeners paying great attention to the various acoustical effects achieved by the musicians and to the overall structure (or lack thereof); in the IMPRO condition, the music was often described as a kind of communicational or relational process, with descriptions that largely interweaved music-specific terms and more broadly social terms. Overall, this experiment shows that our listening experience can be dramatically affected by modal considerations, i.e., by how we think the music was produced. More specifically, it sheds some light on what constitutes the core of the aesthetic experience of improvisation by exhibiting what is centrally at play (and what is not) when we listen to collectively improvised music.
TEACHING READING THROUGH KNOW-WANT-LEARNED (KWL) STRATEGY: The effects and benefits

This study aimed to chart out the effects of teaching reading using Know-Want-Learned (KWL) strategy of Blachowicz and Ogle. This study employed quantitative method by having one pre-test and posttest research design, conducted at SMPN 2 Kejuruan Muda, Aceh Tamiang District, with a total sample of 26 students. The test was analyzed by using t-test. The study revealed that there was an improvement from the pre-test to the posttest and that the teacher had implemented KWL strategy accordingly so that the students improved their reading comprehension skill. In addition, KWL is one of the basic strategies developed to know the extent of learners’ ability and the way they commit it on their own. Thus, the strategy itself has an effect to trigger the students to be autonomous learners. However, it is suggested that the teachers modify some steps in the KWL procedures to eventually motivate the students in learning reading.
ON THE ANALYTICAL SOLUTION OF A VOLTERRA INTEGRAL EQUATION FOR INVESTIGATION OF FRACTAL PROCESSES

Context. We consider a Volterra integral equation of the first kind which may be applied to the data filtration and forecast of fractal random processes, for example, in information-telecommunication systems and in control of complex technological processes.Objective. The aim of the work is to obtain an exact analytical solution to a Volterra integral equation of the first kind. The kernel of the corresponding integral equation is the correlation function of a fractal random process with a power-law structure function.Method. The Volterra integral equation of the first kind is solved with the help of the standard Laplace transform method. The inverse Laplace transform leads to the calculation of the line integral of the function of complex variable. This integral is calculatedas a sum of a residue part and integrals over the banks of cut. The corresponding integrals are obtained on the basis of the known expansions of special functions.Results. We obtained an exact analytical solution of the Volterra integral equation the kernel of which is the correlation function of a fractal random process. The paper is based on a model where the structure function of the corresponding process is a power-law function. It is shown that the part of the solution that does not contain delta-function is convergent at any point if the Hurst exponent is larger than 0.5, i.e. if the process has fractal properties. It is shown that the obtained solution is a real-valued function. Theobtained solution is verified numerically; it is also shown that our solution gives the correct asymptotic behavior. Although the solution contains an exponentially growing function of time, at large times the integral of the obtained solution asymptotically behaves as a power-law function. Conclusions. It is important to stress that we obtained an exact solution of the Volterra integral equation under consideration rather than an approximate one. The obtained solution may be applied to the data filtration and forecast of fractal random processes. As is known, fractal processes take place in a huge variety of different systems, so the results of this paper may have a wide field of application.
SPEECH ACTS ANALYSIS OF DONALD TRUMP’S SPEECH

This research deals with the types of illocutionary acts in Donald Trump’s Inaugural Speech. The research concerns with illocutionary act produced by Donald Trumps as a President of American. The aim of this research was to analyze the types of illocutionary speech act which was dominantly used in that speech. This research applied descriptive qualitative method and speech act theory by Yule. There were 63 utterances and the percentage of utterances were Representative 46%, Expressive 11%, Directive 16%, Commissive 12,7%, and Declarative 14,3%. The result showed that Donald Trump assert to the audience about the nation will be.It is found that Trump’s speech acts in his speech are intended as statement of fact and assertion. Disscussion of hopes implied in Trump’s speech acts. As seen on the table above, it can be seen that Trump hoped that his audiences would be persuaded to act
Graph Cut-Based Human Body Segmentation in Color Images Using Skeleton Information from the Depth Sensor

Segmentation of human bodies in images is useful for a variety of applications, including background substitution, human activity recognition, security, and video surveillance applications. However, human body segmentation has been a challenging problem, due to the complicated shape and motion of a non-rigid human body. Meanwhile, depth sensors with advanced pattern recognition algorithms provide human body skeletons in real time with reasonable accuracy. In this study, we propose an algorithm that projects the human body skeleton from a depth image to a color image, where the human body region is segmented in the color image by using the projected skeleton as a segmentation cue. Experimental results using the Kinect sensor demonstrate that the proposed method provides high quality segmentation results and outperforms the conventional methods.
Architecture Design of Datacenter for Cloud English Education Platform

Considering the importance of datacenter to cloud English edu-cation platform (CCEP), this paper designs a novel datacenter for the CEEP which realizes resource pooling on the storage, network, computation and other infrastructures through integration of virtualization technology. Meanwhile, the intelligent, automatic and centralized service orchestration and scheduling man-agement were implemented via the global CEEP system. On this basis, a com-plete datacenter solution was developed for the CEEP. Through performance analysis, it is concluded that the solution can significantly improve the effec-tiveness, resource utilization, service deployment efficiency as well as operation and maintenance management.
Optimization of Intelligent Data Mining Technology in Big Data Environment

At present, storage technology cannot save data completely. Therefore, in such a big data environment, data mining technology needs to be optimized for intelligent data. Firstly, in the face of massive intelligent data, the potential relationship between data items in the database is firstly described by association rules. The data items are measured by support degree and confidence level, and the data set with minimum support is found. At the same time, strong association rules are obtained according to the given confidence level of users. Secondly, in order to effectively improve the scanning speed of data items, an optimized association data mining technology based on hash technology and optimized transaction compression technology is proposed. A hash function is used to count the item set in the set of waiting options, and the count is less than its support, then the pruning is done, and then the object compression technique is used to delete the item and the transaction which is unrelated to the item set, so as to improve the processing efficiency of the association rules. Experiments show that the optimized data mining technology can significantly improve the efficiency of obtaining valuable intelligent data.
Full-field Strain Measurements for Microstructurally Small Fatigue Crack Propagation Using Digital Image Correlation Method

A novel measurement approach is used to reveal the cumulative deformation field at a sub-grain level and to study the influence of microstructure on the growth of microstructurally small fatigue cracks. The proposed strain field analysis methodology is based on the use of a unique pattering technique with a characteristic speckle size of approximately 10 µm. The developed methodology is applied to study the small fatigue crack behavior in body centered cubic (bcc) Fe-Cr ferritic stainless steel with a relatively large grain size allowing a high spatial measurement accuracy at the sub-grain level. This methodology allows the measurement of small fatigue crack growth retardation events and associated intermittent shear strain localization zones ahead of the crack tip. In addition, this can be correlated with the grain orientation and size. Thus, the developed methodology can provide a deeper fundamental understanding of the small fatigue crack growth behavior, required for the development of robust theoretical models for the small fatigue crack propagation in polycrystalline materials.
Legendre Wavelets with Scaling in Time-delay Systems

This research presents the integration, product, delay and inverse time operational matrices of Legendre wavelets with an arbitrary scaling parameter and illustrates how to design this parameter in order to improve their accuracy and capability in handling optimal control and analysis of time-delay systems. Using the presented Legendre wavelets, the piecewise delay operational matrix is derived to develop the applicability of Legendre wavelets in systems with piecewise constant time-delays or time-varying delays. With the aid of these matrices, the new Legendre wavelets method is applied on linear time-delay systems. The reliability and efficiency of the method are demonstrated by some numerical experiments.
Measuring the quality of e-commerce websites using analytical hierarchy process

Improving website quality for e-commerce website is indispensable since it affects customer satisfaction. There are several aspects of website quality that should be considered. Unfortunately, what criteria that should be prioritized is still under research. This research aims to identify the priority of website quality criteria and incorporate these criteria to measure the quality of ten e-commerce websites in Indonesia using Analytical Hierarchical Process (AHP). This study interviews two experts to assign priority for each criterion. In the end, this study has found that availability is the uttermost aspect to consider. Furthermore, this study also found that OLX.com is the best Customer-to-Customer (C2C) e-commerce in Indonesia in terms of website quality. This research is useful for any e-commerce technical developer to improve his/her website in twofold: 1) criteria priority to improve the quality of C2C e-commerce websites and 2) website quality ranking of C2C e-commerce.
Recursive linear optimal filter for Markovian jump linear systems with multi-step correlated noises and multiplicative random parameters

This paper presents the state estimation problem for discrete-time Markovian jump linear systems with multi-step correlated additive noises and multiplicative random parameters (termed as MCNMP). A recursive linear optimal filter for the considered MCNMP (which is abbreviated as RLMMF) is derived based on state augmentation between the original state and mode uncertainty, with the help of estimating the multi-step correlated additive noises online simultaneously. A maneuvering target tracking example under one-step and two-step correlated additive noises scenarios with different cases (i.e. Gaussian/Gaussian mixture distribution and no multiplicative noises) is simulated to validate the designed filter.
A New Framework for Social Media Content Mining and Knowledge Discovery

Social media has come up with many popular websites such as Facebook, Twitter, Instagram, LinkedIn etc for the use of the generation to share each other's views.Social Media Content Mining is the process of extracting useful information i.e.Text, Video, Audio, Images from the Web by applying Data Mining techniques such as classification, clustering, regression, Outlier Detection and association rules etc can be applied to discover knowledge from web data.This paper presents some existing social media content mining techniques and proposed a new approach for efficient Data Mining frame work to extract useful knowledge from the web data.