Reviewerly/exHarmony huggingface dataset:
Intention to Implement IT Instruction for Teacher Learning

Humanity's competitive advantages in the society are based on the intelligence and knowledge developed.One important way for human development is education.Under the global trend of decreasing birth rate, how can talents be developed to create advantages?It is obvious that talents are important resources in knowledge creation, as well as the foundation of a country.This study adopted the Decomposed Theory of Planned Behavior with the attitude aspect replaced by the teaching belief aspect, the perceived behavioral control aspect decomposed into perceived self-efficacy and condition of supportive resources, and the subjective norm aspect decomposed into teacher's superiors (the Department of Education), teacher's peers (colleagues in school), and teacher's teaching subjects (students and their parents).These three major aspects were used as the main points to design the questions for the interviews regarding teachers' intention to implement the idea of flipped classroom (Tucker, 2012).This study adopted the in-depth interview method and interviewed each subject individually in a face-to-face manner.A total of 5 teachers were selected for the interviews.In-depth interviews can be categorized into structured interviews, unstructured interviews, and semi-structured interviews depending on how structured they are.Due to the limitations of interview durations and the environment, the researcher wasn't able to take down all the contents on-site.Thus, with the approval of the subjects, all the interviews were recorded.When verifying the research findings, the subjects were asked to confirm the correctness of the content analysis results.In addition, some experts, scholars, and peers were invited to assist the analyses of the interview data to prevent biases caused by the researcher's subjective interpretations.This study showed the data analysis results to the subjects so that they could confirm if there was anything that didn't look right in data analyses and interpretations.If there was something that the subjects did not agree with, it would be reviewed for modification.The influence of teachers' teaching beliefs is on their intention to implement IT instruction.This study aimed to explore teachers' intention to replace the traditional teaching method with IT instruction based on the theories of behaviors.Moreover, the research findings showed that the influences of teachers' teaching belief, perceived behavioral control, and subjective norm on their intention to implement IT instruction were all significant.
Cloud-based learning service platform for multilingual smart class

The spread of cloud computing is accelerating universities to seek a better educational approach for capitalizing on its capabilities.With the practice of cloud computing in Saudi Arabia universities, instructors are yet unable to interact with the students face-to-face due to the gender separation practice.It becomes worse in a multilingual environment as the students are less fluent in the first language formally practiced in the university.With the current best practice, the students are able to utilize one app to translate the presentation slides and another app to translate the instructor's speech.The student prefers to comprehend the learning materials, although it delays the process of teaching.Therefore, there is a need to integrate current practices.A prototype of a smart class based on cloud computing is developed to ease the use of a mobile device.This project is evaluated by utilizing both direct and indirect assessment spanning across three academic semesters.
The willingness of teachers to use modern educational technologies in educational process

Among the most significant trends in the development of vocational education, introduction and use of educational technologies can be distinguished. Since the main goal of higher schools is to prepare a competent competitive specialist, they search for the most suitable methods that contribute to rapid achievement of results. The use of technologies by educational institutions is explained by their advantage over other methods of developing professional competence. Due to implementation of a certain algorithm of actions, they allow you to achieve guaranteed results in a timely manner. The purpose of the article is to form future teachers ' the level of competency development, reflecting students’ willingness to adapt, adjust and use educational technologies in professional teaching activities. To do this, we checked the level of motivation of future teachers, the level of knowledge of the theoretical foundations of educational technologies, their design and implementation in professional and pedagogical activities, and the level of formation of the ability to reflect. The development of competencies reflecting the student’s readiness to adapt, adjust and use educational technologies in professional pedagogical activity was carried out in a certain logical sequence, so that the student possessed versatile knowledge about educational technologies, was capable of their systematic creative application and his reflective activity was associated with self-actualization and striving for the implementation of constructive professional activities. The development process was carried out in the course of students solving research and design problems. Research activity was carried out by students in the study of the course “Pedagogical technologies”, fundamental for future teachers of vocational training. The implementation of the projects was carried out during the development of the course "Project Activities of a Professional Education Teacher".
Development of a Biomechatronic Device for Motion Analysis Through a RGB-D Camera

This work investigates the validity and reliability of a novel biomechatronic device providing an interactive environment in Augmented Reality (AR) for neuromotor rehabilitation. A RGB-depth camera and telemonitoring/remote signaling module are the main components of the device, together with a PC-based interface. The interactive environment, which implements some optimized algorithms of body motion capture and novel methodologies for human body motion analysis, enables neuromotor rehabilitation treatments that are adaptable to the performance and individual characteristics of the patient. The RGB-Depth camera module is implemented through a Microsoft Kinect, ORBBEC ZED2K devices; the telemonitoring module for teleassistance and therapy supervision is implemented as a cloud service.&#x0D; Within the module of body motion tracking, the abduction and adduction movements of the limbs of the full-body structure are tracked and the joints angles are measured in real-time; the most distinctive feature of the tracking module is the control of the trunk and shoulder posture during the exercises performed by the patient. Indeed, the device recognizes an incorrect position of the patient's body that could affect the objective of the exercise to be performed. The recognition of an incorrect exercise is associated to the generation of an alert both to the patient and the physician, in order to maximize the effectiveness of the treatment based on the user's potential and to increase the chances of a better biofeedback.&#x0D; The experimental tests, which have been carried out by reproducing several neuromotor exercises on the interactive environment, show that the feature recognition and extraction of the joints and segments of the musculo-skeletal structure of the patient's, and of wrong posture during exercises, can achieve good performance in the different experimental conditions. &#x0D; The developed device is a valid tool for patients affected by chronic disability, but it could be extended to neurodegenerative diseases in the early stages of the disease. Thanks to the enhanced interactivity in augmented reality, the patient can overcome some difficulties in interaction with the most common IT tools and technologies; at the meanwhile she/he can perform rehabilitation at home. The physician can also check in real time the results and customize the care pathway.&#x0D; The enhanced interactivity provided by the device during rehabilitation session increases both the motivation by the patient and the continuity of the care, as well as it supports low-cost remote assistance and telemedicine by optimizing therapy costs.&#x0D; The key points are:&#x0D; &#x0D; i) making rehabilitation motivating for the patient, becoming a "player";&#x0D; ii) optimize effectiveness and costs;&#x0D; &#x0D; iii) possibility of low-cost remote assistance and telemedicine.
Hybrid Recommendation System with Clustering and Classification Method Based on Case of Clinic XYZ

With the development of IT systems in many areas, all data that generated hold potential value that waiting to be unlock, one way is to do product recommendation system based on it.Clinic XYZ is one of the largest beauty aesthetic services in Indonesia.After implementing its ERP systems, Clinic XYZ wants to gain more benefit by doing cross sell of their product to existing customer.Product recommendations system is the solution that developed for it.Product recommendation system is an information-filtering system that handles overload of information and give a recommendation to user a recommendation-based preference, interest of user behavior that observed form those data.The common use algorithm for product recommendation system is collaborative filtering and done directly to whole data.This research applies 2 steps of algorithm, clustering, to separate the customer into different cluster based on the master data that available and classification to each cluster for product recommendation in the clinic to the existing customer.The result show that the 2 steps method generate better result.
A University Landscape for the Digital World

Abstract As the digital transformation clearly highlights the role of universities and institutes of higher education in shaping a higher education system that is more open and provides education to everyone who can benefit from it, this study seeks to analyze, in more detail, what developments are having an impact on higher education and develops future scenarios for education in 2030. The UK study Solving future skills challenges implies that the linear model of education–employment–career will no longer be sufficient in the future, requiring new combinations of skills, experience, and collaboration from educators and employers. This UK study serves as a starting point for the AHEAD trend analysis for a higher education landscape in 2030. Five premises ranging from “No naive innovation view” to “Realistic approach,” and “Diversity in higher education” provide the basis for a search for concepts for the higher education of the future.
Correlation Filters With Adaptive Multiple Contexts for Visual Tracking

The local contexts define the target and its surrounding background within a constrained region, and have been proved useful for visual tracking, but how to adaptively employ them for building robust models remains challenging. By using the spatial weight maps, the correlation filter (CF) methods with spatial regularization provide an alternative to exploit the local contexts for appearance modeling. However, they generally utilize naive spatial weight map functions, and fail to flexibly regulate the effects of the target and background on model learning, thereby restricting the tracking performance. In this paper, we address these issues by presenting an adaptive multiple contexts correlation filter (AMCCF) framework. In particular, a novel sigmoid spatial weight map is first proposed to control the impacts of local contexts for learning more effective CF models. Based on this, different levels of local contexts (multiple contexts) are further modeled by incorporating the spatial weight maps with different parameters into multiple CF models. To adaptively utilize the local contexts on the tracking stage, the minimal weighted confidence margin loss function with a weight prior constraint is adopted for jointly estimating the target position and adaptive fusion weights of response maps from different CF models. To validate the proposed method, extensive experiments are conducted on four tracking benchmarks. The results show that our AMCCF can adaptively leverage the local contexts for robust tracking, and performs favorably against the state-of-the-art trackers.
Novel and Optimal Luma Modification-Based Chroma Downsampling for Bayer Color Filter Array Images

The Bayer color filter array (CFA) pattern has been widely used in modern digital color cameras, and the captured image is called the Bayer CFA image I Bayer .Chroma downsampling is a necessary and important step in the coding system.In this paper, a novel and optimal luma modificationbased chroma downsampling (LMCD) method is proposed for I Bayer .For each converted 2 × 2 YUV block, the downsampled (U, V)-pair and the modified luma values by the proposed LMCD method can be optimally determined, and the optimal variant of our LMCD method associated with the two modified luma values has the best quality among all sixteen variants of LMCD.Based on the Kodak and IMAX datasets, on the High Efficiency Video Coding (HEVC) platform HM-16.18, the Versatile Video Coding (VVC) platform VTM-8.0, and the Joint Photographic Experts Group (JPEG) platform, the comprehensive experimental data showed the substantial quality and quality-bitrate tradeoff merits of the reconstructed images by our optimal LMCD method relative to the traditional and state-of-the-art chroma downsampling methods.
An Information Recognition System for Complex Images

An approach to objective assessment of ultrasound examination is presented. To this end, modern information technologies and a set of mathematical methods in the form of a package are proposed. In this paper, diagnosis is viewed as a three-step process, and closed sub-objects are investigated using complex images, which pertains to the earliest diagnostic stage. For this purpose, three new features related to the disclosure of a growth are included in the paper. A system that performs the detection of the growth and finds the coordinates, area, gravity center and color palette of the obtained image is developed. By means of the created software package, the image is cleared from noise, filtering operations are performed, boundaries are defined more clearly and recognition by the mathematical morphology method is completed using selected classifiers. The main purpose is to direct doctor's attention to the presence of the pre-indicator of a non-specific symptom and to control the future development of the growth. The accuracy of the system is confirmed by the detection and identification of closed growths in the images taken in an ultrasound examination of internal organs of the human body. The system's operability has been tested directly on the ultrasound images (138 cases investigated), with the result of 98.8% at the diagnostic stage, 92, 03% at the early diagnostic stage; 2 cases have been recorded at the earliest diagnostic stage in 2018 and the frequency of monitoring has been determined.
Allen’s Interval Algebra Makes the Difference

Allen’s Interval Algebra constitutes a framework for reasoning about temporal information in a qualitative manner. In particular, it uses intervals, i.e., pairs of endpoints, on the timeline to represent entities corresponding to actions, events, or tasks, and binary relations such as precedes and overlaps to encode the possible configurations between those entities. Allen’s calculus has found its way in many academic and industrial applications that involve, most commonly, planning and scheduling, temporal databases, and healthcare. In this paper, we present a novel encoding of Interval Algebra using answer-set programming (ASP) extended by difference constraints, i.e., the fragment abbreviated as ASP(DL), and demonstrate its performance via a preliminary experimental evaluation. Although our ASP encoding is presented in the case of Allen’s calculus for the sake of clarity, we suggest that analogous encodings can be devised for other point-based calculi, too.
Acoustic-Phonetic Approach for ASR of Less Resourced Languages Using Monolingual and Cross-Lingual Information.

The exploration of speech processing for endangered languages has substantially increased in the past epoch of time. In this paper, we present the acoustic-phonetic approach for automatic speech recognition (ASR) using monolingual and cross-lingual information with application to under-resourced Indian languages, Punjabi, Nepali and Hindi. The challenging task while developing the ASR was the collection of the acoustic corpus for under-resourced languages. We have described here, in brief, the strategies used for designing the corpus and also highlighted the issues pertaining while collecting data for these languages. The bootstrap GMM-UBM based approach is used, which integrates pronunciation lexicon, language model and acoustic-phonetic model. Mel Frequency Cepstral Coefficients were used for extracting the acoustic signal features for training in monolingual and cross-lingual settings. The experimental result shows the overall performance of ASR for cross-lingual and monolingual. The phone substitution plays a key role in the cross-lingual as well as monolingual recognition. The result obtained by cross-lingual recognition compared with other baseline system and it has been found that the performance of the recognition system is based on phonemic units . The recognition rate of cross-lingual generally declines as compared with the monolingual.
Research on Video Recommendation Algorithm Based on Bullet Screen

With the rapid development of Internet industry in the era of Web 2.0, the creation and sharing of content becomes more and more easy, and people's access to information changes accordingly. For information consumers, the exponential growth of information reduces the efficiency of information utilization, and users are eager to obtain more valuable information; for information producers, it is also hoped that there is a way to make information be more concerned by potential users. In order to solve this problem, personalized recommendation system is widely used in all aspects. The emergence of video recommendation system enables users to upgrade from active search to automatic recommendation in the way of obtaining videos, which reduces the time for users to find interested videos, makes video browsing more efficient, and greatly improves the user experience of video websites. In this paper, a video recommendation algorithm based on bullet curtain is proposed to improve the existing recommendation algorithm, and improve the relevance of recommendation results with video and users.
Rapid super resolution for infrared imagery

Infrared (IR) imagery is used in agriculture for irrigation monitoring and early detection of disease in plants. The common IR cameras in this field typically have low resolution. This work offers a method to obtain the super-resolution of IR images from low-power devices to enhance plant traits. The method is based on deep learning (DL). Most calculations are done in the low-resolution domain. The results of each layer are aggregated together to allow a better flow of information through the network. This work shows that good results can be achieved using depthwise separable convolution with roughly 300 K multiply-accumulate computations (MACs), while state-of-the-art convolutional neural network-based super-resolution algorithms are performed with around 1500 K MACs. MTF analysis of the proposed method shows a real ×4 improvement in the spatial resolution of the system, out-preforming the diffraction limit. The method is demonstrated on real agricultural images.
Movie Classification Based on Genre Using Map Reduce

This project elaborates on the methodologies adopted for the classification of movies based on their genre using MapReduce. To do this public database Movie Analysis has been used. The details of characteristic features of around thousand (1000) movies were used. The features such as animation oriented, comedy, melodrama, suspense thriller, romance etc numbering around twenty were elicited. The collected features were transformed into. csv format to blocks in HDFS and loaded to MapReduce. The outcome of this work was found to be excellent in the sense that accurate classification of movies into the appropriate category befitting the genre of the movie was rendered.
Testing multiple polynomial models for eye-tracker calibration

The straightforward approach to eye-tracker calibration considers that the calibration data do not have erroneous associations, and the calibration function is defined. The violation of the non-erroneous assumption could cause an arbitrarily large bias. The MMransac algorithm proposed in this paper is a modified version of the Random Sample Consensus. that achieves robust calibrations. On the other hand, polynomials in two variables (i.e., with terms in the form κxayb) are commonly used to map eye-tracker measurements to points on the screen. High-degree polynomials tend to be more accurate; however, as the order is increased, the function becomes more complex and less smooth, which could cause over-fitting. In this sense, this paper proposes an algorithmic approach that enables model selection criteria even in the presence of outliers. This approach was tested using different model selection criteria. Results show that more accurate calibrations are obtained with the combined robust fitting and model selection approach using the Akaike information criterion (AIC) and the Kullback information criterion (KIC).
Storyboarding in Medical Animation

Storyboards are a series of thumbnail images that act as a planning document for your overall animation project. Each thumbnail image is part of what is known as a shot, and shots are strung together to create scenes, which in turn create a whole animation. Medical animations, compared to film or animated movies, have rigid and specific requirements; however, by utilizing storyboards as a planning document we can infuse our medical animations with emotion, life, and beauty. This chapter will review common storyboarding fundamentals, like camera moves and editing techniques, and how they may be applied to the medical world. Camera moves are important to string shots together and can help add emphasis to specific sections. Zooms and pans are used extensively within medical animation and it is important to note which circumstance might call for either one. Zooms are great for moving to a drastically different microscopic magnification. Pans create visual interest for a stepwise reaction. Techniques such as lighting, depth, and insets and labels create a more balanced composition and save time in the production phase. Storyboards are ultimately about problem solving early to save time and money during the production phase. [M1].
Local search algorithms based on benchmark test functions problem

&lt;p&gt;Optimization process is normally implemented to solve several objectives in the form of single or multi-objectives modes. Some traditional optimization techniques are computationally burdensome which required exhaustive computational times. Thus, many studies have invented new optimization techniques to address the issues. To realize the effectiveness of the proposed techniques, implementation on several benchmark functions is crucial. In solving benchmark test functions, local search algorithms have been rigorously examined and employed to diverse tasks. This paper highlights different algorithms implemented to solve several problems. The capacity of local search algorithms in the resolution of engineering optimization problem including benchmark test functions is reviewed. The use of local search algorithms, mainly Simulated Annealing (SA) and Great Deluge (GD) according to solve different problems is presented. Improvements and hybridization of the local search and global search algorithms are also reviewed in this paper. Consequently, benchmark test functions are proposed to those involved in local search algorithm.&lt;/p&gt;