Reviewerly/exHarmony huggingface dataset:
Enriching Educational Content through Subgraph Semantic Annotation

Semantic web capabilities can improve the potential of eLearning systems and applications. Semantic annotation as a main step of semantic web techniques can be used in creating metadata for eLearning resources to foster adaptive eLearning systems. This paper proposes a semantic annotation approach to enrich educational content with some related concepts in the DBpedia repository. In this approach, unknown concepts in educational content are extracted through applying natural language processing, statistical analysis, and domainspecific ontology. Each extracted concept is linked to the DBpedia relevant concept and for further explanation, the DBpedia subgraph of related concepts is attached. Navigating the DBpedia graph and applying a context matching technique, we can find some relevant concepts in the offline phase. The proposed approach is applied on the set of eLearningdomain documents extracted from the SlideShare repository. Generated results show the acceptable system precision and recall. Our approach is generic; it can be used in any educational content related to any domain.
The Connotative Meaning of Expression Found in Adele’s Song Lyrics 21 “Japanese Edition” Album

This study is entitled "The Connotative Meaning of Expression Found in Adele's Song Lyrics 21 "Japanese Edition" Album". It focuses on describing the types of connotative meaning and connotative expression found in the song lyrics. This study mainly uses the theory proposed by Leech and Cruse. Methods applied in this study were qualitative methods and descriptive analysis. The data here was taken from the internet through the Genius website. The data was collected by downloading the song lyrics and then printing them out. Then, closed reading was implied to identify the expression containing connotation. Furthermore, the data were analyzed based on a semantic theory proposed by Leech (1981) and Cruse (1997). Four songs were analyzed based on the theory proposed by selecting the expression which contains connotative meaning. The result of this study shows that the expression found in Adele's song lyrics contains three types of connotative meaning, such as Positive, Negative, and Neutral. However, the negative connotations are mostly used in the expression. The use of the expression in Adele's song lyrics is to express her feelings towards someone in her past relationship.
Türkçe’de Kanıtsallığa Kullanıma Dayalı Yapı Gramer Yaklaşımı: Kanıtsallık Yapısı

Coupled with corpora, usage-based construction grammar aims to provide cognitive plausibility for linguistic phenomena. In this vein, this paper combines construction grammar and usage-based approaches to analyze evidentiality in Turkish. While Turkish has been analyzed from a usage-based perspective, evidentiality has not been taken up in a usage-based constructionist approach. By using corpora, association measures, and construction as a notion and a framework, this paper defines the Unevidentiality Construction in a taxonomic space. First, it outlines its semantic properties and then it uses association measures such as faith, delta (∆) p, and ITECX to determine its usage pattern and statistical biases based on corpora. The paper demonstrates a superordinate and lower-level, item-specific instantiations of the construction. The results from association measures and the family of unevidentiality constructions can serve for future linguistic endeavors.
URAMDS: Utterances Relation Aware Model for Dialogue Summarization

Dialogue summarization, which aims to make summarization on the given dialogue automatically, is a challenging task in natural language processing. Compared to text summarization, it needs to consider the interaction between speakers and the colloquialization of expression to generate better summarization. Many existing methods treat dialogue as a plain sequence of text and simply ignore the structural information, which could be important for summarization generation of the dialogue. In this paper, to alleviate the above challenges, we propose a deep-learning-based method that combines a serialized model and a graph model. More specifically, we utilize a Sequence to Sequence (Seq2Seq) as the backbone to cope with the informal text and a Graph Neural Network (GNN) to take advantage of the structural information. The two models are combined by sharing the key information with each other. Besides, special attention is drawn to the specific speaker in our proposed method. Extensive experiments have shown the effectiveness of our proposed method.
Digital Teaching Management System Based on Deep Learning of Internet of Things

In order to solve a series of problems similar to the repetitive construction of resources and low degree of resource sharing in cruciform teaching, this paper studies the digital disarming management. Based on the in-depth analysis of the actual needs of the digital teaching resource service system and the key problems in the system, taking the resources and business process as the starting point, based on Java Web related technology, combined with the related processes of resources and business processing, this paper designs the digital teaching resource system of colleges and universities. The system has the functions of resource digitization, process management, and interaction with other platforms. The system database platform and operation platform are built; the development environment is configured; and the user login service function, data conversion service function, process management service function, and system management service function are completed. The function is tested by unit test, and the performance is tested and analyzed according to user requirements and design objectives. The system adopts Java web development technology, uses S2SH framework as the development basis, and takes Oracle database as the data storage platform through Tomcat6.0 web server for program publishing. Through system testing and analysis, the software can operate normally and achieve the expected functions and can be put into use.
Quality assessment of the VR-UC virtual reality educational application based on performance and functionality improvements

The development of interactive educational applications that include 3D environments and spaces for immersive interaction, which leads to many considerations in development such as good performance, smoothing in rendering, immersion, experience provided to the user, among others, being the experience generated in the student the main one since it contains most of the considerations and factors. Taking into consideration the previous mentioned, the present research aims to measure a group of Engineering students, the quality of the improved VR-UC application in relation to the effectiveness, efficiency, satisfaction, usefulness, trust, pleasure, and comfort, when analyzing the results of the Likert scale, which they have been positive in the scales with the highest valuation. It is concluded that an educational virtual reality application must consider tools and techniques that ensure good performance and functionality to give users a good experience and facilitate their learning.
Impact of Electronic Learning in Rural Universities: A Case of WSU Ibika Campus

Electronic learning has become the backbone of institutions of higher learning even those located in rural areas. This research evaluated the impact of electronic learning in rural universities in teaching and learning. The research focused on the usage of e-learning tools, factors that hinder successful usage, and the effects of e-learning in a rural environment. Walter Sisulu University (WSU) was used as the case study in this research. WSU Ibika campus being a campus located in the rural and remote area of Butterworth faces challenges such as shortage of water, poor ICT infrastructure, and poor service delivery. The research adopted a quantitative approach to conduct this research study. A closed-ended questionnaire instrument was used to collect the data. The results showed that the majority of students used learning management systems to conduct assessments while few students used them to access study material and conduct group discussions.
Utilizing Improved Analytic Hierarchy Process for Multiple Evaluation of Practical Curriculum

Practical curriculum is becoming more and more popular in application-oriented university. By analyzing the characteristics of practical curriculum, this paper constructs a multivariate evaluation model from four dimensions such as class attendance, process performance, practical achievement, final examination and a two-layer-hierarchical structure. Then taking the practical course of E-commerce Cognitive Experiment as an example, it makes an empirical analysis based on cluster analysis and improved analytic hierarchy process. And it proves the model has strong operability and feasibility, can become a tool for curriculum teaching quality evaluation, and provide some theoretical support for undergraduate curriculum teaching evaluation and quality improvement as well.
Encompassing Chaos in Brain-inspired Neural Network Models for Substance Identification and Breast Cancer Detection

The main purpose in this work is to explore the fact that chaos, as a biological characteristic in the brain, should be used in an Artificial Neural Network (ANN) system.In fact, as long as chaos is present in brain functionalities, its properties need empirical investigations to show their potential to enhance accuracies in artificial neural network models.In this paper, we present brain-inspired neural network models applied as pattern recognition techniques first as an intelligent data processing module for an optoelectronic multi-wavelength biosensor, and second for breast cancer identification.To this purpose, the simultaneous use of three different neural network behaviors in the present work allows a performance differentiation between the pioneer classifier such as the multilayer perceptron employing the Resilient back Propagation (RProp) algorithm as a learning rule, a heteroassociative Bidirectional Associative Memory (BAM), and a Chaotic-BAM (CBAM).It is to be noted that this would be in two different multidimensional space problems.The later model is experimented on a set of different chaotic output maps before converging to the ANN model that remarkably leads to a perfect recognition for both reallife domains.Empirical exploration of chaotic properties on the memory-based models and their performances shows the ability of a specific modelisation of the whole system that totally satisfies the exigencies of a perfect pattern recognition performance.Accordingly, the experimental results revealed that, beyond chaos' biological plausibility, the perfect accuracy obtained stems from the potential of chaos in the model: (1) the model offers the ability to learn categories by developing prototype representations from exposition to a limited set of exemplars because of its interesting capacity of generalization, and (2) it can generate perfect outputs from incomplete and noisy data since chaos makes the ANN system capable of being resilient to noise.
Investigation of type 1 and type 2 fuzzy logic controllers performance: application of speed control of BLDC motor

In this paper, a simulation study enhanced to model that the speed control of brushless direct current (BLDC) motors used in electric vehicles with intelligent control methods. The simulation study was prepared in Matlab/Simulink environment. The first control method is Type-1 fuzzy logic control (T1FLC), and the second control method is the Intermittent Type-2 fuzzy logic control (IT2FLC) model. Membership functions for different membership numbers have been created for both types of FLC models. These are 3×3, 5×5, 7×7. Control methods are prepared in Matlab/M-file environment. The model is defined as the input variable of the error, which is the difference between the reference speed and the motor speed, and the output variable of the Pulse Width Modulation (PWM) signal applied to the motor. The simulation study maintains the speed of the BLDC motor up to the reference speed with T1FLC and IT2FLC controllers, depending on the reference speed and applied load values. Depending on the number of different memberships, the effects of controller performances on the control of motor speed have been observed. The graphs and findings of the experiment are shown in the results and discussion section.
Facial Attribute Analysis for Emotion Recognition using Deep Learning

Computerized test highlights are involved every day in an assortment of ways of understanding biomedical examination, picture control, between PC connections, electronic gadgets and other business exercises. The principal motivation behind the venture is to work on the workplace. Orientation uniformity, skin tone, and feeling are assumed a key part in deciding the course of a PC. This task requires the development of a Convolutional Neural Network (CNN) for age and orientation. The structure is intended to highlight a pay of around 8 years and 2 names of orientation. As well as counting the years, looks a significant part in PC vision. It is marked as 6 passionate and 6 racial characteristics. Non-verbal relational abilities, like looks, looks, and signals, are utilized in numerous human PC capacities. Utilizing the Viola-Jones calculation, pictures are eliminated from CNN. With the right utilization of the client, the normal outcomes will be apparent. Knowing individuals' feelings, the directing framework can be further developed. Assuming that the underlying face is impartial or quiet, present day strategies work best as a worldwide picture, and there is an absence old enough forecast when the facial highlights change. The information is taken from IMDb-WIKI by age and orientation, while Fer2013 and Kaggle depend on feeling and racial character. The undertaking has two models, one for age and one for foreseeing orientation correspondence through the Resnet vault, and the other for passionate mindfulness. Another closure such as old enough, feeling, skin tone and orientation, the presence of the world, spoiling and arranging resources are offered. Specifically, the huge changes in reality that are not sifted by the picture handling calculation that plans and cycles those pictures are considered before they are communicated on CNN.
User Perception of Smooth Pursuit Target Speed

Gaze-aware interfaces should work on all display sizes. This paper researches whether angular velocity or tangential speed should be kept when scaling a gaze-aware interface based on circular smooth pursuits to another display size. We also address the question of which target speed and which trajectory size feels most comfortable for the users. We present the results of a user study where the participants were asked how they perceived the speed and the radius of a circular moving smooth pursuit target. The data show that the users’ judgment of the optimal speed corresponds with an optimal detection rate. The results also enable us to give an optimal value pair for target speed and trajectory radius. Additionally, we give a functional relation on how to adapt the target speed when scaling the geometry to keep optimal detection rate and user experience.
The Development of the Information and Educational Environment of the University through the Improvement of a Multifunctional Technological Platform

The article shows how the system of vocational education implements the tendency of increasing the role of innovative information and communication technologies to improve the quality of training specialists. The organization of the activities of the «Moscow Financial and Law University MFUA» is described through the use of the electronic information and educational environment (EIEE) of the university in the educational process. The main purpose of EIEE is the acquisition of knowledge, skills, abilities and competencies by students in accordance with federal state educational standards, regardless of the time and location of students. Modules, components and elements that consist the technological platform of EIEE are presented in this article such as: the service of the ability to create individual courses and modules for the use in the formation of individual educational trajectories of the students; the service of accounting of requests of representatives of business communities; the service of the electronic admission committee with the possibility of remote submission of documents and exams; the service of increase in the objectivity of the assessment; the attendance tracking system.It is argued that the development of an effective model of a digital university based on EIEE allows maintaining a high level of training for the digital economy, creating comfortable conditions for training and successful realization of the educational potential of the future specialist.
Chinese Named Entity Recognition Fusing Lexical and Syntactic Information

Chinese named entity recognition is an important research field in natural language processing and is a significant factor in multitasking. However, the accuracy of Chinese named entity recognition is impaired by the lack of entity boundary information and the neglect of potential syntactic information. In this context. In the present study, a Chinese named entity recognition fusing lexical and syntactic information was proposed, and the steps are as follows. Firstly, input text is mapped to character vectors, and external lexical information is introduced with an improved word set matching method and integrated into the input representation of each character. Secondly, BiLSTM is used to obtain the context vector based on the input representation of the character. Thirdly, the syntactic vector is constructed by means of the key value memory network, and the feature vector is obtained by the weighted fusion of context vector and syntactic vector by the gating mechanism. Finally, the feature vector is input into CRF to realize Chinese named entity recognition. The experimental results reveal that the proposed method had better performance on Resume, Weibo and MSRA, and was superior to the current mainstream Chinese named entity recognition methods.
Optimal Resource Provisioning for Data-intensive Microservices

With the continuous progress of cloud computing, many microservices and complex multi-component applications arise for which resource planning is a great challenge. For example, when it comes to data-intensive cloud-native applications, the tenant might be eager to provision cloud resources in an economical manner while ensuring that the application performance meets the requirements in terms of data throughput. However, due to the complexity of the interplay between the building blocks, adequately setting resource limits of the components separately for various data rates is nearly impossible. In this paper, we propose a comprehensive approach that consists of measuring the resource footprint and data throughput performance of such a microservices-based application, analyzing the measurement results by data mining techniques, and finally formulating an optimization problem that aims to minimize the allocated resources given the performance constraints. We illustrate the benefits of the proposed approach on Cortex, an extension to Prometheus for storing monitored metrics data. The data-intensive nature of this illustrative example stems from real-time monitoring of metrics exposed by a multitude of applications running in a data center and the continuous analysis performed on the collected data that can be fetched from Cortex. We present Cortex’s performance vs resource footprint trade-off, and then we build regression models to predict the microservices’ resource consumption and draw a mathematical programming formulation to optimize the most important configuration parameters. Our most important finding is the linear relationship between resource consumption and application performance, which allows for applying linear regression and linear programming models. After the optimization, we compare our results to Cortex’s recommendation, leading to a CPU reservation reduced by 50-80%.
Atmospheric Correction for Polarimetric Images Based on Spectral Segregation

In hazy weather, light's penetration power is wavelength related, the longer wavelength, the less attenuation. Although traditional polarimetric image-dehazing algorithms have demonstrated their ability in enhancing grayscale images, but their ignorance of the spectral difference will lead to serious color distortion when utilizing these algorithms for color images. To conquer that problem, we propose a new method base on spectral segregation. 15 spectral bands are selected and dehazed with the polarimetric dehazing algorithm separately to obtain the best dehazing effects. The blue, green and red channels of the dehazed image, which are acquired through image fusion of the spectral bands, are adjusted with different coefficients to correct the color distortion. 10 infrared bands are added to the short-wavelength channels to enhance the details of the objects especially the trees. Experiment and data analysis demonstrate the effectiveness of our method in increasing visibility and preserving color information. The amount of color distortion can be reduced by 89.6% compared with the polarimetric image-dehazing algorithm without spectral segregation.
A Graph Based Approach Towards Exploiting Reviews for Recommendation

Textual reviews, pervasive on many e-commerce websites, contain a lot of information. Many neural network models have been proposed to use the information of reviews to improve the performance of recommender systems. However, existing models usually use convolutional neural networks to learn the features of the reviews, often focus on the local interactions of words and lack the ability to capture long-distance and non-consecutive word interactions. Meanwhile, their ability should be strengthened on modelling the high-level interactions between users and items. Therefore, we propose a multi-view Graph based Approach towards exploiting Reviews for recommendation (GAR). It integrates the information of review content and user-item graph. In review view, we build an individual word co-occurrence graph for each review and use gated graph convolutional network to learn the features of reviews. In graph view, we use graph attention network to model high-order multi-aspect relations in the user-item graph. Both views use a graph based method. The representation of users and items learned from the two views are integrated to predict the final rating. Experiments on the benchmark datasets show that GAR achieves significantly better rating prediction accuracy compared to the state-of-the-art methods.
Egyart_classify: an approach to classify outpainted Egyptian monuments images using GAN and ResNet

Since Egypt is the cradle of civilizations, it contains many monuments and historical places. Tourist guides need the help of machine learning techniques to aid foreigners in getting to know Egypt's great history. This paper introduces a system to recognize monuments and provides a detailed description. A significant challenge is tackled in this paper, which is recognizing cropped monuments. When a tourist captures a monument image in real life, it may be occluded by an object. The human brain fills in the blanks and completes the image automatically. Our main contribution is to use generative adversarial deep learning techniques (GAN) to outpaint the cropped image. The outpainted image is then fed into state of art classifier RESNET to classify the monument and show a detailed explanation of its remarkable history. We trained the system with a dataset collected by the team of authors. After 1000 epochs of training, the Adversarial Loss of training GAN is 0.28344184 and the validation loss is 0.30181705. The performance measures of the RESNET classifier in testing are 97.0% for accuracy, 97.1% for precision, 97.0% for recall, and 97.0% for F1-measure.