Reviewerly/exHarmony huggingface dataset:
Application of Dual-Channel Convolutional Neural Network Algorithm in Semantic Feature Analysis of English Text Big Data

The current Internet data explosion is expecting an ever-higher demand for text emotion analysis that greatly facilitates public opinion analysis and trend prediction, among others. Therefore, this paper proposes to use a dual-channel convolutional neural network (DCNN) algorithm to analyze the semantic features of English text big data. Following the analysis of the effect of CNN, artificial neural network (ANN), and recurrent neural network (RNN) on English text data analysis, the more effective long short-term memory (LSTM) and the gated recurrent unit (GRU) neural network (NN) are introduced, and each network is combined with the dual-channel CNN, respectively, and comprehensively analyzed under comparative experiments. Second, the semantic features of English text big data are analyzed through the improved SO-pointwise mutual information (SO-PMI) algorithm. Finally, the ensemble dual-channel CNN model is established. Under the comparative experiment, GRU NN has a better feature detection effect than LSTM NN, but the performance increase from dual-channel CNN to GRU NN + dual-channel CNN is not obvious. Under the comparative analysis of GRU NN + dual-channel CNN model and LSTM NN + dual-channel CNN model, GRU NN + dual-channel CNN model ensures the high accuracy of semantic feature analysis and improves the analysis speed of the model. Further, after the attention mechanism is added to the GRU NN + dual-channel CNN model, the accuracy of semantic feature analysis of the model is improved by nearly 1.3%. Therefore, the ensemble model of GRU NN + dual-channel CNN + attention mechanism is more suitable for semantic feature analysis of English text big data. The results will help the e-commerce platform to analyze the evaluation language and semantic features for the current network English short texts.
ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations

Understanding how events are semantically related to each other is the essence of reading comprehension.Recent event-centric reading comprehension datasets focus mostly on event arguments or temporal relations.While these tasks partially evaluate machines' ability of narrative understanding, human-like reading comprehension requires the capability to process event-based information beyond arguments and temporal reasoning.For example, to understand causality between events, we need to infer motivation or purpose; to establish event hierarchy, we need to understand the composition of events.To facilitate these tasks, we introduce ESTER, a comprehensive machine reading comprehension (MRC) dataset for Event Semantic Relation Reasoning.The dataset leverages natural language queries to reason about the five most common event semantic relations, provides more than 6K questions, and captures 10.1K event relation pairs.Experimental results show that the current SOTA systems achieve 22.1%, 63.3% and 83.5% for token-based exact-match (EM), F 1 and event-based HIT@1 scores, which are all significantly below human performances (36.0%, 79.6%, 100% respectively), highlighting our dataset as a challenging benchmark.
Accurate Fine-Grained Layout Analysis for the Historical Tibetan Document Based on the Instance Segmentation

Accurate layout analysis without subsequent text-line segmentation remains an ongoing challenge, especially when facing the Kangyur, a kind of historical Tibetan document featuring considerable touching components and mottled background. Aiming at identifying different regions in document images, layout analysis is indispensable for subsequent procedures such as character recognition. However, there was only a little research being carried out to perform line-level layout analysis which failed to deal with the Kangyur. To obtain the optimal results, a fine-grained sub-line level layout analysis approach is presented. Firstly, we introduced an accelerated method to build the dataset which is dynamic and reliable. Secondly, enhancement had been made to the SOLOv2 according to the characteristics of the Kangyur. Then, we fed the enhanced SOLOv2 with the prepared annotation file during the training phase. Once the network is trained, instances of the text line, sentence, and titles can be segmented and identified during the inference stage. The experimental results show that the proposed method delivers a decent 72.7% average precision on our dataset. In general, this preliminary research provides insights into the fine-grained sub-line level layout analysis and testifies the SOLOv2-based approaches. We also believe that the proposed methods can be adopted on other language documents with various layouts.
Visualization of Graph Models: An Approach to Construction of Representation Metaphors

The paper presents development of the authors' approach to visualization of graph models of various types based on the use of visualization metaphors and aimed at increasing cognitive clarity of these models.One of the key problems of this approach is investigatednamely, formalization of the process of constructing representation metaphors for graph models.Features of graph models that allow formalizing the process of their visualization are considered, the necessary terminology is introduced.A number of principles have been formulated that must be considered when forming metaphors for representing graph models.On the basis of the introduced principles, a general approach to the construction of representation metaphors for visualization of arbitrary graph models is proposed.The main ideas for applying the proposed approach are demonstrated by examples of constructing representation metaphors for two types of graph models: fuzzy cognitive maps and Bayesian networks.In order to discuss the results, a contradiction between the volume of a representation metaphor and its cognitive clarity is formulated, and a hypothesis is proposed about the relationship of this contradiction with Hick's law.The feasibility of experimental study of this relationship and the refinement of its parameters, including with the aim of developing recommendations for the construction of efficient representation metaphors of graph models, is noted.In the future, the presented approach can become an important component of an integrated approach to building a visualization mechanism for an arbitrary graph model, which provides support for efficient visual analysis throughout all stages of modeling.
Are Factuality Checkers Reliable? Adversarial Meta-evaluation of Factuality in Summarization

With the continuous upgrading of the summarization systems driven by deep neural networks, researchers have higher requirements on the quality of the generated summaries, which should be not only fluent and informative but also factually correct.As a result, the field of factual evaluation has developed rapidly recently.Despite its initial progress in evaluating generated summaries, the meta-evaluation methodologies of factuality metrics are limited in their opacity, leading to the insufficient understanding of factuality metrics' relative advantages and their applicability.In this paper, we present an adversarial meta-evaluation methodology that allows us to (i) diagnose the fine-grained strengths and weaknesses of 6 existing top-performing metrics over 24 diagnostic test datasets, (ii) search for directions for further improvement by data augmentation.Our observations from this work motivate us to propose several calls for future research.We make all codes, diagnostic test datasets, trained factuality models available:
Towards Figurative Expression Enhancement: Effects of the SVVR-Supported Worked Example Approach on the Descriptive Writing of Highly Engaged Students

During the past decades, many researchers have attempted to explore effective teaching methods for developing students’ descriptive writing performance. In this study, the worked example was implemented as an effective way of guiding students to provide step-by-step solutions to learning tasks. Moreover, a spherical video-based virtual reality (SVVR) environment was provided to place students in real-world situations which enabled them to experience the learning contexts in depth. A pretest-posttest quasi experimental study was conducted to explore the influence of the SVVR-supported worked example approach and engagement level on students’ Chinese descriptive writing performance. A total of 79 fourth-grade elementary school students participated in this study. The experimental group used SVVR with worked examples to complete Chinese writing assignments, whereas the control group used videos and worked examples. The results showed no significant effects of the SVVR-supported worked example approach compared with the conventional worked example approach regarding organization, sensory details, or creativity dimensions. As for the figurative expression dimension, students in the SVVR-supported worked example approach condition scored significantly higher. Moreover, high engagement students significantly outperformed low engagement students in all four writing performance dimensions. Additionally, a significant interaction effect between learning approach and engagement level on figurative expression was found.
Classification of Blood Cells from Blood Cell Images Using Dense Convolutional Network

 A Dense CNN model is proposed for white blood cell classification  Comparison in terms of accuracy, precision, recall, and F1 measure with other classifiers  The overall accuracy rate of 94% was obtained with DenseNet121 model  Use of publicly available dataset for state-of-the-art comparison with other studies This study aims to classify white blood cells from blood cell images.Input image goes along multiple convolutions and acquire high-level features with standard CNN.In Dense Convolutional Network (DenseNet), each layer acquires additional inputs from all previous layers and transfers on its own feature-maps to all next layers as shown in graphic.DenseNet is a dense-connected convolutional network that forward connects each layer to the other layers.DenseNet gives superior performance for various computer vision problems.DenseNet reduces the gradient descent problem, increases feature reuse, and reduces parameter usage.The advantage of DenseNet architectures is to allow feature propagation and feature reuse.Thus it minimizes the number of parameters.The main structure block of DenseNet121 is the Dense Block.These Dense Blocks consist of convolution layers.DenseNet121 contains 121 connected convolutional layers with a fully-connected layer of 1000 units as the final output layer.Figure A. Fundamental architecture of one dense block in DenseNet
A Two-Stage Differential Evolution Algorithm with Mutation Strategy Combination

For most of differential evolution (DE) algorithm variants, premature convergence is still challenging. The main reason is that the exploration and exploitation are highly coupled in the existing works. To address this problem, we present a novel DE variant that can symmetrically decouple exploration and exploitation during the optimization process in this paper. In the algorithm, the whole population is divided into two symmetrical subpopulations by ascending order of fitness during each iteration; moreover, we divide the algorithm into two symmetrical stages according to the number of evaluations (FEs). On one hand, we introduce a mutation strategy, DE/current/1, which rarely appears in the literature. It can keep sufficient population diversity and fully explore the solution space, but its convergence speed gradually slows as iteration continues. To give full play to its advantages and avoid its disadvantages, we propose a heterogeneous two-stage double-subpopulation (HTSDS) mechanism. Four mutation strategies (including DE/current/1 and its modified version) with distinct search behaviors are assigned to superior and inferior subpopulations in two stages, which helps simultaneously and independently managing exploration and exploitation in different components. On the other hand, an adaptive two-stage partition (ATSP) strategy is proposed, which can adjust the stage partition parameter according to the complexity of the problem. Hence, a two-stage differential evolution algorithm with mutation strategy combination (TS-MSCDE) is proposed. Numerical experiments were conducted using CEC2017, CEC2020 and four real-world optimization problems from CEC2011. The results show that when computing resources are sufficient, the algorithm is competitive, especially for complex multimodal problems.
contrastive study of grammar translation method and direct method in teaching of English language to primary school pupils

There have always been different methods of teaching and learning of English language and throughout the years these methods have changed and improved. The Grammar Translation Method is one of the first and the main methods in teaching and learning of English language. This method enables the learning through the translation of various texts and the study of grammatical rules, which is its main activity. Moreover, in a class where Grammar Translation Method is used the target language is used less while the students are mainly taught in their national language, i.e. L1. Therefore, since this method was less effective in preparing the learners to use the target language communicatively, a new method was introduced. Thus, the Direct Method became used and popular among learners and teachers.
Understanding MOOC Reviews: Text Mining using Structural Topic Model

Understanding the reasons for Massive Open Online Course (MOOC) learners' complaints is essential for MOOC providers to facilitate service quality and promote learner satisfaction.The current research uses structural topic modeling to analyze 21,692 programming MOOC course reviews in Class Central, leading to enhanced inference on learner (dis)satisfaction.Four topics appear more commonly in negative reviews as compared to positive ones.Additionally, variations in learner complaints across MOOC course grades are explored, indicating that learners' main complaints about high-graded MOOCs include problemsolving, practices, and programming textbooks, whereas learners of low-graded courses are frequently annoyed by grading and course quality problems.Our study contributes to the MOOC literature by facilitating a better understanding of MOOC learner (dis)satisfaction using rigorous statistical techniques.Although this study uses programming MOOCs as a case study, the analytical methodologies are independent and adapt to MOOC reviews of varied subjects.
SCLAiR: Supervised Contrastive Learning for User and Device Independent Airwriting Recognition

Airwriting Recognition is the problem of identifying letters written in free space with finger movement. It is essentially a specialized case of gesture recognition, wherein the vocabulary of gestures corresponds to letters as in a particular language. With the wide adoption of smart wearables in the general population, airwriting recognition using motion sensors from a smart-band can be used as a medium of user input for applications in Human-Computer Interaction. There has been limited work in the recognition of in-air trajectories using motion sensors, and the performance of the techniques in the case when the device used to record signals is changed has not been explored hitherto. Motivated by these, a new paradigm for device and user-independent airwriting recognition based on supervised contrastive learning is proposed. A two stage classification strategy is employed, the first of which involves training an encoder network with supervised contrastive loss. In the subsequent stage, a classification head is trained with the encoder weights kept frozen. The efficacy of the proposed method is demonstrated through experiments on a publicly available dataset and also with a dataset recorded in our lab using a different device. Experiments have been performed in both supervised and unsupervised settings and compared against several state-of-the-art domain adaptation techniques. Data and the code for our implementation will be made available at https://github.com/ayushayt/SCLAiR.
Enabling Identity for the IoT-as-a-Service Business Model

The IoT-as-a-Service (IoTaaS) business model has already been identified by some people from both industry and academia, but has not been formally defined. IoTaaS offers IoT devices on demand, with considerable cost savings and resource optimization. In addition, it enables different applications to reuse the existing devices. However, this business model is associated with different technological challenges that need to be addressed, one of which is the identity problem. Focusing on this, self-sovereign identity (SSI) schemes have proven to provide better privacy and scalability than traditional identity paradigms, which is especially important in the IoT owing to its characteristics. In this paper, we formally analyze an IoTaaS business model, identifying and detailing its main technological challenges. In addition, we tackle the identity problem of this business model and propose an SSI-based identity management system, which is compliant with the existing standards from the W3C, and include a performance evaluation.
Development of an incentive model for renewable energy resources using forecasting accuracy in South Korea

Abstract This study presents an incentive model for a renewable energy source (RES) supplier, based on forecasting accuracy. The incentive model imposes responsibility of forecasting to RES suppliers by providing an additional revenue or penalty, according to forecasting accuracy. For this, a profit analysis reflecting the current environment was performed. The structure of the incentive model is designed based on the results of profit analysis. Subsequently, the price parameter was determined using an artificially generated forecasting error of the RES. In addition, the utilization of energy storage system (ESS) is analyzed, including the operation algorithm and optimal sizing, to verify the utility of the ESS when the incentive model is applied. In the case study, the actual price data and RES data from South Korea were used. The results show that the incentive model can provide a high level of profit or penalty for the RES supplier, according to the forecasting accuracy. It is also shown that the optimal capacity of the ESS based on econometrics can be reduced by more than 50% compared with the current environment, thereby reducing the burden on the ESS investment of RES suppliers.
Acceptance, Readiness and Intention to Use Augmented Reality (AR) in Teaching English Reading among Secondary School Teachers in Malaysia

Abstract: This research reports on Malaysian English teachers’ readiness and acceptance in using Augmented Reality (AR) in English instruction and their intention to use AR in teaching English reading. Through a survey on 181 Malaysian English language teachers in Klang Valley, Malaysia, it was found that Malaysian English teachers possess quite a high level of acceptance in using Augmented Reality in the teaching of English reading with 60.8 % (n = 110); and have a high level of readiness in using Augmented Reality in the teaching of English reading at 63.0% (n = 114). The researchers also found significant correlation between level of acceptance and behavioral intention (r = .935**, p = .00, p &lt; .05) between teachers’ acceptance with their intention to use Augmented Reality in teaching of English reading at .05 level of significance. There is also high correlation between teachers’ readiness with intention to use augmented reality in teaching English reading, with the correlation coefficient (r), at .924. In conclusion, Malaysian secondary school teachers’ acceptance and readiness to use Augmented Reality (AR) in teaching English reading are high and there is a positively significant relationship between acceptance and readiness with intention to use AR in teaching English reading.&#x0D; &#x0D; Keywords: Acceptance, Augmented Reality, English Language Learning, English Reading, Readiness, Intention to Use
Research on the Influence Mechanism of Block Chain on the Credit of Transportation Capacity Supply Chain Finance

This paper aims at the promotion of the application of inclusive financing into transportation capacity financing by combining transportation capacity supply chain with block chain technology as a brand-new financing topic. It focuses on the influencing mechanism by block chain on credit access, credit line, and credit supervision. From the perspective of “transportation” finance, the application of block chain in different scenarios is demonstrated after analyzing the attenuation process of credit transmission in the supply chain, the reviewing of two credit line evaluation methods of business self-compensation and credit guarantee, and the reviewing of regulatory requirements in transaction closed-loop, delivery closed-loop, and capital closed-loop; therefore the 3 major influencing mechanisms by block chain on the transportation capacity supply chain financing credit granting are discovered, indicating the effective improvement of financial institutions participation and better credit line for the financing of micro, medium, and small transportation enterprises (SMEs) by the application of block chain technology.
DISTANCE EDUCATION IN THE PANDEMIC ERA: THE VIEWS OF GREEK GENERAL AND SPECIAL EDUCATION TEACHERS OF SECONDARY EDUCATION REGARDING READINESS, CHALLENGES AND THE POST-COVID EDUCATIONAL PRACTICES

&lt;p&gt;The present study, via the use of a questionnaire, examined the views of 138 Greek teachers, of general and special education, on the readiness for distance education, the challenges they face during the courses, and the measures to be taken in education in case of a next crisis such as Coronavirus pandemic (COVID-19). Educators’ views on the measures to be taken by educational institutions in order to be ready to provide distance education in the post-COVID era were also examined. The analysis of results showed that Special Education teachers appeared to be more ready for distance education; indeed, their field of employment itself, namely teaching, their years of service, the age and the level of studies were found to have a statistically significant influence on their answers. The greater challenge that both groups of teachers faced were the difficulties in communicating with their students. It became apparent also the great need for training teachers in issues related to distance education as well as the protection of their personal data of their students. &lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0977/a.php" alt="Hit counter" /&gt;&lt;/p&gt;