Reviewerly/exHarmony huggingface dataset:
College Students’ Preferences on Principles for the Effective Instructional Video Design for Online General English Classes in Korea

The present study investigates the preferences of students regarding the principles for the effective design of instructional videos to identify factors that influence engagement. A questionnaire was distributed to 232 students enrolled in online liberal arts classes at a private university in South Korea. Frequency analysis was conducted to determine preferences, whereas an independent sample t-test and one-way analysis of variance were administered to verify any differences in preferences according to gender and grade. The findings are as follows. First, out of the 12 principles that should be considered in the design of instructional videos, the students most preferred the review quiz principle. Moreover, this factor was found to exert the greatest influence on engagement. Second, incorporating real-life situation principles into instructional videos also had a significant impact on engagement. Third, female students expressed higher levels of preference than did male students in terms of the preview, course content on screen, and review quiz principles. Fourth, sophomores preferred the review quiz principle more than the freshmen did. The results of the present study are in line with those of previous research in that the effective instructional design of multimedia lessons requires reducing extraneous processing, managing essential processing, and fostering generative processing. Particularly, the study found that Korean students value video lectures with generative activities for meaningful learning. Based on the findings of the study, pedagogical considerations of the design of recorded lectures and its structure for active engagement, and suggestions for future studies are provided.
A Deep Learning and Clustering Extraction Mechanism for Recognizing the Actions of Athletes in Sports

In sports, the essence of a complete technical action is a complete information structure pattern and the athlete's judgment of the action is actually the identification of the movement information structure pattern. Action recognition refers to the ability of the human brain to distinguish a perceived action from other actions and obtain predictive response information when it identifies and confirms it according to the constantly changing motion information on the field. Action recognition mainly includes two aspects: one is to obtain the required action information based on visual observation and the other is to judge the action based on the obtained action information, but the neuropsychological mechanism of this process is still unknown. In this paper, a new key frame extraction method based on the clustering algorithm and multifeature fusion is proposed for sports videos with complex content, many scenes, and rich actions. First, a variety of features are fused, and then, similarity measurement can be used to describe videos with complex content more completely and comprehensively; second, a clustering algorithm is used to cluster sports video sequences according to scenes, eliminating the need for shots in the case of many scenes. It is difficult and complicated to detect segmentation; third, extracting key frames according to the minimum motion standard can more accurately represent the video content with rich actions. At the same time, the clustering algorithm used in this paper is improved to enhance the offline computing efficiency of the key frame extraction system. Based on the analysis of the advantages and disadvantages of the classical convolutional neural network and recurrent neural network algorithms in deep learning, this paper proposes an improved convolutional network and optimization based on the recognition and analysis of human actions under complex scenes, complex actions, and fast motion compared to post-neural network and hybrid neural network algorithm. Experiments show that the algorithm achieves similar human observation of athletes' training execution and completion. Compared with other algorithms, it has been verified that it has very high learning rate and accuracy for the athlete's action recognition.
Effect of Peer Feedback on Paragraph Writing Performance among High School Students

Writing is one of the four important skills in learning a foreign language.However, most high school students find it uneasy to write due to many different both objective and subjective reasons.Finding effective solutions to this problem is always appreciated.This study, therefore, hopes to investigate whether peer feedback has any positive effect on this group's writing performance.In order to support this investigation, the authors attempted to deploy a writing teaching strategy using peer feedback as the main instrument to foster students' ability to write their paragraph.Two groups of the participants were recruited: 37 control students and 39 experimental ones in a high school in a rural district in Vietnam.After six weeks of intervention with this strategy, most students in the experimental group were awarded a better score than their counterparts in the control groups in general.The authors applied a 10 -scale rating rubric with three paragraph rating domains to measure their paragraph writing performance.Then, the authors also made an attempt to see the experimental students' attitudes towards peer feedback after their application in class and they were satisfied with it afterwards.It revealed that overall they showed positive attitudes towards peer feedback.
WSNet – Convolutional Neural Networkbased Word Spotting for Arabic and English Handwritten Documents

This paper proposes a new convolutional neural network architecture to tackle the problem of word spotting in handwritten documents. A Deep learning approach using a novel Convolutional Neural Network is developed for the recognition of the words in historical handwritten documents. This includes a pre-processing step to re-size all the images to a fixed size. These images are then fed to the CNN for training. The proposed network shows promising results for both Arabic and English and both modern and historical documents. Four datasets – IFN/ENIT, Visual Media Lab – Historical Documents (VML-HD), George Washington and IAM datasets – have been used for evaluation. It is observed that the mean average precision for the George Washington dataset is 99.6%, outperforming other state-of-the-art methods. Historical documents in Arabic are known for being complex to work with; this model shows good results for the Arabic datasets, as well. This indicates that the architecture is also able to generalize well to other languages.
IMPROVING MULTI-LABEL TEXT CLASSIFICATION USING WEIGHTED INFORMATION GAIN AND CO-TRAINED MULTINOMIAL NAÏVE BAYES CLASSIFIER

Over recent years, the emergence of electronic text processing systems has generated a vast amount of structured and unstructured data, thus creating a challenging situation for users to rummage through irrelevant information. Therefore, studies are continually looking to improve the classification process to produce more accurate results that would benefit users. This paper looks into the weighted information gain method that re-assigns wrongly classified features with new weights to provide better classification. The method focuses on the weights of the frequency bins, assuming every time a certain word frequency bin is iterated, it provides information on the target word feature. Therefore, the more iteration and re-assigning of weight occur within the bin, the more important the bin becomes, eventually providing better classification. The proposed algorithm was trained and tested using a corpus extracted from dedicated Facebook pages related to diabetes. The weighted information gain feature selection technique is then fed into a co-trained Multinomial Naïve Bayes classification algorithm that captures the labels' dependencies. The algorithm incorporates class value dependencies since the dataset used multi-label data before converting string vectors that allow the sparse distribution between features to be minimised, thus producing more accurate results. The results of this study show an improvement in classification to 61%.
Hybrid Domain Attention Network for Efficient Super-Resolution

Image SR reconstruction methods focus on recovering the lost details in the image, that is, high-frequency information, which exists in the region of edges and textures. Consequently, the low-frequency information of an image often requires few computational resources. At present, most of the recent CNN-based image SR reconstruction methods allocate computational resources uniformly and treat all features equally, which inevitably results in wasted computational resources and increased computational effort. However, the limited computational resources of mobile devices can hardly afford the expensive computational cost. This paper proposes a symmetric CNN (HDANet), which is based on the Transformer’s self-attention mechanism and uses symmetric convolution to capture the dependencies of image features in two dimensions, spatial and channel, respectively. Specifically, the spatial self-attention module identifies important regions in the image, and the channel self-attention module adaptively emphasizes important channels. The output of the two symmetric modules can be summed to further enhance the feature representation and selectively emphasize important feature information, which can enable the network architecture to precisely locate and bypass low-frequency information and reduce computational cost. Extensive experimental results on Set5, Set14, B100, and Urban100 datasets show that HDANet achieves advanced SR reconstruction performance while reducing computational complexity. HDANet reduces FLOPs by nearly 40% compared to the original model. ×2 SR reconstruction of images on the Set5 test set achieves a PSNR value of 37.94 dB.
MobileNets Can Be Lossily Compressed: Neural Network Compression for Embedded Accelerators

Although neural network quantization is an imperative technology for the computation and memory efficiency of embedded neural network accelerators, simple post-training quantization incurs unacceptable levels of accuracy degradation on some important models targeting embedded systems, such as MobileNets. While explicit quantization-aware training or re-training after quantization can often reclaim lost accuracy, this is not always possible or convenient. We present an alternative approach to compressing such difficult neural networks, using a novel variant of the ZFP lossy floating-point compression algorithm to compress both model weights and inter-layer activations and demonstrate that it can be efficiently implemented on an embedded FPGA platform. Our ZFP variant, which we call ZFPe, is designed for efficient implementation on embedded accelerators, such as FPGAs, requiring a fraction of chip resources per bandwidth compared to state-of-the-art lossy compression accelerators. ZFPe-compressing the MobileNet V2 model with an 8-bit budget per weight and activation results in significantly higher accuracy compared to 8-bit integer post-training quantization and shows no loss of accuracy, compared to an uncompressed model when given a 12-bit budget per floating-point value. To demonstrate the benefits of our approach, we implement an embedded neural network accelerator on a realistic embedded acceleration platform equipped with the low-power Lattice ECP5-85F FPGA and a 32 MB SDRAM chip. Each ZFPe module consumes less than 6% of LUTs while compressing or decompressing one value per cycle, requiring a fraction of the resources compared to state-of-the-art compression accelerators while completely removing the memory bottleneck of our accelerator.
Enhancement of the efficiency of solar energy cells by selecting suitable places based on the simulation of PV System

At present, the increasing demand for electrical energy and the presence of renewable sources in various forms in the world and, particularly in Iraq, such as solar energy and wind energy, have become the focus of researchers' attention.Huge efforts are focused on finding ways to use ecologically friendly energy to generate electricity and eliminate fossil fuels.In this study paper, we propose the use of a simulation program to discover the ideal location for a solar cell and the amount of time to be exposed to the sun's rays, so that a home powered by solar energy can be built.Also, through this program, the losses were calculated that accompany the conversion of light energy into electrical energy to find the necessary solutions to make the solar cell work with high efficiency.
WebXR Asset Management in Developing Virtual Reality Learning Media

Extended Reality (XR) is the technology that allows blending virtual and physical worlds using augmented reality (AR) and Virtual Reality (VR) to create a fully immersive experience. While WebXR is a web based XR environment built on a JavaScript framework that enables interactivity of AR and VR devices within the web browser. Contemporarily, educators are demanded to explore the variety of new media in practicing interactive learning, specifically in an academic environment. Arguably, VR has recently been identified as one of the most popular gamification mediums. It can create an environment that is both physically and digitally engaging. This paper reviews, simplifies as well as recommends a better component management system in the VR production pipeline using practice-based research methodology.
A Study on Evolutionary Algorithms and Its Applications

Evolutionary methods are a horror-based approach to solving problems that are not easily solved in polynomial time, for example, classical NP-heart problems and take longer to complete.Evolutionary methods are commonly used to provide good approximate solutions to problems that cannot be easily solved using other techniques.Many optimization issues fall into this category.It can be very calculated-finding a suitable solution is serious but sometimes the optimal solution is enough.Major classes of contemporaries (in the order of popularity) E.A. Genetic algorithms (GAs), evolutionary strategies (ESs), differential evolution (DE) and distribution algorithm evaluation (EDAs) are used.Evolutionary methods are based on the concepts of biological evolution.The 'population' of possible solutions to the problem will be created first, and each solution will be evaluated using a 'fitness function'.The population develops over time and (hopefully) identifies the best solutions.
Natural Language Processing for Virtual Reference Analysis

Objective – Chat transcript analysis can illuminate user needs by identifying common question topics, but traditional hand coding methods for topic analysis are time-consuming and poorly suited to large datasets. The research team explored the viability of automatic and natural language processing (NLP) strategies to perform rapid topic analysis on a large dataset of transcripts from a consortial chat service. Methods – The research team developed a toolchain for data processing and analysis, which incorporated targeted searching for query terms using regular expressions and natural language processing using the Python spaCy library for automatic topic analysis. Processed data was exported to Tableau for visualization. Results were compared to hand-coded data to test the accuracy of conclusions. Results – The processed data provided insights about the volume of chats originating from each participating library, the proportion of chats answered by operator groups for each library, and the percentage of chats answered by different staff types. The data also captured the top referring URLs for the service, course codes and file extensions mentioned, and query hits. Natural language processing revealed that the most common topics were related to citation, subscription databases, and finding full-text articles, which aligns with common question types identified in hand-coded transcripts. Conclusion – Compared to hand coding, automatic and NLP processing approaches have benefits in terms of the volume of data that can be analyzed and the time frame required for analysis, but they come with a trade-off in accuracy, such as false hits. Therefore, computational approaches should be used to supplement traditional hand coding methods. As NLP becomes more accurate, approaches such as these may widen avenues of insight into virtual reference and patron needs.
Assured Mission Adaptation of UAVs

The design of systems that can change their behaviour to account for scenarios that were not foreseen at design time remains an open challenge. In this article, we propose an approach for adaptation of mobile robot missions that is not constrained to a predefined set of mission evolutions. We implement an adaptive software architecture and show how controller synthesis can be used both to guarantee correct transitioning from the old to the new mission goals with runtime architectural reconfiguration to include new software actuators and sensors if necessary. The architecture brings together architectural concepts that are commonplace in robotics such as temporal planning, discrete, hybrid and continuous control layers together with architectural concepts from adaptive systems such as runtime models and runtime synthesis. We validate the architecture flying several missions taken from the robotic literature for different real and simulated UAVs.
Virtual and face-to-face team collaboration comparison through an agent-based simulation

Abstract The ongoing COVID-19 pandemic has accelerated the acceptance of virtual team collaboration as a replacement for face-to-face collaboration. Unlike face-to-face collaboration, virtual collaboration is influenced by unique factors, such as technology mediation. However, there is a lack of rigorous research that assesses the impact of virtual collaboration on the engineering design process. Therefore, the current study investigates the effect of virtual team collaboration on design outcomes by means of the MILANO (Model of Influence, Learning, and Norms in Organizations) framework. To tailor MILANO for virtual collaboration, this paper first presents an empirical study of human design teams, which shows how model parameter values for face-to-face collaboration (like self-efficacy, perceived influencers, perceived degree of influence, trust and familiarity) differ from appropriate parameter values for virtual collaboration. The simulation results for both virtual and face-to-face collaboration show how design outcomes differ with collaboration mode. Unlike teams with a few well-defined influential individuals, the mode of collaboration does not have a significant impact on teams where all individuals are equally influential. Virtual collaboration also results in lower exploration and variety than face-to-face collaboration.
A New Adaptive Federated Cubature Kalman Filter Based on Chi-Square Test for SINS/GNSS/SRS/CNS Integration

As an emerging means of transportation for the intelligent transportation system (ITS) in aviation and aerospace, hypersonic cruise vehicles (HCVs) have received numerous research interests during the past several decades. However, the navigation and positioning strictly limit the progress and application of HCVs due to their special characteristics on dynamics and environments. To improve the stability of navigation in HCVs, a chi-square test-based adaptive federated cubature Kalman filter (CAFCKF) is proposed in this paper. In the proposed approach, the chi-square test is adopted for the estimation of the measurement noise statistics firstly. Subsequently, a new adaptive information fusion factor is designed for the federated filter to adjust the contribution of each subsystem. Finally, the information sharing factor, which is used for the amendment of the state covariance of each subsystem, is refined based on the judging index of the chi-square test accordingly. Simulation results show that the proposed CAFCKF can be used to improve the accuracy and stability of the navigation system.
Fake accounts detection system based on bidirectional gated recurrent unit neural network

&lt;span&gt;Online social networks have become the most widely used medium to interact with friends and family, share news and important events or publish daily activities. However, this growing popularity has made social networks a target for suspicious exploitation such as the spreading of misleading or malicious information, making them less reliable and less trustworthy. In this paper, a fake account detection system based on the bidirectional gated recurrent unit (BiGRU) model is proposed. The focus has been on the content of users’ tweets to classify twitter user profile as legitimate or fake. Tweets are gathered in a single file and are transformed into a vector space using the GloVe word embedding technique in order to preserve the semantic and syntax context. Compared with the baseline models such as long short-term memory (LSTM) and convolutional neural networks (CNN), the results are promising and confirm that using GloVe with BiGRU classifier outperforms with 99.44% for accuracy and 99.25% for precision. To prove the efficiency of our approach the results obtained with GloVe were compared to Word2vec under the same conditions. Results confirm that GloVe with BiGRU classifier performs the best results for detection of fake Twitter accounts using only tweets content feature.&lt;/span&gt;
Rotating machinery anomaly detection using data reconstruction generative adversarial networks with vibration energy analysis

Rotating machines, such as engines, turbines, or gearboxes, are widely used in modern society. Their mechanical components, such as rotors, bearings, or gears, are the main parts, and any failure in them can lead to a complete shutdown of the rotating machinery. Anomaly detection in such critical systems is essential for the healthy operation of rotating machinery. As the requirement of obtaining sufficient fault data of rotating machinery is challenging to satisfy, a new anomaly detection model is proposed for rotating machinery, which can achieve anomaly detection without fault samples. The model combines vibration energy features, adversarial learning mechanism, and long short-term memory to develop a novel anomaly detection model based on data reconstruction. The model was validated using two different datasets. The proposed model achieves the highest accuracy of 98.90% for anomaly detection under different working conditions, outperforming similar models.
Lexicalization in the developing parser

We use children's noun learning as a probe into the nature of their syntactic prediction mechanism and the statistical knowledge on which that prediction mechanism is based. We focus on verb-based predictions, considering two possibilities: children's syntactic predictions might rely on distributional knowledge about specific verbs–i.e. they might be lexicalized–or they might rely on distributional knowledge that is general to all verbs. In an intermodal preferential looking experiment, we establish that verb-based predictions are lexicalized: children encode the syntactic distributions of specific verbs and use those distributions to make predictions, but they do not assume that these can be assumed of verbs in general.