Reviewerly/exHarmony huggingface dataset:
ATTITUDE OF GRADE VI PUPILS IN THE IMPLEMENTATION OF ONLINE DISTANCE LEARNING

This study sought to determine the attitude among Grade VI pupils in the implementation of online distance learning of Borongan City Division in school year 2020-2021. Specifically, it aimed to identify their reading abilities and their association with their attitude toward online distance learning. To address the objectives, this study used a correlational design. A total of 217 Grade 6 pupils from the five districts of Borongan City Division served as respondents of this study. A survey questionnaire was adapted to gather necessary data on the reading levels of learners and their attitudes toward the online distance learning modality. Meanwhile, inferential statistics such as Pearson r Coefficient were used to identify the association between the reading levels of pupils and their attitude toward online learning which was found to have a significant relationship. KEYWORD: attitude, online distance learning, correlation
Research on the Application of Big Data Technology and Collaborative Filtering Recommendation System in Accurately Guiding the Employment of College Students in the Context of COVID-19

Under the influence of COVID-19, the employment situation of college students is severe, and the employment guidance of college students is also facing new challenges and opportunities.Based on the analysis of the difficulties faced by college students' employment guidance in the context of the epidemic, this paper explores the application of big data technology and the role of collaborative filtering recommendation algorithm in student employment, to accurately guide college students' employment and improve the effect of employment guidance, alleviate the impact of the epidemic, and effectively improve the employment situation of college students.
The Influence Mechanism of Humble Leadership on Employee Helping Behavior: A Moderated Mediation Model

Based on social cognition theory and social exchange theory, this paper constructs a model of the relationship between humble leadership and employee helping behavior, and discusses the mediating role of employee psychological security and the moderating role of defensive regulatory focus.On the basis of reliability and validity analysis, a structural equation model, Bootstrap and hierarchical regression analysis were used to analyze the data.The empirical results show that humble leadership has a significant positive impact on employee helping behavior, psychological security plays a mediating role between humble leadership and employee helping behavior, and defensive regulatory focus significantly moderates the relationship between humble leadership and employee helping behavior
IMPROVING STUDENT CREATIVITY AND LEARNING OUTCOMES THROUGH THE PODCAST-BASED INQUIRY LEARNING MODEL

This study aimed to increase creativity and student learning outcomes through a podcast-based inquiry learning model. This research was conducted at SMA PGRI 2 Jambi City in February-April 2022, with the subjects of this research being students of class X IPS 2. This type of research was classroom action research which was conducted six times. The data were obtained using questionnaires, observations, interviews, documentation studies, and field notes which were then analyzed through qualitative descriptive techniques. The results showed that the application of the podcast-based inquiry learning model to history subjects had gone well. The test results showed that the average value of learning outcomes had increased from 70.5 in cycle I to 83.3 in cycle VI. The average student creativity scores also increased from cycle I, with a score of 70.5 to 84.2 in cycle VI. These numbers show that the podcast-based inquiry learning model can improve student learning outcomes and creativity.
Personal Exercise Assistant: Correcting Exercise Posture using Modified Open pose

Fitness exercises or yoga are very bene?cial to personal health and ?tness; however, they can also be ineffective and potentially dangerous if performed in correctly by the user. Exercise mistakes are made when the user does not use the proper form, or pose. In our work, we introduce Personal Exercise Assistant, an application that detects the user’s exercise pose and provides personalized feedback. Modified Open Pose is used as a Pose estimator in this application. Open Pose is a pre trained model composed of a multi-stage CNN to detect a user’s posture. This application then evaluates the vector geometry of the pose through an exercise to provide helpful feedback. Pose estimation is a method in which spatial locations of key body joints is calculated using image or video of the person. Modified open pose is a light weight (8bit Quantized version) Open pose. Personal Exercise Assistant works on yoga exercises and supports any Windows or Linux computer with a GPU.
Accuracy Evaluation and Prediction of Single-Image Camera Calibration

This paper proposes an application to statistically predict the accuracy of single-image geometric camera calibration that uses given 2D-3D correspondences.Deriving both camera intrinsics and extrinsics from correspondences between a single image and a 3D shape, is important for the scene analysis when the optical system of the camera is lost, such as in the analyses of traffic accidents.It is unclear whether the single-image calibration will be successful in practice, particularly when the number of 2D-3D correspondences is small, even if we could assign accurate correspondences by manual labor.To this end, we perform a systematic evaluation of the camera parameter accuracy using synthetic environments.Based on the statistics observed during the experiments, our application predicts the calibration accuracy from simple variables (e.g., the area that correspondences could be given).Since the prediction process does not rely on 3D shapes, it provides an estimate of the success of the calibration before time-consuming processes, i.e., 3D scanning and 2D-3D correspondence mapping.
Real-Time Highway Abnormality Detection Using an Image Processing Algorithm

Due to highway real-time monitoring issues, manual monitoring cannot quickly detect abnormalities. Incorrect detection and costly monitoring make it hard to determine traffic's operating state. Experiments show that the YOLOv3 algorithm has improved. Real-time detection improves accuracy. Improving the deep-sort vehicle feature description can speed up vehicle tracking. The system now detects vehicle speeding and congestion in real-time utilizing an upgraded vehicle object recognition, tracking algorithm, and expressway traffic monitoring picture. First, the highway corporation's monitoring system was inspected. After monitoring highway footage in various places, lighting, weather, and other conditions, a comprehensive vehicle data set was developed. A deep learning model for vehicle target detection is built on this data. Second, the labeling tool divides the data set and labels each sample. YOLOv3's border regression loss function increased with the enhanced vehicle detection algorithm. This was achieved through vehicle target identification algorithm research. Build a model of accurate vehicle detection, test the algorithm model, and use the results of the experimental examination of the various loss functions and training data to improve target identification performance and speed. After discussing target tracking, the real-time multi-target monitoring-based deep-sort algorithm will be explained. Trials assess the improved algorithm. If the detection impact is the same, increasing the tracking speed will improve the anomaly detection system's real-time performance. This is true even without boosting tracking speed. Anomaly detection was developed from vehicle target identification and tracking studies. Debugging and analyzing an abnormal event detection system may automatically recognize the speed and congestion of the video vehicle during abnormal occurrences, such as those in expressway surveillance video. It accurately detects real-time anomalous events. Thus, this technology can better track vehicle traffic and predict and identify emergencies. It also supports timeliness and on-site rescue, ensuring lane traffic flows as planned and improving highway business service.
Enhanced Word Sense Disambiguation Algorithm for Afaan Oromoo

In various circumstances, the same word can mean differently based on the usage of the word in a particular sentence.The aim of word sense disambiguation (WSD) is to precisely understand the meaning of a word in particular usage.WSD utilized in several applications of natural language to interpret an ambiguous word contextually.This paper enhances a statistical algorithm proposed by Abdo [36] that performs a task of WSD for Afaan Oromoo (one of underresourced language spoken in East Africa by nearly 50% of Ethiopians).The paper evaluates appropriate methods that used to increase the performance of disambiguation for the language with and without morphology consideration.The algorithm evaluated by 249 sentences with four evaluation metrics: recall, precision, F1 and accuracy.The evaluation result has achieved state of the art for Afaan Oromoo.Finally, future direction is highlighted for further research of the task on the language.
Phishing URL Detection using LSTM Based Ensemble Learning Approaches

Increasing incidents of phishing attacks tempt a significant challenge for cybersecurity personals. Phishing is a deceitful venture with an intention to steal confidential information of an organization or an individual. Many works have been performed to build anti-phishing solutions over the years, but attackers are coming with new manoeuvres from time to time. Many of the existing techniques are experimented based on limited set of URLs and dependent on other software to collect domain related information of the URLs. In this paper, with an aim to build a more accurate and effective phishing attack detection system, we used the concept of ensemble learning using Long Short-Term Memory (LSTM) models. We proposed ensemble of LSTM models using bagging approach and stacking approach. For performing classification using LSTM method, no separate feature extraction is done. Ensemble models are built integrating the predictions of multiple LSTM models. Performances of proposed ensemble LSTM methods are compared with five different machine learning classification methods. To implement these machine learning algorithms, different URL based lexical features are extracted. Mutual Information based feature selection algorithm is used to select more relevant features to perform classifications. Both the bagging and the stacking approaches of ensemble learning using LSTM models outperform other machine learning techniques. The results are compared with other anti-phishing solutions implemented using deep learning methods. Our approaches have proved to be the more accurate one with a low false positive rate of less than 0.15% performed comparatively on a larger dataset.
Automatic Analysis of Human Body Representations in Western Art

The way the human body is depicted in classical and modern paintings is relevant for art historical analyses. Each artist has certain themes and concerns, resulting in different poses being used more heavily than others. In this paper, we propose a computer vision pipeline to analyse human pose and representations in paintings, which can be used for specific artists or periods. Specifically, we combine two pose estimation approaches (OpenPose and DensePose, respectively) and introduce methods to deal with occlusion and perspective issues. For normalisation, we map the detected poses and contours to Leonardo da Vinci's Vitruvian Man, the classical depiction of body proportions. We propose a visualisation approach for illustrating the articulation of joints in a set of paintings. Combined with a hierarchical clustering of poses, our approach reveals common and uncommon poses used by artists. Our approach improves over purely skeleton based analyses of human body in paintings.
The effect of using Emoji and Hashtag in sentiment analysis on Twitter case study: Indonesian online travel agent

Twitter is one of the world’s largest social media, reaching 330 million active users every month. Therefore, conducting sentiment analysis on Twitter should give a representative picture related to public perception. Twitter allows its users to use Hashtags and Emoji while posting a Tweet, and those become common on Twitter. Emojis and Hashtags can sometimes be decisive information for a Tweet’s sentiment. Therefore, a study was conducted to find out how far Emoji’s and Hashtag’s influence if included as information when conducting sentiment analysis. The results showed that when doing sentiment analysis using the Naïve Bayes model, the one that includes Emoji got slightly better overall performance than other variants, with accuracy 77.55%, precision 0.819, recall 0.887, and F1-score 0.852. We conclude that in our case, Emoji and Hashtag do not significantly improve the performance but are worth considering in future research.
Spatially-varying Warping for Panoramic Image Stitching

Panoramic image stitching is widely implemented to many application scenarios, such as satellite remote sensing detection, street view mapping and medical image generation. In this paper, we study two processes that have great impact on the visual effects of the stitched images and are the most concerned by researchers, namely image registration and stitching seam removal. In contrast to the traditional panoramic image stitching algorithms that use only one global projection transformation matrix for image alignment, this paper proposes a spatially-varying deformation method that uses multiple local transformation matrices, including the APAP (As-Projective-As-Possible) method that can effectively reduce the image alignment error and geometric transformation distortion, the SPHP (Shape-Preserving Half-Projective) method that can combine good alignment accuracy and reduced projection distortion, and the AANAP (Adaptive As-Natural-As-Possible) method that can improve the perspective of non-overlapping regions of the stitched image and good accuracy. The image stitching performance can be well improved.
Empowering IoT through Improving Technology

The Internet of Things (IoT) which is dynamic, intelligent, and ubiquitous. Academic and commercial organizations mostly prefer the Internet of Things for research. Day by day, IoT publicity is increasing. An intelligent network that concatenates all things (IP or non-IP based) to the Internet for information interchange and communication is the Internet of Things (IoT). This paper provides the IoT system with its applications, challenges, and open issues and discusses the state-of-the-art methods of the IoT system and its layered architecture. Sensors and actuators are combined in the IoT system for new services and products with highly efficient, low costs, and user friendly. The IoT is ordinarily composed of various advanced technologies like software, actuators, sensors, and electronics because of Micro-Electro-Mechanical Systems (MEMS) comprising electronic and mechanical components atthe micron level.
Role of virtual reality in the life of ageing population

Virtual reality (VR) has been on the scene for several decades already. Its first applications were in gaming. However, hardware and software were expensive and thus not for everybody. Since that time, the development of technology proceeded fast and enabled to open new application areas for VR. Currently many commercial systems are available for gaming, training and education, simulations, design, and also for medical purposes. In the article we focus on VR applications in healthcare. First we present existing commercial solutions, and research studies showing the potential of VR in healthcare. In recent years there have appeared many interesting projects and applications aimed at ageing population as target users. We present examples of such projects. Based on our previous experience and after analysis of available solutions, we propose a conceptual architecture od software environment for development of such applications and discuss their potential use. Finally, the implementation of the proposed architecture for interactive application of experience sets is described.
Multi-View Surgical Camera Calibration with None-Feature-Rich Video Frames: Toward 3D Surgery Playback

Mounting multi-view cameras within a surgical light is a practical choice since some cameras are expected to observe surgery with few occlusions. Such multi-view videos must be reassembled for easy reference. A typical way is to reconstruct the surgery in 3D. However, the geometrical relationship among cameras is changed because each camera independently moves every time the lighting is reconfigured (i.e., every time surgeons touch the surgical light). Moreover, feature matching between surgical images is potentially challenging because of missing rich features. To address the challenge, we propose a feature-matching strategy that enables robust calibration of the multi-view camera system by collecting a set of a small number of matches over time while the cameras stay stationary. Our approach would enable conversion from multi-view videos to a 3D video. However, surgical videos are long and, thus, the cost of the conversion rapidly grows. Therefore, we implement a video player where only selected frames are converted to minimize time and data until playbacks. We demonstrate that sufficient calibration quality with real surgical videos can lead to a promising 3D mesh and a recently emerged 3D multi-layer representation. We reviewed comments from surgeons to discuss the differences between those 3D representations on an autostereoscopic display with respect to medical usage.
Quantum Machine Learning for Distributed Quantum Protocols with Local Operations and Noisy Classical Communications

Distributed quantum information processing protocols such as quantum entanglement distillation and quantum state discrimination rely on local operations and classical communications (LOCC). Existing LOCC-based protocols typically assume the availability of ideal, noiseless, communication channels. In this paper, we study the case in which classical communication takes place over noisy channels, and we propose to address the design of LOCC protocols in this setting via the use of quantum machine learning tools. We specifically focus on the important tasks of quantum entanglement distillation and quantum state discrimination, and implement local processing through parameterized quantum circuits (PQCs) that are optimized to maximize the average fidelity and average success probability in the respective tasks, while accounting for communication errors. The introduced approach, Noise Aware-LOCCNet (NA-LOCCNet), is shown to have significant advantages over existing protocols designed for noiseless communications.
Comparison of Machine Learning Algorithms in Domain Specific Information Extraction

Information Extraction is an essential task in Natural Language Processing.It is the process of extracting useful information from unstructured text.Information extraction helps in most of the NLP applications like sentiment analysis, named entity recognition, medical data extraction, features extraction from research articles, feature extraction from agriculture, etc.Most of the applications in information extraction are performed by machine learning models.Many research work shave been carried out on machine learning based information extraction from various domain texts in English such as Bio medical, Share market, Weather, Business, Social media, Agriculture, Engineering, and Tourism.However domain specific information extraction for a particular regional language is still a challenge.There are different types of classification algorithms.However, for a selected domain to select the appropriate classification algorithm is very difficult.In this paper three famous classification algorithms are selected to do information extraction by classifying the Gynecological domain data in Tamil Language.The main objective or this research work is to analyze the machine learning methods which is suitable for Tamil domain specific text documents.There are 1635 documents being involved in classification task to extract the features by these selected three algorithms.By evaluating the classification task of each model it has been found that the Naive Bayes classification model provides highest accuracy value (84%) for the gynecological domain data.The F1-Score, Error rate and Execution time also evaluated for the selected machine learning models.The evaluation of performance has proved that the Naï ve Bayes classification model gives optimal results.It has been concluded that the Naï ve Bayes classification model is the best model to classify the gynaecological domain text in Tamil language
A technology-enhanced learning intervention for statistics in higher education using bite-sized video-based learning and precision teaching

&#x0D; &#x0D; &#x0D; Adjustments to life and learning following the COVID-19 pandemic have transformed user acceptance of online learning methods. It is, therefore, imperative to analyse factors relating to user performance and preferences for such interactions. In this study, we combined video-based learning with precision teaching to reinforce previously learnt statistics skills in university students without a mathematical background. We developed a learning design consisting of eight ‘bite-sized’ online learning episodes. Each episode started with a brief learning video followed by a practice phase and an end-of-episode assessment. The practice phase differed in two groups of participants, matched on statistics attainment pre- intervention. A precision-teaching intervention group (N = 19) completed practice guided by a frequency-based approach aiming at building fluency in statistics. A control group (N = 19) completed self-directed practice for the same amount of time as the intervention group. All participants completed a statistics attainment test and a questionnaire on their attitudes towards statistics pre- and post- intervention, and a review of the learning materials post-intervention. The intervention group achieved, consistently, higher scores in all end-of-episode assessments compared to the control group. Both groups showed significant and comparable improvements in statistics attainment post-intervention. Both groups also reported more positive feelings towards statistics post-intervention, while the review of the learning materials suggested that the video-based learning design was well-received by students. Our results suggest that video-based learning has great potential to support, as a supplementary teaching aid, university students in learning statistics. We discuss future research directions and implications of the study.&#x0D; &#x0D; &#x0D;