Reviewerly/exHarmony huggingface dataset:
Comparative Study of Stemming Strategy for Hadith Text Classification

Arabic language is a language has a deep meaning. Text Classification has become trending in this era due to increasing data on the Internet. However, there is only few researchers only work on Arabic language due to the difficulties of Arabic language. In this study, the researcher using Hadith Arabic Text Dataset with different Stemming strategy method. The researcher managed to collect 1253 documents of Hadith sahih from different kitab which are Bukhary, Muslim and Tirmizi. Convolutional Neural Neural with Linear Support Vector Machine were used as classifier in order to investigate the performance of the model. The experiment were conducted with three different Arabic strategy method; light stemming, Khoja Stemming and no stemming method. The non-stemming strategy managed to get higher accuracy compared to other strategy method which is 89.05% of accuracy.
INTERACTIVE WORKLOAD TRACKING SYSTEM

Tracking system that are being used in the companies are outdated and time consuming. By creating a dashboard, it will increase efficiency and reduce the workload and the workflow hinderance. Researches show that current workload tracking system are behind paywall and on average would cost 10$ per to use. This project aims to determine how to reduce those average and create an interactive tracking workload system. In order to get a good working interactive workload system, this project aims to achieve theobjectives such to identify the importance of the employees by tracking their workload and to develop an interactive dashboard that will be able to track the employees. To achieve those objectives, I am aiming to follow the SDLC (Software Development Life Cycle) and waterfall model. And I have divided my project workflow into 4 parts which are research tools, database construction, front-end construction, and testing. The 4 divided covered as the SDLC and waterfall models that I have followed the methods to finish the project. The result of the project is the working interactive workload tracking system where the users can see their tasks and their tasks progress. And the manager or supervisor can also see their employee progress with the help of pie chart. The website and the interface will satisfy the importance of the employee progress and the employees’ workloads. 6
Conceptual and Algorithmic Models of Air Manipulation System

The chapter describes the conceptual model and the general structure of the aerial manipulator system, new tasks for manipulator control related to the instability of its base and interaction with ground-based objects. The analysis of the destabilizing factors affecting the deviation of the movement of the end working mechanism from the specified trajectory is carried out. The main task of this study is formulated and related to the design of the control system of the manipulator mounted on the UAV and maintaining the center of mass on the vertical axis when interacting with ground-based objects. Then we describe a number of developed algorithms that implement the task of motion control and stabilization of the air manipulation system.
Performance Evaluation of Hybrid Feature Selection Technique for Sentiment Classification based on Food Reviews

This paper presents an evaluation of the performance efficiency of sentiment classification using a hybrid feature selection technique. This technique is able to overcome the issue of lack in evaluating features importance by using a combination of TF-IDF+SVM-RFE (Term Frequency-Inverse Document Frequency (TF-IDF) and Supports Vector Machine (SVM-RFE)). Feature importance is measured and significant features are selected recursively based on the number of significant features known as k-top features. We tested this technique with a food reviews dataset from Kaggle to classify a positive and negative review. Finally, SVM has been deployed as a classifier to evaluate the classification performance. The performance is observed based on the accuracy, precision, recall and F-measure. The highest accuracy is 80%, precision is 82%, recall is 76% and F-measure is 79%. Consequently, 24.5% of the features to be classified in this technique have been reduced in obtaining these highest results. Thus, the computational resources are able to be utilized optimally from this reduction and the classification performance efficiency is able to be maintained.
A Study on the Possibility of Liberal Theatre Class Employing Documentary Theatre Focusing on Die Ermittlung and The Acting Motivation

Documentary theatre is not only notable for its trends among contemporary theatre, but also for its various characteristics, many of which can connect learners' lives and society, as well as draw upon their critical cognition and convergent thinking skills. This article notes the educational implications found in documentary theatre, and discusses the possibility of liberal theatre courses employing documentary theatre. Classes like this convey knowledge of the theatre and is attempts to learn through it. In addition, various practical activities can be carried out by utilizing the convergence properties of the theatre, and from these activities learners can experience something quite aesthetic in nature that they otherwise might have missed. In this sense, this paper focuses on Die Ermittlung and The Acting Motivation. In traditional documentary theatre, Die Ermittlung, which deals with social issues, can provide an opportunity to critically recognize social problems. On the other hand, from the auto-documentary theatre we have The Acting Motivation, which expands the concept of documentary theatre that was previously discussed, and also reflects on the lives of young people and the society they must relate to. Not only can learners acquire knowledge of the theatre, but they can also reflect on society and themselves through its productions. In addition, the step-by-step activities attempted in conjunction with this class aim at helping students solve problems, and to express themselves using a theatrical-based imagination. The purpose of the final step of the program--the appreciation and reflection process--is to integrate learning with individual experiences, and to further find new questions. Through these discussions, the possibility of using documentary theatre as a practical model for convergence education and another class models is presented.
Design Thinking For Technology Design

This paper describes the workshop titled "Design Thinking For Technology Design." Design thinking is a collaborative design process that keeps the user's needs at the center. This workshop will introduce design thinking methodology to the participants through hands-on activities. The workshop participants will work on a problem and design a low fidelity prototype of a potential solution. This workshop is open to everyone.
Adaptive Correlation Filters Feature Fusion Learning for Visual Tracking

Tracking algorithms based on discriminative correlation filters (DCFs) usually employ fixed weights to integrate feature response maps from multiple templates. However, they fail to exploit the complementarity of multi-feature. These features are against tracking challenges, e.g., deformation, illumination variation, and occlusion. In this work, we propose a novel adaptive feature fusion learning DCFs-based tracker (AFLCF). Specifically, AFLCF can learn the optimal fusion weights for handcrafted and deep feature responses online. The fused response map owns the complementary advantages of multiple features, obtaining a robust object representation. Furthermore, the adaptive temporal smoothing penalty adapts to the tracking scenarios with motion variation, avoiding model corruption and ensuring reliable model updates. Extensive experiments on five challenging visual tracking benchmarks demonstrate the superiority of AFLCF over other state-of-the-art methods. For example, AFLCF achieves a gain of 1.9\(\%\) and 4.4\(\%\) AUC score on LaSOT compared to ECO and STRCF, respectively.
Adaboost Hybrid Algorithm with Color Space Transformation and Lighting Compensation Method to Detect Face Based on Emotion

In this study, facial recognition was performed using the Adaboost hybrid algorithm with the method of color space transformation and lighting compensation (CST-LC) to detect faces based on emotions. This study aims to improve facial recognition in the research. Face detection was carried out based on a combination of the skin color segmentation algorithm with the AdaBoost algorithm. The experimental results show that the value of the initial processing parameters using the CST-LC method is an average of PSNR 7.586 and MSE 50.86 and the results of face detection using a dataset with an accuracy value of 87.74 %. The value of the accuracy results in this study is still better than the contribution research above, which is 65.3% where the overall face detection performance in this study is 25.57% better than the research contribution above.
A Comparative Study on Fault Detection Using Twin Support Vector Machines for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are composed of distributed small sensors that are deployed in various environmental conditions to collect data such as temperature, moisture, light speed, etc. These sensors are subjected to various damages, noises and low battery life as a result of which sensors account for abnormal data readings. These anomalies must be detected for the reliable and efficient working of a WSN. This study presents a comparative study for fault detection using Twin Support Vector Machines (TSVM) by utilizing different kernel functions. TSVM uses two non-parallel hyperplanes to classify data and is insensitive to class size imbalance. The choice of kernels significantly influences the performance of TSVM. The proposed work is estimated using different standard WSN datasets by inserting various types of fault. The performance of the work is measured using standard performance indexes.
국어교사의 신념과 읽기 수업 실행 양상

In this study, we aimed to examine the constructivist beliefs of Korean language arts teachers and the relationship between their beliefs and classroom practices in teaching reading. A survey on teacher beliefs and practices in the teaching of reading was conducted on 216 Korean language arts teachers in secondary schools. To measure teacher beliefs, the Literacy Orientation Survey (LOS) was translated from English into Korean and validated with confirmatory factor analysis. The mean score on the LOS for the participants was 3.39, indicating that their constructivist orientation was relatively moderate. Among their beliefs about the content of reading instruction, “cultivating a positive attitude” and “reading in daily life” were highly correlated with the LOS. “Textbooks” were most often selected as reading material, but they were negatively correlated with the LOS. The extent to which they believed “the contents of the textbook” were important for evaluation and the extent to which they were using “multiple-choice tests” were both negatively correlated with their constructivist beliefs. The results of this study indicated that Korean language arts teachers’ beliefs were mostly congruent with their practices. But they were often engaged with the practices that they reported they did not support. Further research is needed to understand the subtle relationship between teachers’ beliefs and practices for teaching reading.
Towards many-to-one neural style transfer method

Neural style transfer aims at transforming the artistic style of a painting into a photograph. The classic methods are based on the one-to-one principle, where stylization is performed based on a single style image. In the paper, an approach is proposed that allows transferring the style based on multiple images of the same reference painter multiplied, in addition, by data augmentation. Comparing to the original method, the proposed approach produces visibly better results, and allows for greater flexibility in the transferring process. The proposed many-to-one method is illustrated by an example of transferring the style of two Polish painters using the same content image
Research on the Emotion Regulation Effect of Home Sports Based on Social Media Data Analysis and Online Experimental Investigation

Effective in preventing the spread of Covid-19 though, a long period of home quarantine would also impose a negative impact on the public emotion. Therefore, this study aimed to explore the emotional regulation effect of home exercises. The social media data during Wuhan’s lockdown was used to analysis the emotional orientation of the masses. This thesis has conducted data on the subjective emotional status of subjects before and after home exercise from an one-week online experiment, when the regulating effect on emotion from various types of home exercise was quantitatively evaluated in accordance with different emotional weights. The experimental results showed that generally home exercise imposed a positive impact on the regulation of emotion during the epidemic period, while different exercise forms saw their impacts varying. Based on the results and analysis, we suggest that people should embark on the exercise of the second maximum intensity/duration within 10 to 60 minutes/moderate energy consumption for emotion regulation. The experimental results of this study have shown a significance for picking up proper home exercise forms during the epidemic period, as well as the practice of intelligent exercise during home quarantine in the future.
Extraction of non-uniform reflected laser lines based on multi-channel region constraint method

At present, the existing laser line extraction algorithms need to be in good lighting conditions to get better results. However, in practical application, due to the uneven illumination of the scene or the inconsistent reflection degree of the scanned object surface, the laser line extraction results will be greatly reduced. In some conditions, in order to improve the scanning efficiency, the grid laser line is used, but the grid laser is used Lines usually cause uneven distribution of laser lines. This paper proposes a method of extracting grid laser lines under the condition of non-uniform reflection. In the first step, the influence of global light can be eliminated using the difference between global illumination intensity and its first moment, and the image blocks can be classified using the average illumination intensity of local area to obtain the Region of interest (ROI). In the second step, a constrained region can be calculated by using edge gradient variance of local area and edge gradient maximization. In the third step, the center coordinates of sub-pixel laser line are finally obtained by combining the constrained region and illumination intensity map with Steger algorithm. The method used in this paper needs less empirical parameters to be set manually, and has a wider range of application. The accuracy of extraction is consistent with that of Steger algorithm. From the experimental results of this paper, we can see that we get more effective data, lower error rate and stronger robustness.
A block cipher algorithm developed using Magic Square in the seventh Order

Abstract This paper use the magic square of order 7 to develop Block encryption algorithms that work for encoding color images and text in GF(P) and GF(2 8 ). The algorithm use key with length = 35 and a message with length =14 are used, First, the keys are placed in the previously agreed positions and the remaining positions remain for the message, then the magic sum for each of them is calculated. Complexity, velocity, NIST calculations, and histogram calculations were calculated and the results were compared with the 5th degree magic square, where the complexity was with using GF(P) = (256) 14 × (P) 35 and using GF(2 8 ) = (256) 14 × (256) 35 , The magic square of the seventh degree is better than the magic square of the fifth degree in cryptography in terms of complexity and a slight difference in speed.
Covariance Analysis of the Receding Horizon Optimal FIR Filter

In this paper, we study properties of the receding horizon optimal FIR (RHOFIR) filter. The used approach based on analysis its error covariance matrix (ECM). Our contributions are as follows. First, the monotonicity and convergence of the ECM with increasing the horizon size of the sliding window (SW) are established. One important consequence of obtained results is that the ECM trace of the RHOFIR filter does not reach its lower bound (a steady state) on compact sets. This allows formulating a rule for selecting a horizon size of the SW determining a moment when the ECM trace enters a neighborhood of the steady state. Second, an upper bound is obtained for the decomposition of the ECM into two terms in which one of them does not depend on the noise of the dynamics. This makes it is possible to specify an estimate for the horizon size of the SW using information only about the noise intensity of the measurements. Third, an error sensitivity analysis to the dynamics and measurements noises of the ECM is carried out.
Methodology for the Assessment of the Text Similarity of Documents in the CORE Open Access Data Set of Scholarly Documents

This paper describes the methodology of data preparation and analysis of the text similarity required for plagiarism detection on the CORE data set. Firstly, we used the CrossREF API and Microsoft Academic Graph data set for metadata enrichment and elimination of duplicates of doc-uments from the CORE 2018 data set. In the second step, we used 4-gram sequences of words from every document and transformed them into SHA-256 hash values. Features retrieved using hashing algorithm are compared, and the result is a list of documents and the percentages of cov-erage between pairs of documents features. In the third step, called pairwise feature-based ex-haustive analysis, pairs of documents are checked using the longest common substring.
Real-time 16K video coding on a GPU with complexity scalable BPC-PaCo

The advent of new technologies such as high dynamic range or 8K screens has enhanced the quality of digital images but it has also increased the codecs' computational demands to process such data.This paper presents a video codec that, while providing the same coding features and performance as those of JPEG2000, can process 16K video in real time using a consumer-grade GPU.This high throughput is achieved with a technique that introduces complexity scalability to a bitplane coding engine, which is the most computationally complex stage of the coding pipeline.The resulting codec can trade throughput for coding performance depending on the user's needs.Experimental results suggest that our method can double the throughput achieved by CPU implementations of the recently approved High-Throughput JPEG2000 and by hardwired implementations of HEVC in a GPU.
Deblurring Processor for Motion-Blurred Faces Based on Generative Adversarial Networks

Low-quality face image restoration is a popular research direction in today's computer vision field. It can be used as a pre-work for tasks such as face detection and face recognition. At present, there is a lot of work to solve the problem of low-quality faces under various environmental conditions. This paper mainly focuses on the restoration of motion-blurred faces. In increasingly abundant mobile scenes, the fast recovery of motion-blurred faces can bring highly effective speed improvements in tasks such as face matching. In order to achieve this goal, a deblurring method for motion-blurred facial image signals based on generative adversarial networks(GANs) is proposed. It uses an end-to-end method to train a sharp image generator, i.e., a processor for motion-blurred facial images. This paper introduce the processing progress of motion-blurred images, the development and changes of GANs and some basic concepts. After that, it give the details of network structure and training optimization design of the image processor. Then we conducted a motion blur image generation experiment on some general facial data set, and used the pairs of blurred and sharp face image data to perform the training and testing experiments of the processor GAN, and gave some visual displays. Finally, MTCNN is used to detect the faces of the image generated by the deblurring processor, and compare it with the result of the blurred image. From the results, the processing effect of the deblurring processor on the motion-blurred picture has a significant improvement both in terms of intuition and evaluation indicators of face detection.
Parametric q-Gaussian distributed stochastic neighbor embedding with Convolutional Neural Network

For dimensionality reduction, t-distributed stochastic neighbor embedding (t-SNE) is famous. This technique represents the similarity between the pair of the samples in the high-dimensional space as Gaussian distribution. Then the similarity between the pair of the samples in the low-dimensional embedding space is also represented by using t-distribution to obtain the feature vectors in the embedding space from the high-dimensional data. The authors proposed q-Gaussian distributed stochastic neighbor embedding (q-SNE) as an extension of the t-SNE. The q-Gaussian distribution can express many distributions by setting hyperparameter q$= and includes the Gaussian distribution and the t-distribution as the special cases with hyperparameter q close to 1.0 and q = 2.0. However, these methods are applicable for a given data set and it is not possible to map new samples into the embedded space. To address this problem, the parametric t-SNE is proposed to construct the non-linear mapping by using a feed-forward neural network. In this paper, we propose a novel technique called parametric q-SNE with Convolutional Neural Network without pre-training. On MNIST, FashionMNIST, and COIL-20, the effectiveness of the parametric q -SNE is shown by using the visualization on 2-dimensional mapping, and the classification by using k nearest neighbors (k-NN) on the embedded space.
Concentration of the Collision Estimator

This work establishes strong concentration properties of the natural collision estimator, which counts the number of co-occurrences within a random sample from a discrete distribution.Although this estimator has a wide range of applications, including uniformity testing and entropy assessment, prior works don’t give any insights into its stochastic properties beyond the variance bounds.We circumvent technical difficulties, applying elegant techniques to bound higher moments and conclude concentration properties. Remarkably, this shows that the simple unbiased estimator in many settings achieves desired accuracy on its own, with no need for confidence boosting.
Multi-Task Deep Neural Networks for Multi-Document Reading Comprehension

Machine Reading Comprehension (MRC) is a challenging nature language processing (NLP) task and has become enormously popular, which aims to guide the machine to comprehend unstructured textual data and answer the given question. Recently, there has been much progress in single-document MRC, However, MRC in the real-world usually requires the machine to answer questions using analyzing multiple documents not only a single document, especially for real web search engine. Compared with single-document MRC (MRC task on a single document), multi-document MRC is much more challenging, since it needs to get multiple verifiable answer candidates from different documents. In the paper, we address the problem by using multi-task learning. We present a multi-task neural network framework for multi-document MRC task, in which different loss functions are used for multiple task objectives. Extensive experiments on publicly available large-scale multiple documents MRC dataset (i.e., MS-MARCO [1]) demonstrate the effectiveness of proposed model. Empirical results show that our proposed model outperforms all tested baselines with a wide margin and achieves satisfactory performance on MS-MARCO, and we also achieves better results on another challenging MRC dataset (i.e., SearchQA [2]).