Reviewerly/exHarmony huggingface dataset:
Analisis Pengalaman Pengguna Game Visual Novel Asal Usul Kota Surabaya Menggunakan Metode Usability Testing

A visual novel game is a visual communication design product that integrates graphic design through visual styles, visual character designs, and narrative designs accompanied by interactive communicative story construction. Games with the best storytelling and the ability to communicate historical chronology and cultural facts are synonymous with visual novel games. In the previous study, the Visual Novel Folklore Game of the Origins of the City of Surabaya featured puzzle and dialogue mechanics to build a more interactive narrative with the user. In addition, there is a branching narrative where users can make decisions by choosing which point of view the user wants to play. Several stages in this study include data collection, usability testing, data analysis, and conclusion. The results of this user experience analysis research overview several usability aspects that should be concerned in the subsequent game development. The usability aspects tested are Learnability, Memorability, Efficiency, Error, and Satisfaction. Of the 114 respondents involved in this study, the highest average score was obtained for the satisfaction aspect, which was 4.17, while the lowest average score was for the error aspect, which was 3.83, the games is easy to play and get satisfying results in every aspect of usability, the games is easy to play and achieves satisfactory results in each usability parameter.
Comparison of Stereo Matching Algorithms for the Development of Disparity Map

Stereo Matching is one of the classical problems in computer vision for the extraction of 3D information but still controversial for accuracy and processing costs. The use of matching techniques and cost functions is crucial in the development of the disparity map. This paper presents a comparative study of six different stereo matching algorithms including Block Matching (BM), Block Matching with Dynamic Programming (BMDP), Belief Propagation (BP), Gradient Feature Matching (GF), Histogram of Oriented Gradient (HOG) and Fixed-Window Aggregated Cost (FWAC). In addition, three cost functions, namely, Mean Squared Error (MSE), Sum of Absolute Differences (SAD), and Normalized Cross-Correlation (NCC) were utilized and compared. The stereo images used in this study were obtained from the Middlebury Stereo Datasets provided with perfect and imperfect calibrations. It was observed that the selection of matching function is quite important and also depends on the image properties. Results showed that the BP algorithm in most cases provided better results achieving accuracies over 95%. Accordingly, BP algorithm is highly recommended based on rapidity and performance and for applications with the need of detection of small details, HOG is advised.
Classification of Russian textbooks by grade level and topic using ReaderBench

Textbooks are essential resources for classroom and offline reading, while the quality of learning materials guides the entire learning process.One of the most important factors to be considered is their readability and comprehensibility.Therefore, the correct pairing of textbook complexity and student grade level is paramount.This article analyzes automated classification methods for Russianlanguage textbooks on two dimensions, namely the topic of the text and its complexity reflected by its corresponding school grade level.The studied corpus is a collection of 154 textbooks from the Russian Federation from the second to the eleventh grade levels.Our analysis considers machine learning techniques on the textual complexity indices provided by the open-source multi-language framework ReaderBench and BERT-based models for the classification tasks.Additionally, we explore using the most predictive ReaderBench features in conjunction with contextualized embeddings from BERT.Our results argue that incorporating textual complexity indices improves the classification performance of BERT-based models on our dataset split using 2 strategies.More specifically, the F1 score for topic classification improved to 92.63%, while the F1 score for school grade-level classification improved to 54.06% for the Greedy approach in which multiple adjacent paragraphs are considered a single text unit up until reaching the maximum length of 512 tokens imposed by the language model.
Autonomous flight insurance method of unmanned aerial vehicles Parot Mambo using semantic segmentation data

Autonomous navigation of unmanned aerial vehicles (UAVs) has become in the past decade an extremely attracting topic, also due to the increasing availability of affordable equipment and open-source control and processing software environments. This demand has also raised a strong interest in developing accessible experimental platforms to train engineering students in the rapidly evolving area of autonomous navigation. In this paper, we describe a platform based on low-cost off-the-shelf hardware that takes advantage of the Matlab/Simulink programming environment to tackle most of the problems related to UAV autonomous navigation. More specifically, the subject of this paper is the autonomous control of the flight of a small UAV, which must explore and patrol an indoor unknown environment. Objectives: to analyse the existing hardware platforms for autonomous flight indoors, choose a flight exploration scenario of unknown premises, to formalize the procedure for obtaining a model of knowledge for semantic classification of premises, to formalize obtaining distance to obstacles using data camera horizontally employment and building on its barrier map. Namely, we use the method of image segmentation based on the brightness threshold, a method of training the semantic segmentation network, and computer algorithms in probabilistic robotics for mobile robots. We consider both the case of navigation guided by structural visual information placed in the environment, e.g., contrast markers for flight (such as path marked by a red tape), and the case of navigation based on unstructured information such as recognizable objects or human gestures. Basing on preliminary tests, the most suitable method for autonomous in-door navigation is by using\ object classification and segmentation, so that the UAV gradually analyses the surrounding objects in the room and makes decisions on path planning. The result of our investigation is a method that is suitable to allow the autonomous flight of a UAV with a frontal video camera. Conclusions. The scientific novelty of the obtained results is as follows: we have improved the method of autonomous flight of small UAVs by using the semantic network model and determining the purpose of flight only at a given altitude to minimize the computational costs of limited autopilot capabilities for low-cost small UAV models. The results of our study can be further extended by means of a campaign of experiments in different environments.
EFFECTIVENESS OF ETHICAL LEADERSHIP ON EMPLOYEE PERFORMANCE: SYSTEMATIC LITERATURE REVIEW

This paper's goal is to develop an appropriate systematic literature review of the research that has investigated how ethical leadership affects employees' performance within an enterprise. The growth of multinational corporations provides a new dimension to the investigation of leadership that takes into account the effect of diverse country cultures. This systematic literature review analyzed publications and articles published within the last decade (2009- 2020). Based on the overall research of the 19 analyzed studies top managers, leaders, and supervisors are supposed to set a good example for their team members and employees by modeling ethical behavior. The effectiveness of top managers and leaders on employees and their performance has been thoroughly investigated in many research. All hypotheses were supported by the data collected from 19 papers, which showed that ethical leadership improves employees' in-role job performance. These findings have important research and practical consequences. Additionally, since ethical leadership is seen to be essential in enhancing the accepted business strategy in the achievement of organizations’ objectives and goals, this research will concentrate on the effects of an ethical leadership style on the performance of employees.
Economic Prospect of Cryptocurrency: Nigeria as a Case Study

The emergence of blockchain technologies has experienced rapid growth and generated lots of activities around the globe, where people use cryptocurrencies for transactions and as investments.Nigeria's cryptocurrency market is surging due to the devaluation of the naira and has caused local currency to depreciate.Cryptocurrencies and blockchain technologies are part of a broader wave of technologies that facilitate peer-to-peer (P2P) commerce, and personalisation of market products.The technology create money without central banks and facilitate payments without financial institutions.This study investigates the economic prospect of cryptocurrency with a focus on Nigeria as a case study.The study employed the use of qualitative data gathered using a structured online questionnaire.The result revealed that 69% of the participants were male, and 48.6% were between the ages of 25-34.Furthermore, 46.8% of the respondent are in support of cryptocurrency regulation by the Nigerian government, rather than the total ban placed on the technology.A large proportion (73%) of the respondents are not in support of Nigerian government prohibiting commercial banks from dealing with cryptocurrency.In addition, the majority (91%) of the participants had knowledge of cryptocurrency.The study concluded that the apex banks should deliberately adopt cryptocurrency and create a department that rolls out policies to control its value.
Lightweight high-precision pedestrian tracking algorithm in complex occlusion scenarios

Aiming at the serious occlusion and slow tracking speed in pedestrian target tracking and recognition in complex scenes, a target tracking method based on improved YOLO v5 combined with Deep SORT is proposed.By merging the attention mechanism ECA-Net with the Neck part of the YOLO v5 network, using the CIoU loss function and the method of CIoU non-maximum value suppression, connecting the Deep SORT model using Shuffle Net V2 as the appearance feature extraction network to achieve lightweight and fast speed tracking and the purpose of improving tracking under occlusion.A large number of experiments show that the improved YOLO v5 increases the average precision by 1.3% compared with other algorithms.The improved tracking model, MOTA reaches 54.3% on the MOT17 pedestrian tracking data, and the tracking accuracy is 3.7% higher than the related algorithms and The model presented in this paper improves the FPS by nearly 5 on the fps indicator.
Robust Exponential Graph Regularization Non-Negative Matrix Factorization Technology for Feature Extraction

Graph regularized non-negative matrix factorization (GNMF) is widely used in feature extraction. In the process of dimensionality reduction, GNMF can retain the internal manifold structure of data by adding a regularizer to non-negative matrix factorization (NMF). Because Ga NMF regularizer is implemented by local preserving projections (LPP), there are small sample size problems (SSS). In view of the above problems, a new algorithm named robust exponential graph regularized non-negative matrix factorization (REGNMF) is proposed in this paper. By adding a matrix exponent to the regularizer of GNMF, the possible existing singular matrix will change into a non-singular matrix. This model successfully solves the problems in the above algorithm. For the optimization problem of the REGNMF algorithm, we use a multiplicative non-negative updating rule to iteratively solve the REGNMF method. Finally, this method is applied to AR, COIL database, Yale noise set, and AR occlusion dataset for performance test, and the experimental results are compared with some existing methods. The results indicate that the proposed method is more significant.
Psychology of the Interaction of Understanding and Forecasting Processes in Creative Mathematical Thinking

Based on the results of the analysis of research on mathematical thinking, its creative nature has been ascertained. The results of research on creative mathematical thinking were analyzed and the expediency of studying the psychological essence of the interaction of thought processes of understanding and forecasting when solving creative mathematical problems was ascertained.The aim of the article is to find out the psychological essence of the inte­raction of thought processes of understanding and forecasting in creative ma­thematical thinking. To study the interaction of the processes of understanding and forecasting in mathematical thinking, the method of analyzing students’ search actions du­ring solving creative mathematical problems of different classes was used.the results of the research. It was established that creative mathematical thinking is a complete system of interrelated actions, with the help of which the thinking mathematical result is achieved.It was established that the processes of understanding mathematical prob-lems and predicting thinking results function throughout the entire process of solving mathematical problems.It was found that the content of search actions aimed at understanding the problem and predicting thinking results depend on the stages of solving the problem (study of the condition, search for a solution, verification of the found solution), in which their procedural and dynamic side is not only manifested, but is also being formed. At the same time, the process of understanding a creative mathematical problem and the process of forecasting are complementary.It is established that the understanding of the condition of the problem forms the content of forecasting actions, and the process of forecasting cont­ributes to the formation of understanding of the mathematical problem. It was established that in the search mathematical process it is not possible to record such a state of understanding of the problem that would ensure the emergence of a hypothesis regarding the solution.It has been found that forecasting, which takes place throughout the en-tire search process, can generate a solution hypothesis at different stages of the solution, with different states of understanding of the mathematical problem.The hypothesis of solving the problem is an indicator of the state of un-derstanding of the problem, and its approbation contributes to deepening the understanding of the essence of the problem itself. At the same time, the content of the hypothesis, its approval determines the state of understanding of the problem.conclusion. The process of the subject’s understanding of a creative math-ematical problem and the process of prediction take place throughout all stages of the solution process and are mutually complementary.
Distributed Focused Web Crawling for Context Aware Recommender System using Machine Learning and Text Mining Algorithms

In today's world, Recommender System (RS) is the most effective means used to manage the huge amount of multimedia content available on the internet.RS learns the user preferences and relationships among the users and items.It helps the users to discover new interesting items and make use of different media types such as text, audio, video and images.RS can act as an information filtering model which can overcome the issues related to over-fitting and excess information.In this work, a new distributed framework named DAE-SR (Deep AutoEncoder based Softmax Regression) is introduced for context-aware recommender systems which focus on user-item based interaction and offers personalized recommendations.The proposed model is implemented in PYTHON platform.The dataset used for experimentation is Foursquare dataset.The performance of the proposed context-aware RS is beneficial to both the users and service providers.Its helps in decision making process and can offer relevant recommendations to users.The performance is evaluated in terms of various metrics such as accuracy, recall, precision and so on.From the implementation outcomes, the proposed strategy achieved good accuracy (98.33%), precision (98%), run time (1.43 ms) and recall (98.1%).Thus, it is proved that the proposed DAE-SR classifier performs better compared to other models and offer dependable and relevant recommendations to users.
Proxy Re-encryption Scheme based on the Timed-release in Edge Computing

With the growth of Industrial Internet, various types of data show explosive growth.Data is being moved from the cloud to the edge for computing more frequently and edge computing becomes an important factor affecting the deep application of Industrial Internet platforms.However, the security issue of data transmission sharing is not addressed.Therefore, this paper proposes a security scheme based on timedrelease, multi-dimensional virtual permutation, and proxy reencryption(PRE), to protect the confidentiality of the data during the transmitter; symmetric cryptography is employed to encrypt the transmission data.At the same time, a time server is used, making it impossible for data receivers to get the information about the data before the specified time arrives, it solves the application scenario of data transmission and sharing with timed-release requirements and the efficiency of a large number of data is solved, and the security of data is improved.Finally, the security of the scheme was proved theoretically.Compared to existing PRE schemes, this scheme adds timed-release controlled access, has resistance to ciphertext attacks and end-to-end security features, and uses fewer bilinear pair operations in the algorithm.The performance was tested experimentally and the results show that the scheme improves efficiency while ensuring security and has significant advantages in terms of data security and private data protection.
Summoning Method using Freehand Interaction for Occluded Object Selection in Handheld Augmented Reality

Ray-casting is often used for object manipulation that intersects with the ray that uses the intersection between rays extending from the user and a virtual object. In Augmented Reality (AR), ray-casting has been triggered by a single touch event on the screen using smartphone or handheld device. Object selection in AR implemented in a simple single touch experience, which should be easily understandable for users. However, to select or hover the distant object or occluded object becoming one of the issues need to be solved in handheld AR. Besides, to work with real hand gesture for handheld AR may be challenging. Therefore, the ray intersection will be one of the advantages to manipulate virtual object in AR when works with bare hand or freehand gesture. As aiming a ray out of one's hand towards out of reach item is a very abstract interaction, summoning method has been proposed to confirm the object selection. In handheld AR, summoning method has not been explored, this paper aims to explore the summoning method with raycasting in handheld AR using real hand gesture. It also compares the implementation of summoning using touch-based, the conventional method in handheld AR.
Identification and Counting of Blood Cells Using Machine Learning and Image Processing

Doctors view the total number of leukocytes in a person’s blood as a crucial sign of that individual’s general health. Historically, blood cell counting was performed manually using a hemocytometer and a few more lab equipment and chemicals. The process is slow and laborious. Red blood cells (RBC), white blood cells (WBC), and platelets are the three kinds of blood cells that can be detected and counted automatically using picture segmentation and S-CNN (Suit-Convolutional Neural Network) machine learning algorithms, as demonstrated in this body of work. Red blood cells, white blood cells, and platelets may be located and counted automatically using an open-source database of blood smear pictures. When the trained model was evaluated using smear images from a distinct dataset, it was observed that the learned models were relatively simplistic. The computer-aided tracking and identification technique allows us to count blood cells in less than one second from photographs of character assassination. It is helpful for real-world applications and has an approximate 92% degree of accuracy.
Sentiment Analysis on Moroccan Dialect based on ML and Social Media Content Detection

As technology continues to evolve, humans tend to follow suit, and currently social media has taken place as the defacto method of communication.As it tends to happen with verbal communication, people express their opinions in written form and through an analysis of their words, one can extract what an individual wants from a product, a topic, or an event.By looking at the emotions expressed in such content, governments, businesses, and people can learn a lot that can help them improve their strategies.Therefore, in this study, we will use different algorithms to improve the Moroccan sentiment classification.The first step is to gather and prepare Moroccan Dialectal Arabic Twitter comments.Then, a lot of different combinations of extraction (n-grams) and weighting schemes (BOW/ TF-IDF) and word embedding for feature construction are applied to get the best classification models.We used Naive Bayes, Random Forests, Support Vector Machines, and Logistic regression and LSTM to classify the data we prepared.Our machine learning approach, which incorporates sentiment analysis, was designed to analyze Twitter comments written in Modern Standard Arabic or Moroccan Dialectal Arabic.As a final benchmark of our paper, we were simply a sliver shy away from the 70% mark in our accuracy by relying on the SVM algorithm.Although not a game-changing result, this was enough to encourage us to continue developing our model further.
An Automated Text Document Classification Framework using BERT

Due to the rapid advancement of technology, the volume of online text data from numerous various disciplines is increasing significantly over time.Therefore, more work is needed to create systems that can effectively classify text data in accordance with its content, facilitating processing and the extraction of crucial information.Since these non-automated systems use manual feature extraction and classification, which is error-prone and time-consuming by choosing the best appropriate algorithms for feature extraction and classification, traditional procedures are typically resource intensive (computational, human, etc.), which is not a viable solution.To address the shortcomings of traditional approaches, we offer a unique text categorization strategy based on a well-known DL algorithm called BERT.The proposed framework is trained and tested using cutting-edge text datasets, such as the UCI email dataset, which includes spam and non-spam emails, and the BBC News dataset, which includes multiple categories such as tech, sports, politics, business, and entertainment.The system achieved the highest accuracy of 91.4% and can be used by different organizations to classify text-based data with a high performance.The effectiveness of the proposed framework is evaluated using multiple evaluation metrics such as Accuracy, Precision, and Recall.