Reviewerly/exHarmony huggingface dataset:
Manajemen Pembelajaran Online dalam Meningkatkan Kemampuan Berkomunikasi Bahasa Inggris Mahasiswa

The research stems from concerns among graduate students who lack proficiency in English communication, essential in today's era of global competition. This study aims to comprehensively analyze the online-based English language learning management's role in enhancing students' English communication skills. Specific objectives encompass English Learning Planning, Organizing, Implementation, and Evaluation, while addressing inhibiting factors and devised solutions. Employing the POAC management function (Planning, Organizing, Actuating, Controlling) by G.R. Terry, the research follows a qualitative case study approach. Outcomes demonstrate that effective planning with vision, mission, objectives, and schedules significantly improves students' English communication skills. Organizing online English learning through an optimized structure and dedicated teaching team facilitates the process. Curriculum-guided implementation utilizes native speaker guest lecturers for an enriched experience. Evaluations are consistently refined through iterative processes. Despite challenges such as human resources, infrastructure, and institutional policies, alternatives are established to overcome barriers. Solutions encompass human resources, facility enhancements, ICT advancements, and policy adaptations, all fostering English communication skills. Online-based English learning yields graduates with proficient English communication abilities, positively impacting their overall quality. However, the study highlights that the current online-based management hasn't reached its full potential in enhancing communication skills. Bridging the gap between online and practical learning can more effectively train students in English communication, gradually achieving the desired proficiency
Analysis of Financial Fraud Cases of Le.com

Le.com (formerly LeTV), as the first online video company listed in A shares in China, which plays a role as a weather vane for the online video industry, was reported to had committed financial fraud for up to ten years from 2007 to 2016, mainly by fabricating business with related companies and third-party companies to construct a false capital cycle. On May 2, 2021, the China Securities Regulatory Commission (CSRC) issued a penalty decision. By introducing the background of Le.com and its main fraud methods, based on the three major reasons of case analysis and fraud triangle theory, this paper focuses on the problems of its ownership concentration and imperfect audit system, and analyzes the specific motivation analysis of Le.com financial fraud.
The Influence of Movie Release Modes on Movie Revenues: Take Lost in Russia and Lost in Thailand as Examples

In recent years, the competition between China's streaming movies and theatrical movies has intensified. The epidemic has further promoted the development of streaming movies and enhanced the competitiveness of streaming platforms. Traditional theater companies have felt the threat of being "replaced". This article takes the two movies Lost in Russia and Lost in Thailand as examples to analyze the impact of different release modes on the tbox office and concludes that the streaming movie Lost in Russia has a higher box office. Alt-hough there are some accidental factors in the success of Lost in Russia, this study guides the future development of streaming media platforms and theater companies. This article finds that streaming movies have advantages in publicity and cinema chains have ad-vantages in production through SWOT analysis, but they also have inherent disadvantages. Since the streaming media platform and the theater chain cannot replace each other for the time being, and the coexistence of the two release modes is unavoidable, the streaming media platform and the theater chain company should learn from each other's advantages and develop together.
Performance Evaluation of AquaFeL-PSO Informative Path Planner Under Different Contamination Profiles

The use of Autonomous Surface Vehicles allows streamlining the task of monitoring the water quality parameters of water resources, reducing costs and time spent. In addition, the vehicles are capable of taking water measurements in hard-to-reach areas. This chapter evaluates the performance of the AquaFeL-PSO monitoring system in different contamination profiles. The AquaFeL-PSO is an algorithm based on Particle Swarm Optimization, Gaussian processes and Federated Learning technique. The operation mode of the algorithm is two-phase, the exploration phase, which is responsible for covering the largest possible area of the water resource in order to detect contaminated areas, and the exploitation phase, which is responsible for accurately characterizing the water quality parameters in the contaminated areas. This system is evaluated using different benchmark functions in order to observe the performance of the system under different contamination profiles. The results show that the AquaFeL-PSO was able to generate models of the water quality parameters in eight of the ten contamination profiles. In addition, it had the best performance in detecting pollution peaks and generating the model of the water quality parameters of the entire water resource.
Semi-automatic ontology development for supporting personalized tutoring

Many researches have working the last two decades on ontology learning, using NLP, data mining, and machine learning, but experiments show, that every ontology learning method have his precision and recall less than 1, and automatically developed ontologies need from human evaluation and modification. So, participation of experts is a must in ontology development to guarantee high quality and the semi-automatic ontology development is the only approach having potential to ensure cheaper development of high-quality ontologies. In this research we will discuss semi-automatic support of ontology engineering process, based on automated knowledge extraction from text, semi-structured or structured sources. Most of automated ontology development methods and algorithms are domain – dependent. We analyze specifics of semi-automatic ontology development in the e-learning domain and propose software architecture of semi-automatic ontology development framework for educational ontologies.
Biomechanical constraints on escape from threat in virtual reality: Preliminary findings

Humans exhibit complex and diverse movements when encountering threats. In this study, we examined spatio-temporal parameters, trunk segment angle, and movement intensity during escape behavior in virtual reality (VR) scenarios. What biomechanical constraints influence human escape decisions during VR threat scenarios? We analyzed the data from our previous study by Sporrer et al. [1], where a fully-immersive VR environment was designed with a 5 ×10 m physical space in which participants could move freely. Participants (n=29) were instructed to forage for fruit on a bush and to avoid contact with various threats over 68 independent episodes. Participants were given a safe shelter to avoid the approaching threat (Fig. 1a.). Threat distances and speeds were such that at assumed escape speed, participants had at least 1.5 s time to start escaping. Participants were equipped with a VR headset (HTC Vive Pro Eye HMD), hand controllers, and three position-tracking sensors on the waist and each foot. The waist tracker was used to derive velocity and acceleration; head and waist trackers were used to estimate the trunk segment angle; and all of the trackers were used to calculate movement intensity [2] (Fig. 1b.). Fig. 1. (a) Example scenario of the VR environment featuring bushes for foraging, a safehouse for escaping, and a panther as a predatory threat. (b) Location of the position-tracking sensor and the equations used to calculate relevant variables. (c) Results from the first 1.5 s after threat appeared (black plot), and the previous 1.5 s before the end state (blue plot).Download : Download high-res image (215KB)Download : Download full-size image Participants who were caught by the threat had a lower maximum velocity within the first 1.5 seconds of the threat appearing (0.19±0.02 m/s) compared to those who escaped (0.63±0.02 m/s). Participants who were caught by the threat also leaned forward more (p<0.001) within the first 1.5 seconds after the threat appeared. Movement intensity was higher in all trackers during the first 1.5 seconds after the threat appeared in successfully escaped trials (Fig. 1c.). However, in the last 1.5 seconds before the end state, the participants who were caught by the threat exhibited higher velocity (p<0.001), acceleration (p<0.001), and overall movement intensity than the escaped participants, which may indicate that they were still trying to escape. Our findings suggest that although participants had at least 1.5 s decision time in all scenarios, moving less quickly during this period would make later virtual death more likely. The forward trunk inclination during this period may also have imposed a biomechanical constraint that affected their ability to move, walk, or run in another direction. Overall movement intensity across all body parts may also predict if the participants will successfully escape from the threat. Further studies are required to elucidate biomechanical constraints on human escape.
Forecasting dining times in a full-service Thai hotpot restaurant Using Random Forest Classifier

In the foodservice industry, time is a crucial factor that impacts both consumers and management. Machine learning (ML) is increasingly used to improve the quality of services through prediction. In this study, we aim to develop a model for predicting meal duration using Random Forest Classification algorithm. The study uses data from the Point-of-Sale (POS) system of a full-service Thai hotpot restaurant, with a focus on two commercial areas in Bangkok. The variables that we used include the branch, the number of customers, the number of items, the day of the week, and the time of the day. As a result, the overall accuracy of the model was 86% and the F1-score was 0.81. The discussion of the potential use of this approach in connection with the existing system in a restaurant could also be beneficial, aiding the restaurant in planning management more efficiently and gaining a better understanding of consumer behavior. This study will discuss the results of the model along with additional perspectives for future work.
Ontology-based case study management towards bridging training and actual investigation gaps in digital forensics

The training programs in digital forensics have contributed many case study models to guide digital forensic analyses. However, they only account for a small number of real cases and they are usually too abstract while actual cybercrime investigations are more diverse and complex. This gap leads to difficulties in giving immediate and straightforward actions for law enforcement during cybercrime investigations. In this paper, we propose an ontology-based knowledge map model, which is a foundation model for building a case study management system for Digital Forensic Intelligence (DFINT) and Open Source Intelligence (OSINT) in digital forensics. The main idea of this proposed model is to encode specific training cases of cybercrime into knowledge map representations, then the system uses the knowledge from the ontology to provide more information on the context and enrich them to match actual cybercrime scenes. Therefore, this approach can be used to bridge the gap between training case studies and the actual investigation environment. To illustrate our approach, we build a DFOSINT ontology for DFINT and OSINT domain; develop a prototype of the case study management system, and evaluate it in two aspects, ontology validation and case study validation with existing case studies of digital investigations.
Effective utilization of solar energy in sustainable agricultural irrigation a provincial example from Turkiye

Food supply security is one of the most important problems facing the world today. Although fertile agricultural lands are decreasing day by day due to climate change caused by global warming, the demand for food is increasing with the increasing population from around the world. In addition, energy input costs in food production are increasing day by day. One of the energy input costs is the energy spent for agricultural irrigation. Meeting the energy required for agricultural irrigation from the Sun will both prevent environmental problems caused by fossil fuels and reduce agricultural irrigation costs. In this study, the province of Mus, which is one of the least developed cities in Turkey, with a high potential for agriculture and solar energy, is considered as a case study. In this study, firstly, the equipment that can be used in the solar-powered irrigation system was determined, after the necessary formulas and calculations were made in detail, finally, the cost analysis of this irrigation system was made and the time that the system could pay for itself was calculated. With this study, when the size of a field and the distance of the water source from the field are known, the required water pump size, PV panel size and installation cost can be easily calculated. Thus, a tool has been developed that can be easily used by every farmer. It is believed that this program will make a great contribution to encouraging farmers to en-gage in irrigated agriculture by utilizing solar energy.
Research advanced in offline handwritten signature verification

Biometrics technology has penetrated into various fields of our lives that require authentication and verification, such as criminal investigation, check processing and legal procedures, which is a focus of debate in the scientific research community. As a biometric feature that is easy to obtain, verification of handwritten signatures has attracted great interest in the past several decades. Although the online handwritten signature verification (OnHSV) system can obtain more information and has a higher accuracy rate, the offline handwritten signature verification (OfHSV) system remains the focus of research, since most signatures we can obtain are offline. In this paper, according to the difference of steps of OfHSV, we classify and introduce the advanced research work of OfHSV from four aspects: datasets, preprocessing techniques, feature extraction methods, and neural network structures. Additionally, we introduced the background of signature verification system and the experimental results of existing technologies. Finally, we presented some notable challenges, leading the reader to current trends and future directions in this field.
Optimalisasi Overload Traffic Dan Request Cloud Environment Menggunakan Metode Content Delivery Network Dan Private Zone Di RCTI+

Since 2019 Indonesia has had rapid development, especially in the digital industry, recorded at the 2020 World Economic Forum. Indonesia has 2,193 startup companies engaged in the digital field. It can also affect the ability of these companies to address existing or future digital problems. As is known, down or system failure in a server can cause losses in the company's economy. For a company the size of Facebook alone, it could suffer a loss of 99.75 million US Dollars. One of the causes of server failure is Overload Traffic and Request Cloud Environment caused by the large number of requests submitted to the server. Requests submitted to the server are recorded into 2 data, namely, CCU (Count Current User) and Traffic. In this study, the author uses the CDN and Private zone methods on Alibaba Cloud to share the server load so that there is no downtime or server failure caused by Overload Traffic and Request Cloud. It is hoped that this research can be helpful to networking engineers in overcoming Traffic Overload and Request Cloud, especially on Alibaba Cloud.
Naive Bayes Classification for Email Spam Detection

Email is one of the cheapest forms of communication that every internet user utilizes, from individuals to businesses. Because of its simplicity and wide availability, it is vulnerable to threats by perpetrators through spam with malicious intents, known to have resulted in huge financial losses and threatened the privacy of millions of individuals. Not all spam emails are malicious; however, they are a nuisance to users regardless. Because of these reasons, there is a dire need for good spam detection systems that are automatically able to identify emails as spam. This chapter aims to do exactly that by proposing a Naïve Bayes approach to create a spam detection system by using a combination of the Enron Email dataset and the 419 fraud dataset. The datasets are lemmatized in order to boost performance in terms of execution time and accuracy. Grid search is one technique adopted to maximize accuracy. Finally, the model is evaluated through various metrics and a comparative analysis is performed.
Application of Machine Learning for Disabled Persons

Communication with the disabled person who is deaf-mute person is very difficult. Even though sign language is crucial for deaf-mute persons to communicate with other people and with themselves, regular people still pay it little attention. Normal people often overlook the value of sign language unless they have family members who are deaf-mute. Using sign language interpreters is one way to communicate with those who are deaf-mute. However, hiring sign language interpreters can be expensive. A model that can automatically convert their motions into words can be used as a low-cost replacement for the interpreters. This chapter gives the description about the development of a model which helps automatic detection of actions with real time using the Mediapipe model and then with the help of sign language conversion model it translates into the textual format.
Improving the BiLSTM model performance for tweet sentiment analysis

Twitter is a microblogging website where users can publish brief entries known as tweets. These tweets can occasionally reveal the users' attitudes and feelings. This paper analyses three emoticon processing methods with the BiLSTM model to discover the efficiencies of different methods in helping deep learning models classify tweet sentiments. Firstly, the simply removing method, the replacing with description text method, and the replacing with predefined sentiment method are established. Then the BiLSTM model is used to train and test with different methods on the Sentiment140 dataset. The performances of all models are evaluated by accuracy, F1 score, precision score, and recall score. The experimental results show that the replacing with predefined sentiments method provides the highest accuracy which is 0.84. The simply removing method also produces the testing accuracy as 0.84, but it performs worse in the last epoch, the training and validation accuracy, and the training and validation loss. The replacing with description text method produces the worst accuracy which is 0.83. It indicates that predefining the most possible sentiments of the popular emoticons has a reliable efficiency in optimizing the performance of deep learning models when the tweets with emoticons take a small proportion.
The Correlation between Students’ Reading Speed and Their Reading Comprehension in Descriptive Text

The purpose of the research is to find out the correlation between students’ reading speed and their reading comprehension of descriptive text among the tenth-grade students of SMAN 14 Kabupaten Tangerang in the academic year 2018/2019. The population of this research is the whole students in tenth grade at SMAN 14 Kabupaten Tangerang. The writers chose 73 students as the sample of this research by using simple random sampling. In this research, the writers applied principles of the quantitative method with correlation form. The data was collected by using Reading Speed as variable X and Reading Comprehension as variable Y. The data is analyzed by using Pearson correlate and the result of it is 0.398. It means the result of r-xy (0.398) is higher than the r-table (0.329). So it can be concluded that there is a positive correlation between students’ reading speed and their reading comprehension among tenth-grade students of SMAN 14 Kabupaten Tangerang in academic years 2018/2019.
Circle detection algorithm based on neighborhood density clustering

Circle detection in images is one of the key technologies in machine vision, pattern recognition, and artificial intelligence. However, conventional circle detection methods are sensitive to complex scenes, noise, and occlusion in images. Solving the impact of these situations is still the focus of circle detection algorithm research. Therefore, a circle detection algorithm based on neighborhood density clustering (NDC) is proposed. The proposed algorithm calculates circle parameters of connected regions after extracting corners, corroding the corners, and marking the connected regions. Then, NDC of the circle parameters is executed to classify arcs belonging to the same circle into one category to acquire a virtual connected region. And the circle parameters of the virtual connected region are clustered using NDC again to obtain the circle parameter dataset of the circle to be detected. The precise circle parameters are further estimated by calculating the centroid of each category. To prevent false positives, candidate circles are verified through a ratio rule. Extensive experiments using both synthetic and real images were performed. The results compared with those of representative state-of-the-art methods demonstrate that the proposed algorithm can be applied to a variety of complex scenes and has several advantages: good anti-occlusion effect, more robustness against noise, high accuracy, and better performance.
Fast naviation with icosahedral golden gates

An algorithm of Ross and Selinger for the factorization of diagonal elements of PU(2) to within distance $\varepsilon$ was adapted by Parzanchevski and Sarnak into an efficient probabilistic algorithm for any element of PU(2) using at most effective $3\log_p\frac{1}{\varepsilon^{3}}$ factors from certain well-chosen sets associated to a number field and a prime $p$. The icosahedral super golden gates are one such set associated to $\mathbb{Q}(\sqrt{5})$. We leverage recent work of Carvalho Pinto, Petit, and Stier to reduce this bound to $\frac{7}{3}\log_{59}\frac{1}{\varepsilon^3}$, and we implement the algorithm in Python. This represents an improvement by a multiplicative factor of $\log_259\approx5.9$ over the analogous result for the Clifford+$T$ gates. This is of interest because the icosahedral gates have shortest factorization lengths among all super golden gates.