Reviewerly/exHarmony huggingface dataset:
A MODEL OF TRAINING STUDENTS OF TOURIST HIGHER EDUCATION INSTITUTIONS IN THE FIELD OF HOTEL SERVICE

Today there is a contradiction that has developed between the need to strengthen the technological component training of students at a tourist university in hotel specializations, on the one hand, and an insufficient level of scientific support in professional tourism education aimed at improving the quality of technological training of students in a tourist university in the field of hotel service, on the other. This contradiction determined the research problem: what should be the content and organization of technological training of students at a tourist university in the field of hotel service. The problem and the degree of its development determined the purpose of the study: the development of the content and organization of technological training of students at a tourist university in the field of hotel service, ensuring a high level of their knowledge, skills and competitiveness in the labor market and professions. To solve the set tasks and test the hypothesis, the following research methods were used: theoretical analysis of philosophical, pedagogical, psychological, sociological literature; survey (questionnaire, conversation); modeling; observation; methods of studying and summarizing pedagogical experience; methods of statistical processing of results. As a results of the study, the following conclusions were formulated: the model of technological training of students at a tourist university in the field of hotel service, developed in this study, makes it possible to organize more effective technological training of students of a tourist university in the field of hotel service, and, accordingly, increase their attractiveness in the labor market as highly qualified managers of the hospitality industry. Practical implications. The materials of this study are recommended for creating a professional development program for teachers of educational institutions of the tourism and hotel profile, in particular, for teachers of specialized disciplines in hotel service (business). The materials of the discipline "Technology of hotel service" are recommended for improving typical professional service standards in hotels, which is very important for the development and improvement of hotel activities and improving the quality of guest service. Value/originality. The results of the study allow us to determine further prospects for studying this issue, which consists in finding new directions that ensure the strengthening of the technological orientation of training students of a tourist university in the field of hotel service. It seems to us that the results obtained in the course of the study will serve as the basis for further comprehensive study and wide application in practice of new large-scale and innovative approaches in the technological training of students at tourist universities in the field of hotel service.
Using Hot Seat Game to Improve Pupils’ English Vocabulary Mastery

Malaysian pupils generally have been introduced to English language since pre-school level.However, the pupils still have a problem of lack of English vocabulary which hinders them from being proficient in the language.Teachers need to try different teaching strategies that can enrich pupils' English vocabulary.Thus, the research's objective was to determine whether teaching vocabulary by using hot seat game can improve the pupils' vocabulary mastery.This action research involved 26 Year 3 pupils in a rural area primary school in Pahang.Data for this study was collected quantitative and qualitatively through vocabulary pre and post-tests, observation and semi-structured interviews.The mean of the pupils' score in Cycle 1 was 65.8 in the pre-test and went up to 77.1 in the post-test.During Cycle 2, the mean of the pupils' score was 75.2 in the pre-test and increased to 81.2 in the post-test.Therefore, it can be concluded that hot seat game was able to improve English vocabulary mastery of the Year 3 pupils after only two months of implementation.For further researches, a longer research's duration and more frequent use of hot seat game are advised as it might shows a better result in pupils' English vocabulary mastery.
Spam Detection and Spammer Community Detection in Social Media Platform Using Machine Learning Techniques

In recent years, social media platforms have experienced a surge in popularity, leading to an increase in spam and spammer communities. This paper presents a comprehensive study on spam detection and spammer community detection in social media platforms using machine learning techniques. The proposed approach focuses on three main features: Review-Behavioral (RB) Based features, Review-Linguistic (RL) Based Features, and User-Behavioral (UB) Based Features. By combining these features, we aim to create a robust and accurate spam detection model that effectively identifies spam content and spammer communities in social media networks. The Review-Behavioral (RB) Based features focus on the patterns and tendencies observed in user-generated content, such as the frequency of posting, the time between posts, and the distribution of ratings. Review-Linguistic (RL) Based Features, on the other hand, analyze the linguistic characteristics of the content, including the use of specific keywords, the complexity of the text, and sentiment analysis. Lastly, User-Behavioral (UB) Based Features examine the behavior of users in the social media platform, including their social connections, interactions, and activity patterns. By incorporating these features into a machine learning model, we aim to develop an effective spam detection and spammer community detection system that can be used to protect social media platforms from malicious activities. The results of this study will provide valuable insights into the effectiveness of these features in identifying spam and spammer communities, as well as contribute to the ongoing efforts to improve the security and integrity of social media networks.
Efficient Algorithms for Mining the Concise and Lossless Representation of High Utility Itemsets

Data mining is the process of extracting new, possibly useful information from vast data bases that is not straightforward. Market basket analysis, a kind of data mining used in retail research, is used to analyse client transactions. The association between the things that occur in transactions more frequently was the focus of earlier data mining techniques. They don't take an item's significance or utility into account while often mining an itemset. Utility mining is a new field that has emerged as a result of the limits of common mining goods. When mining, the profitability or utility of an object is taken into account. In a transaction, an item's utility refers to its significance or financial gain. Finding the item set with utility values over the specified threshold is the main goal of mining high utility goods. We give a literature review on several mining methods in this work. The support-confidence framework, used in traditional association rule mining, gives users an objective way to quantify the rules that are important to them. Despite several research being done, the mining operation may perform worse with regard to of processing speed as well as memory. efficiency when consumers are presented with too many high utility itemsets by existing approaches. In this research, the system proposes a unique framework for mining closed high utility itemsets that acts as a lossless yet compact representation of high utility itemsets.
UTILIZATION OF INFORMATION AND COMMUNICATION TECHNOLOGY IN THE DEVELOPMENT OF KINDERGARTEN LEARNERS UNDER THE NEW NORMAL

The study determined the utilization of Information Communication Technology in the Development of Kindergarten Learning during this new normal. As we are all experiencing this pandemic, it dramatically affects the educational system wherein face-to-face became forbidden and online classes became the alternative way to continue the learning process. There are five utilization of ICT focused in this study: Language arts, Numeracy, Science, Music and arts, and social skills. 1-4 Likert scale questionnaires were used to gather the data needed. A total of 80 respondents, 50 parents and 30 teachers were selected to answer the questionnaire in this descriptive type of research. Weighted mean, frequency count, percentage and Z-Test were among the tools utilized in analyzing the gathered data. The study found that utilization of ICT was very useful ranging 3.25-4.00. Correlational analysis showed that learners can develop their competencies or domain without the utilization of ICT. To improve the utilization of ICT during this pandemic it is important to strengthen the practices of teachers as well as the parents. KEYWORDS: development, information communication technology, kindergarten, learners, new normal, utilization
Hiding the Data Using Hybrid of LSB and AES in Encrypted Image

&#x0D; &#x0D; &#x0D; &#x0D; Abstract&#x0D; In this technique, which is known as "Reserving Room before Encryption," we first allotted some space for data embedding. Then, the picture was encrypted using the private key, which helps to safeguard the encryption process. Using a different private key, the data is then securely hidden in an encrypted image. Following points, we receive two private keys from the transmitter side, which increased the encryption and decryption processes' security. The data hiding the encrypted image and the two private keys are now sent to the receiver. Receivers can only retrieve data and images if they have access to the encryption key and the data concealing private key, respectively. Thus, the image and data were not easily recovered by the hackers. Because of this, despite the AES algorithm's mathematical soundness, the primary underlying issues with it, like as speed and the use of subpar or tiny prime integers that have resulted in severe security breaches, may be resolved. A basic and commonly utilised technology worldwide is public key cryptography. The majority of public key algorithms, including AES, are based on modular arithmetic. As a result, performing public key encryption and decryption requires performing several modular multiplications on very large values. As a result, symmetric key algorithms are known to be faster than public key algorithms. It has recently become increasingly commonplace to use GPUs for general-purpose computing. The extremely parallel features have been used to significantly improve the performance of many computer problems.&#x0D; &#x0D; &#x0D; &#x0D;
Fine-tuning Pretrained Transformers for Sentiment Analysis on Twitter Data

Due to the noise and informal language present in Twitter data, it is difficult to perform sentiment analysis on the platform. In recent years, a number of transformer models have been developed that can perform well in this type of task. This study aims to analyze the performance of these models on Twitter data. The study utilizes a publicly-available dataset of tweets with neutral, positive, or negative sentiment. It preprocesses the data and tokenizes it using WordPiece. Three transformer models are then tuned using the labeled tweets' pre-defined weights and the models' training weights from large language modeling projects. The models are trained on a 5-phased scale. The three models' performance was evaluated using various metrics, such as accuracy, recall, and F1 score. The results show that the models performed well overall, with ELECTRA leading the way with an accuracy of 85.8%, followed by XLNet and BERT with 84.3% and 84.5% accuracy, respectively. The study also looked into the hyperparameters' impact on the performance. It revealed that batch sizes and learning rates have a significant effect on the models' performance. The results indicate that the models performed better with larger batch sizes and lower learning rates. The study concluded that the three pre-trained transformer models, namely XLNet, ELECTRA, and BERT, were able to perform well in terms of their performance when it came to analyzing Twitter data. Their findings can be beneficial for those working in the field of sentiment analysis on social media platforms.
A Deep Learning-Based Approach for Real-Time Object Detection and Recognition

Object detection and recognition is an essential task in computer vision with numerous real-world applications such as surveillance, self-driving cars, and robotics. In recent years, deep learning-based approaches have significantly improved the accuracy and speed of object detection and recognition. The You Only Look Once version 3 (YOLOv3) algorithm is a popular deep learning-based approach that can detect and recognize objects in real-time. The Common Objects in Context (COCO) dataset is a large-scale dataset with over 330,000 labeled images and more than 2.5 million object instances, making it a popular choice for object detection and recognition tasks. In this paper, we propose a deep learning-based approach for real-time object detection and recognition using the YOLOv3 architecture and COCO dataset. We evaluate our approach based on several performance metrics, including mean average precision (mAP), frames per second (FPS), total object detection time, object detection accuracy, false positive rate, number of detected objects, and mean intersection over union (mIoU). Our results show that our approach achieves a mean average precision of 0.76 on the COCO dataset and a real-time performance of 40 frames per second on a single GPU. Additionally, our approach achieves an object detection accuracy of 93.5%, a false positive rate of 6.5%, and a mean intersection over union of 0.65. Our proposed approach shows promising results for real-time object detection and recognition and can be applied to various real-world applications.
Patent Disclosure Requirement for AI-Assisted inventions: A Comparative Study of EU and India

Patent is based on the concept of quid pro quo. It requires the applicant to draft the application in such a way so that it satisfies the doctrines of written description, enablement, and best mode. However, in case of AI-related invention, the situation is tricky. Most of the inventions carried out by AI has black box scenario—where the inventor also may not be aware of the fact how the invention was exactly carried out by the AI system. The current study delves into the situation regarding how patent offices in Europe and India are dealing with such scenarios. Focussing on the AI-assisted inventions, the study finds out that in Europe, there is a clear guideline about AI-related inventions. The guidelines are tighter than those of the US. On the other hand, India relies on guidelines on computer related invention.
Discussion on the Application of BIM Technology in HVAC Design

With the sustained and steady growth of China’s economy, HVAC technology is becoming more and more advanced. BIM technology is used in ventilation and air conditioning design, which changes the limitations of traditional design. In the new period, it improves the construction efficiency and quality of HVAC engineering and optimizes the air conditioning performance. This paper mainly analyzes the application of BIM technology in HVAC design, and introduces the application of BIM technology in HVAC design in detail by introducing the characteristics of BIM technology. Keywords: BIM technology HVAC technology application Introduction: With the continuous development of information technology, HVAC design technology has also made continuous progress. In recent years, BIM technology has been used in HVAC design and played an important role. With the progress of the times and the development of science and technology, HVAC has appeared in people’s lives more frequently. The combination of BIM technology and HVAC design of air conditioning is conducive to promoting the connection between different professional fields in HVAC design and further improving HVAC design. Therefore, in order to give full play to the advantages of BIM technology in HVAC design, relevant departments and staff should strengthen the application research of BIM technology. However, there are many problems in the application of BIM technology, and the phenomenon of energy waste is serious, which is not conducive to China’s economic development. At present, the design of BIM technology in HVAC system must analyze the actual situation, pay attention to resource conservation and reduce environmental pollution.
Vibrator similarity test based on GPS timing, synchronization and nodal system (post)

Summary Wireline similarity test is a popular method to verify the synchronization of recording system and vibrator in seismic exploration industry. This method originated from the initial period when vibroseis was put into seismic exploration, and has boasted constant good reliability and operability. However, with the development and wide application of the high-efficiency vibroseis acquisition, the wireline similarity test gradually exposes some shortcomings. This paper gives a new similarity test method based on GPS timing, synchronization and node. The method gets good results in real applications and overcomes the shortcomings of wireline similarity test.
Automatic detection of microseismic waveform based on machine learning

Summary Effective waveform detection techniques are the premise of data processing and interpretation in microseismic monitoring. With the wide applications of microseismic monitoring in oil and gas exploration, waveform recognition techniques have been more mature. In this abstract, the proposed method used convolutional autoencoder to detect effective waveforms. Firstly the time-frequency feature map is obtained by S-transform (ST). We then use convolutional autoencoder (CAE) to extract features related to effective signals. Finally the K-Means clustering algorithm is used to recognize the effective signals. We tested synthetic and field microseismic data and the results show that the proposed method can recognize effective microseismic waveforms well with synthetic data and field microseismic data.
PERSEPSI MAHASISWA AKUNTANSI TERHADAP PENERAPAN MATA KULIAH AKUNTANSI FORENSIK DALAM KURIKULUM JURUSAN AKUNTANSI DI UNIVERSITAS (STUDI KASUS PADA UNIVERSITAS 17 AGUSTUS 1945 SURABAYA)

This research aims to analyze the perception of accounting students towards forensic accounting courses in the curriculum of the accounting major at the University. Entering forensic accounting courses in accounting departments is the answer to the development of the forensic accountant profession in several Government and KAP agencies that require quality labor produced by universities to produce graduates who are ready to enter the workforce. The object of research is a student in the accounting department. This research is an empirical study of purposive sampling methods in data collection. The data was obtained by distributing questionnaires of 100 pure students and students of workers in the accounting department who had taken forensic accounting courses at the University of August 17, 1945 Surabaya. Data analysis conducted with pls program version 2.0 research results obtained the influence of perception of students majoring in accounting University August 17, 1945 by 90% on forensic accounting courses in the curriculum at the college is very close. The results of hypothesis testing showed that all independent variables had a positive influence on forensic accounting courses
Object Detection Using Tensorflow Lite

Object detection is a computer vision and has numerous applications, including autonomous vehicles, security systems, and robotics.TensorFlow is a popular opensource framework for machine learning and deep learning, and it provides pre-trained model tools of object detection.we use TensorFlow for object detection in Android applications.We investigate various pre-trained models, their accuracy, and their performance on mobile devices.We also explore different techniques to optimize the models for mobile devices, such as quantization, pruning, and compression.We implement a demo Android application that uses TensorFlow for object detection and evaluate its performance on different Android devices in the advent of deep learning techniques, object detection has seen significant improvements in accuracy and speed.TensorFlow is a popular used for object detection and Android is the most widely used mobile operating system, and there is a growing demand for object detection in Android applications.In this project, we propose an implementation of object detection using TensorFlow in Android.We-trained the model using the TensorFlow Object Detection API and converted it to a TensorFlow Lite model for deployment on Android.We developed an Android application that can take images from the camera or the device's storage andperform object detection.The application uses the TensorFlow Lite model for object detection and displays the results to the user.In thisThe proposed system can be used in various applications such as security cameras, traffic monitoring, and augmented reality.
Ketika Penyebaran Berita Palsu Melahirkan Industri Baru: Studi Kasus Drone Emprit

The spread of disinformation caused by the anonymity and openness present in social media networks is becoming increasingly easy.The emergence of the anti-disinformation industry on social media can be actor who fights against the spread by the ability to analyze the spread of fake news using Artificial Intelligence capabilities so that it can become a new industry commodity that is excellent on social media, including Twitter.The study adopted a mixed qualitative approach to explore how Twitter users respond to the spread of disinformation as well as the rise of an anti-disinformation industry whose role as truth-checkers is emerging as an actor in countering the spread of disinformation on social media.The results of this study indicate that (1) some disinformation that occurs on Twitter causes its users need the presence of third parties to provide data-based views (2) there are values that are trying to be displayed by fact-checkers including objective, independent, transparent, and accountability (3) criticism from users on the results provided by fact-checkers.This study concludes that the emergence of a trend or phenomenon that can be consumed by all Twitter users must be accompanied by
Design of an Information System for Recipients of the Family Hope Program (PKH) at the Office of the Wali Nagari Tebing Tinggi Web-Based

The Family Hope Program (PKH) is a program of providing conditional social assistance to very poor families (KSM) designated as beneficiary families of PKH. population welfare. However, to get this PKH assistance, the community must register first by coming directly to the wali nagari office. In this case, it takes a long time for the community to register until the data is received by the village guardian's office. Data collection techniques in this study were carried out by collecting data through observations, interviews and literature studies. The data collected is in the form of PKH participant data and PKH recipient data. Therefore, it is necessary to design a web-based information system for recipients of the Family Hope Program (PKH) for the Tebing Tinggi Wali Nagari Office. With this system, the public can input personal data online, the wali nagari office employees and PKH assistants get data reports online, and the public can view information directly that their names are included in the data of PKH beneficiaries.