Reviewerly/exHarmony huggingface dataset:
INTERACTIVE ARTIFICIAL INTELLIGENCE

Interactive AI is a field of artificial intelligence that allows computers to interact with humans in a natural and intuitive way. This technology uses machine learning algorithms to interpret and respond to voice, facial expressions, and other forms of input. Interactive AI has numerous applications in various fields, including natural language processing, computer vision, robotics, and virtual reality. It can be used to create more natural and intuitive user interfaces and to automate tedious tasks.However, the implementation of interactive AI also comes with challenges and opportunities. One of the primary challenges is the need for significant resources and expertise to develop and deploy this technology. In addition, there are concerns about privacy and security, as well as the potential for bias and discrimination in the algorithms. Despite these challenges, there are also significant opportunities associated with interactive AI. This technology has the potential to revolutionize the way we interact with computers, making them more user- friendly and efficient. It can also lead to the development of new products and services, and improve the overall user experience.To fully unlock the potential of interactive AI, further research is needed to address these challenges and ensure that the technology is developed in an ethical and responsible manner. This includes developing algorithms that are transparent and unbiased, ensuring that user data is protected and secure, and promoting diversity and inclusivity in the development process. Overall, interactive AI has the potential to transform numerous industries and enhance human-computer interactions, making it a field with great promise for the future International Scientific Journal of Engineering and Management ISSN: 2583-6129 Volume: 02 Issue: 04 | April – 2023 DOI: 10.55041/ISJEM00281 www.isjem.com An International Scholarly || Multidisciplinary || Open Access || Indexing in all major Database &amp; Metadata © 2023, ISJEM (All Rights Reserved) | www.isjem.com | Page 2 KEYWORDS: Interactive AI, Artificial Intelligence, Machine Learning, Natural Language Processing, Computer Vision
Development of Flashcard Media On Indonesia for Studen At Grade 2 Elementary School

Learning media is a very essential object in the teaching and learning process. This research develops a flashcard EBI teaching aid product by containing Indonesian Spelling material. The purpose of this research is to develop a valid, attractive, and effective flashcard EBI teaching aid product during teaching and learning activities at SDN Ceweng. This study involved 17 students of SDN Ceweng in the even semester of the 2021/2022 academic year. This development research uses the ADDIE development model. the results of this study indicate that: (1) the results of the validity of the media obtained a percentage value of 86.76% with a very valid category and suitable for use without improvement; (2) the results of the validity of the material obtained a percentage value of 82.35% with a very valid category and suitable for use without improvement; (3) interesting implementation results, namely 89.82% with very interesting category. Based on the acquisition value, the flashcard EBI flashcard media is declared suitable for use in the learning process.&#x0D; Keywords: Flashcard, Indonesian Spelling&#x0D; &#x0D;
English Learning Community in English Village dan Meningkatkan Berkomunikasi Kelas 5(A) MI Al-Hidayah

Language is a bridge to the world and education is one form of initial capital in learning so that students are able and can understand the importance of changes in learning that are applied to students with the progress of globalization and the language used using English because this international language that I apply at MI Al-hidayah and learning models that are fun and students are easy to understand gem and song learning models that apply songs and games so that elementary school children are more happy in learning because students do not feel learning but they get maximum results can communicate and students can memorize vocabulary prose words English is easy and easy, we as researchers are looking for an easy method and in learning so that students can learn without any pressure. Here students learn English, there is no coercion and they enjoy this method with pronunciation and practice in class and students. a is invited to play gems and enjoy the learning. The problems studied in this study are about how the phenomena of student learning take shape, how to cultivate student discipline and how appropriate methods in democratic learning can foster student learning discipline at MI Al-hidayah. student learning, describe how to foster curiosity and increase student learning. This research uses a qualitative research approach with descriptive qualitative research. The subjects of this study were students, education staff and language village students. This research was conducted from February 2022 to June 2022. The data collection methods used were observation, interviews and documentation methods. The validity of the data was obtained by source triangulation method.
A Study on Visual Attention in Health Literacy Imaging -Comparison and Analysis of Live and Animated Images-

본 연구는 헬스 리터러시에 기반한 국내 대학병원들의 병원 안내 영상들을 실사와 애니메이션 2가지로 분류하여 활용 현황과 특성을 알아보고 상호비교, 분석을 진행하였다. 연구 방법 및 범위는 국내 헬스 리터러시 영상 중 실사 영상과 애니메이션 모션 그래픽 영상으로 시청자의 이해도를 비교분석 하기 위해 유사한 주제와 내용 구성으로 이루어져 있는 10분 이내의 콘텐츠를 주제별 1개씩, 4개를 선별하여 영상이 갖는 시각적 ‘주목성’, ‘교육성’, ‘이해도’, ‘심미성’, ‘친화성’으로 분류해 대학병원 방문 이력이 있는 성인 연령층 (20∼70대)를 대상으로 114명에게 실시하였다. 조사 결과 실사 영상은 주목성과 심미성, 친화성에서 애니메이션 영상에 비해 낮은 만족도를 보였으나, 교육성과 이해도는 서로 비슷하거나 실사 영상에서 좀 더 높은 만족도가 나타났다. 실사 영상과 비교해 애니메이션 영상은 시청자의 주목성을 높이며 디자인에 사용된 색상 구성이 시청자가 대상을 쉽게 인지, 기억할 수 있는 색들로 구성하고 구체적인 내용 전달이 필요하며 예시로 사용된 이미지의 이해가 쉽고 캐릭터를 바탕으로 한 전달 내용에 대한 흥미를 높일 수 있다. 또한 전문 용어의 사용 시 시청자의 이해를 위해 이미지, 타이포그래피 등을 사용한 부가적 설명, 시각적 주목성을 갖는 색상 구성, 시청자가 의료기관에 갖는 두려움과 부담감에 대해 더 고민한다면 향후 의료기관 교육 콘텐츠 개발에 많은 도움에 기여되길 바란다. In this study, hospital guidance videos of domestic university hospitals based on health literacy were classified into two categories: due diligence and animation to find out the status and characteristics of use, and to conduct mutual comparison and analysis was conducted. In order to compare and analyze the understanding of viewers with live-action video and animation motion graphic video, the research method and scope are visual ‘attention’ and ‘education’ of the video by selecting four contents and one for each subject. It was classified into “understanding”, “aesthetic”, and “friendliness” and conducted on 114 adult age groups (20s to 70s) with a history of visiting university hospitals. As a result of the survey, live-action videos showed lower satisfaction than animation videos in terms of attention, aesthetics, and affinity, but education and understanding were similar to each other or higher satisfaction in live-action videos. Compared to live-action videos, animation videos increase viewers’ attention, and the color composition used in the design is composed of colors that viewers can easily recognize and remember, specific content is required, and the image used as an example is easy to understand, and interest in character-based delivery can be increased. In addition, if you think more about additional explanations using images, typography, etc., color composition with visual attention, and the fear and burden of viewers in medical institutions for the understanding of professional terms, it can help develop educational content in medical institutions in the future.
The Coriollis Illusion Scenario for Spatial Disorientation Simulator with the 6-DoF and Infinity Yaw Motion Platform

We propose the Coriolis illusion scenario for 6-DoF and infinity yaw motion platforms. The proposed scenario can be applied to a simulator that includes a motion platform supporting 6-axis and infinite yaw motion. During the training, a trainee ascends to 1500 feet after take-off, continues level flight to the direction 145, and then turns to the direction 050 while maintaining a bank of 15 degrees. During the right turn, the motion platform rotates to the right at a speed of 30 degrees per second, and this movement implies the Coriolis illusion to the trainee. When the trainee reaches the 360 direction, a trainer orders the trainee to look his feet quickly. As a result, the trainee experiences abnormal sensations due to the Coriolis illusion. The result section shows the proposed scenario generates the Coriolis illusion to trainees.
Discrimination and expression

One common area of inquiry by lawmakers and policymakers working on laws prohibiting discrimination concerns the line between rules concerning hate speech, on the one hand, and anti-discrimination law, on the other.
PEDESTRIAN DETECTION AND TRACKING OF THEIR MOVEMENT TRAJECTORY USING THE BACKGROUND SEGMENTATION METHOD BASED ON KNN

Problem statement. Target detection and video image tracking is one of the important topics of computer vision, as well as a problem that needs to be urgently addressed in practical applications. Interference makes it difficult to get the target position. Faced with this problem, scientists have proposed many tracking algorithms. Purpose. In real video monitoring, the system can automatically detect the foreground and draw the trajectory of the foreground. Methods. Use the KNN background segmenting algorithm in combination with OpenCV to detect the foreground and track the trajectory of the video. Novelty. It can continuously detect the foreground in the video and is also applicable to the new foreground in the video. This method is easy to call, does not require the use of a large amount of computer performance resources and can achieve real-time detection and tracking. Result. In a real test, we got good test results, we successfully identified moving pedestrians on video and drew their trajectories. Practical relevance. The algorithm can be applied to road traffic, can determine the trajectory of a vehicle to track vehicles, and can also be used to detect pedestrians to pave the way for subsequent recognition of pedestrian behavior.
DEXTERITY OF ACCEPTING AND DECLINING ORAL INVITATIONS OF SMK STUDENTS IN SPEAKING PERFORMANCE

Accepting and declining invitations bridges the speakers and hearers involved in direct communication to get mutual understanding. In a classroom context, the knowledge of accepting and declining invitations is essential since both teachers and students should be able to create a dialogical relationship. Therefore, this study aims to determine whether the eleventh-year students of SMK Negeri Nibaaf, Kefamenanu, have mastered accepting and declining the invitation, describe their difficulties, and categorize their level of mastery. To describe the whole process, the researchers applied a quantitative method. To get the data, this study used an oral test given to eleven students learning English in the 2020/2021 school year. This study used mobile phones to record the oral test. Their performance was analyzed using the parts of accepting and declining an invitation and four aspects of speaking: fluency, vocabulary, grammar, and pronunciation. The result shows that their average score is 89.5. It indicates that their dexterity is categorized as very good, and their level of mastery is excellent. However, they made 109 (48%) right and 23 (8%) wrong answers. They also faced pronunciation difficulties (11 (or" 4%) right and 55 (or: 20%) wrong answers) and grammar20 (or: 7%) right and 46 (or: 17%) wrong answers). It implies that teaching speaking should pay more attention to accepting and declining an oral invitation.
Sorokh-Poth: A Balanced Bangladeshi Road Vehicle Image Dataset Integrated With a Detection System Based On Deep Convolutional Neural Networks

When it comes to the administration of highways, the identification of intelligent vehicles is becoming an increasingly important component. Applications for vehicle detection are essential for both military and civilian usage, including the monitoring of highway traffic, administration, and the control of urban traffic. The vast amount of vehicle image data available online has the potential to encourage the development of increasingly sophisticated object recognition and classification models and algorithms. But finding an organized, balanced, and meaningful dataset continues to be a major challenge. This study proposes Sorokh-Poth, a new complete balanced image dataset based on Bangladeshi road travel that is compatible with a number of CNN-based architectures. The majority of the photographs in the collection were taken with a smartphone. The dataset includes 9,809 classified and annotated images of ten different types of vehicles, including autorickshaws, bicycles, buses, cars, CNG-powered vehicles, lagoon rickshaws, trucks, and vans. This research work utilizes the residual network ResNet-50 model, a CNN-based architecture. Here, features specific to the type of vehicle were automatically retrieved and grouped. Accuracy, precision, recall, and f1-score were just a few of the metrics used for evaluation during the research. The proposed model exhibited an increasing accuracy despite the vehicles shifting physical characteristics. The purpose of this work is to implement various CNN-based residual networks ResNet-50 and ResNet-150V2. Our proposed ResNet-50 model achieves an accuracy of 98.00% in the detection of native Bangladeshi vehicles, according to result comparisons and evaluations.
Generation of RBC, WBC Subtype, and Platelet Count Report Using YOLO

Abstract: Artificial intelligence introduced a way to combine machines’ computing ability with human intelligence. Machine learning is a sub-branch of AI that consists of different algorithms to implement concepts of AI in practical terms. But when a machine has to process numerous amounts of data, deep learning algorithms come into the picture. It is observed that when a computing system has to deal with image data then neural network algorithms give efficient ways to process them and draw unique patterns from them. Object detection is the task of identifying required objects from an image. This type of technology plays a crucial role in medical image processing. Some algorithms can efficiently identify and classify objects from an image. It is observed that Fast R-CNN and Faster R-CNN, mask R-CNN, have given pretty good accuracy while performing such tasks. But when time is a concern for a system, such methods put an obstacle of training time and architectural complexity. The You Only Look Once (YOLO) object detection method has introduced a new way of processing images in a single pass. This algorithm is famous because of its speed and correctness. There are numerous models of YOLO developed now which include YOLOv1 to YOLOV8. This paper gives the performance of the latest version of YOLO on the blood cell dataset.
Database Security

Abstract: Database security is a crucial factor in safeguarding sensitive data in today's information-centric world. As organizations increasingly rely on databases for storing and managing vast volumes of data, ensuring the security of databases has become a top priority. This paper proposes a methodology for enhancing database security by addressing identified challenges and attacks. The methodology involves various steps, including identifying and assessing potential threats, evaluating existing security measures, implementing multi-layered defense, enforcing the principle of least privilege, regularly updating and patching DBMS, conducting regular security audits, providing user awareness training, implementing data encryption, monitoring and logging database activities, developing an incident response plan, staying updated with the latest research and best practices, and continuously improving security measures. By following this methodology, organizations can effectively enhance the security of their databases and safeguard critical data from potential attacks and breaches. Emphasis is placed on regular review and updates to security measures to proactively address evolving threats and ensure continuous protection of sensitive data.
Micro-Analysis of Rainfall Using Statistical Techniques

Abstract: Rainfall over a region across the earth is the prima face reason of its uniqueness. Data is taken from official website of Indian Meteorological Department. Figure 2 shows that the district wise comparison of average rainfall in Maharashtra. Figure 3 shows that the district wise comparison of average rainy days in Maharashtra. There is significant difference between actual and normal (expected) rainfall of the all districts over the period 1998-2017. There is significant difference between average actual and normal (expected) rainfall of the districts in Maharashtra.
Blockchain Based Transaction Management System

Abstract: The technology that supports bitcoin, known as blockchain, is still in its development. Blockchain technology has the potential to improve existing corporate procedures to make them more collaborative, transparent, secure, and effective.. The banking sector is among the first to take advantage of this technology's disruptive potential. One of the most intricate bank payment systems in the entire globe is the Indian banking system. The current infrastructure that the Indian banking system uses is centralized and based on the real-time gross settlement system. This centralized architecture causes transactions to process slowly and result in wasting. Additionally, it costs a lot for security and recovery considerations. Systems for real-time gross contracts should indeed meet high expectations for speed, stability, and security. The primary objective is to develop a system that provides security, confidentiality, and a decentralized money lending mechanism rather than converting old system to the blockchain platform. Here, a novel strategy that encourages a decentralized system and services built on the Ethereum blockchain is progressed. The system enables a number of services using distributed ledger technology, including loan checking, money transfer, and money deposit, among others.
A Statistical and Machine Learning Approach for Summarising Computer Science Research Papers

Academics, researchers and students usually read a lot of papers for their research or to keep up-to-date with the latest works.The high number of papers available makes the process time-consuming.A solution is to summarise the papers and allow the reader to decide if the papers are relevant to their work and whether they require more attention.A system has been built to generate extractive summaries of computer science research papers.We demonstrate how the intrinsic statistical characteristics of computer science research papers such as the document length or the presence of certain keywords can help train a machine learning classifier model that can achieve state-of-the-art performance.Human and automatic evaluation using ROUGE has been carried out to measure performance.Results show that the proposed model performs better than TextRank and BERT on both human and automatic evaluation.It also does better than BART on human evaluation.
Modular labelled calculi for relevant logics

&#x0D; &#x0D; &#x0D; In this article, we perform a detailed proof theoretic investigation of a wide number of relevant logics by employing the well-established methodology of labelled sequent calculi to build our intended systems. At the semantic level, we will characterise relevant logics by employing reduced Routley-Meyer models, namely, relational structures with a ternary relation between worlds along with a unique distinct element considered as the real (or actual) world. This paper realizes the idea of building a variety of modular labelled calculi by reflecting, at the syntactic level, semantic informations taken from reduced Routley-Meyer models. Central results include proofs of soundness and completeness, as well as a proof of cut- admissibility.&#x0D; &#x0D; &#x0D;
Deep Learning Model for Automatic Detection of Oral squamous cell carcinoma (OSCC) using Histopathological Images

Oral squamous cell carcinoma (OSCC), is a type of cancer that causes the loss of the structural formation of layers and membranes in the oral cavity region.With the recent advent of Deep learning (DL) in biomedical image classification, the automated early diagnosis of oral histopathological images can aid in effective treatment of oral cancer.This work attempts to perform an automated classification of benign and malignant oral biopsy histopathological images by implementing a DL-based convolutional neural network (CNN) model for the initial analysis of OSCC.For this research, four recently developed candidate pre-trained DL-CNN models namely NASNetLarge, InceptionNet, Xception, and DenseNet201 are selected through the approach of transfer learning.These pre-trained models are then modified with additional layers for effective OSCC detection.The efficacy of these modified models is examined on an oral cancer histopathological image database.It is examined that the pre-trained DenseNet201 model with modified structure has surpassed other models in terms of performance parameters by recording an accuracy of 91.25% and is considered as our proposed DL-CNN model.
Eye Movement Interpretation for Detecting Dyslexia Using Machine Learning Techniques

Dyslexia is a learning disorder.It hampers an individual's ability to comprehend the words, making it difficult for the person to read, spell, and write.The studies exemplify that timely detection and support proves as an advantage in mitigating the negative effects of dyslexia.the traditional manual method takes more time and effort to identify dyslexia, to overcome these issues the presented paper incorporated a machine learning based screening technique to detect dyslexia.As eye movements possess the ability to provide insights into reading disorders.Understanding the patterns that eye movements make while reading a text paragraph can help distinguish between dyslexic and non-dyslexic readers.The raw data consist of right-eye and left-eye movement positions along the x-axis and y-axis of 185 students were captured while reading a text paragraph.These eye movements were captured using an eye tracker based on the principle of human-computer interaction.The features such as fixation, saccadic movements were extracted for better prediction, later the classification was performed using XGBoost, support vector machine (SVM) and random forest (RF).The results show that XGBoost provides an accuracy of 95%, SVM 94% and RF 91%.To further validate the machine learning models author has used the performs measured called confusion matrix, precision, recall and F1 scores.The obtained results shows that the SVM achieved an F1 score of 94%, Recall of 94.5% and precision of 96%, whereas RF achieved an F1 score of 90%, Recall of 92% and precision of 89%.Finally, XGBoost achieved an F1 score of 95%, Recall of 95.5% and precision of 95%.The results imply that XGBoost achieves better result compare to other models.