Reviewerly/exHarmony huggingface dataset:
Maneuvering Target Tracking with Modified IMM Algorithm Based on Array Radars

Maneuvering target detection and tracking encounter great challenges due to the large measurement error. The characteristics of accurate measurement and different perspectives by array radar and Bistatic radar are used to obtain accurate measurement. To this end, the precise radial-velocity information from bistatic array radar is introduced, and then, we propose a modified interacting multiple model algorithm to improve the tracking accuracy. Simulation results show that the proposed algorithm is superior to traditional tracking algorithms in non-linear scenarios.
Dynamic Path Planning for QoS Improvement in Multiple Automated Guided Vehicles

This paper studies the trajectory optimization of an automated guided vehicle (AGV) for a smart factory where material handling with high wireless communication quality is required. This design aims to maximize the sum of spectral efficiency under complex continuous-value constraints on movement and combinatorial constraints on cooperative movements. To tackle the NP-hard optimization problem, this paper proposes a hybrid algorithm that combines global and local optimization, where global optimization meets the constraints with relaxation and local optimization considers all constraints on movements. Specifically, the AGVs travel by local path planning based on dynamic window approach (DWA), given the globally optimal trajectory that is solved using dynamic programming (DP). Finally, simulation results provide the high communication quality achieved by the proposed algorithm compared to the benchmark algorithms.
An Approach to Data Representation and Processing of Knowledge from the Bulgarian Folklore

In the context of global and high-tech development, cultural fields are often overlooked during these transitions. Therefore, it is essential to pay close attention to their inclusion. Cultural-historical heritage, which has undergone centuries of changes, deserves to be introduced into the technological world and made as accessible as possible to society, without territorial or border restrictions. Digitizing a part of this heritage, including folklore, can make it high-tech while preserving its character, peculiarity, and identity. This article examines the digitalization of Bulgarian cultural and historical heritage. It explores existing international standards for presenting cultural heritage and proposes a formalism for an adequate presentation of knowledge in the field of Bulgarian folklore. This article presents the first version of a model for the formal description of objects from Bulgarian folklore. We provide examples of the formal presentation of objects from Bulgarian folklore and their corresponding computer implementation.
Identifying Emotions for Virtual Reality Based on Eye Tracking and Cloud Computing

With the rapid development of virtual reality technology, immersive experiences have become the new generation’s sought-after entertainment feast. Nowadays, various industries have also started utilizing virtual reality for vocational training in fields such as education, healthcare, entertainment, and military. In virtual environments, users can interact socially with others through virtual avatars as their representatives. In this study, we aim to integrate virtual reality devices, eye-tracking devices, and public cloud computing services to infer emotions based on changes in eye data. We evaluate the average accuracy of the proposed model and hope to enhance the facial emotions of avatars to better align with users’ intentions. This study will contribute to improving the authenticity of social interaction and providing users with a better social experience in virtual reality environments.
Design of Graph Filter Using Hilbert Matrix and Vandermonde Matrix

The graph filter is a useful tool for processing signals collected from various networks. This paper presents the closed-form design of a graph filter. Depending on whether the graph frequency is known or not, there are two types of designs studied here. The first design is the graph-unaware method, where the graph frequency does not need to be known in advance. In this case, the filter coefficients are determined by minimizing the integral squared errors between the actual response and the ideal response. The optimal solution is then obtained by using the inverse of the Hilbert matrix. The second design is the graph-aware method, where the graph frequency needs to be known in advance. In this case, the filter coefficients are obtained by letting the actual response be equal to the ideal response at the prescribed frequency points. The solution is obtained by using the inverse of the Vandermonde matrix. Finally, the temperature data denoising of sensor networks with different sizes are used to demonstrate the effectiveness of the designed graph filters.
Leveraging feature fusion for improved NER for tourism field

To tackle the problem of inadequate semantic associations and considerable gaps among tourism-related documents, a feature fusion method for the tourism domain was proposed. Relevant tourism feature information was extracted from the Baidu Baike knowledge base and encoded using one-hot encoding. The encoded feature vectors were concatenated with the original input vectors and input into the BERT model for training. Simultaneously, the Conditional Random Fields (CRF) model was employed to extract the sequence with the highest global probability, resulting in the identification of relevant tourism entities. A number of datasets were constructed for the entity recognition task, using Jiangxi Province tourism as an example. The performance of the CRF, BILSTM_CRF, BERT_CRF, BERT_IDCNN_CRF, and BERT_BILSTM_CRF models were evaluated on the given dataset. The comparative analysis revealed that the proposed model demonstrated a satisfactory F1 score of 87.3% on the same dataset.
THE NEXUS OF IT CAPABILITIES, SUPPLY CHAIN CAPABILITIES, AND PERFORMANCE: A MEDIATION ANALYSIS IN THE RETAIL INDUSTRY

This study aims to make a distinctive contribution to the supply chain management literature by providing unique insights into the interplay between information technology capabilities, supply chain capabilities, and supply chain performance in the UAE retail industry. This study employed a quantitative survey-based approach, focusing on the top 15 of 376 furniture stores in Dubai. Employing a convenience sampling method, an online survey was distributed via email to the participants. Of the 248 collected questionnaires, 215 were subjected to analysis. The respondents included employees from information technology departments and top management in the surveyed companies. The study used partial least squares (PLS)-structural equation modeling (SEM) software. Contemporary analysis enhances prior research on IT and SC capabilities. Statistical analysis offers robust evidence that both information technology and supply chain capabilities significantly influence supply chain performance. Moreover, this study reveals that additional supply chain capabilities mediate the impact of information technology capabilities on supply chain performance. This research stands out because of the increasing recognition of IT as a strategic enabler of supply chain management. Understanding the underlying mechanisms through which IT capabilities impact overall supply chain performance is paramount. This study introduces the concept that supply chain capabilities serve as mediating factors, translating the influence of IT capabilities into enhanced supply chain performance. By unraveling this mediating relationship, this study contributes to the existing literature by providing a deeper understanding of the intricate dynamics between IT capabilities, supply chain capabilities, and supply chain performance. Keywords : Information Technology Capabilities, Supply Chain Capabilities, Supply Chain Performance DOI： https://doi.org/10.35741/issn.0258-2724.58.4.57
BLOCKCHAIN AUTHENTICATION ACCESS CONTROL FRAMEWORK TO SECURE IOT DEVICES IN SMART CITIES

The purpose of this article is to propose a framework named IoT-AC/Bc to secure smart cities. This article describes a new method that integrates Role-Based Access Control (RBAC) and Zero-Knowledge Proof (ZKP) models based on IoT and blockchain technologies. This method enables fast access authentication on smart city gates using ZKP, which provides an extra layer of security to address access control challenges. The framework consists of an Internet of Things Access Control-based Blockchain Smart Contract that manages user authentication, access session identities, and asset real-time interactions; and a Blockchain Ledger Management Smart Contract that implements distributed access control and tracks the ledger history of different activities. The framework achieves availability, scalability, multi-factor authentication, and decentralization, which strengthens distribution, role authority, access rights, and concurrent identity access requests. The results are demonstrated through a comparative analysis focused on the number of multithreaded requests, the cost of smart contract deployment, the multi-factor authentication performance for users’ interactions with the smart city gates, and the security strength levels. The framework’s performance effectiveness is evaluated through a case study that consists of various scenarios. The framework allows improving the security level by 94% under low and medium attack strength and 89% under high attack strength for 18 security attacks. This paper is novel because the framework uses ZKP over a blockchain network as an extra cryptographic technique with RBAC as a logical access control model to achieve fast accessibility authorization for users’ transactions while preserving their private identity on smart city gates. Keywords: Access Control, Blockchain, Internet of Things, Role-Based Access Control, Smart Cities, Multi-Factor Authentication DOI： https://doi.org/10.35741/issn.0258-2724.58.4.56
XR unmasked

Abstract As XR pushes the boundaries of immersive technology, the challenge of creating a flawless simulated reality presents unique design hurdles. In this critical analysis, we unpack the fundamental issues, identifying potential solutions and forecasting future developments in the XR landscape.
IMPLEMENTATION OF CONTAINER ORCHESTRATOR MANAGEMENT IN LEARNING MANAGEMENT SYSTEM

The pandemic that started in 2019 made technology even more important. Accelerating digital adaptation and transformation in the education sector, namely the Learning Management System (LMS) is very helpful in the teaching and learning process. Various LMS platforms are widely used and developed by various agencies. Pradita University is also developing an LMS which is made with the node.js programming language and the next.js framework which can be accessed on the pradita.digital site. The choice of using the wrong technology can cause the problematic LMS that is used to become slow and heavy, to the point where it cannot be accessed. The use of technology with the hypervisior concept which was previously commonly used has various weaknesses. For this reason, the Pradita LMS was developed using container orchestrator technology and Rancher Kubernetes as container orchestrator management. By utilizing Rancher as a container orchestrator management, it can make it easier to use with a user friendly GUI. In this study aims to prove that the use of Rancher in improving the quality and performance of LMS Pradita. Among them by identifying system efficiency, providing better scalability, increasing the availability of Pradita LMS, Rancher is able to manage applications and containers used for Pradita LMS, and saving costs used in implementing physical devices. By using the experimental method applied in this study, the results of system efficiency testing and scalability testing carried out with a load of up to 200 users can run properly and efficiently, then the availability test proves that the LMS is still available even when there are workers who are not operating, and testing rancher management capabilities lead to conclusions Rancher runs optimally and uses resources efficiently in accordance with the objectives in this study.
PROGRESSIVE WEB APP-BASED ONLINE REPAIR SHOP APPLICATION USING MAPBOX AND GEOLOCATION API

Motorcycle users in Indonesia continue to increase every year. This has implications for the increasing need for motorcycle repair or maintenance service providers. This need is an emergency, but this is a problem because the lack of information regarding the location of the nearest workshop makes the process of finding a repair shop consuming a lot of time and energy. This study aims to develop an Online Workshop application that can present a map with the nearest repair shop location using Mapbox and the Geolocation API to make it easier for motorbike users to find a repair shop. In addition, this application allows users to be able to call a mechanic to come to the location. The Bengkel Online application is built using Firebase as a database and applies progressive web apps technology. The results of this study indicate that the online workshop application can be used to order repair services and call a mechanic to the location properly and accurately as indicated by the results of application testing which shows that all features function properly.
Bag of Visual Words Model - A Mathematical Approach

Information extraction from images is now incredibly valuable for many new inventions. Even though there are several simple methods for extracting information from the images, feasibility and accuracy are critical. One of the simplest and most significant processes is feature extraction from the images. Many scientific approaches are derived by the experts based on the extracted features for a better conclusion of their work. Mathematical procedures, like Scientific methods, play an important role in image analysis. The Bag of Visual Words (BoVW) [1, 2, 3] is one of them, and it is helpful to figure out how similar a group of images is. A set of visual words characterises the images in the Bag of Visual Words model, which are subsequently aggregated in a histogram per image [4]. The histogram difference depicts the similarities among the images. The reweighting methodology known as Term Frequency – Inverse Document Frequency (TF-IDF) [5] refines this procedure. The overall weighting [6] for all words in each histogram is calculated before reweighting. As per the traditional way, the images are transformed into the matrix called as Cost matrix. It is constructed through two mathematical: Euclidean distances and Cosine distances. The main purpose of finding these distances is to detect similarity between the histograms. Further the histograms are normalized and both distances are calculated. The visual representation is also generated. The two mathematical methods are compared to see which one is appropriate for checking resemblance. The strategy identified as the optimum solution based on the findings aids in fraud detection in digital signature, Image Processing, and classification of images.
Development of interactive e-book for science learning in fifth grade elementary school students

The type of research used by the researchers is generally aimed at developing a learning media or improving existing products to make them more effective. This study is development research, specifically focusing on the development of an interactive E-book-based media for Science learning in fifth-grade elementary school students, specifically covering the topic of the human movement organ. The method employed in this research is Research and Development (RnD), using the 4-D Thiagarajan model (Definition, Design, Development, Dissemination). The research instruments used include Validation Sheets (media experts and subject matter experts), Questionnaires (student responses), and Test Sheets (Learning Outcome Tests). The study results in the creation of an electronic medium, an interactive E-book, that completed high-quality criteria in terms of validity, practicality, and effectiveness. The media's validation results by the subject matter experts were 87.5%, and by the media experts were 86.5%, both categorized as "Highly Feasible." The practicality of the media was rated at 92.2% with the criterion of "Practical" based on student learning outcomes. The effectiveness test showed that 87% of students achieved scores that completed the Minimum Mastery Criteria (KKM). In conclusion, the developed product obtained high-quality criteria in terms of suitability, practicality, and effectiveness.
Revolutionizing Healthcare with AI: Predicting Future Medication Models and Improving Patient Outcomes

This paper addresses the problem of predicting future medication models in a database using a powerful AI system [1][2]. The background highlights the importance of accurate predictions in healthcare for effective decision-making and improved patient outcomes. The proposed solution involves the development of an AI model trained on a diverse dataset of historical medication models, incorporating advanced machine learning algorithms and techniques. The key technologies and components of the program include data preprocessing, feature selection, algorithm comparison, and performance evaluation [3]. Challenges encountered during the project, such as data quality and model generalizability, were mitigated through careful data cleaning and fine-tuning of the AI model [4][5]. The application of the system to various scenarios during experimentation demonstrated its robustness and versatility. The most important results include high prediction accuracy, precision, and recall in forecasting future medication models. The system showed promising performance across different patient populations, suggesting its potential for personalized treatment planning and decision-making. The idea presented in this paper offers a valuable solution for healthcare professionals and researchers seeking accurate predictions in medication modeling, facilitating better patient care and optimized treatment strategies.
Surveillance Video Summarization based on Target Object Detection

The recent trend increases the use of surveillance cameras in many of the private and public premises, which causes the number of surveillance videos to grow exponentially. The information gained from these surveillance videos not only helps the owner of the property, but also helps in crime investigations for police and security officials. Though there are several applications of such videos, yet their storage, management and retrieval processes are still challenging. Hence, it is important to develop an efficient technique to describe a long video into a shorter video with semantic information by eliminating the redundant and unimportant frames. This technique makes the larger video to shrink in length for efficient storage and also helps the users to attain a complete knowledge of the video by only watching the shorter video, without spending more time in watching the original longer video. To achieve this objective, this paper proposes a video summarization technique for summarizing the surveillance videos by extracting the target object using YOLO then discarding the remaining frames and finally combining the extracted key frames into a single video. This method first detects the target object related in the original video frames and then eliminates the remaining frames that are irrelevant without prominent objects, resulting in video with only the key frames which are into the interest of the user, finally those frames are combined to form a summarized video.
Conversational General College Enquiry Intelligent ChatBot

In place of direct interaction with a live college administration, a chat-bot is a software programme that is used to conduct an online chat conversation via text or text-to-voice. We provided a ChatBot that creates a dynamic Answer for online student inquiries in the suggested method. The ChatBot used by the Proposed System is powered by artificial intelligence. This suggested ChatBot recognises the student and teacher context that initiates the specific answer purpose. The desired reaction will be generated for the student and teacher because it is responding dynamically. The suggested method uses machine learning algorithms to train the ChatBot by observing different requests and responses from students and teachers. These days, chatbots are starting to be quite capable since artificial intelligence helps the human touch in every discussion, enables chatbots to understand users’ questions, and enables them to deliver accurate responses. The goal of this project is to decrease the requirement for several systems for various procedures and to lessen the reliance of an administration on humans. Developing a ChatBot over connected data presents a variety of difficulties, such as supporting numerous knowledge bases and comprehending student inquiries. We initially design and create an architecture to offer an interactive user experience in order to overcome these issues. In order to comprehend user intents and produce SPARQL queries, we also offer a machine learning approach based on intent classification and natural language understanding. For that, we must include enough specifics on the requirements for students and professors, including the availability of text books, staff timetables, student mark information, and semester outcomes.
Black Widow Optimization Algorithm for Virtual Machines Migration in the Cloud Environments

Cloud data centers use virtualization technology to manage computing resources. Using a group of connected Virtual Machines (VMs), corresponding users can compute data efficiently and effectively. It improves the utilization of resources, thereby reducing hardware requirements. Repossession of affected services requires VM-based infrastructure overhaul schemes. Clarifications concerning dedicated routing are also desirable to improve the reliability of Domain Controller (DC) services. The migration of a VM experiencing a node failure challenges maintaining reliability. The selection of VMs is influential in limiting the number of VM migrations. Choosing one or more potential VMs for migration reduces the servers' workload. This paper presents an energy-aware VM migration method for cloud computing based on the Black Widow Optimization (BWO) algorithm. The proposed algorithm was implemented and measured using JAVA. Afterward, we compared our results against existing methodologies regarding resource availability, energy consumption, load, and migration cost.
Chatbot Program for Proposed Requirements in Korean Problem Specification Document

In software engineering, requirement analysis is a crucial task throughout the entire process and holds significant importance. However, factors contributing to the failure of requirement analysis include communication breakdowns, divergent interpretations of requirements, and inadequate execution of requirements. To address these issues, a proposed approach involves utilizing NLP machine learning within Korean requirement documents to generate knowledge-based data and deduce actors and actions using natural language processing knowledge-based information. Actors and actions derived are then structured into a hierarchy of sentences through clustering, establishing a conceptual hierarchy between sentences. This is transformed into ontology data, resulting in the ultimate requirement list. A chatbot system provides users with the derived system event list, generating requirement diagrams and specification documents. Users can refer to the chatbot system's outputs to extract requirements. In this paper, the feasibility of this approach is demonstrated by applying it to a case involving Korean-language requirements for course enrollment.