Reviewerly/exHarmony huggingface dataset:
Comparison of Object Detection Technique for Applications in ETAT Automation Training Set

Object detection is a type of artificial intelligence (AI) that is currently being developed and used widely. It can classify and detect objects in images or videos. This research aims to investigate object detection that is suitable and accurate for detecting the color of the workpieces in the training set. This study will experiment comparison of three object detection models: 1. OpenCV Python code via the Thonny software. Using the BGR color separation principle, the range of color values must be specified so that the model can separate different colors. 2. V4-tiny and 3. V7-tiny are models that use model training principles in the CiRA-CORE Platform. In this study, 1,271 images of workpieces processed with the Augmentation Technique were utilized to train the model. and 240 images for testing the model. To analyze of find a suitable and accurate model for detecting the color of workpieces Because light is important in visual or object detection in real-time this study divided the experiment into two conditions: LED Lighting and without LED Lighting. The results showed that V7-tiny is an object detection model that can detect the color of the target in both lighting conditions. The V7-tiny model has an accuracy of 97.92%.
Prediction of Number of Accesses by Thumbnail Image Classification on Video Sites

In recent years, video sites such as YouTube have become popular on the Internet, and it is a fact that Internet users spend a lot of time watching videos on these video sites every day. It is thought that users select the sites and channels they want to view based on some criteria such as need or interest, but the selection criteria are not clear, and it is necessary to provide some kind of indicator. In this paper, we propose that thumbnail images may be one of the factors when viewers select a video from among a large number of video sites and channels. Thumbnail images are a standard that channel operators can actively participate in creating, and we hypothesize that the quality of thumbnail images can increase the number of accesses. As a result of testing, we found that it was possible to predict the number of accesses from thumbnail images.
The Logo Detection in an Illegitimate Web Page

The growth of the online market is rapid. On the other hand, the fraud of brand products are significant serious issue. To mitigate these issues we proposed an intelligent system called The logo detection on an illegitimate web page. we use Convolutional Neural Networks(CNN) for detecting online fake logos on unauthorized web pages. moreover, the evaluation of our performance is presented. Finally, we apply our method to the real world through an online search of the Mae Fah Luang University’s logo on the illegitimate web page. The result outstanding shows us that our proposed is useful for the fake logos detection.
BLSO: Broad Learning System-Based Scheme for Adaptive Task Offloading in Industrial IoT

In the Industrial Internet of Things (IIoT) enabled by multi-access edge computing (MEC), numerous heterogeneous devices are capable to offload computing-heavy tasks to MEC servers, thereby reducing the network congestion and accelerating task processing. Since the tasks are constrained by deadlines, the main goal of making a task offloading decision is to minimize the total time consumption of the system. Machine learning (ML) have been widely employed to address this problem. However, the popular schemes like deep learning and reinforcement learning require extensive training time, impacting the promptness of task offloading decisions. Additionally, when new devices enter the system, these schemes become unsuitable and necessitate retraining. To solve the problem, this paper proposes a broad learning system-based task offloading scheme (BLSO) to make task offloading decisions. Compared with previous works, BLSO significantly reduces training time while preserving the accuracy of task offloading decision. Moreover, when the environment changes frequently such as upon the addition of new devices joining in or the data distribution of tasks has been changed, BLSO can be readily updated, maintaining adaptiveness to environmental changes. Experimental results demonstrate that BLSO considerably enhances the efficiency of task offloading decision-making with high accuracy.
Understanding the Cryptocurrency Free Giveaway Scam Disseminated on Twitter Lists

This paper presents an end-to-end analysis of the cryptocurrency free giveaway scam disseminated in a new distribution channel, Twitter lists. To detect scams disseminated in this channel, unlike existing detection systems that rely on manual effort, this paper develops a fully automated detection system, GiveawayScamHunter, which can continuously collect lists from Twitter and utilize a Nature-Language-Processing (NLP) model to automatically detect the free giveaway scam and then extract the associated scam cryptocurrency address.By running GiveawayScamHunter from Jun. 2022 to Jun. 2023, we have collected 95,111 free giveaway scam Twitter lists created by thousands of Twitter accounts. Through analyzing the profile of scam list creators, our work reveals that different strategies have been adopted to spread the scam scheme, including compromising popular accounts and registering spam accounts on Twitter. Our analysis result shows that 43.9% of spam accounts remained active before our report. Furthermore, we collected 327 free giveaway domains and 121 new scam cryptocurrency addresses. By tracing the transaction history of all scam addresses, our work uncovers that over 365 victims have been attacked by the scam, resulting in an estimated financial loss of 872K USD. Overall, our work sheds light on the tactics, scale, and impact of free giveaway scams disseminated on Twitter lists, emphasizing the urgent need for effective detection and prevention mechanisms to protect social network users from such fraudulent activity.
iBCTrans: A Practical Blockchain-Based Framework for Cellular Vehicular-To-Everything Networks

As Intelligent Transportation Systems and Connected Vehicles become increasingly prevalent, establishing secure, efficient, and scalable communication infrastructures has become imperative. This paper introduces iBCTrans, a novel framework designed specifically for Cellular Vehicular-to-Everything (C-V2X) Networks, leveraging a blockchain-based approach to address the challenges in vehicular communication scenarios. iBCTrans integrates blockchain technology with Self-Sovereign Identity (SSI) principles and Decentralized Identifiers (DIDs) to facilitate secure and trustworthy V2X applications among vehicles, infrastructure, and networks. In this framework, vehicles can authenticate their identities in a secure and privacy-preserving manner, ensuring the integrity and reliability of the communication. A detailed overview of the iBCTrans architecture, components, protocols, and workflows is presented and potential applications in various vehicular communication scenarios are discussed. The proposed iBCTrans framework aims to provide a robust solution for the emerging demands of V2X communications, paving the way for the next generation of intelligent vehicular networks.
Performance Evaluation of Programming Languages as API Services for Cloud Environments: A Comparative Study of PHP, Python, Node.js and Golang

Over the past decades, the speed and bandwidth of internet systems have dramatically improved. Alongside this, the expansion of cloud server providers, in terms of both price and efficiency, has paralleled the development of various website, applications and household devices that can be accessed via the internet. The development of API Services to support these various services necessitates several considerations, one of which is the choice of language for API Service development. In this study, we compare the performance of four popular languages used for developing Web services or API Services: PHP, Node.js, Python, and Golang. We experiment three abilities: data querying and transmission, file management, and CPU utilization for computations. The outcomes are measured in terms of response time and CPU usage. Our experiments reveal that when the system has a low number of users, both Node.js and Golang respond well and have similar response times. However, as the number of users in the system increases, Golang responds better than Node.js.
WOA-FNN: An innovative hybrid optimization technique for effective detection of shot boundaries

The presented research proposes a unique approach for Shot Boundary Detection (SBD) of videos using the Whale Optimisation Algorithm (WOA) to identify both abrupt and gradual transitions. The Whale Optimization Algorithm optimizes the weights and bias of the Feed-Forward Neural Network (FNN) to classify transition frames and normal frames from a set of available frames, with the aid of an experimental threshold. WOA has a unique characteristics of fast convergence and searching for optimal solutions with high precision. The proposed system has adopted a dual stage system comprising of possible and confirmation stage to increase the effectiveness of the system interms of F1 Score. WOAFNN gives a better results interms of precision and F1 score when compared with the state-of-art techniques.
Research on English Semantic Intelligent Analysis System Based on Neural Network Algorithm

English semantics has been a popular topic for a long time. According to modern linguists, language is a system of symbols that conveys meaning through speech. The relationship between language and meaning is viewed from the perspective of difference. Cognitive semantics is also controversial because of its difficulty in identifying the core mechanism for explaining complex linguistic phenomena, and its research methods often lack empirical evidence and even practicality. For this reason, this paper explores a neural network (NN)-based semantic analysis method with English semantic analysis as the entry point, and introduces the concepts related to neural networks and semantic analysis separately, then constructs an intelligent analysis system based on neural network algorithms for English semantics, and the final experimental results prove that the BRF network is more effective in semantic recognition (the recognition accuracy of the BRF network is 97.41%, AUC is 0.87).
Online English Intelligent Question Answering System Based on K-Means Algorithm

In the current information age, people's demand for information acquisition is becoming increasingly urgent. However, traditional search engines often face problems such as high information content and uneven quality, making it difficult to meet users’ needs. Therefore, how to improve the credibility and pertinence of search results has become an urgent problem to be solved. Therefore, this paper proposes a new wireless web crawler strategy, which aims to provide users with more accurate and efficient information search services. This paper aims to explore a wireless web crawler strategy based on K-means algorithm. From the perspective of users, this article utilizes intelligent question answering systems and specific dictionary content and non keyword retrieval techniques to achieve the search and generation of target information. Afterwards, this article conducted functional testing on the system, and the test results showed that the overall similarity and accuracy of the system were high, indicating that the system performed well in practical application scenarios. The fusion application of online English intelligent question answering system based on K-means algorithm has made some research findings. Here are some related research findings: The K-means algorithm can effectively classify user questions and generate corresponding answers. By conducting cluster analysis on a large number of Q&A datasets, the system can automatically learn the features and patterns of the problem, and classify the problem into groups that should provide the same or similar answers based on similarity.
Fusion Application of New Media Communication and Intelligent AI Algorithms in the Metaverse Ecology

The metaverse has played an important role in promoting scientific and technological progress and economic development in the new wave of technological revolution. Compared with traditional language and word processing methods, high-tech technologies such as digital technology and intelligent manufacturing have stronger advantages. This article would study the metaverse algorithm. Based on this, this article proposes an optimization method to solve the problem and make it more in line with practical needs. Finally, simulation software is used to verify whether the system is feasible. The experimental results show that the F1 value of the model is between 0.7 and 0.9, and a larger value indicates better model performance. Through experimental research, it has been found that combining new media communication with intelligent AI algorithms can achieve a more real-time and interactive way of communication. In the metaverse ecosystem, users can immediately comment, like, or share. With the help of virtual reality technology, combined with new media communication and intelligent AI algorithms, users can gain a more immersive experience, enter the virtual world and interact with other users.
Breast Cancer Detection Using Machine Learning Algorithms

The second most normal sort of cancer in ladies all over the planet is breast disease. Individuals can live longer assuming they are found and tried early, which can assist with cutting down the high demise rate. The most famous ways of seeing whether somebody has disease are computer-aided diagnosis (CAD), mammogram, computed tomography (CT), ultrasound, and biopsy. This study proposed a PC helped outfit strategy for diagnosing bosom malignant growth utilizing a ReNet18 and a support vector machine (SVM). The ReNet18 model is utilized to catch highlights from a X-ray picture, and the SVM is utilized to recognize the disease. To further develop execution, murkiness decrease is utilized to work on the nature of the photos, and afterward the histogram-based K-implies technique is utilized to part the cancer region from the remainder of the picture. The aftereffects of the investigations were taken a gander at utilizing the Break His dataset, which has two gatherings: ordinary and destructive. The recommended model is tried for four extension factors: 40x, 100x, 200x, and 400x. The consequences of the investigation show that the proposed model is more precise by 92.6% at 200x amplification. For the 100x amplification factor, the best accuracy and precision were viewed as 93.1% and 86.5%, separately. The outcomes showed that the proposed plan functions admirably for characterizing pictures of breast cancer cells found in histopathology.
Lightweight American Sign Language and Gesture Recognition using YOLOv8

Hand gestures are a kind of nonverbal communication due to their inherent presence in interpersonal conversations, serving as a natural means for individuals to connect with one another. Hand gesture recognition pertains to the cognitive process of understanding and categorizing significant motions generated by an individual's hands. This methodology is used to engage in interpersonal communication with individuals. Hand Hand sign recognition is a challenging task due to the variability of hand gestures, background clutter, and occlusion. Using DeepSORT and YOLOv8, we suggest a simple method for hand sign recognition in this paper. A tracking algorithm called DeepSORT uses deep learning to track multiple objects in a video even when they are obscured. To find hands in a video, use the quick and precise object detection algorithm YOLOv8. First, YOLOv8 is used by our system to find hands in videos. The hands are then monitored over time using DeepSORT. The tracked hands are subsequently divided into various hand signals. Real-time hand sign recognition is possible with our system, which is a lightweight solution. It is also sturdy.
Implementation of IR Lock on Poledrone (Polman Drone Education) for Precision Landing with ROS

Poledrone or known as quadcopter constitutes an Unmanned Aerial Vehicle (UAV) system that has four symmetrical landing rotors and has the same heights of each corner. Apart from that, Poledrone must have a good stability and be able to minimize distraction on every motion by optimizing the parameter of controller system. However, as a 6 DoF system, Poledrone has a dynamic complex with high non-linear, strong clutch and underactuated that makes it difficult to change into control algorithm. Poledrone with PID controller often suffers from overshoot causing it to be unstable which results is precision landing failure without any damage. By taking into account several strong points of PID methods, the researcher forms the implementation IR Lock with PID control on Poledrone by using ROS as the middleware. The result of result of test with IR Lock is the precision of the landing are good and the average of landing travel time is 11.073 s. But without IR-Lock The precision of the landing is quite good and the average of landing travel time is 5.84 s. This research is that the result can be used as learning aid in order to obtain accurate point more precision Poledrone landing system. So, for future work the research will combine camera with IR-Lock for making the result more accurate.
A Prediction Method of Enterprise Evaluation Based on Genetic Taboo Algorithm

Enterprise evaluation is a specialized activity derived with the development of economy, but in the process of enterprise evaluation, much changes will affect the appraisal results and reduce the accuracy of appraisal. This paper proposes a genetic contraindication algorithm to analyze the evaluation method, can the level of enterprise evaluation, and shorten the formulation time of the evaluation method. Then, a comprehensive evaluation of the real data is conducted. Finally, continuous monitoring is used for the evaluation method, and the final evaluation results are output. The results show that the genetic contraindication algorithm can accurately perform enterprise evaluation. Improve the level of evaluation methods, and the accuracy is greater than 95%, which is better than the traditional algorithm. Tabu algorithm can constrain enterprise evaluation and prediction, reduce external interference and improve the accuracy of prediction. Therefore, the genetic contraindication algorithm can meet the requirements of enterprise evaluation and is suitable for continuous analysis of enterprise evaluation. Compared with traditional methods, genetic taboo can improve the accuracy of calculation results, and make its accuracy more than 90%. At the same time, genetic tabu algorithm can meet the actual needs of enterprise evaluation, and better track evaluation and analysis.
Application and Research of Chaos Algorithm in Information Security Systems

Digital images, as an important component of multimedia files, protecting digital image security has become a research hotspot. The article proposes a visual image encryption algorithm based on LSB, which adopts a new two-dimensional chaotic model improved by a cascaded coupling model and processes the output of the model through linear operations to obtain a cryptographic stream. The two schemes are simulated, and the encryption and decryption speed, key space, random characteristics of chaotic sequence, statistical characteristics, sensitivity analysis, information entropy and invisibility are analyzed. The security performance of the visual image encryption algorithm based on chaos proposed in this paper is verified when transmitting images, among the 1024 * 1024 images, encryption takes the longest time, reaching around 7.7 seconds. The entropy value of plaintext images is basically consistent at around 7.9, and the randomness of plaintext images is significantly lower than that of ciphertext images.
Design and Application of Intelligent Recognition Model for English Translation Based on HMM Algorithm

With the development of economy and society, people have gained a new understanding of intelligent model design and application, and the application of intelligent English translation is also increasing. This allows intelligent recognition systems to quickly enter people's lives, and recognition systems are also an important research direction in the current scientific research field. In order to solve the problem of relatively poor accuracy of traditional algorithmic models, an improved Hidden Markov Model (HMM) method was adopted to achieve intelligent recognition of English translations. On the basis of this method, a phrase corpus labeled with tens of thousands of English and Chinese words was established, thus realizing automatic retrieval of phrases. In addition, an intelligent identification mode for English translations was established, and the intelligent identification mode for data collection, processing, and output was designed; according to the characteristics of the English translation, there was a systematic collection, processing, and extraction of the feature parameters of the English translation, thereby completing the intelligent recognition of the English translation. On this basis, an experimental study was conducted using the developed automatic English translation recognition system, and the experimental results were statistically analyzed. The viewpoint that syllables that redefined phonemes were the least effective units in the time domain was proposed. This paper presents the design and application of an English translation intelligent recognition model based on HMM algorithm. The model was discovered through the use of qualitative and quantitative methods to analyze the existing English translation intelligent recognition system.
Aktivitas Berbagi Pengetahuan Dalam Meningkatkan Literasi Fisik Anak Usia Dini Studi Kasus di motoricshool Halo Kids Indonesia

This research aims to explain how knowledge-sharing activities occur at Halo Kids Indonesia, identify its impact on children's physical literacy, and identify the knowledge sharing process. The qualitative method is used with a case study approach, involving data collection through relevant informants, and the data is presented in narrative form. This research shows that Halo Kids Indonesia applies the concept of sharing knowledge effectively through the stages of socialization, externalization, combination, and internalization. Activities such as sharing parenting, visual documentation, and training play an essential role in children's physical literacy development. In conclusion, Halo Kids Indonesia successfully applies knowledge sharing to increase physical literacy in early childhood.&#x0D; Keywords: Knowledge Sharing, Physical Literacy, Early Childhood, Motor Training