Reviewerly/exHarmony huggingface dataset:
Behavior NPC Prediction Using Deep Learning

Playing video games is one way to break up the monotony of an otherwise boring day. However, a significant number of people quickly tire of playing some of the games that are available to them. This study will result in the development of a strategy that will give a rapid response during combat and may boost NPC abilities. This will be done so that this problem can be circumvented. There is a non-player character that is considered to be one of the most important in the game (NPC). NPCs that are autonomous and adaptive may change their behavior in reaction to the decisions made by the player as well as the conditions in their environment. Previous research has made use of the neural network methodology to forecast the behavior of NPCs; however, this method has a drawback in that the predicted behavior is not necessarily as desired, which leads to a poor level of accuracy. This study attempts to answer the problem of inadequate accuracy by using as its three input parameters the power of the non-player character (NPC), the distance between the player and the NPC, and the power of the opponent. The outcomes of the test indicate that the machine learning technique may be used to identify the results of the NPC behavior analysis as well as the level of accuracy reached.
Teaching Information Management System Based on Blockchain Technology

The teaching management system is a systematic and complete database system, including key modules such as teacher information, student information, and course information. The purpose of this paper is to research teaching information management system based on blockchain technology. This paper proposes the research content of building a teaching management system based on blockchain, and analyzes the significance of system building. In-depth research on the key technologies of building a blockchain-based teaching data management system, including blockchain core technology, asymmetric encryption verification mechanism, IPFS technology, Merkle transaction tree and perceptual hash function, and front-end and back-end technologies related to system implementation. The specific requirements of the teaching management system based on blockchain are analyzed, the overall structure of the system is designed, and the specific structure division and function design are completed for the teaching data tracking module and digital rights management module. Corresponding usability tests and functional tests are carried out for various functions of the system. The abnormal rate of the system is 0, and it can run normally under different platforms and browser environments.
The Design and Application of College Aesthetic Education Micro-course Based on Intelligent Mobile Terminal

In the context of mobile technology and computers, major changes have taken place in the form of organization, dissemination and the final presentation of information. Media forms with “micro” as the key word are rapidly born and developed, showing significant differences and dynamics. The purpose of this paper is to study the design and application of college aesthetic education micro-lectures based on intelligent mobile terminals. This paper studies the key technologies for the development of the micro-course mobile learning platform, including the Android platform and MySQL. This paper proposes a general model of micro-course teaching design for public art education in colleges and universities, summarizes the design of micro-course modules, and applies it to the course of “Chinese Folklore”. A questionnaire survey was conducted on the application effect of micro-lectures in college aesthetic education. Through the implementation of the curriculum, the form of public art teaching in colleges and universities has been expanded, students' learning resources have been enriched, and the teaching of public art education has been improved to a certain extent.
Complex Odia Handwritten Character Recognition using Deep Learning Model

One of the ongoing uses of offline OCR is the identification of Odia character pictures. Here, an effort has been made to create a useful feature extraction method that will aid in recognizing handwritten digits, basic scripts, and compound characters in Odia. Here, character recognition has been implemented using three different types of techniques. First, Three feature extraction techniques are used separately to identify the different characters. a feature collection is combined with a set of common machine learning methods after that. In the second strategy, well-known RNN and CNN carry out character recognition by offering the same set of features but delaying instantaneous visuals, unlike conventional networks. The final challenge was to include a set of complex characters from the Odia language into our proposed framework for character recognition. On this dataset with 120 classes of characters, the suggested technique obtains a recognition accuracy of 88.76%.
The Application of Computer Aid in English Teaching under the Background of Artificial Intelligence-Based on Awrite Survey Data Analysis

The basic skills required to learn a foreign language are listening, speaking, reading, writing, and translation. Among them, learning to write can more effectively promote the internalization of language knowledge, which is the written expression of language production. It can also objectively reflect the learner's language proficiency and thinking ability. This is also a key indicator for comprehensive English use ability research and testing. For a long time, English creation courses are mainly based on classroom teaching, the learners' individual thinking activities are greatly restricted, which greatly inhibits the learners' main body ability. Based on the application of Awrite software, the thesis explores the practical application of computer-aided creation in comprehensive English teaching under the background of artificial intelligence technology. On the basis of literature information, it explores the relevant theories of computer-aided writing textbooks and comprehensive English classrooms. The problem was analyzed, and then based on the Awrite software, the effect of computer-assisted application in English teaching under the background of artificial intelligence was investigated. The results of the survey showed that the introduction of Awrite software into English writing teaching improved the students' autonomous writing ability.
A New Annotation System for Dynamic Web Pages Driven by NLP

Web annotation has become an essential technique to express people's thoughts and feelings by attaching annotations to web content. Annotators with the same interests can exchange their ideas and experiences by conducting universal online collaborations. The idea of submitting annotations depends on attaching vocal or textual notes with the contents of websites. However, annotating dynamic data is a problem that encourages researchers to work on proper solutions. Losing annotations because of the change in website annotated contents will definitely lead to losing the intended collaboration between annotators. This work is related to annotating dynamic websites by computing the textual similarity between erased (or relocated) annotated text and the remained text in dynamic websites by exploiting NLP (Natural Language Processing) algorithms. The attached annotation with dynamic content will be attached to the most related text on the website. By this, annotators will not lose their annotations and replies and hence their collaboration will remain. The experimental tests conducted in this work reflect promising results.
Action recognition based on temporal skeleton analysis: a new method for IoT scenarios

With the rapid development of the Internet of Things (IoT) and the enhancement of the computing power of embedded devices, research on the security monitoring of human beings under the IoT framework is attaching greater importance. However, the current person action recognition algorithms still suffer from low accuracy or high computational cost in complex scenarios. To solve these problems, we combined the Fast-ReID model with HRNet to extract and follow the person’s nodes in real time under multiple target states. Then, we replaced the base convolutional module of HRNet with a depthwise separable convolutional layer and reduced the depth of the network to further reduce the computational complexity of the network. At the end of the system, we introduced 3D separable convolution as our action classification module. The multi-model combination of person re-identification (ReID) and action recognition system is suitable for deployment in edge computing devices. The system enables efficient and accurate real-time person ID tagging, skeleton detection, and action recognition, allowing continuous and stable tagging and recognition of all targets in complex situations. In addition, we also proposed a dynamic skeleton data automatic generation algorithm, which could obtain high-quality dynamic skeleton data stably and efficiently.
OOV Handling Using Partial Lemma-Based Language Model in LF-MMI Based ASR for Bahasa Indonesia

One of the common problems in ASR is the out-of-vocabulary word in an utterance that can degrade the performance of the system. Bahasa Indonesia, as an agglutinative language, uses affixation to generate words from a set of affixes and root words. We propose the use of a partial lemma-based language model (LM) and lexicon that can handle words created from affixation. The partial lemma-based LM and lexicon are created from the original ones using morphology analyzer output as a reference. The experiment shows that using the LM in ASR with LF-MMI cost function gives a better WER when the heuristic to insert inter-word short pause is modified to also consider the affixes.
Design of Teleconsultation System with Artificial Intelligence Based Chatbot Using Docker Platform

The implementation of the National Health Insurance Program (JKN) caused an increase in the number of visits to the first-level health facilities, while there are still clinics that lack medical personnel. Teleconsultation is defined as the possibility of contact between patients and doctors through a telemonitoring platform. Chatbot is a program that automatically provides services by talking to users through many types of communication media. A study shows a positive result in the use of support-chatbot for breast cancer patients, and that doctor agrees that chatbot can help in carrying out some simple tasks in healthcare scenario. Microservice is a system architecture that allows improvement of stability and security. We proposed a chatbot with three main functions: as a teleconsultation platform where doctors can arrange consultation questions that can either be reused for some patients with similar conditions or a specific set of questions for a particular patient; an artificial intelligence based disease diagnosis function made with the decision tree model using data that was collected from the results of literature studies; and disease information search function where the data was obtained from the results of web scraping. Each function was made in their respective services, allowing the usage of different technology according to their specific needs while providing easier maintenance, testing, and deployment. The chatbot's ability to lead the conversation flow was tested using predetermined test cases and direct user testing, the predetermined test cases shows positive results with the chatbot being able to correctly answer all the input as expected, while the user testing shows some areas of the system that could be improved further, including the addition of more commonly used disease term in the disease information database and more training for the chatbot.
Federated Learning with Blockchain: A Study of the Latest Decentralized Couple

Federated learning is a method for machine learning that avoids the need to move data by training an algorithm in parallel on a large number of edge devices or servers that hold copies of the training data in their own memory. Conventional decentralized approaches often assume that local data samples are equally distributed, whereas typical centralized machine learning techniques involve uploading all local datasets to a single server. Federated learning has flaws like privacy and insufficient data. Blockchain’s rise offers a safe and effective method for Federated Learning implementation. In this paper, the collaboration of Federated learning and blockchain is discussed. Then the possible applications that can be developed using federated learning and blockchain are discussed. Furthermore, the challenges and opportunities for collaboration between these technologies are explained.
Hybrid PAD Classroom Intelligent Teaching System Based on Optimization Algorithm

The vigorous development of electronic products, computer technology and Internet technology has rewritten the world once again. The education industry also needs to optimize development in such a torrent of times. Online education has broken the traditional way of learning and brought a new direction for China's teaching reform. In order to meet the new teaching methods in the network age, the paper first discusses the design process of genetic algorithm in the intelligent classroom teaching system and the characteristics of the optimization algorithm, and then based on the optimization algorithm and the characteristics of the PAD classroom, the PAD classroom intelligent teaching system to optimize the application. This paper adopts mature probability theory and mathematical statistics method, which is scientific and credible, and the optimal scheme provided also has extensive adaptability.
FPGA Implementation of Radix-4 Modular Montgomery Multiplier over Prime Fields

Modular multiplication is one of the most commonly used primitive in cryptographic algorithms. Montgomery multiplication is one of the preferred techniques for modular multiplication since it replaces the costly division operations with series of multiplications. This paper presents the implementation details of Radix-4 Montgomery multiplication algorithm over prime fields targeted for FPGA platform. The proposed design achieves a good balance between resource utilization and the throughput. The efficiency (Mbps/LUT) of the proposed implementation is improved by 55-86% compared to Radix-2 Montgomery multiplication. Compared to similar Radix4 modular multipliers, the improvement in efficiency is of the order of 42-150%. The paper presents the implementation results for prime field sizes ranging from 192 to 512 bits followed by detailed comparison with previous implementations in terms of frequency, Slice LUTs, throughput, area x time per bit and efficiency.
An Approach for Word Segmentation from a Line Segment in Odia Text Using Quartiles

This paper deals with word segmentation from a given line segment. These line segments may have alphabets and matras in one single line segment or the alphabets and matras of a line text in two different line segments. The line text is segmented into alphabets and the associated matras in two different line segments are reconstructed using Reconstruct Module. The approach introduced in this paper has three phases: Pre_Processing Module, Find_White_Spaces Module, and Analyse_White_Spaces Module. The Pre_Processing module is responsible for reading the input line segment, converting it to a gray image, removing white spaces that encapsulate the whole text, and then converting it to a binary image. The Find_White_Spaces module is responsible for finding the start and end of the white spaces between the words. The Analyse_White_Spaces module is responsible for analysing the widths of the white spaces using quartiles and storing the segmented words in the directory, ‘Segmented Words’. The proposed system has been tested with images of line segments consisting of only alphabets and alphabets with matras. The overall correctness accuracy of 99.9% has been achieved in this approach for word segmentation.
Semantic Segmentation in Medical Image Based on Hybrid Dlinknet and Unet

Medical imagery segmentation has been widely using deep learning approaches, which are quickly evolving in semantic segmentation. Nevertheless, due to their poor performance, newly proposed methods like Fully Convolutional Network, U-Net, LinkNet and SegNet still require enhancement to offer better semantic segmentation while identifying breast cancer. Therefore, this article presents a hybrid encoder and decoder framework in histology images of tissue slides. The novel network is designed with DlinkNet and UNet with a fusion of medical imagery data. An Attention Gate Module (AGM) is inserted into the proposed network to enrich the network learning’s capacity. This way, semantic features are extracted and semantically segmented in the tumors in the input image set. The effectiveness of the planned network is investigated concerning various parameters like Maximum Symmetric Surface Distance (MSSD 35.98), Accuracy (99.2%), Relative Absolute Volume Difference (RAVD 3.41), Jaccard index (0.83), Average Symmetric Surface Distance (ASSD 0.629), Sensitivity (91%) and Dice Coefficient (DICE) (0.928) with existing networks of FPN and SENet.
Analysis of the Intelligent Fault Detection Platform of Seismic System Based on Machine Recognition

This paper introduces the basic situation of fault detection platform of seismic observation system based on machine recognition. The platform mainly uses computer language to judge possible faults of seismic observation system by using abnormal waveform change characteristics of seismic records, and prompts maintenance personnel to deal with them accordingly. This study can improve the time-effectiveness of instrument fault judgment and better guarantee the normal operation of seismic observation system.
Research on Virtual Reality Exhibition Teaching Platform in Teaching Reform of New Economics

With the popularization of the concept of “Internet + education” and the advent of the 5G era, the information-based auxiliary teaching platform has gradually become a common teaching method for education at all levels. As an emerging information-based teaching platform, the virtual Exhibition teaching platform based on Virtual Reality, Human-computer interaction and Gesture Recognition technology can break through the limitations of time and space, allow students to experience the MICE site immersively, and provide effective solutions for education and teaching through modern educational technology means and information technology. And through the realization of the reform of educational concepts and the innovation of teaching models, so as to establish a new model of intelligent teaching, the virtual Exhibition teaching platform and the traditional teaching methods are combined, so that students can fully absorb theoretical knowledge at the same time, but also in the virtual MICE activities to practice, strengthen the optimization and integration between teaching modes, improve the teaching effect of MICE courses.
Design of Online Teaching Assistance System in Colleges Based on Intelligent Optimization Algorithm

The continuous development of computer technology has changed the operation mode of many industries and departments, and more and more users have begun to use the Internet. For the education sector, integrating computer technology into the education system is becoming an important way to modernize education. This paper aims to study the design of online teaching assistance system in colleges and universities based on intelligent optimization algorithm. On the basis of analyzing the advantages of J2EE, intelligent optimization algorithm and system performance requirements, the online teaching assistance system in colleges and universities is designed, and finally the system is designed. Performance was tested. The test results show that the performance of the system designed by this paper is good, the test pass rate is above 95%, and the actual response time of the system is also within the expected response time, which basically meets the requirements of this paper.
Application of Virtual Reality Technology in Interior Design Teaching

Virtual reality simulates the three-dimensional space virtual environment formed by the computer multimedia information processing system, and makes the user feel immersed in the gold through the relevant sensing devices, so as to achieve a good interaction between the user and the environment and let them feel immersed in it environment effect. This paper focuses on the research on interior design teaching, based on VR virtual reality technology, the three virtual reality technology and interior design teaching are closely combined. In the interior design teaching application designed in this paper, students can use virtual reality technology to enter the simulated three-dimensional scene, and they can experience the final effect of their own plan. In this way, students can not only evaluate and analyze the rationality of the plan, In order to optimize the teaching plan and obtain better application results, we can also keep in mind the reasonable and unreasonable aspects of the plan through personal experience, and accumulate experience for designing better works in the future. The final results of the research show that with the increase of the frame design area, the restoration degree of the virtual teaching frame model will decrease slightly, but it remains above 90% restoration degree. When the design area is 56m2, the restoration degree of the frame model is 98.4. %, in order to ensure the authenticity of the restoration degree of the virtual teaching framework model, the design area of the framework should be properly controlled.
Research on the Application of Computer Big Data Technology in College Japanese Grammar Teaching

Big data is relative to data in the general sense. It refers to such a data collection: the amount of data is growing so fast that it is impossible to collect, process, store and store data within a certain period of time with conventional data tools. Calculated data set. The era of big data has changed people's social life and way of thinking. In the field of education, it has also promoted the reform of teaching mode, especially the teaching of foreign languages. At the same time, it also brings challenges and new ideas to Japanese grammar teaching.
English Grammar Learning Model Construction System based on Interactive Genetic Algorithm

English grammar learning is an important part of English language learning, which directly affects the formation of students' comprehensive language ability. Grammar learning is not the ultimate goal of language learning, but an effective way and a bridge to the other side of communication. With the continuous development of science and technology, it also has a great impact on English grammar learning. Based on interactive genetic algorithm (IGA), this paper designs and analyzes the English grammar learning model(EGIM) system, discusses the design principles of EGIM, and constructs the EGIM through IGA; The experimental results show that the learning model proposed in this paper has great advantages in improving grammar learning interest and learning efficiency; It optimizes the grammar learning process and improves the learning efficiency, which is of great significance for English learning.