Reviewerly/exHarmony huggingface dataset:
Field programmable gate array based moving object tracking system for robot navigation

This paper proposes a method in which an object tracking robot system is implemented on field programmable gate arrays (FPGAs). The OV7670 camera provides real-time object pictures to the system. To improve picture quality, images are put via the median filter phase. The item is distinguished from the backdrop based on color (red), after which it is subjected to a mathematical morphological approach of filtering to eliminate noise. To send the robot control signals, the object's (new) coordinates are found. In this method, the median filter, color separation, hardware IP cores, and morphological filter are all part of the embedded system on FPGA. Through the direct memory access (DMA) controller, these cores may communicate and perform high-speed pipeline computing at higher data rates. The entire system is executed in real-time on Xilinx's spartan-6 FPGA KIT. The results show practical and promise.
Image preprocessing analysis in handwritten Javanese character recognition

The handwriting produced by each person is unique, so each person has a different stroke, even though they write the same letter. Handwritten Javanese is an exciting topic to study, in addition to scientific purposes and preserving Indonesian culture. The Javanese character image dataset is aksara Jawa: aksara Jawa custom dataset from the Kaggle database consists of 2,154 train data and 480 evaluation data. This research proposed to analyze the impact of some preprocessing methods in recognizing handwritten Javanese characters. The preprocessing methods are dilation, skeletonization, and noise reduction. The first process is segmentation for region of interest (ROI) extraction, then various preprocessing is used, and finally, the recognition step neural network (NN) to measure the effectiveness of the preprocessing method. The experiment shows that all preprocessing methods (dilation, skeletonization, and noise reduction) give excellent results, especially on the black background color, reaching 98% accuracy. Other experimental findings show that in any preprocessing combination, the black background accuracy is better than the white one.
An ameliorated Round Robin algorithm in the cloud computing for task scheduling

Cloud computing is an advanced technology that offers types of assistance on requests. Because of the huge measure of requests got from cloud clients, all requests should be managed efficiently. Therefore, the task scheduling is critical in cloud computing. The provision of computational resources in cloud is controlled by a cloud provider. It is necessary to design high-efficiency scheduling algorithms that are compatible with the corresponding computing paradigms. This paper introduces a new task scheduling method for cloud computing called an ameliorated Round Robin algorithm (ARRA). The proposed algorithm develops an optimal time quantum based on the average of task burst time using fixed and dynamic manners. The experimental results showed that the ARRA significantly outperformed other algorithms including improved RR, enhanced RR, dynamic time quantum approach (ARR) and enhanced RR (RAST ERR) in terms of the average waiting time, average turnaround time and response time.
Ultrafast silicon threshold circuitry for chaotic laser time series

Photonic computing has been intensively studied to explore the ultrahigh bandwidth of lightwaves. However, electronic support is indispensable for the post-processing and control of photonic systems owing to the difficulties encountered in all-optical processing. Herein, we demonstrate an ultrafast silicon circuitry capable of conducting thresholding operations on incoming chaotically oscillating high-bandwidth signals. Such circuits are critical elements in ultrafast random-number generators and photonic reinforcement learning that exploit chaotically oscillating time series. The circuit design, including active inductors for bandwidth expansion, and proof-of-principle fabricated device operations are demonstrated using a 180 nm silicon complementary metal–oxide–semiconductor technology node.
Database Translation Mechanism: Generating Data Dictionary for Relational Database

PDF HTML XML Export Cite reminder Database Translation Mechanism: Generating Data Dictionary for Relational Database DOI: 10.21655/ijsi.1673-7288.00291 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:In order to optimize workflow and improve efficiency, modern enterprises usually entrust software providers to build the Enterprise Information System (EIS). However, the software providers generally do not provide the data dictionary for the EIS relational database, which brings great difficulties for enterprises to use the data stored in the EIS database. This paper proposes a method named database translation mechanism for generating the data dictionary of the EIS relational database which only utilizes the data collected from the interfaces of EIS. Inspired by the great success that Graph Neural Networks (GNNs) have achieved, the study builds a graph structure for the EIS relational database by mining the relationships between columns and then train a GNN-based classifier to predict to which column the value belongs. In addition, a table-based sampling method is designed to construct graph datasets for mini-batch training on the large-scale graph structure. Furthermore, a uniform encoding method and a hybrid aggregator function are proposed to improve the performance of the GNN-based classifier. The trained GNN-based classifier can be used to predict the matching relationships between the columns of the EIS database and the tables extracted from the EIS interfaces given the fact that the data in the database is entered at the interfaces. In this way, the detailed information at the interface to translate the database can be used. Experimental results on a real-world ERP relational database demonstrate the superior performance of the proposed method, which efficiently exploits and utilizes the graph structure information in the relational database. Reference Related Cited by
Exploration in Potential Use of Chatbots

The long-term goal of the research is to develop an efficient natural language processing application for both healthcare and education purposes.Chatbots are identified as a program within a website or an application that simulates human conversations using NLP.The objective of the current study is to provide a comprehensive review of chatbot's possible impacts on the healthcare and education industry.To be specific, the study has the following subobjectives: The result of this study will be valuable to the healthcare and education industry as well as related software providers in developing better proactive and tools for understanding the intent behind individuals' requests.
Design of a game-based learning environment for basic rhythmic patterns in music

Rhythm is the basic learning content in music teaching. A game-based learning environment with basic rhythm is constructed in this paper by using 3D virtual world environment. The virtual island with the theme of “Music Playground” is designed to allow learners to learn basic music theory knowledge such as musical notes, time values, beats, staves, etc. Further understand the concept and classification of rhythm type and the importance of rhythm for music learning through game experience.
An Atypical Induction Control with User-Defined Rules

Under traditional induction control methods, phase sequence combination can't be adjusted dynamically by traffic flow characteristics.To solve this problem, this paper put forward an atypical induction control with user-defined rules, which is supported by real-time traffic data such as vehicle presence, queue length, and the number of vehicles in the interval.Under this control, users can define the identification and triggering rules of the traffic state and design the combination rules of the conventional and extended stage chain according to the characteristics of the actual scene.Based on the analysis of the local highresolution log data of the signal controller at the intersection of Jiaotong Avenue and Tianxian Road in Xiaogan City, it is concluded that the maximum queuing length of the average intersection cycle decreases from 160 m to 95 m after the implementation of the scheme, a decrease up to 40.6%.The results show that the atypical induction control with user-defined rules has good flexibility, controllability, and expansibility and is extremely suitable for traffic scenes with dynamic traffic characteristics.
Intelligent question answering system for medical knowledge

In recent years, intelligent question answering system has been widely used in various interactive services. Intelligent question answering system is to arrange the accumulated disordered corpus information in an orderly and scientific way, and establish a classification model based on knowledge to support the question answering of various forms. In this paper, vertical medical website data and electronic medical record data are fused to solve the mapping problem of colloquial medical vocabulary and medical professional vocabulary. PLSA (probabilistic latent semantic analysis) theme architecture model solves the hidden theme content crawling problem of web page crawler. The application of vector space model and knowledge graph in medical knowledge question answering system solves the problem of low efficiency in traditional interactive question answering system. After testing, it can accurately understand the user’s intention, and the accuracy of question and answer is high.
Bronze inscription classification based on rare-class sample generator

Bronze Inscriptions is essential to the study of ancient Chinese history. We have obtained a set of materials containing 102,338 images of bronze texts collected by experts, but the distribution of these acquired images is imbalance, with a large sample of common words but the opposite for rare words. To solve this problem, we have adopted an end-to-end enhanced rare data generation module that can generate feature maps for rare classes during the training phase. The experiment results show that the proposed model can obtain an overall average accuracy of 77.38% and a minority accuracy of 68.24%.
Research and application on the digital mine platform

This paper introduces a digital mine platform which integrates functions such as data storage, deep mining and intelligent management. The digital mine platform encapsulates the software and hardware resources, and uses resource virtual to respond the users quickly, solves the standardization problem of message transmission process through XML documents, integrates process calls of existing heterogeneous system, and collects massive signals resource. The digital mine platform lays the foundation for digital mines.
Research on the model of SaaS platform based on Docker container technology

Although the rapid development of information technology makes rooms for many small and medium-sized enterprises on program deployment through Virtual Machines (VMs), the physical resources is not fully occupied since some programs are not as large as the virtual systems. Therefore, we applied the Docker container technology, which is an application-level isolation. It is more flexible and smaller than virtual machines. This paper studies the standardized migration, automatic deployment and application cluster monitoring of Docker container technology and applies them to the Saas platform. It provides visual operation function for enterprise project management and reduces the steps of system deployment, operation and maintenance.
Research on the design of household coagulation testing product service system

In this study, the product service system was introduced to study and design the postoperative health management of patients with coronary heart disease, aiming at solving the problems such as difficult medication guidance and high reexamination time cost for patients with coronary heart disease. The user journey map, which focuses on user experience, was used to identify the requirements and pain points of postoperative health management of patients with coronary heart disease. Combined with Kano model, the functions transformed from requirements and pain points were classified and prioritized to guide the design of product service system. Through the research, the framework of product service system is established, and the household coagulation testing product service system is visually displayed through the service blueprint. The design of household coagulation tester, interactive interface of mobile and computer software is produced, so as to improve the efficiency and effectiveness of health management for patients with coronary heart disease and realize a one-stop service platform integrating medical diagnosis, medication guidance and health management. The findings of this study propose a new noninfectious chronic disease (NCD) management mode taking coronary heart disease health management as an example, and provide a reference for the research of NCDs management system in China.
YOLOv3 tiny vehicle: a new model for real-time vehicle detection

Existing vehicle detection has the problem of unbalanced detection accuracy and speed. Aiming at this problem, this paper proposes a new real-time vehicle detection model named YOLOv3 Tiny Vehicle. The proposed network replaces the Maxpooling layers of the original network with the convolutional layers to ensure that the characteristic information of the vehicle was preserved to the greatest extent. On this basis, our work adds a dense connection structure to the original network, which greatly reduces or even eliminates the overfitting problem during network training. The experimental results show that the mean Average Precision (mAP) of the model on the Beijing Institute of Technology vehicle (BIT-Vehicle) dataset can reach 96.80%, the Frames per second (FPS) can reach 188. At the same time, it also shows that our model has preeminent generalization ability.
Application of artificial intelligence technology in 5G Edge IoT proxy communication terminal of power system

With the construction of new power system, the number of new energy power stations, energy storage stations and other constructions will increase rapidly, which puts forward higher requirements for standardized operation and personnel safety in power infrastructure construction and operation inspection. The traditional safety management mode with on-site supervision as the main method no longer meets the requirements of current power grid scale, organization setting and the number of production activities. 5G edge IoT proxy communication terminal of power system is developed in this paper, which adopts the artificial intelligence technology of face recognition and safety helmet recognition, and uses the characteristics of 5G high speed, low latency, large connection and Beidou high-precision positioning, which can effectively expand the monitoring range and control ability. Using 5G edge IoT proxy communication terminal of power system to build an intelligent, all-round and information-based safety supervision and management system can effectively improve the safety supervision level of personnel at all levels of operation inspection and infrastructure construction site, realize the whole process safety control, risk analysis and early warning function of operation environment and process, and support the digital transformation of State Grid.
Research on machine learning strategy based on voting model

In this research, machine learning algorithms such as decision tree, random forest, and BP neural network are used to predict a certain dataset, and then a voting prediction model is built based on the above three machine learning algorithms.To verify the performance of this voting model, we introduced confusion matrix and F1 score to evaluate the effectiveness of machine learning.The experimental results show that the performance of the machine learning strategy based on the voting model outperforms that of a single machine learning algorithm and that adjusting the voting weights of a single algorithm can also affect the performance of the whole model.This result is well worth further study.
Mixed mode of instruction database principles and applications

Thanks to the rapid development of Internet technology, the popular mixed mode of instruction combines the advantages of online learning and offline classroom learning, which can give full play to the advantages of online learning and provide rich and high-quality learning resources for teachers and students. In this context, teachers can keep abreast of students’ learning progress and the improvement of knowledge consolidation level, guide students to think, stimulate students’ learning motivation and enthusiasm, and improve students’ ability to solve practical problems and comprehensive professional ability through offline classroom teaching, so as to give full play to students’ subjective initiative. Through the online and offline mixed teaching practice of database principle and application, a student-centered teaching mode is introduced, that is, students are changed from passive recipients to active learners and knowledge internalization is deepened, which not only improves students’ learning initiative, but also strengthens the communication between students and teachers, thus effectively improving teaching efficiency.
A smart contracts-based searchable encryption scheme with forward privacy

As an important link of the Internet of Things, wireless sensor network has great potential in industry, agriculture and daily use of residents. To ensure cloud data security, data is encrypted before being uploaded to the cloud. However, how to ensure the searchability of ciphertext in cloud is challenging. To solve the above problems, a searchable encryption scheme for sensor networks is proposed. The sensor node collects the data, encrypts it and uploads it to the smart contract after collecting the data from the local gateway. In this scheme, the concept of version information is introduced to authenticate ciphertext version so as to realize access control of trap gates of different versions. In order to avoid data leakage caused by the information storage center, the searchable encryption technology is combined with the block chain technology, and the smart contract is used as the data storage center, so as to ensure efficient ciphertext retrieval and eliminate malicious behavior on the server side. Through security analysis, it is proved that the scheme satisfies ciphertext indistinguishability and forward security based on DBDH hard problems. Experimental efficiency analysis shows that the scheme has certain advantages in computational efficiency.
Research on COVID-19 network public opinion events based on topic model and community detection algorithm

COVID-19 has caused a large number of online public opinion incidents. How to timely and effectively guide the resulting network public opinion has become an urgent problem to be solved. This paper collects more than 130,000 original Weibo posts during the Wuhan “city closure” incident, and analyses the topic characteristics of the incident on the basis of user classification through topic models and community detection algorithms. It was found that during this period, the government responded quickly to the epidemic and gained public support. For different Weibo users, officially certified users mainly publish information about the epidemic and epidemic prevention measures. Personally certified users mainly forwarded and transmitted official information actively, and they also expressed their opinions and made suggestions. Non-certified users actively expressed their emotions and opinions, so they were important users that reflect public opinion.
Computing direction similarity based on two-layer domain graph

A quantitative calculation model for the direction similarity of composite spatial objects is introduced in this paper. First, it quantifies the compound expression model, defines the distance between the domain map and direction slices suitable for the model; then, it uses the decomposition idea to decompose the composite spatial object, decompose the reference target, and the source target as a whole; finally, it uses the balanced transmission problem. The minimum element method is used to solve the distance between the direction relationship matrix of the compound expression model, and finally the similarity between the composite spatial objects is obtained. The directional similarity experiment of composite spatial objects at different scales proves the feasibility, and the results are in line with human cognition.
Effect of Online Learning Characteristics on Self-determination Needs, Self-efficacy and Academic Achievement

Online education has good reason to study its value in that it can standardize learning content and teaching methods and secure objectivity, away from various controls and restrictions such as space and time. The purpose of this study is to present conditions for online education to become a learner-centered teaching plan that can increase problem-solving-oriented learning outcomes suitable for the era of the 4th industrial revolution. This study attempted to investigate the relationship between the characteristics of online learning, self-determination needs and academic achievement based on previous studies. The questionnaire was distributed to 117 Korean university students with online learning experiences living in Seoul, Busan, and Gyeonggi-do, and the questionnaire was composed of a 5-point scale. To verify the hypothesis, regression analysis was performed using SPSS 23.0.&#x0D; The results of the study are summarized as follows. First, the characteristics of online learning have a positive effect on academic motivation and achievement. Second, the characteristics of online learning have a positive effect on satisfying self-determination needs. Third, satisfying self-determination needs has a positive effect on academic motivation and achievement. Fourth, the degree of satisfaction of self-determination needs was found to play a mediating role in the relationship between the characteristics of online learning and academic satisfaction. Therefore, in designing an online learning program, it suggests that it is necessary to design it to satisfy the self-determination needs.