Reviewerly/exHarmony huggingface dataset:
Research on the Computer Aided Physical Education Distance Teaching System

This paper builds a network physical education system based on WEB services and SQL database. The architecture of the platform is divided into four levels: infrastructure, logic, support and distance learning platform. On the browser side, the Spring architecture is used to complete the control design. The Hibernate architecture and Spring JDBC wrapper classes are used to convert between the underlying database and business logic layers. The practical application and experiment show that the system can meet the needs of modern education technology. And it can integrate all kinds of learning styles well. The user interface is good. The physical education distance teaching system is a good man-machine teaching method.
Fast Exam Video Summarization Using Targeted Evaluation of Scene Changes Based on User Behavior

As the demand for distance learning has increased, so has the need for electronic exams (e-exams). In e-exams, where video monitoring is used, one of the main tasks of the proctors is to detect cheating in exam videos. Monitoring a large number of videos is challenging for proctors, and summarizing videos to focus on cheating scenes can significantly reduce the difficulty of the monitoring task. Several important points should be taken into account in e-exam video summarization, such as various background, the clothes of each examinee, and the variety of video surveillance devices. One of the most effective methods for video summarization is the use of deep neural networks. However, the existing methods require a large dataset for training which is time consuming, besides, they are unable to accurately summarize the video containing a new background scene. In this paper, a deep learning-based method is proposed to recognize the objects in the scene and monitor any change in the examinee’s body parts with different priorities. In this approach, the abnormal behaviors of the examinee can be automatically detected without the need for a pre-training with multiple background, then the video is automatically summarized. The experimental results demonstrate that the proposed method, while having a high processing speed, can summarize exam videos up to 70%.
New AMR(Automatic Modulation Recognition) algorithm based on the phase difference tendency of unknown communication signal

This paper is about the practical algorithm of an AMR (Automatic Modulation Recognition) algorithm that automatically classifies the modulation scheme without no prior knowledge of communication signal. In general, most of the previously proposed AMR algorithms assume that a certain level of information known in advance but most of all make it difficult to apply to signals in real environments such as hostile communication.In this paper, to obtain modulation information for the target signal, the phase difference tendency of the input signal is used to classify it into PSK (Phase Shift Keying) and FSK (Frequency Shift Keying) classes and then the characteristics of the M-signal are used. Squared Auto Correlation Function (ACF) processed vector to determine modulation index M for MPSK signal. This paper is for an algorithm that proposes a method to determine the modulation index M of an MPSK signal without complex calculations using the M-square characteristic of ACF.
Research on Digital Media Application System Based on Computer Artificial Intelligence Technology

A digital media design system based on computer artificial intelligence technology is proposed. The design uses TMS320C6657 and DDR31333 as the hardware core of the system. At the same time, the high-speed external memory is used to expand the massive data transmission channel. The software uses a plug-in architecture to develop image plug-in and sound plug-in. The various functional modules are integrated together in the form of plug-ins to form a complete overall framework. In this paper, a virtual reality-based digital media production platform is developed by using the method of joint development of software and hardware. The analysis of the actual operation of the system proves that the system works stably and reliably, and the realization of each function module has reached the design index of the system.
Distributed Computing and Data Processing Technology under the Power Data Middleware Architecture

With the establishment and development of integrated monitoring platforms and communication information platforms for intelligent substations, the data volume of the power system is showing explosive growth. However, existing data centers have poor processing capabilities for massive amounts of data, making it difficult to meet the multiple service quality requirements of users. How to effectively manage massive and complex data, fully utilize the value of data, improve service quality, reduce operational costs, and achieve rapid response to users is an urgent problem to be solved. Therefore, based on the actual needs of power enterprises, this article proposed a cloud computing power data middleware architecture based on distributed technology, which effectively managed, cleaned, converted, and evaluated power data sources through cloud computing platforms. This article mainly analyzed the shortcomings of traditional power data processing platforms and the feasibility of cloud computing based power data platforms. Finally, through load balancing bias comparison and pressure testing experiments under different task numbers, it was proven that cloud computing systems outperformed traditional power data processing systems in terms of utility value, completion time, and load balancing. Moreover, in the pressure testing experiment, under the condition of 10 concurrent users and 673 system request transactions, 91.3% of the interface response time in the cloud computing system was within 50ms. Therefore, cloud computing systems have certain advantages in processing power data.
A Contactless Control Mechanism for Computerized Systems using Hand Gestures

This article presents a systematic way to develop a hand-sign recognition system aimed at detecting and interpreting user hand-signs for specific functionalities, enabling an easy contactless user experience. The process unfolds through several sequential steps. Firstly, the data collection phase involves employing a custom camera integrated with OpenCV, capturing a diverse range of hand-signs crucial for model training. Subsequently, the model training step encompasses extracting hand landmark data using mediapipe and leveraging scikit-learn in conjunction with a random forest/SVM classifier. Once the model is trained, a classifier is assigned to recognize distinct hand-signs, achieved by mapping gestures to keyboard shortcuts. Lastly, the system’s functionality is encapsulated within a tkinter GUI, yielding a cohesive user experience. This approach achieves efficient and accurate hand-sign recognition, enabling seamless interaction and activation of functions through gesture-based input in a contactless manner.
Symmetry Verification and Circuit Implementation using Qiskit

For near-term quantum computer, Quantum Error Mitigation (QEM) has recently attracted attention as a solution to reduce errors in quantum computers. In this paper, we introduce one of the QEM protocols, symmetry verification, and implement it using Qiskit. To check the performance, we use the VQE (Variational Quantum Eigensolver) to find the ground-state energy of hydrogen molecule (H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ) on two qubits.
P2P System for Private Business Data Aggregation

The paper addresses the challenge of enabling secure and privacy-preserving data aggregation within decentralized business communities. The specific application scenario involves small communities of big companies that seek to access detailed aggregated data from their community without revealing their individual contributions to competitors or any centralized authority. The absence of a neutral and trusted organization to collect and aggregate data necessitates the development of a decentralized solution. To meet these requirements, we propose a novel P2P algorithm that ensures efficient connection establishment among nodes while maintaining privacy and anonymity. By leveraging the network topology and employing specific connection request types, nodes can establish connections without disclosing their specific business details. Experimental results validate the algorithm’s effectiveness of securely constructing a cyclic overlay network. The solution offers a robust and scalable approach to data aggregation, enabling actors to access aggregated information without compromising their individual data or the confidentiality of their contributions.
A Market Transaction Model Based on Blockchain Technology for Multimicrogrid Networks

In order to solve the reliability and security problems of multi-micro-networks participating in market transaction data, a multi-micro-network market transaction model based on blockchain technology is proposed. The multi-microgrid system and the models of wind turbines, photovoltaic units and energy storage in the microgrid are analyzed. The blockchain and its main technologies are introduced, and the multi-microgrid public chain model is analyzed. The Nash bargaining method is analyzed, and the microgrid cost model and transaction model are established, with the goal of minimizing the cost of microgrid alliance and maximizing the profit distribution rate of multi-microgrid. The simulation analysis of the model established in this paper shows the effectiveness of the model.
Real Time Object Tracking Using OpenCV

Object tracking has been widely used in the field of computer vision. The OpenCV Library offers a wide range of Target Tracing Algorithms, which have been shown to be robust and effective. But as technology advances, precision and robust can be improved. In this overview, we look at the latest developments and new developments in OpenCV’s object tracking.The focus is to provide an overview of some of the latest methods used to improve object tracking accuracy and robustness. We discuss the strengths and weaknesses of each approach and evaluate their performance based on standard benchmarks datasets. From our analysis, we observe that deep learning-based methods, coupled with motion models, have recently demonstrated impressive results. We also highlight the limitations and challenges faced in real-world scenarios with complex environments, occlusions, and deformable objects. Finally, we discuss interesting future research directions, including exploring hybrid methods that combine several techniques to improve tracking accuracy and robustness. With the advent of IoT, drones, and autonomous vehicles, object tracking has become increasingly relevant in our daily lives, and thus, the pursuit of accurate and robust algorithms is as crucial as ever. In conclusion, this review provides insights into some of the recent advancements and challenges in object tracking in OpenCV. The review offers a valuable resource for researchers and practitioners working in the field and highlights gaps in current research that require further investigation.
Text Clustering Based on Binary Particle Swarm and Spherical K-Means Algorithm

Internet technology advancement has led to an exponential surge in text data. Among the pressing data types today, natural language data stands out. Leveraging natural language processing (NLP) technology, computers can comprehend human language and extract valuable information from extensive textual data. This paper introduces a novel approach for text clustering, combining binary particle swarm optimization with the spherical K-means algorithm. Empirical findings on a Chinese text dataset demonstrate the effectiveness of the feature selection method introduced in this paper, effectively diminishing feature overlap and enhancing clustering precision.
Simplification of Continuous Chains in 3D CAD Models for Industrial AR Applications

The use of Augmented Reality (AR) devices like HoloLens has gained significant acceptance for training assembly line operators in various industries. When employing Computer-Aided Design (CAD) models to create assembly line instructions for training purposes, preserving all redundant information becomes unnecessary. Utilizing simplified CAD models leads to improved run-time performance of the applications in which they are employed. This paper introduces a novel solution to simplify CAD models by identifying continuous chains within the CAD model, with the help of deep learning techniques. The proposed method makes it possible to suppress such features that do not impede visualization when removed.
Surface-Assisted In-Air Gesture Recognition

In-air hand gestures can be effectively utilized to support immersive interactions in an online meeting. In this study, we propose a novel system to support in-air hand gestures by leveraging the vibrations introduced by the gestures. When a user makes hand gestures while putting her elbow on a table, vibrations are produced due to the gestures and travel through the table’s surface. Based on this observation, we collect gesture-driven vibrations and use them for gesture recognition. Our evaluations with real-world users demonstrate that we can precisely recognize in-air hand gestures (e.g., an average accuracy of 96.02%) using only commercial devices, such as smartphones and smartwatches.
A CNN-based model for low-textured object detection and tracking

This paper presents a specialized Single Shot Multi-box Detector (SSD) [9] tailored to the unique challenges of detecting low-textured objects in complex scenes. We harness the Fruit 360 dataset [10], featuring low-textured images of various fruits and vegetables, for both training and validation purposes. Our primary objective is to enable the integration of this streamlined SSD model [9] into mobile devices for the enhancement of mixed and augmented reality experiences. Remarkably, our custom model delivers a fourfold increase in processing speed compared to the original SSD [9], all while preserving or even enhancing detection accuracy on the designated dataset. Implementation is carried out in Python using TensorFlow
A Numerical Computer Simulation System for the Influence of Physical Fitness Training Loads on Basketball Players

Artificial intelligence technology is used to develop a set of basketball load effect simulation system. The perceptual interaction subsystem uses the virtual environment producer to build a 3D training load to affect the scene, and then uses the view module in the model calculus subsystem to draw the picture in the 3D scene. The logic module will deduce the physical state of the player, and then feed the inferred results to the visual interaction system through the interface, affecting the generator. The interaction of basketball load on it is shown. The experimental results show that this system can simulate the physical strength decline of basketball very well. It has good load capacity. It can improve the load effect of physical exercise on athletes and the simulation efficiency of competition. After use, it can improve the performance of basketball players.
Research on Intelligent Data Mining and Processing System Based on Computer Genetic Algorithm

In this paper, a genetic algorithm for DSS modeling is proposed by integrating GA and DSS. Firstly, SOFM algorithm is used to convert the quantized sample information into semantic value. The decision system is transformed into a fuzzy set of semantic values. The original fuzzy interval is reduced by using fuzzy set similarity analysis and fusion method. This enhances the credibility of the model. Then genetic algorithm is used to further optimize the rules obtained. The corresponding data were selected from UCI database as the test object. The correctness of the method applied in quantitative data is verified.
HOG Based Surveillance System for Chain Snatching Detection

In certain countries, the usage of closed-circuit television (CCTV) cameras for public safety and crime prevention has increased recently. Traditional CCTV systems, on the other hand, have a reputation for being ineffective at spotting suspicious activity. By utilizing artificial intelligence and machine learning techniques to identify odd behavior, the Abnormal Activity Detection work seeks to get over this constraint. A sophisticated methodology is used in the study to identify suspicious activity in CCTV footage. The proposed system employs two methods for abnormal activity detection in chain snatching incidents. These methods utilize the HOG and SIFT features and are implemented using various classifiers, including random forest, XGBoost, logistic regression, decision tree, and SVM. The system is designed to analyze video frames and detect chain snatching incidents in real-time. The proposed system is novel in its ability to be trained on various types of video footage with different categories, making it a versatile tool for crime prevention and public safety. Ultimately, the Abnormal Activity detection system offers a comprehensive approach to CCTV surveillance that enhances public safety and aids in crime prevention.
Application Research of Computer Artificial Intelligence Technology in Cultural Confidence Online Open Course Platform System

An intelligent learning system based on online resource open course was proposed based on Semantic Web ontology technology. The system mainly includes experimental content, online test, video on demand, teaching forum and other modules. Firstly, according to the characteristics of the learning behavior data collected by the online resource open course platform, the characteristics of learners are selected. Then, a knowledge tracking model is constructed based on Bayesian network, model parameters are set based on empirical probability, and the difficulty of problems is introduced into the model. Finally, we define the positive characteristics of students’ attitude and build a learning attitude tracking model based on classification algorithm. The design of the system is reasonable, the operation is simple, the maintenance is convenient, the operation is stable, the security is high, and the teaching method of the basic course of computer application is optimized, and the computer application ability of the students is greatly improved.
Research on the Application of Intelligent ChatGPT in Computer Intelligent Computing System

An intelligent ChatGPT based Transformer neural network architecture is proposed. It not only has the function of understanding the text, but also has the function of creating the text. The system correlates large data sets. The algorithm in this paper can strengthen the mining of semantic information at sentence level, improve the understanding of specific text information and the learning of text information. It effectively reduces the overall computational complexity of the model. This method can improve the universality of the model under the actual distribution. The convergence rate in the parameter space can be accelerated. Use Rotowire's public data to verify method validity. Experimental results show that the proposed method performs better than the existing data-to-text generation model. The algorithm can be used to automatically convert structured data into continuous text automatically generated.
Enhance Image Style Transfer with Depth Spatial Modulation

Style transfer technology is an image editing technique that creates new images by blending the content features of content reference images and the style features of style reference images. In past style transfer algorithms, the transfer results often failed to strictly follow the guidelines given by the content and style references, resulting in discrepancies between the contours of the generated objects and the content references, as well as instances where the colors within the objects bled out. In this work, we attempt to augment traditional style transfer algorithms with a structural branch, filling in structural information using spatial modulation. As this approach does not disrupt the existing architecture of the generative network, it maintains a degree of universality. Experimental results suggest that our proposed method can enhance the transfer effectiveness of existing style transfer networks in terms of authenticity, content, and style.
Activity Monitoring and Unusual Activity Detection for Elderly Homes

The elderly population is increasing rapidly worldwide, and with age comes an increased risk for falls, accidents, and other health issues. In order to ensure the safety and well-being of the elderly, it is crucial to have a system in place to monitor their activities and detect any unusual behavior. This paper discusses the importance of activity monitoring and unusual activity detection in elderly homes and suggests using current state-of-the-art technology for this purpose.
Research on Cloud Security Power Grid Model Simulation System Based on Computer Big Data Technology

In this paper, a model of active distribution network safety monitoring system based on big data mining platform is established. This model combines multiple detection techniques and uses three-stage detection methods in the analysis equipment engine. A fuzzy encryption processing model of power flow information is proposed, which can improve the computing efficiency of static security analysis of power system under cloud computing environment on the premise of ensuring power flow information security. Through the simulation analysis of the fault diagnosis example, the results show that the proposed fault diagnosis algorithm is fast, accurate and effective.
Cross-race Effect in Face Recognition: An Observation on Southeast Asian

Face recognition technology has been widely applied for many biometrics verification purposes, including surveillance, access to personal devices, and transaction permission, to name a few. While numerous studies have been conducted over time to improve the state-of-the-art in face recognition, previous research has primarily focused on the majority population, overlooking the distinctive facial features among different races. Consequently, the accuracy of face recognition may vary and may not be optimal for ethnic minorities. This study aims to determine racial bias, particularly the cross-race effect in face recognition for Southeast Asians. Three face recognition frameworks were utilized: (i) Principal Component Analysis (PCA) for feature extraction with Kth Nearest Neighbor (KNN) for the nationalities' classification, (ii) Principal Component Analysis (PCA) for feature extraction with Support Vector Machine (SVM) for the nationalities' classification, (iii) Fourier Transform (FT) to extract texture before applying PCA with SVM. Racial bias was measured based on the recognition rate for each race, and the cross-race effect was measured based on the number of false negatives. The results indicated that there was a racial bias in experiment (i) in which Vietnamese, Indian, and Cambodian nationalities have a very low recognition rate as low as 37.5% in comparison to that of Japanese, Chinese, and Caucasians with a very high recognition rate of up to 100%. However, experiments (ii) and (iii) resulted in a more consistent recognition rate across races, ranging from 71% to 100% which means less racial bias compared to the first experiment. Additionally, the confusion matrix revealed a cross-race effect, with Cambodian individuals often misclassified as Vietnamese and vice versa. Future research should explore deep learning approaches on larger Southeast Asian datasets, and gain a better understanding of the relation between Cambodian and Vietnamese faces.