Reviewerly/exHarmony huggingface dataset:
Tag Analysis of Multimedia Training in Sports Classroom Combined with Computer Complex Decentralized Platform

This paper studies the tag analysis of multimedia training in sports classroom combined with computer complex decentralized platform. The network entity has two pairs of keys: one is the public-private key pair contained in the certificate, which is recorded as the certificate key; the other is the key pair used for issuing the certificate, which is recorded as the issuing key. The scheme contains two types of certificate transactions: one is a transaction signed by the network entity with its own issuance key pair for issuing certificates periodically; the other is a transaction for initializing and resetting the issuance key pair. Hence, with the consideration of the mentioned algorithms, the designed multimedia training in sports classroom combined with computer complex decentralized platform is further implemented. Through the comparison testing, the performance is then shown.
Concurrent Testing of School Student Information Management System based on Multi-Terminal Information Upload Optimization Network

This paper designs and implements multimedia information query based on mobile terminal, and realizes real-time or non-real-time sending and receiving display of video and image information. At the same time, the multi-objective optimization model that minimizes the investment cost is optimized and solved by using the simulated annealing algorithm. The overall development process of college student information management system. Firstly, it briefly introduces the research background, significance and research status of the system, and then introduces the system ASP.NET related concurrent development technology and database development tools. On this basis, the system needs to be analyzed and obtained.
Plan of Block-chain Enabled Confirmed Key Management Protocol for Internet of Medical Things Development

With the emergence and influence of the Internet on the healthcare industry, it necessitates the need to preserve, access, and update medical records from anywhere and at any time. Medical discrepancies associated with Electronic Health Records (EHR) must be addressed, as it has privacy and security issues. The lack of consistency was certainly the most critical issue, but it is far from the only issue with EHR. Decentralized cloud models based on blockchain platforms have previously been proposed and deployed to address interoperability and privacy concerns. Other issues, though, remain. Three of the most critical concerns are scalability, usability, and accessibility. This research study presents a Secure eHealth Framework (SeFra) based on a progressive temporal blockchain method to overcome the aforementioned concerns. Context-based Merkle Tree (CBMT) and Context-Based Access Control (CBAC) are two of SeFra's core building blocks that are specifically focused on establishing security, increased integrity checking, and access control approaches. Interoperability and scalability issues are also handled through the use of temporal characteristics in conjunction with HL7 protocols, as well as data management using the Interplanetary File System (IPFS). The results show that temporal shadow concepts and context-based features have an impact on the proposed framework's capacity to safely manage the Personalised Micro Ledger (PML). It also aids in the collection of medical data in text format with temporal aspects and its dissemination to all other peers in line with the restrictions set in the context-based smart contract.
A Hadoop Framework Realization of Online Physical Education Practice System based on Large-Scale Data Analysis

A Hadoop framework realization of the physical education practice research based on large-scale data analysis is studied in the paper. Different types of data are obtained with the support of the device interfaces, including structured, semi-structured and unstructured types of data. At the same time, there may be errors, redundancy or useless big data. Hence, in the designed model, the Hadoop is combined. In order to realize the high-performance MapReduce cluster design based on GPU acceleration, a high-performance MapReduce state fusion and task scheduling model is established by using open source coding and program loading methods. With the implementation of the designed model, the framework realization of the physical education practice research has been studied. The overall performance is validated through testing.
Self-Organization Modeling and Data Tracking Algorithm of Overall Functional Data of Party Organizations in Secondary Colleges of "Internet" Electronic Information Platform

Self-organization modeling and the data tracking algorithm of overall functional data of the party organizations in secondary colleges of "Internet" electronic information platform is studied in the paper. The designed algorithm first converts the high-dimensional and sparse feature representation into low-dimensional dense vectors and random distributed sets through the embedding layer factor vector. The diversity of basic recommendation results is an off-line index of the general recommendation system. There is a temporary lack of the deep learning to optimize the diversity of recommendation results. This method is mainly used to establish research models of complex systems under strong adversarial situations. In general, the concept of "liveness" is mainly based on the ability to maintain a stable system information function in a complex system and the detection-based multi-target tracking can be divided into online tracking and also offline tracking. The system is implemented considering the mentioned issues, and is verified through the systematic verification.
Research on Computer Desktop Image Compressed Sensing Framework based on FCM Algorithm

This article first introduces the kernel-based FCM algorithm (KFCM), which has a good clustering effect on data sets with ambiguity and uncertainty; the image compression sensing algorithm has a strong global optimization ability, but it is easy to fall into the local area. Optimal. For this reason, with the help of quantum theory, the particles are "quantized", the movement of the particles is updated through the quantum revolving gate, and the quantum NOT gate is used to increase the diversity of the population; and compared with the FCM, FCMS, FCMS1 and FLICM algorithms. Evaluate algorithms and experimental results according to relevant evaluation criteria.
Biomedical Image Security Based on 2D and 3D Chaotic Systems

Recently, identifying abnormalities via medical images became vital. As these images are transferred across the IoMT network, they require high safety. Unauthorised use of the data contained in these images might have disastrous consequences. There are numerous approaches to protecting images in IoMT. One of the most effective approaches for safeguarding medical images is encryption. Encryption methods rely heavily on the concepts of confusion and dispersion. This research developed effective encryption and decryption algorithms for secure image transmission based on the chaotic maps with two and three dimensions for the IoMT application. The RGB colour system is first converted to the YCbCr colour space via a colour transformation. Then each of these components will be divided into 16 blocks, and a block permutation will be used to alter the position of these blocks. To perform more confusion a pixel permutation technique is further used. As permutation is alone not sufficient to achieve a satisfactory encryption, the contents again undergone diffusion for getting the final ciphertext image. The provided encryption technique discussed in this paper has been verified and studied on certain colour images. The findings demonstrated that the statistical and differential assaults had a substantial impact on the security and validity of the results. Additionally, the ciphered image has a near-optimal information entropy and correlation coefficients close to zero.
A New Otsu Thresholding Method on the Circular Histogram

Using H component of HSI color model to segment the color images is an alternative way. Considering the H component histogram is a typical example of the circular histogram, a new Otsu thresholding version on the circular histogram is proposed based on the theory of circular statistics. Different from the three Otsu thresholding methods on circular histogram defined by Lai and Rosin, the proposed version define the threshold selection criterion by minimizing the angles distance between each point in the class and the mean of the class. The experimental comparison with Lai and Rosin’s three related thresholding methods and the maximum entropy thresholding method on circular histograms shows that the proposed Otsu thresholding version is competitive.
Rollator with Random Forest Implementation for Footstep Prediction using IMU Sensor

The problem that often and commonly occurs in the elderly or people with walking disorders is falling when walking using a static assistive device. This causes the fall rate of people with walking disorders to be high. One way to prevent this is to use a non-static device that can move more flexibly, one of which is a rollator which has a system that can assist the user by detecting the user’s footstep with Random Forest method and then processed by the rollator to assist user with motor DC and servo for brake angle. The reason why we use Random Forest Classifier method is because in our preliminary experiments, the Machine Learning Random Forest Classifier method is effective enough to be used in microcontrollers with limited memory because the memory usage is relatively low but is quite effective in reading the user’s footsteps. In the technique of reading the user’s footsteps which is the most important thing, we use the IMU 6 DoF sensor which consists of three accelerometer axes and three gyroscope axes with a total of 4650 data per gesture recorded which is divided into 31 sets of movements, 150 samples from 3 accelerometer axes and 3 gyroscope axes then used as a dataset to be processed as trained data on machine learning Random Forest method for footstep detection and then apply it to actuators to help users with dc motors through fuzzy with several parameters. The average total accuracy of 90% is continuous in the machine learning process that is run with a microcontroller which is quite good and accurate.
The Big Data Analysis and Visualization of Mass Messages under “Smart Government Affairs” Based on Text Mining

Based on the records of people’s political inquiries, comments from public sources on the Internet and the data of the relevant departments’ responses to some people’s messages, this study uses text analysis, text feature extraction, model building, text mining, and other evaluation methods to study and evaluate the three aspects of government services: analysis of public comments, mining of hot issues and evaluation of replies, which aims to prompt the government to understand the needs of the people quickly and solve the relevant problems in a timely and effective manner. The results show that the final classification accuracy using BERT is 3.4% and 1.8% higher than that using TF-IDF and Word2vec, respectively. Multi-classification of message data was realized by BERT combined with the LinearSVC algorithm, and the crowd message was accurately divided into seven types of problems, with an accuracy of 96.7%. It is intended to be transferred to relevant departments for processing. For problems related to people’s livelihood, law, economy, and other aspects, different departments should take countermeasures to solve them and achieve systematic, departmental, and regional coordination. This will enhance the ability of government platforms to deal with problems. Through the definition of hot indexes, hot issues mining can timely find the outstanding problems reflected by the masses. At the same time, the feedback evaluation system can comprehensively evaluate the work of relevant departments from the perspectives of relevance, completeness, and interpretability. Big data analysis technology based on text mining is a feasible way to solve the difficulties of text data analysis. The analysis model constructed in this study is suitable for mining and analyzing unstructured data with short text features, and the results can provide guidance for government decision-making.
A Prediction Method of Anti-electricity Stealing Based on Big Data of Electric Power

This paper introduces the diversification and harmfulness of power theft methods in recent years, then collects and analyzes the power theft data of a power company from the power information collection system and the marketing business application system, and establishes the anti-theft characteristic orientation scale by using big data analysis method, and then forms the anti-theft expert sample data. By using the optimized mathematical model. This paper analyzes the principle of machine learning classification algorithms such as decision tree, support vector machine, K nearest neighbor and BP artificial neural network, select the optimal algorithm, train and model the sample data, and write a program to predict the new data. MATLAB tool software is used in the experiment, and the practical model of the optimal classification machine is finally formed by constantly adjusting the classification algorithm and parameters. Through cross-experiment verification, the optimal classification prediction results are obtained, which can effectively improve the accuracy of electricity theft recognition.
A Gaussian Mixture PHD Filter Based on Energy Accumulation

Aiming at the problem that tracking performance of the Gaussian mixture probability hypothesis density (GM-PHD) filter is inaccurate under low signal-to-noise ratio (SNR), a GM-PHD filter based on energy accumulation of trajectory is proposed. Firstly, several constant frames are selected to be the input of the pipeline filter, remaining possible straight lines within the speed limit. Then, accumulate the pixel value of the possible straight paths as their energies to further reduce false alarms. Finally, the number and status of targets are obtained through the iterative update of the classic PHD filter. The simulation results show that the proposed method achieves a favourable tracking performance, especially in the accuracy of the target number and target state estimates.
The Category-Sample and Category-Loss Human Body Parsing Algorithm

Aiming at the long-tailed distribution of the dataset and the similarity among the categories, which leads to the reduction of the accuracy of human parsing, the human parsing model combining category sampling and category loss is proposed. Firstly, the images are parsed by semantic segmentation module. Next, the parsing results and the ground truth labels are sampled by category balanced sampling module. And then the sampled parsing result and sampled ground truth labels are utilized to calculate the master loss. Meanwhile, the intra-category loss between the heatmap features from semantic segmentation model and ground truth labels are calculated in the category loss module, and the master loss and the intra-category loss are optimized at the same time to get a more accurate model. The experimental results based on the MHPv2.0 dataset show that the model in this paper improves the mIoU(mean intersection over union) more than 1.3% without changing the structure of the semantic segmentation model. The algorithm effectively reduces the impact of the long tail distribution problem and similarity among categories.
Path Planning Based on Deep Reinforcement Learning Towards Human-Robot Collaboration

Visual navigation is required for many robotics applications, ranging from mobile robotics for movement manipulation to automated driving. One of the visual navigation technologies that are often used is path planning. This method considers a way to find a valid configuration sequence to move from the starting point to the destination point. Deep reinforcement learning (DRL) provides a mapless trainable approach by integrating path planning, localization and image processing in a single module. Therefore the approach can be optimized for a specific environment. However, DRL-based navigation is mostly validated in a simple simulation environment with a size that is not too large. Therefore, we propose a new visual navigation architecture method using deep reinforcement learning. We have designed a realistic simulation framework that resembles a room’s state with several models of goods in it. Agents in the simulator will carry out the learning process by applying deep reinforcement learning to path planning with the support of A2C network, LSTM and auxiliary tasks. We evaluated the agent’s method in a simulation framework conducted 10 times, and each experiment was carried out in 1000 randomly generated environments. Training takes about 18 hours on a single GPU. The result is that in the broader simulation environment, our method has a success rate of 99.81% in finding the destination of a given image. These results make the proposed method can be applied to a broader environment and this approach can be used towards human-robot collaboration.
Semi-automatic PFU Counting Method by Double Thresholding and Two-Stage Segmentation

Virus quantification is the necessary step for measuring the vaccine efficiency. Plaque assay is the most popular method and quantified the virus in term of a Plaque Forming Unit (PFU). In this paper, a semi-automatic method for plaque assay from image taken by a mobile phone is proposed. Two thresholds in different color spaces are used to extract the PFUs: one for PFU at the center and one for PFU at the edge of a well. The cluster of PFU is segmented in two stages. In the first stage, watershed segmentation based on distance transform is applied to segment the distinct and large PFUs. In the second stage, the size is used to separate the blurry PFUs. The proposed method achieved 94% precision, 92% sensitivity and 6% counting error in the experiment on 30 samples stained with neutral red color.
Face Template Protection Through Incremental Learning and Error-Correcting Codes

Face template protection are increasingly applied in identity authentication systems in recent years since it can provide high matching performance. Face template protection aims to provide an effective solution to ensure the security of face features. In most of the end-to-end face template protection schemes, the main challenge of the problem is that the unavailability of the enrolled users’ data makes it difficult to effectively update the model’s parameters. To overcome this challenge, we proposed a KDAT (Knowledge Distillation and Attention Transfer) scheme updating the parameters by incrementally training the model, using new users’ data and public available unlabeled data. The total loss applies a binary cross-entropy loss to learn the new data, and a distillation loss and attention loss to retain the knowledge acquired from the enrolled users. The experimental results on the Extended Yale B, PIE, and FEI datasets show that this scheme can still maintain a high recognition accuracy after multiple incremental training.
Design and Realization of UGV Robot with Combined of Geared Wheel and Walked Mechanism for Uncertain Terrain in Volcanic Observation

This paper focuses on the manufacture of mobile robots for volcanic observation. The robot moves with a semi-autonomous system, which use a remote, IMU sensor and lidar/ultrasonic sensor to assist the navigation. A new mechanical model incorporating the system and crawling is proposed to improve mobility in irregular areas of the volcano. And the proposed control method is behavior-based control, which shows that the IMU sensor and lidar/ultrasonic sensor are able to assist the robot's navigation to get past obstacles and the robot's initial destination, even if it is given a disturbance to change direction. With the new mechanical model the robot can pass through sandy terrain and other ground conditions with ease.
Spatial-Temporal Visualization of Tuberculosis Vulnerability in East Java, Indonesia, Using the K-medoids Clustering Algorithm

East Java is one of the provinces in Indonesia with a high tuberculosis rate. East Java has the second-highest rate of tuberculosis cases in Indonesia, and it still has a significant health issue that has to be resolved. An overview of the correlation between geographic locations and susceptibility levels can be obtained from tuberculosis susceptibility mapping. Using spatial analysis, community-based tuberculosis initiatives can be planned more successfully by identifying the pattern of illness distribution and potential causes. The K-medoids clustering technique is used in this paper to provide a new approach for mapping the degree of tuberculosis susceptibility in East Java. The overall number of TB cases, unhealthy houses, population density, and health facilities are all utilized as variables that affect vulnerability. These parameters are highly correlated with one another to assess the level of TB susceptibility. There are three categories for tuberculosis susceptibility levels: low, medium, and high from 2016 to 2020. According to the K-medoids clustering 2016-2020 data’s average silhouette, there are 0.445 clusters with a number of 3, 0.423 clusters with a number of 4, and 0.342 clusters with a number of 5. The silhouette value demonstrates that using 3 clusters improves the accuracy of the K-medoids computation. The average variance for the K-medoids approach is 0.041, whereas the average variance for the K-means method is 0.048. This demonstrates that the K-medoids algorithm is greater to K-means at forming clusters.
A Hierarchical Ensemble Deep Learning Activity Recognition Approach with Wearable Sensors Based on Focal Loss

Abnormal activity in daily life is a relatively common symptom of chronic diseases, such as dementia. There will probably be a variety of repetitive activities in dementia patients' daily life, such as repeated handling of objects and repeated packing of clothes. It is particularly important to recognize the daily activities of the elderly, which can be further used to predict and monitor chronic diseases. In this paper, we propose a hierarchical ensemble deep learning activity recognition approach with wearable sensors based on focal loss. Seven basic everyday life activities including cooking, keyboarding, reading, brushing teeth, washing one's face, washing dishes and writing are considered in order to show its performance. Based on hold-out cross-validation results on a dataset collected from elderly volunteers, the average accuracy, precision, recall and F1-score of our approach are 98.69%, 98.05%, 98.01% and 97.99%, respectively, in identifying the activities of daily life for the elderly.
Clustering directional data through depth functions

Abstract A new depth-based clustering procedure for directional data is proposed. Such method is fully non-parametric and has the advantages to be flexible and applicable even in high dimensions when a suitable notion of depth is adopted. The introduced technique is evaluated through an extensive simulation study. In addition, a real data example in text mining is given to explain its effectiveness in comparison with other existing directional clustering algorithms.