Reviewerly/exHarmony huggingface dataset:
Design of Physical Training System for Police Cadets Based on Machine Vision Technology

Aiming at the defects of the current physical training system of police colleges, such as incomplete error consideration and poor effect, a physical training system based on machine vision technology is designed. Firstly, the video images of physical training are collected to obtain the motion trajectory of training joint points, and the measurement error of cadets' physical training is analyzed according to the motion trajectory and error detection method; Then, according to the error score intelligent evaluation module, evaluate the cadets' warm-up exercise intensity, exercise training response time, exercise training action accuracy and stability, and give training suggestions to improve the exercise effect; Finally, the simulation results show that compared with other systems, the proposed system has fast response time and high motion control accuracy, and can obtain ideal motion training error detection effect.
Deep learning-Based Quality Enhancement Algorithms for Background of Video

In this work, three quality enhancement algorithms are proposed to decrease the noise between the foreground and the background. The proposed algorithm is performed on the decoder side target to enhance the quality of the background with a higher quantization parameter to the same level of the foreground. The simulation results show that all three algorithms can improve the PSNR when using the proposed algorithms. The performance of these three algorithms is also discussed.
Research on Intelligent Management System of Practical Courses Based on Artificial Intelligence Technology

On the basis of detailed analysis of the hot issues of artificial intelligence algorithm teaching, a method and system of applying practical courses in colleges and universities is proposed, and an intelligent system of practical courses is established. Teachers can know the concrete performance of students in all aspects of practical teaching, give fair scores, and introduce the scores into the unified score entry system of colleges and universities. The system uses B/S mode, sends a request to the Web server through the browser, accesses the background database through the intermediate component, and returns the operation result to the front browser in the form of HTML page, thus realizing the system management of the professional practice courses in colleges and universities. The system works well by using PHP+Dreamweaver+MySQL scheme.
Formal UML-based Modeling and Analysis for Securing Location-based IoT Applications

We present a process and a tool to apply formal methods in Internet of Things (IoT) applications using the Unified Modeling Language (UML). As there are no best practices to develop secured IoT systems, we have developed a plug-in tool that integrates a framework to validate UML software models and we present the design of a location-based IoT application as a use case for the validation tool.
Multi-Branch Spatial-Temporal Attention Graph Convolution Network for Skeleton-based Action Recognition

Skeleton-based human action recognition has attracted considerable research interest due to its robustness to dynamic environments and complex backgrounds. Models based on graph neural networks have achieved great success in this field. However, most of these graph neural network based methods adopt a single-branch or two-stream structure with limited input features. Due to the separation of space and time, many methods cannot pay attention to the critical information of human skeleton sequence, and multi-scale spatial-temporal dependent attention modeling becomes the key to modeling. This paper proposes a novel multi-branch Spatio-temporal attention graph convolutional neural network (MB-STAGCN) to recognize human actions from skeleton data. The proposed model adopts a multi-branch structure, fuses three-stream features, and then inputs them into the backbone network to extract features from multiple perspectives to a greater extent. Besides, we design a new temporal convolutional block for multi-scale extraction of information in the backbone network. We also propose an attention block named Spatio-temporal Concat Attention (STCatAtt) to capture critical information. Experiments on benchmark datasets show remarkable performance for human action recognition, demonstrating the effectiveness of our method.
A High-efficient Training Strategy for Deep Q-learning Network Used in Robot Active Object Detection

Active object detection (AOD) aims to guide a robot to make appropriate moving actions to get close to the target object, which is significant for the service robot to complete tasks in the indoor household environment. At present, most of the research on the robot AOD is developed based on reinforcement learning (RL) methods. However, the training efficiency and testing performance can be further improved. Therefore, a novel high-efficient training strategy is designed for the DQN model of AOD in this paper. Different from the existing RL-based training algorithm, the presented training strategy can avoid the repeat data with negative reward. The experiments have been implemented in an AOD dataset, proving that the proposed training strategy is more efficient than the raw DQN training algorithm.
A Model for Identifying Objects in Images Based on the Concept of a Receptive Field of Neurons

The article discusses a new method for identifying objects in images, which is based on the model of registration and neural coding of radiation at the periphery of the human visual system (mainly in the retina). The proposed method combines both known data on the structure and functioning of the human visual system and modern machine learning methods for image processing and analysis. A characteristic feature of the approach is the representation of the recorded data (images) by a sample of counts formed by an ideal visualization device. The refinement of the classical statistical estimation, hypothesis testing, etc., arising from this approach and related to the issues of object recognition in images, are discussed. The synthesized numerical algorithms of the developed procedures are illustrated by the results of a computer simulation.
One-Shot Synthetic Disguised Face Generation to improve Robustness against Disguise Attacks

Disguised face recognition can be challenging in real-world circumstances because the disguise add-ons are usually used to hide the key facial features. It is very important to solve this problem especially at sensitive places. In order to design disguised facial recognition system good quality disguised face database is required. We propose to solve this problem by presenting a one-shot disguised face generation method that can augment normal non-disguised faces and help improve the model accuracy when tested on disguised faces. The proposed method utilizes patchwise contrastive loss and cycle-consistency loss to take benefit of both approaches. The patchwise contrastive loss allows training with as few as one sample per image however, to further condition the networks for better results cycle-consistency among domains is applied. The results are satisfactory given that the models were trained with only one image per subject per add-on.
Distributional Semantics of Line Charts for Trend Classification

Line charts are often used to convey high level information about time series data. Unfortunately, these charts are not always described in text, and as a result are often inaccessible to users with visual impairments who rely on screen readers. In these situations, an automated system that can describe the overall trend in a chart would be desirable. This paper presents a novel approach to classifying trends in line chart images, for use in existing chart summarization tools. Previous projects have introduced approaches to automatically summarize line charts, but have thus far been unable to describe chart trends with sufficient accuracy for real-world applications. Instead of classifying an image’s trend via a convolutional neural network (CNN) system, as has been done previously, we present an architecture similar to bag-of-words (BoW) techniques for computer vision, mapping the image classification problem to an analogous natural language problem. We divided images into matrices of image patches which we then each treated as a series of “visual words” which were used to classify each image. We utilized natural language processing (NLP) word embeddings techniques to to create embeddings of visual words that allowed us to model contextual similarity between patches. We trained a linear support vector machine (SVM) model using these patch embeddings as inputs to classify the chart trend. We compared this method against a ResNet classifier pre-trained on ImageNet. Our experimental results showed that the novel approach presented in this paper outperforms existing approaches.
Secure Government Data Sharing Based on Blockchain and Attribute-Based Encryption

In the social informatization, more and more government data are interacted in it. With the development of information technologies, traditional government data sharing systems may not be suitable for modern social inforatization. In this paper, a secure data sharing scheme based on the revocable attribute CP-ABE and blockchain is proposed, with which fine-grained access control and flexible updating can be achieved through the attribute revocation. In the proposed scheme, a combined on-chain and off-chain storage model is used, and the computational cost of encryption and decryption is reduced through smart contract technology, where the cryptographic hash is uploaded to the blockchain and the access control is implemented by smart contracts. With the identity verification and access management technologies in blockchain, the security of government data is enhanced on chain. Furthermore, it also enables decentralised, transparent and traceable access processes.
The Application of eXtended Reality for Learning in Higher Education

The eXtended reality technologies can enable the teachers for improving their outcomes in learning. This may be achieved by using the student’s interactivity, engagements and collaboration. This study aims to show the interest of students for 3D content and 360 degree lessons on eXtended reality environment. Three groups of students for different lectures take part on the questionnaire. The results show an increasing the interest, attention and motivation of students on learning by eXtended reality.
Predicting the number of Covid-19 cases in Surabaya using hybrid extreme machine learning with particle swarm optimization

Covid-19 has spread to various countries in the world, including Indonesia. Surabaya becomes one of the big cities in Indonesia where the spread of Covid-19 is very fast, so the number of positive cases of Covid-19 is very large and more than 1000 people die because of this disease until November 2020. Prediction of the number of positive cases of Covid-19 can be used to regulate the availability of facilities in hospitals and make plans and policies to overcome this disease outbreak. Many prediction methods have been found, one of which is the Extreme Learning Machine (ELM). ELM has high training speed and accuracy. However, the performance of ELM depends on the number of neurons. When the number of neurons is not precisely determined, the accuracy of prediction becomes worst. Particle Swarm Optimization (PSO) is used to decide the number of neurons. For this reason, this paper proposes a prediction of the Covid-19 cases in the City of Surabaya using the hybrid of ELM and PSO (ELM-PSO). The experiments show that the comparative performance of the proposed methods with several activation functions in the prediction of the Covid-19 cases in the City of Surabaya.
Human-Robot-Collaboration in the Healthcare Environment: An Exploratory Study

There is an existing shortage of skilled personnel in the healthcare environment. The growing need for healthcare professionals due to the increasing number of elderly people represents a sociopolitical and economic challenge. It is expected that human-robot collaboration in healthcare will rise in the near future because it could be a great advantage to relieve healthcare professionals with technical systems. To promote the acceptance of such technical systems and digital aids, it is important to involve the health care staff from the very beginning. Therefore, the aim of this study was to examine differences in the acceptance of and the general attitude towards robots in the healthcare sector. In focus was the difference between people working in the health care environment and those who don’t. An exploratory study was conducted to find out if - and if yes how - the attitude of people towards robots in the healthcare sector differ and whether the type of robot has an influence on the attitudes towards robots. The results show that participants working in the healthcare sector have a less positive attitude towards robots than those not working in the healthcare sector. Furthermore, significant differences can be shown regarding the assessments of the different robots in the different scenarios. The results of the study should help to understand how people working in the healthcare sector evaluate the potential use of different robots in healthcare.
Pengembangan Sistem Pelayanan Publik Melalui Sistem Administrasi Kependudukan Menggunakan Metode Extreme Programming

Population administration is a public service system for the community in providing services regarding population needs. The problem that often occurs is related to data storage. Files sent by the public to take care of administration in the form of hard copies are vulnerable to loss and damage if they are not stored and archived properly. In addition, the public cannot monitor the extent to which the files they submit, whether their files are appropriate or not or complete or not. This study aims to develop a public service system through a population administration system using the development of an extreme programming (XP) system. XP is used because it is able to develop systems with needs that change very quickly. The population administration system that is managed is the administration related to births, deaths and changing of domiciles. The system was developed web-based, so that it can be used anywhere and anytime. In addition, the system developed is equipped with file monitoring facilities, so that the public can easily find out from the files or data submitted. The resulting application is in the good category, because based on usability testing shows an average value of 88% in the good category, so the application is feasible to use and as needed.
Strategic Planning and its Relationship to School Performance among Private Secondary School Principals in the Capital Amman from the Teachers Point of View

The study aims at identifying the relationship between strategic planning and school performance among private secondary school principals in Jordan from the teachers point of view. The researchers used the descriptive correlative approach, and the study sample consisted of (310) male and female teachers who work in private secondary schools in Marka District in the capital (Amman) in Jordan, they were selected by simple random sampling method. To achieve the objectives of the study, a questionnaire was developed that included (44) items. The results of the study showed that the degree of application of strategic planning, and the level of school performance was high,. They also showed that there were statistically significant differences due to the gender variable in favor of males, and the educational qualification variable in favor of the bachelor’s degree between the average responses of the study sample to the degree of strategic planning application. The results also showed that there were differences due to the gender variable in favor of males for the level of school performance. The study revealed a positive correlation between the degree of strategic planning application and the level of school performance The researcher recommended the need to continue developing and encouraging the application of strategic planning in order to reach the desired goals.
Hyperspectral Salient Object Detection Using Extended Morphology with CNN

Salient object detection using hyperspectral images is crucial for various image processing and computer vision applications. Many studies considering spectral information have been developed, extracting only low-level features from a hy-perspectral image. In this research work, a dataset specifically developed for salient object detection called HS-SOD is considered exploiting both spatial and spectral information equally. To include spatial information, Extended Morpho-logical Profile (EMP) has been considered. EMP incorpo-rates spatial characteristics by including nearby pixel information. A convolution neural network (CNN) is integrated with extended morphology to extract high-level features. It detect objects of multiple spatial scales and ratios, preserving boundary edges. We observed an improvement of 5 % in overall accuracy while using EMP with CNN compared to that of using EMP without CNN. Thus, the experimental re-sults demonstrate the effectiveness of EMP with CNN on the hyperspectral datasets.
Using GPUs to Speed Up Genetic-Fuzzy Data Mining with Evaluation on All Large Itemsets

In fuzzy data mining, the membership function significantly influences exploration performance. Therefore, some scholars have proposed genetic-fuzzy mining to determine a set of good membership functions for effectively mining fuzzy association rules. Some scholars proposed evaluating the membership functions using both the number of large 1-itemsets and the suitability of chromosomes. They only considered large 1-itemsets instead of all large itemsets because of the time-consuming problem. We analyzed the time-consuming reason and found that there are many independent calculations in the mining process. Given this, we adopt the GPU devices and propose a GPU-based mining algorithm with evaluation on all large itemsets to improve obtained membership functions and reduce time cost. Experimental results also show the efficiency of using GPUs on genetic-fuzzy data mining.
The development of Actuarial Mathematics E-Book using Sigil application

Teaching materials are an important component in the teaching and learning process. The development of science and technology and the demands of the curriculum in the era of industrial revolution 4.0 are currently forcing teachers to think creatively using science and technology to create teaching and learning processes that can improve student understanding. The current condition forces more learning to be done online, therefore lecturers must be able to develop practical print teaching materials assisted by video tutorials. With this teaching material, it is hoped that it can help students better understand the teaching material independently because This research was conducted to improve the process and learning outcomes of the Actuarial Mathematics course in the Mathematics Department, Faculty of Mathematics and Natural Sciences (FMIPA) Universitas Negeri Medan. The aims of this study are: (1) to develop an e-book for Actuarial Mathematics using the Sigil application, which is tailored to the achievement of student competence in the Actuarial Mathematics course, (2) to describe the steps and results of the validation of the e-book for Actuarial Mathematics so that it is suitable for learning, (3) describing the effectiveness of the Actuarial Mathematics e-book with the Sigil application on student learning outcomes in the Actuarial Mathematics course. This study uses research and development methods with the ADDIE development model consisting of five research stages: need analysis, planning, development, implementation, and evaluation. The results of this study indicate that based on the e-book content validity test, it was found that Actuarial Mathematics teaching materials were in a good category. The contents of the e-book are categorized both in terms of language use, the accuracy of learning materials, and appearance. Then the expert validity test results show that the material developed in the Actuarial Mathematics e-book is in a very consistent category. Furthermore, student responses to e-books used during learning indicate that e-books contribute positively during the learning process. The learning outcomes of students who use e-books are better than those of students who do not use e-books.
Hand Detection and Hand Recognition Application Design for Human Computer Interaction Using SSD and Hand Landmark

This research is intended to support computer interaction technology, especially the operation of computers to minimize the adverse effects of the dangers of direct physical contact between hands and technological objects. This research was conducted with the aim of utilizing the movement of the limbs of the hand as an object of interaction that bridges between humans and computers. With this research, it is hoped that the design results can minimize the risk of spreading bacteria and viruses that cause health problems. The objects used in the design will focus on the pattern/shape and movement of the hands, and in training the pattern/shape of the hand as a pointing tool and keyboard shortcut input. The design was built by using a webcam as a sensor to capture images. This design used the help of the field of artificial intelligence, hand detection using the Single-Shot Detector method with hand recognition using Hand Landmark. The value of success in functionality obtained from the application was 94.73%. The results of the model evaluation obtained that the final average recall value was 96%, the precision value was 100%, and the accuracy value was 96%. From the research that has been done, it is possible to replace the mouse function and several keyboard shortcuts with right- and left-hand movements.
Recognizing Microexpression as Macroexpression by the Teacher-student Framework Network

Microexpressions have great research value because of their high reliability in revealing human emotions. However, microexpression recognition is challenging due to its low amplitude. In this paper, we propose a teacher-student framework for microexpression magnification. First, we input the macro-expression samples into an action transfer model first order motion model (FOMM) for image animation to extract the variation features from neutral expression to macroexpressions as the teacher model. Similarly, we extracted features from neutral expressions to microexpressions. Then, we designed a dual-channel encoder-decoder network, which was guided by the middle-layer feature map of the teacher model, to learn the jump from microexpression features to macroexpression features. To ensure the magnification effect and image quality, we introduced a self-attention mechanism and loss functions. Finally, an end-to-end microexpression magnification model (MEMM) was constructed. We fed our magnified images directly into a simple ResNet50 network for recognition, achieving a competitive score under the MEGC2019 standard, compared with recent complex recognition networks. Our model can also be applied in the wild for microexpression magnification.
Representation of Random Phenomena

We will begin this chapter by better defining the formalism, notation and terminology that will accompany us throughout the rest of the book. Without this very important step, the reader would risk to misunderstand the meaning of most of the basic equations of probability and statistics.