Reviewerly/exHarmony huggingface dataset:
The Development of Android-based Control System for Reinforcing the Electronic Control Subject

Abstract The advancement of technology has brought the control system technology recently to be more precise, stable, and reliable. It is also possible to utilize the sophisticated hardware embedded in the smartphone for controlling or monitoring system through the external modules. This study aimed to develop an Android-based control system that can be used to reinforce the students’ competence, particularly in the Electronic Control subject. The method used in this study was a linear sequential model that comprises analysis, design, coding, and testing. A total number of nine items of functional testing has been conducted, and the results revealed that the prototype control system could successfully react as designed. Nevertheless, the one that should be considered in the future study is the compatibility issue between the external controlled hardware and the smartphone.
Improving Student’s Motivation in Pandemic Covid-19 Era Using an Authentic English Learning App

Abstract A habit of the learning process in the classroom where students only receive lessons from the teacher makes students passive in the learning process. Therefore, students learning motivation decreases when it comes to distance learning. The development of Authentic English Learning App aims to motivate and facilitate students during the distance learning process. This system is designed for students to learn English in an authentic context in their surrounding at home. Moreover, students can collaborate with their peers and give feedback to their peers’ work. The App was tested using black box testing before it used by students. Black Box testing show a Very Good performance of the App. Furthermore, The result of this study shows that in the post-motivation, there is a significant difference of learning motivation between students who use Authentic English Learning App (EG) and students who only read a book (CG) (F = 4.664, p &lt; 0.05). Therefore, Authentic English Learning App can motivate students to learn English effectively. Moreover, Technology Acceptance Model (TAM) questionnaire shows a high level of trust in each dimension (M &gt; 4.5), It shows that Authentic English Learning App can facilitate students during Covid-19 in distance learning.
Fast and Flexible Overlap Detection for Chart Labeling with Occupancy Bitmap

Legible labels should not overlap with other marks in a chart. The state-of-the-art labeling algorithm detects overlaps using a set of points to approximate each mark's shape. This approach is inefficient for large marks or many marks as it requires too many points to detect overlaps. In response, we present a Bitmap-Based label placement algorithm, which leverages occupancy bitmap to accelerate overlap detection. To create an occupancy bitmap, we rasterize marks onto a bitmap based on the area they occupy in the chart. With the bitmap, we can efficiently place labels without overlapping existing marks, regardless of the number and geometric complexity of the marks. This Bitmap-Based algorithm offers significant performance improvements over the state-of-the-art approach while placing a similar number of labels.
Adiabatic Circuits for Quantum Computer Control

How far can quantum computers scale up? Quantum computers have more qubits with longer lifetimes than ever before, yet experimentalists report a scaling limit around 1,000 qubits due to heat dissipation in the classical control system. This paper introduces new classical circuits and architectures that will reduce this dissipation by exploiting the heat difference between room temperature and the cryogenic environment. In lieu of using just cryo CMOS or Single Flux Quantum (SFQ) Josephson junctions (JJs), this paper focuses on cryogenic adiabatic transistor circuits (CATC), which use the same transistors as CMOS and are clocked on a ladder of clock rates, enabling the circuits to exploit varying energy-delay tradeoffs to increase energy efficiency. These design principles could lead to a scale up path for quantum computers that combines aspects of Moore's law with the principles of quantum speedup.
Application of genetic algorithm for broad learning system optimization

In order to solve the problem of waste of resources and low monitoring efficiency caused by Container Freight Station (CFS) safety helmet detection mainly relying on manual operations. The Broad Learning System (BLS) optimized by Genetic Algorithm (GA) is selected as the image recognition classifier for helmet detection, which accurately and quickly marks the images of CFS workers who do not wear safety helmet in the video and issues an early warning. GA is a method of searching for the optimal solution by simulating the natural evolution process. While comparing it with initial BLS, BLS optimized by GA (GA-BLS) can automatically constructs network structure and tunes hyperparameters. Furthermore, using proposed method in safety helmet data set reaches a lower error, reduces operation time than initial BLS and other image detection method, such as support vector machine (SVM).
Performance Study of Multi-target Tracking Using Kalman Filter and Hungarian Algorithm

We present the method for multi-target tracking using the combination of Kalman filter and Hungarian algorithm and test the efficiency of this method with two different data sets. In Data set – I, no target leave or enter the frame and in Data set – II, targets leave and enter the frame at regular intervals. This tracking method deals with the data association problem that arises with multiple targets in a single frame and also the dimensionality problem that arises due to repeated changes in the size of state-space associated with multiple targets. We use 2 important methods to achieve this. The first is the Kalman filter which is an extension of Bayesian filter. It uses a probabilistic approach to deal with the estimation of data. The second one is the Hungarian algorithm, used to overcome the data association problem and data association comes into the picture only when there are multiple targets.
Synthetic Convolutional Features for Improved Semantic Segmentation

Recently, learning-based image synthesis has enabled to generate high resolution images, either applying popular adversarial training or a powerful perceptual loss. However, it remains challenging to successfully leverage synthetic data for improving semantic segmentation with additional synthetic images. Therefore, we suggest to generate intermediate convolutional features and propose the first synthesis approach that is catered to such intermediate convolutional features. This allows us to generate new features from label masks and include them successfully into the training procedure in order to improve the performance of semantic segmentation. Experimental results and analysis on two challenging datasets Cityscapes and ADE20K show that our generated feature improves performance on segmentation tasks.
Combined Convolutional and Recurrent Neural Networks for Hierarchical Classification of Images

Deep learning models based on CNNs are predominantly used in image classification tasks. Such approaches, assuming independence of object categories, normally use a CNN as a feature learner and apply a flat classifier on top of it. Object classes in many settings have known hierarchical relations, and classifiers exploiting these relations should perform better. We propose hierarchical classification models combining a CNN to extract hierarchical representations of images, and an RNN or sequence-to-sequence model to capture a hierarchical tree of classes. In addition, we apply residual learning to the RNN part in order to facilitate training our compound model and improve generalization of the model. Experimental results on a public and a real world proprietary dataset of images show that our hierarchical networks perform better than state-of-the-art CNNs.
Recent Progress on Multiple-Unmanned Aerial Vehicle Collision Avoidance Algorithms

In recent years, the development of small unmanned aerial vehicle (UAV) has greatly promoted the development of UAV swarms. As multi-UAV are widely applied in more scenarios, the collision avoidance problems have attracted more attention. This paper discussed the basic problems of collision avoiding about multi-UAV, and sorted out the collision avoidance algorithms of multiple UAVs. We divide the UAV collision avoidance algorithm into two parts, one is cooperative algorithm and another is non-cooperative algorithm. Then, according to the current algorithm development, the development trend of multi-UAV collision avoidance research in the further are discussed.
The Automatic Liquid Particles Detection for Injection

The problem of injection liquid detection is currently a manual task carried out by trained human visual inspectors. We show that algorithms based on deep neural networks can be used to effectively automate this process. It is aimed at problems such as small injection particle,the great difficulty of observation and many noise interference. We have proposed an automatic method for detecting particles in injection, which combines deep learning detection method and tracking algorithm. Connect detection and tracking together to extract object trajectory to effectively distinguish particles and noise, and achieve accurate detection and tracking of injection liquid particles. Our method first detects the suspect particles in the first frame with unclassified Faster R-CNN and tracks the detected suspect particles through the CSR-DCF tracking algorithm. We thus obtained the trajectory of the suspected particles. Finally, distinguish the particle and noise by classification algorithm we proposed according to the trajectory characteristics. Experimental results in 8 frames show that the average accuracy has reached 97.5% with faster detection speed in our data set.
Research and Application on Power Generation Safety Monitoring and Cloud Platform

Aiming at the problem that the traditional monitoring mode of power generation can't monitor the normative behavior of operators as well as realize the comprehensive monitoring and intelligent decision-making analysis of power generation safety, this paper applies cloud platform, real-time database, OpenPose and TensorFlow to the power generation safety management system to monitor and manage the operation process of staff. The research shows that the application of cloud computing and deep learning technology in power generation safety monitoring system can significantly improve the compatibility, real-time performance and supervision ability of the system, and ensure the accuracy and recognition rate of data. And the application of human posture recognition can improve the safety of staff daily operations.
Blinking Eyes Detection using Convolutional Neural Network on Video Data

The drowsiness conditions at human body can affect to changing some parts of body's behaviour, such as changing behaviour of eyes, mouths, and brains. When people in drowsy conditions, distance between upper eyelid and lower eyelid at their eyes will be shorter than normal behaviour, mouths will be a little more opened rather than usual, and brains will produce very low frequencies of brain's signals. This changing behaviour in some parts of bodies gives a benefit to detect drowsy conditions when some people do not give concern to this. Different behaviour of faces and eyes during drowsy conditions and normal conditions; decreasing distance between upper eyelid and lower eyelid is a worth idea to detect drowsiness, this is a basic idea in this research, we want to build a machine learning to detect blinking eyes based on images running in realtime. In this research, we collect images of faces and eyes to build a dataset and separate this dataset to two label categories based on our target classifications, these labels are ”opened eyes” and ”closed eyes”. There are 3 different datasets in this research, first dataset contains 6000 images, second dataset contains 8000 images, and third dataset contains 10000 images of faces and eyes, each of those datasets is collected from one sample person, one sample person is me (author). There is a little thing different at 10000 image dataset compared by those two existing datasets particularly at closed eyes class category. In closed eyes class category at 10000 image dataset contains two variations of closed eyes images, there are 4000 perfectly closed eyes images and 1000 half-closed eyes images. All of those datasets are trained to convolutional neural network (CNN) so we have 3 different pretrained CNNs. Those three pretrained CNNs are tested to detect blinking eyes of samples running in realtime. There are 11 differences of samples, one sample is me (author) and 10 other samples are from other people. From this test, we get the conclusions that the highest success in detect blinking if those pre-trained CNNs are tested to detect blinking eyes from sampel face of me (author) is exactly placed at center in front of frame/camera. Rate of success in this detection is 0.95 every 20 detection.
Analysis and Design for the Decentralized Network Sharing of Static Resources

Decentralized network research has received substantial attention internationally, resulting in significant innovations in distributed databases, storage, and computing in the areas of cloud, fog, and edge computing. When it comes to decentralized networks, they are widely recognized "a network from the future" that possesses serious limitations in terms of trust and security. In a traditional distributed network, resources (i.e., computing power)are typically concentrated on a single server, or through a core server to "direct". Thus, a core server represents the upper limit of the capacity of the entire service, and it is susceptible to concurrency bottlenecks or excessive loads. Therefore, in a decentralized network, the absence of a core not only means that the load capacity of the entire network has increased significantly, but also that the entire network is more "open", the disaster tolerance is stronger, and the response time performance is faster. In this paper, a simple implementation of file sharing is presented, the feasibility of file sharing in a decentralized network is discussed, and its advantages and disadvantages are studied.
Application of Customized Term Frequency-Inverse Document Frequency for Vietnamese Document Classification in Place of Lemmatization

Natural language processing (NLP) is a problem which attracts lots of attention from researchers. This study analyzes and compares a different method to classify text sentences or paragraphs in Vietnamese into different categories. The work utilizes a sequence of techniques for data-preprocessing, customize learning model and methods before using Term Frequency-Inverse Document Frequency (TF-IDF) for model training. This classification model could contribute positively to many Vietnamese text-analyzing based businesses, such as social network, e-commerce, or data mining in general. This problem’s challenge relies on two main aspects: the Vietnamese language itself and current NLP researches for the Vietnamese language. The paper utilizes the pros of many different classification methods to provide better accuracy in text classification.
Photo matching using skin color histogram without and with Instagram-like modifications

Social networking has become a part of our lives. Web platforms enable communication and exchange of different media for private and business purposes, where beauty or Instagram-like filters are applied for better representation. Among face characteristics, skin color is often used for recognition or matching. In this paper feature vectors corresponding to unmodified and modified query images are based on two color space domains (HSV and Lab). Seven different distance metrics are applied for matching purposes, as well several Instagram related filters, like sepia and kelvin. The results show that image filtering affect color skin based approaches in information retrieval.
Multiple feature‐based portal vein classification for liver segment extraction

Purpose The liver segments divided by Couinaud classification method are used to understand the functional anatomy of liver, which is significant in hepatic resection surgery. In Couinaud classification method, each third‐order branch of the portal vein (PV) defines the supplied territory of a corresponding liver segment. However, the accuracies of the reconstruction and classification of PV are affected by the complicated structure of the vein. The purpose of this paper is to develop a separation and classification method that can accurately extract the liver segments. Methods In this paper, a multiple feature‐based method is proposed to obtain liver segments. Because the portal and hepatic veins usually connect in the vessel segmentation result, the PV is first completely separated based on the different strategies for minimal node cut using fused features of topology and appearance. Meanwhile, all bifurcation nodes of PV are detected. The bifurcation nodes are initial ordered through their linkages to classify the branches. Then, the feature of the vascular topology is used to refine the orders of bifurcation nodes. The bifurcation nodes with the refined orders classify the branches between them, and the third‐order branches of PV are obtained. The liver segments are eventually obtained through the third‐order branches. Results The separation and classification in the proposed method are evaluated on the CT and MR datasets. The average values of Dice, Jaccard, Recall, and Precision obtained by the proposed method are 93.00%, 87.90%, 93.47%, and 93.19%, respectively. Compared with the state‐of‐the‐art methods, the separation results obtained by the proposed method are more accurate. The branches of PV are classified based on the separation result. According to the third‐order branches, eight liver segments correspond to the different functional areas are precisely extracted. Conclusions The proposed method achieves a high accuracy for the liver segment extraction. And the extracted liver segments are significant for the preplanning of resection surgery.
Research on Collaborative Filtering of Food Information Security in E-Commerce Platform

Abstract Recommender systems are one of the most important technologies in the electronic commerce system. In a collaborative filtering recommendation algorithm, similarity calculation is the key to determining the efficiency of the recommendation algorithm. This paper analyzes the shortcomings of traditional similarity measurement methods in recommender systems and proposes a scoring-matrix-filling algorithm. Based on information categories and user interest similarity, the algorithm can reduce the negative influence of data sparsity on the recommendation result to some extent. The research results have certain practical and guiding significance.
Turn Real People into Anime Cartoonization

This paper presents two different methods to turn real people's photos into anime. The first method trains photo2cartoon codes with a totally different dataset. The second method divides cartoonization into three steps, which are: converting original image into line drawing, drawing color hints on line drawing, and finally colorizing. In order to train faster and take smaller spaces, a lighten method called LWAnimeGAN is developed. Our method is easy to implement and understand, and our goal is to save space and time for cartoonists.
Creating Graphical Passwords on a Mobile Phone

An alternative to traditional text based passwords (TP) is graphical passwords (GP). Psychological research has shown individuals to have superior memorability for images compared to text. With smartphones being touchscreen, GPs could be more suitable and provide better usability and security compared to TPs. However, GP is a relatively new area, with problems between usability and security. Some GP schemes involve creating password via drawing, as drawing is also shown to enhance memorability. But current GP schemes’ drawing tools are too complex and difficult to use. Therefore, this project aimed to create a GP scheme specifically for smartphones that provides an easy and simple to use drawing tool while providing high usability and security.
Demagnetization Fault Diagnosis of PMSM Based on Fuzzy Extreme Learning Machine

To improve the accuracy of partial demagnetization fault diagnosis for permanent magnet synchronous motor (PMSM), an improved fuzzy extreme learning machine (F-ELM) algorithm is constructed by integrating fuzzy theory into the extreme learning machine (ELM) in this paper. Firstly, a PMSM field-circuit coupling simulation system under vector control is established by employing finite element analysis method. Secondly, the feature samples affecting classification accuracy can be obtained using wavelet packet decomposition (WPD). Thirdly, by taking the imbalance of demagnetization feature into consideration, a new type of improved ELM, i.e., F-ELM, is proposed by associating input layer with a fuzzy membership. Finally, a comparison with the accuracy of back-propagation neural network (BPNN), support vector machine (SVM), and ELM is performed. The experimental results show that the proposed method outperforms the existing machine learning methods, and can effectively diagnose the partial demagnetization fault of PMSM.
High-Quality HTTP Live Streaming System for Limited Communication Bandwidth

Considering the increased demand of live streaming services, in this paper, we present a novel video transmission way that maximizes the quality when the communication bandwidth is limited. Based on the HTTP live streaming (HLS) protocol, the proposed segment-level interpolation allows to generate the quality-aware sequence without increasing the storage demands of media server, but reducing the amount of unused bandwidth. Simulation results show that the proposed approach drastically enhances the quality of HLS-based services by utilizing bandwidth up to 102.67% compared to the conventional solution.
Strategy and Implementation of Hex

With the development of computer game algorithms, some new chesses are gradually included in the competitions in China. Hex is a new game that Chinese have just begun to focus on recently. Since Hex has a priority problem, and the larger the chessboard, the larger the search space. Therefore, how to reduce the costs of search and calculation is an important problem in hex games. It has proposed two strategies based on geometry of its cell geometry (LRC) and chessboard region (CR) for Hex game, and combined them with Monte Carlo tree search. These strategies can be used in Monte Carlo tree search to filter out unreasonable or optimal results at the current level, so as to reduce the scope of search space. A platform has been designed and developed, it provides human-AI even AI-AI interface to the Hex game.
Study on Software Defect Prediction Model Based on Improved BP Algorithm

Software defect is an important indicator to evaluate software product quality. To reduce the defects of software products and to improve the quality of software is always the goal of software development. This paper combines the simulated annealing (SA) algorithm and JCUDA technology to improve the BP algorithm, to build an improved software defect prediction model with higher prediction accuracy. The experimental results show that the software defect prediction model based on improved BP algorithm is able to accurately predict the software defects, which is better than the traditional BP algorithm.