Reviewerly/exHarmony huggingface dataset:
Systematic error analysis and parameter design of a vision-based phase estimation method for ultra-precision positioning

Ultra-precision position measurement is increasingly important in advanced manufacturing such as the semiconductor industry and fiber optics or photonics. A vision-based phase estimation method we proposed previously performs position measurement by imaging a 2D periodic pattern. In this paper, systematic errors of this method are analyzed and derived mathematically, which are classified into two types: spectrum leakage error caused by image truncation and window function modulation, and sub-pixel error resulting from discrete Fourier transform (DFT) intensity interpolation. Key design parameters are concluded including pattern period T, camera pixel size t and resolution N, as well as the type of window function used. Numerical simulations are conducted to investigate the relationship between the phase errors and design parameters. Then an error reduction method is proposed. Finally, the improved performance of parameter optimization is validated by a comparative experiment. Experimental results show the measurement errors of the prototype are within ∼2 nm in X or Y axis, and ∼1 µrad in axis, which reaches the sub-pixel accuracy better than 10-3pixel.
Efektivitas Pembelajaran Daring Berbasis WhatsApp Group Berbantuan LKPD Ditinjau dari Motivasi Belajar Siswa

ABSTRAK Penelitian ini merupakan penelitian semu (quasy eksperimen) yang bertujuan untuk mengetahui apakah pembelajaran daring berbasis WhatsApp Group berbantuan LKPD lebih efektif daripada pembelajaran daring berbasis WhatsApp Group tanpa berbantuan LKPD yang ditinjau dari motivasi belajar siswa terhadap hasil belajar matematika siswa kelas VII di SMP N 4 Sewon. Teknik pengambilan sampel menggunakan cluster random sampling terpilih kelas VII A dan B. Teknik pengumpulan data menggunakan teknik tes berupa posttest, dokumentasi untuk nilai pretest dan teknik non-tes berupa angket motivasi belajar. Teknik analisis data menggunakan uji-t, uji prasyarat berupa uji normalitas, uji mann-whitney, dan uji kruskal wallis. Hasil dari penelitian ini yaitu pembelajaran yang menggunakan pembelajaran daring berbasis WhatsApp Group berbantuan LKPD sama efektifnya dengan pembelajaran daring berbasis WhatsApp Group tanpa berbantuan LKPD dan hasil belajar matematika siswa dengan motivasi tinggi lebih baik dibanding siswa yang memiliki motivasi sedang dan rendah. ABSTRACT This research is a quasi-experimental researchwhich aims to determine whether online learning based on WhatsApp Group with LKPD assistance is more effective than online learning based on WhatsApp Group without assistance from LKPD in terms of student learning motivation towards mathematics learning outcomes of grade VII students at SMP N 4 Sewon . The sampling technique used was cluster random sampling selected classes VII A and B. The data collection technique used a test technique in the form of a posttest, documentation for thescore pretest and a non-test technique in the form of a learning motivation questionnaire. Data were analyzed using t-test, test preconditions such normality test, mann-whitney, andtest. Kruskal WallisThe result of this research is that learning using online learning based on WhatsApp Group with the assistance of LKPD is as effective as online learning based on WhatsApp Group without assistance from LKPD and the mathematics learning outcomes of students with high motivation are better than students who have moderate and low motivation.
Cyclic quantum state sharing with a nine-qubit entangled state

Abstract In this paper, we present a novel scheme for cyclic quantum state sharing (QSTS) by using a nine-qubit entangled state as quantum channel. In this scheme, each of the three dealers has a different unknown quantum state, and every dealer wants two agents to share his/her quantum state. At the same time, three dealers perform Bell basis measurements on his/her own qubits, and the controllers make a single-qubit measurement on his/her own qubits, after that, the receivers can recover the original states by applying suitable unitary operations, respectively. It is shown that, only if all agents collaborate with each other, the cyclic QSTS can be completed with unit success probability.
Application of a new deep learning method with CBAM in clothing image classification

How to effectively identify the clothing category from the massive clothing image is one of the hot research in the field of image recognition in recent years. However, many existing recognition methods lack attention to clothing details, resulting in poor generalization and long calculation cycle, which is not suitable for practical application. In this paper, a new network structure called VCG is proposed. Firstly, VGG16 is used as feature extraction network. Then the convolutional block attention module (CBAM) is added to the second convolution block to emphasize the channel and spatial information, enhance the attention to the target area and extract the detail information from the image. Finally, the global average pooling is used to replace the full connection layer to vectorize the features, which greatly reduces the calculation parameters and gives each channel the actual category significance to output the recognized clothing category. The experimental results show that compared with several common neural network models, VCG is more accurate and has a good detection effect on clothing category recognition.
Deep Stacked Sparse Autoencoders – A Breast Cancer Classifier

Breast cancer is among one of the non-communicable diseases that is the major cause of women's mortalities around the globe. Early diagnosis of breast cancer has significant death reduction effects. This chronic disease requires careful and lengthy prognostic procedures before reaching a rational decision about optimum clinical treatments. During the last decade, in Computer-Aided Diagnostic (CAD) systems, machine learning and deep learning-based approaches are being implemented to provide solutions with the least error probabilities in breast cancer screening practices. These methods are determined for optimal and acceptable results with little human intervention. In this article, Deep Stacked Sparse Autoencoders for breast cancer diagnostic and classification are proposed. Anticipated algorithms and methods are evaluated and tested using the platform of MATLAB R2017b on Breast Cancer Wisconsin (Diagnostic) Data Set (WDBC) and achieved results surpass all the CAD techniques and methods in terms of classification accuracy and efficiency.
A Novel Learning Based Non-Lambertian Photometric Stereo Method for Pixel-Level Normal Reconstruction of Polished Surfaces

High-quality reconstruction of polished surfaces is a promising yet challenging task in the industrial field. Due to its extreme reflective properties, state-of-the-art methods have not achieved a satisfying trade-off between retaining texture and removing the effects of specular outliers. In this paper, we propose a learning based pixel-level photometric stereo method to estimate the surface normal. A feature fusion convolutional neural network is used to extract the features from the normal map solved by the least square method and from the original images respectively, and combine them to regress the normal map. The proposed network outperforms the state-of-the-art methods on the DiLiGenT benchmark dataset. Meanwhile, we use the polished rail welding surface to verify the generalization of our method. To fit the complex geometry of the rails, we design a flexible photometric stereo information collection hardware with multi-angle lights and multi-view cameras, which can collect the light and shade information of the rail surface for photometric stereo. The experimental results indicate that the proposed method is able to reconstruct the normal of the polished surface at the pixel level with abundant texture information.
Application of Cloud Computing and Blockchain Technology in Intelligent Information Security

The rapid development of information technology promotes the development of universities from digital campus to intelligent campus. Blockchain, as the underlying architecture for building a trusted information environment, can integrate the technical expertise of cloud computing, Internet of Things, artificial intelligence and other technologies to boost the construction of intelligent campus. Based on the basic technology and main characteristics of block chain, this paper analyzes the possible information security problems in the construction of smart campus, and puts forward specific typical applications in combination with the advantages of block chain technology.
Multi-Resolution Attention for Personalized Item Search

Personalized item search has become an essential tool for online platforms---where users interact with a large corpus of items (e.g., click, purchase, like) via a search query---to provide their users with a more satisfactory search experience. The record (or history) of users' past interactions serves as a valuable asset to achieve personalization. While user history data can span over a long period of time, only a part of the history is relevant to a user's current search intent. Moreover, since historical interactions take place at aperiodic points in time, modeling their relevance to the current search query entangles complex temporal dependencies. We propose multi-resolution attention to address these challenges for personalized item search. Our approach captures higher-order temporal relations between user queries and their history across several temporal subspaces (i.e., resolutions), each corresponding to distinct temporal ranges with adaptive time boundaries that are also learned directly from data. We achieve this by coupling the conventional multi-head attention module with a differentiable soft-thresholding mechanism, which essentially operates as a masking function in the temporal domain. Comparisons with strong baselines on an open-source benchmark dataset confirm the efficacy of our approach.
Research of Self-Attention in Image Segmentation

Although the traditional convolutional neural network is applied to image segmentation successfully, it has some limitations. That's the context information of the long-range on the image is not well captured. With the success of the introduction of self-attentional mechanisms in the field of natural language processing (NLP), people have tried to introduce the attention mechanism in the field of computer vision. It turns out that self-attention can really solve this long-range dependency problem. This paper is a summary on the application of self-attention to image segmentation in the past two years. And think about whether the self-attention module in this field can replace convolution operation in the future.
Obstacle avoidance planning combining reinforcement learning and RRT* applied to underwater operations

Obstacle avoidance planning has always been an essential technology for Autonomous Underwater Vehicles (AUV) underwater operations. In view of the complex underwater obstacle environment, planning a path that takes into account the optimal calculation efficiency and energy consumption is of great significance to underwater operations. The rapidly random tree <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> (RR$\text{T}^{*}$) method has been proposed in recent years to solve the path optimization problem. Compared with the original rapidly random tree (RRT), RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> has the feature of gradual optimization of the path, reducing a lot of useless border memory storage, greatly improving the efficiency of search. Since the RR$\text{T}^{*}$ algorithm is essentially a random sampling extension, the following problems will occur: The goal is so weak that the cost of exploring ineffective areas is high. In this paper, the RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm driven by reinforcement learning (RL-RR$\text{T}^{*}$) is used to reduce the detection cost of invalid areas. This method uses Q-Learning to optimize the random tree expansion process of RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> , which not only maintains the random exploratory property of RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> in unknown special environments, but also uses Q-Learning to reduce the exploration cost of invalid regions. Specifically, while satisfying the AUVs kinematics model, by setting the reward function of the extended node, the variable probability parameter of the biased target and the dynamic step function, the invalid nodes are reduced, the exploration process is accelerated, and the path planning efficiency is improved. In the simulation experiment, this method is used in two unknown special maze environments. The experimental results show the feasibility of the RL-RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm and the advantages of efficiency and performance.
Pose-guided pedestrian action recognition with two-stream neural architecture searching

Pedestrians are vulnerable on streets and their actions serve as important cues for motion prediction to avoid collisions. In this paper, we address the problem of pedestrian action recognition for the first time. We first introduce a new dataset, namely, the pedestrian action recognition dataset (PARD), which serves as a database for experiments. Then, we provide an efficient baseline method, MFVGG, reaching comparable performance to previous methods at lower costs. To better handle the canonical problem, we further improve the baseline from the following two aspects: first, we leverage the pose prior to enrich the feature representations; second, we propose a two-stream neural architecture search (NAS) method to obtain the optimal network architecture tailored to our task. From the experimental results on PARD, our method outperforms previous top-performing action recognition methods. The dataset and code are publicly available at <uri xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://https://github.com/Yankeegsj/PARD">https://github.com/Yankeegsj/PARD</uri>
Phony News Detection in Reddit Using Natural Language Techniques and Machine Learning Pipelines

Phony news or fake news spreads like a wildfire on social media causing loss to the society. Swift detection of fake news is a priority as it reduces harm to society. This paper developed a phony news detector for Reddit posts using popular machine learning techniques in conjunction with natural language processing techniques. Popular feature extraction algorithms like CountVectorizer (CV) and Term Frequency Inverse Document Frequency (TFIDF) were implemented. These features were fed to Multinomial Naive Bayes (MNB), Random Forest (RF), Support Vector Classifier (SVC), Logistic Regression (LR), AdaBoost, and XGBoost for classifying news as either genuine or phony. Finally, coefficient analysis was performed in order to interpret the best coefficients. The study revealed that the pipeline model of MNB and TFIDF achieved a best accuracy rate of 79.05% when compared to other pipeline models.
Solar Panel Analysis for Human Activity Monitoring System

The infrastructure development is not equal to the consciousness of keeping it.In many areas, the destruction occurs before the development has been finished.One of them can be seen in Sekanak River restoration.Some parts have been damaged although the development has still be in progress.Therefore, an activity monitoring system that can monitor and give warning to the vandal is really needed.The system can be an alternative to prevent more destructions.However, due to the system will be applied in the public areas that is difficult to be reachable by the electrical source from the State Electricity Company, an independent supply is needed.This paper presents an analysis of the solar panel that can supply the system continuously.The analysis involves the calculation of the supply that can be used to switch on the system for 24 hours a day.From the experimental setting, it can be proved that the proposed solar panel design and construction has worked well.It can supply the total need of the system power of 200 watt for 24 hours.
A Novel Binary QUasi-Affine TRansformation Evolution (QUATRE) Algorithm and Its Application for Feature Selection

QUasi-Affine TRansformation Evolution (QUATRE) algorithm is a new intelligent computing algorithm based on matrix iteration behavior. Binary QUATRE (BQUATRE) is a binary version that can be used to solve binary application problems. From continuous to binary arithmetic is a crucial part of the binary version. In order to convert the continuous type to the binary type, this paper proposes a simple and effective conversion method. After the benchmark function test, it proves that the improved binary QUATRE method has strong competitiveness. Finally, the feature selection problem can be successfully solved in the UCI data set, and a higher classification accuracy can be obtained with a smaller number of features.
Gymnasium Simulation Design and Implementation Based on 3D Virtual Building

Based on the three-dimensional virtual building-simulation design and implementation method of the gymnasium, when the building space is subjected to virtual reality and simulation design, based on the constructed building space coordinate system and scale, the position and parameters of the building are used to construct a mathematical model of the building space component., The mathematical model of each component is integrated to build a mathematical model of the overall building space. OpenGL virtual reality technology is used to expand the target building based on the mathematical model of the target building space. The target building is given materials and texture characteristics to obtain the ideal 3D virtual view of the building space. The 3D virtual view of the building space is 3D rendered and displayed. The vivid 3D virtual renderings of the building space are animated using animation design technology. The experimental results show that the proposed method has good point-line rendering and overall rendering effect, can obtain more realistic 3D virtual building based gym simulation design results, and has high interactivity and practicability.
Application of Hybrid of ACO-BP in Convolution Neural Network for Effective Classification

Convolution Neural Network (CNN) has been widely used in pattern recognition for various applications. Convolution neural network performs non-linear transformation on input to generate the global abstract feature vector. The resulting global feature vector is input to Fully connected Neural Network (FNN) and the activation value at the neuron in the output layer classifies the input data vector. During training of CNN on a given dataset, error at the output layer is minimized using backpropagation with stochastic gradient descent. The weights optimization using backpropagation has a drawback of local minima. Thus, in this research paper hybrid of ACO-BP has been used for initialization of CNN weights using Ant Colony Optimization (ACO) and its further optimization using Backpropagation (BP) to overcome local minima. The performance of CNN shows the improvement since the ability of deep learning architecture to generalize depends on the weight configuration during training phase. Experiment was conducted on MINST data set using k-fold cross validation method to confirm the effectiveness of CNN with hybrid of ACO-BP in pattern recognition. The results show the improvement in the classification accuracy-using hybrid of ACO-BP with CNN in comparison to CNN with BP only.
The Relationship among Total Quality Management, Operational Performance and Competitive Advantage in the Jordanian Banking Sector

The purpose of this study is to examine the relationship between total quality management (TQM), operational performance, and competitive advantage in the Jordanian banking sector, and also to determine whether operational performance mediates the relationship between TQM and competitive advantage. The data from 336 respondents were validated and tested using the partial least squares structural equation modeling. This study was conducted using a self-administered questionnaire distributed among managers of the Jordanian Banking Sector. The result revealed that TQM has a positive significant effect on competitive advantage at 0.001 level of significance (β=0.393, t= 7.113, p&lt;0.001) .The results also showed that TQM had an important effect on operational performance at the significance level of 0.001 (β = 0.632, t = 14.846, p &lt; 0.001).Also, the study discovered that operational performance plays a role as a mediator on the relationship between TQM and competitive advantage in the Jordanian banking sector at the 0.001 level; β = 0.257, t-value = 6.696, p-value = 0.000. The study is beneficial for the banking sector, financial institutions, and other organizations that seek to improve their competitive advantage among their competitors through proper employment of TQM dimensions namely: top-management, customer focus, continuous improvement, employee management, and process management, beside improving operational performance in different activities.
Research on Grid Monitoring Algorithm of Educational Resources Based on Economic Structure Transformation

In order to overcome the problems of the traditional educational resource grid monitoring algorithm, such as high signal noise, high data acquisition time, and high monitoring error rate, an educational resource grid monitoring algorithm based on the transformation of economic structure was proposed. This paper analyzes the structure of educational resource grid and constructs the monitoring structure of educational resource grid by using hierarchical tree structure model. With the support of this architecture, information sensors and data collection detectors are used to collect relevant data, and convolutional neural networks are used to denoise the collected data. According to the processed data, the educational resource grid under the transformation of economic structure is monitored to obtain relevant monitoring results. Experimental results show that the maximum value and minimum value of signal noise are 15 dB and 9 dB, respectively, the data acquisition time is always lower than 0.3 s, and the monitoring error rate is always below 4%, which has high practical application value.