Reviewerly/exHarmony huggingface dataset:
Enhanced Task-Aware Spatial Disentanglement Head for Oil Tanks Detection in High-Resolution Optical Imagery

Task-aware spatial disentanglement (TSD) head could decouple classification and regression from the spatial dimension by using deformable pooling. However, the effect of deformable pooling decreases when the Intersection over Union (IoU) of the positive sample increases as the deformable pooling destroys the learned Region of Interest&#x2019;s (RoI&#x2019;s) pooling feature. Besides, deformable pooling is to find the useful area of an object to predict the object in a proposal, so deformable pooling is not suitable to some extent for negative samples as deformable pooling will find the useful area of an object to predict there is no object in negative samples. We propose an enhanced TSD (ETSD), which adds an auxiliary branch with IoU-based loss weight. The auxiliary branch has double identical structure heads to separate parameters for classification and regression and uses the RoI&#x2019;s pooling features as input to train classification and prediction directly. The loss weight of the auxiliary branch will be doubled for negative samples and increased gradually for positive samples according to the IoU. Double heads in the auxiliary branch make the RoI&#x2019;s pooling feature get optimal feedback in backpropagation. The increase in the loss weights of the auxiliary branch will allow the model to enhance the training of the RoI&#x2019;s pooling feature. The more accurate the RoI&#x2019;s pooling feature, the more accurate the deformable pooling features learned from the RoI&#x2019;s pooling feature. We performed detailed ablation studies on the oil tank dataset extracted from DIOR.
Statistical Analysis of Electroencephalographic Signals in the Stimulation of Energy Data Visualizations

Increasing luxury living standards coupled with technological developments have made energy efficiency in homes much more important. By protecting the environment and preventing the depletion of energy resources, making energy use conscious has an important role in preserving a livable world for future generations. The brain–computer interface (BCI) has been widely used to improve the quality of life of individuals. There have been numerous research projects on predicting the behavior of energy consumers. However, detecting emotional responses and incorporating personal perceptions of individuals is still a challenge to understanding energy users’ behavior. This paper investigates the best selection method for energy data visualization using BCI systems for improving energy users’ experience. An experimental study has been conducted to acquire electroencephalography (EEG) signals of energy users against the stimuli of energy data visualizations to detect emotions and understand the users’ perceptions. A self-assessment manikin (SAM) is used to rate the arousal and valence scales required for emotion classification. Sample entropy (SampEn) and approximate entropy (ApEn) are utilized to analyze EEG data. One-way ANOVA and Tukey’s honestly significant difference test is applied to the entropy values of EEG signals showing some promising results from the conducted statistical analysis.
Globalization and Its Impact on Technological Development

Globalization is the main feature and an inevitable process of the time we live in. It allows for the global movement of people, ideas, information, technologies, and capital. It erases the outdated borders and helps establish more intense and dynamic connections between global and local events. On the other hand, today’s world of turbulent changes shows beyond doubt that modern development depends on innovations and technological development. The subject of this paper is the influence of globalization on technology and its development. This research was made possible through a review of current literature in this field along with two case studies, with the goal of helping the reader become more familiar with the concept of technological development caused by globalization, and providing him an insight into technological changes in production methods, system of business management, new job openings, requirements for additional education and training of the workers, at the same time emphasizing the importance of the maximization of positive, and minimization of negative trends that go along the technology development.
Identification of Synthetic Spoofed Speech with Deep Capsule Network

The state-of-the-art models for speech synthesis and voice conversion have caused a great threat to automatic speech verification (ASV) system. In fact, it is difficult for human beings to perceive the subtle difference between the bonafide speech and spoofed speech from these models. The ASVspoof 2019 challenge, jointly launched by several world-leading research institutions, is the largest and most comprehensive challenge for spoofed speech identification. In this work, a countermeasure system for ASVspoof 2019 is proposed based on cepstrum features and deep capsule network. MFCC and CQCC features are extracted as the input of the proposed network. The convolutional layer and routing strategy of the capsule network are specifically designed to distinguish bonafide speech from spoofed ones. The experimental results on ASVspoof 2019 LA evaluation set show that the proposed deep capsule network can improve the baseline algorithms t-DCF and EER scores by 31% and 37%, respectively.
Super-Resolution of Defocus Thread Image Based on Cycle Generative Adversarial Networks

The dual camera calibration measurement method can realize low-cost and high-precision bolt dimension measurement by using two microscope cameras. But the height difference between the thread crest and root exceeds the depth of field, and the thread image becomes defocus, which seriously affects the measurement accuracy. For this reason, a super-resolution method for defocus thread image based on cyclic generative adversarial networks is proposed. We collected focus thread images and defocus thread images as training data. Two encoders are used in the generation network to extract image defocus features and content features. And a sub-pixel convolution layer is added to the decoder to achieve image super-resolution. A loss function based on adversarial loss and cycle-consistent loss is constructed to realize unsupervised training of the network, thereby achieve super-resolution of defocus thread images. The experimental results show that, in the simulated defocus images, the method has superiority in image detail preservation, sharpness improvement and peak signal to noise ratio. In the bolt dimension measurement task, it can effectively reconstruct the clear thread image and thus provide the measurement accuracy to 0.01 mm.
Hiding Scheme based on Improved Reduced Difference Expansion in Multi-block Shape

Since the image has the correlation among pixels, the single-layer embedding here uses a block with a shape of 3 × 3. If the reference point that cannot be changed intuitively is located in the center of the block, the image quality or the effect of embedding capacity should be the best situation. This study implements the embedding performance of 9 different scanning patterns proposed by Lee et al. (Data Hiding Using Multi-Pixel Difference Expansion. 2018 3rd International Conference on Computer and Communication Systems, pp 56–60, 2018) scholars, and the IRDE scheme is calculated with the difference between the block center as the reference point and its eight neighbor pixels to achieve the purpose of hiding data. In addition, according to above, this paper is improved from the method of Lee et al. (Data Hiding Using Multi-Pixel Difference Expansion. 2018 3rd International Conference on Computer and Communication Systems, pp 56–60, 2018) scholars. Based on the malleability of the block shape, we have extended the block shape so that the maximum capacity of each block is increased from the original 8 bits to 35 bits, and at the same time, it can continue to maintain high-quality images by controlling the overflow/underflow mechanism. In order to further explore the relationship between using different block shapes in exchange for high embedding capacity and image quality, this paper designed 5 different shapes and tested them under single-layer. However, the experimental results show that with the continuous increase block shape, the increase of embedding capacity is limited or even tends to be constant. When considering the embedding performance with embedding capacity, the embedding time and the image quality should also be considered. When the shape is 5 × 5, our scheme can achieve greater performance.
High-Quality Developing Components: Exploring Jetpack

Up to now, you should be able to build Android App independently. However, there is a huge gap between making an app and an app that has quality codebase and good architecture.
Prediction of Primary Water Inventory in Nuclear Power Plant Based on Recurrent Neural Network

During the operation of the nuclear power plant, a large number of human error prevention training and management have been carried out. However, the traditional methods to prevent human error are through the way of human behavior management, that is, improving staff working habits, strengthening staff monitoring behavior and so on. As a contrast, intelligent prediction of the operator manual action has great advantages, which can greatly reduce the possibility of human error, improve safety, and reduce the loss and impact caused by operator’s misoperation. If the influence and evolution trend can be predicted before the operator's command sent, the human error probability can be effectively reduced. In this paper, the function of the primary water inventory control is analyzed and modeled. By mining the correlation information in the historical operation data of the power plant, our network model LSTM-FCN integrating the recurrent neural network and the temporal convolutional neural network is established. Combined with the processing advantages of convolutional neural network and recurrent neural network, the changes of the relevant parameters of the primary water inventory are predicted, which could provide reference information to operators on future development trends.
Enrich Your App with Multimedia

Phones used to be the device that can only be used to make phone calls and send text messages. Now, phones are playing a more and more important role in our daily lives, and we can have all kinds of entertainment through a mobile phone: we can listen to music during the boring commute; we can watch movies during the trip; we can shoot photos whereever we go.
Development and Application of Computer Teaching and Automatic Management System

With the development of economic and cultural level, computer and network technology has also been developed rapidly, and the popularity is also higher and higher. Computer information management technology has also been widely used in the education system. Among them, the use of computer and network technology in school physical education teaching management is still very low, In order to make the A/intranet management technology widely used and promoted in various disciplines, especially in school physical education teaching and management, this paper focuses on the analysis and research of computer physical education teaching and sports network management.
Automated Classification of Argumentative Components in Students’ Essays

Multiple approaches have been proposed for the automated classification of argumentative components. However, few studies have focused on argumentation in students’ essays and how automated classification can support the development of automated writing evaluation (AWE) systems in intelligent tutoring systems. In this study, linguistics features (related to positionality, semantic similarity, part-of-speech tags, named entity, and syntactic dependency) were obtained from 314 essays written by first-year college students. These features were used to build an algorithm to classify 2264 argumentative components found in the essays into four categories (final claim, primary claim, data, and other). Results indicated a Random Forest model (using five repeats of 10-fold cross-validation) achieved an overall F1-score of 0.78 in the classification and that the positionality, semantic similarity, and syntactic dependency features played the most critical roles. The algorithm can help inform the development of AWE algorithms to drive feedback on argumentative essays and help developing writers improve their argumentation.
Everything About UI Development

It is natural for mobile engineers to focus on how to implement the functionality of the app when they use an app. However, users will just focus on the UI and functionality of the app instead of the implementation. While it is not our duty to come up with beautiful designs, we can use the rich UI tools provided by Android to build beautiful UIs.
Template-Based Thinning Method for Handwritten Gujarati Character’s Strokes and its Classification for Writer-Dependent Gujarati Font Synthesis

Handwriting synthesis simulates the human writing style by learning the structural features of handwritten characters. There are two approaches to handwriting synthesis: movement simulation and shape simulation. In movement simulation, the hand's neuromuscular movement is used to synthesis the writing. In shape simulation, glyph or structural features are used to synthesis the writing. Our main objective is to generate handwritten Gujarati font using a shape simulation approach. Therefore, to excerpt the structural features and shape of characters, the detailed strokes analysis from Gujarati handwritten characters is required. Earlier, we have proposed an algorithm for stroke extraction from handwritten Gujarati characters. So here, statistical analysis of stroke extraction has been carried out to verify extraction methodology. The image should be one pixel wide to work with the structural features, so an improved thinning algorithm has also been proposed. Likewise proposed an elegant way of extracting and generating features based on structural features. The classification is carried out on extracted strokes from handwritten Gujarati consonants using k-nearest neighbor (KNN) and neural network. Besides, results compared by tuning different parameters, where our extracted features on the proposed thin image, give an average 98% accuracy in both classifiers.
Proposing the Service Quality Control Model of Wi-Fi System Based on Cloud Theory

A model for controlling the service quality of a Wi-Fi system based on cloud theory is discussed in this paper. The quality of service of each Wi-Fi is determined based on the current and previous data of each Wi-Fi, these data include Availability (Av), Performance (Per) and Secure Parameters (SP). The backward cloud generator model is used to map a quantity of precise data of Av – Per – SP back into the qualitative concept expressed by the expected value (Ex), the entropy (En) and the hyper entropy (He). Using the forward cloud generator model to determine the quality of service of each Wi-Fi. The operating principle of the proposed model is as follows: when the user selects a certain Wi-Fi in the Wi-Fi system to connect through the proposed control model, the control model will perform determine the quality of service of each Wi-Fi in the system through the established cloud model. From there, the control model will make a comparison, to give feedback to the user whether to change the connection to another Wi-Fi or not. The simulation results show that the proposed model has controlled the quality of service of the Wi-Fi system.
Very Short-Term Photovoltaic Power Forecast by Multi-input Long Short-Term Memory Network

Electricity generation from solar power is indispensable in the scenario of environmental and energy crises. Progressively increased share of installing solar photovoltaic (PV) technology widely has occurred for gradual reduction of cost and short gestation period. But besides this, sunlight has diurnal unavailability and irregular tendency due to its weather dependence. To utilize it in a large scale, accurate forecasting of solar power is necessary for assuring the economic and stable operation of the smart grid and participation in bidding. In this paper, long short-term memory (LSTM) network, a type of recurrent neural network, has been used to forecast the PV output power using multiple weather inputs and power. Rigorous testing of multiple-input LSTM networks has been done, and a selection of dominant weather inputs is carried out. The LSTM has been optimized based on the number of neurons, number of layers and also optimized concerning the loss function used. The LSTM network obtained at the end is fully optimized for the solar power data for the prediction of 10 min ahead power forecast.
Real time handwritten digital image recognition system based on edge computing equipment

With the development of edge computing technology, real-time handwritten numeral recognition system deployed in edge computing devices has a bright future. However, the edge computing equipment has weak computing power and limited storage space, so the mainstream image recognition neural network can not guarantee the real-time performance on low-performance edge devices. To solve this problem, this paper designs a handwritten digit recognition system which is suitable for low performance edge computing devices. The system extracts the effective information such as the position of the number in the input image through the image preprocessing module, and infers through a lightweight neural network specially designed for edge devices. In this paper, the proposed handwritten numeral image recognition system is deployed on the edge computing device Jetson Nano. The experimental data show that the inference speed of our model is 10 times faster than that of the original Tensorflow inference, and 60 times higher than the neural network Mobilenet specially designed for mobile devices. At the same time, with the increase of input video resolution, the FPS of the system does not decline significantly, which can meet the needs of most edge tasks. Finally, the system also provides design and deployment experience for other edge AI tasks.
Design and Implementation of a Multi-mode P Code Direct Acquisition Module

Compared with civilian code, P code has higher anti-interference and anti-deception, especially in the military field. However, due to the non-periodicity, high rate and large amount of data processing of P code, the improvement of acquisition speed and acquisition sensitivity is restricted. Based on the parallel frequency algorithm of matched filtering combined with fast Fourier transform (PMF-FFT), this paper designs an FFT module, which is configurable to a certain extent in FFT length and phase search parallelism. Combined with the design of time division multiplexing acquisition mode, it can achieve the flexible switching of multiple acquisition speeds and acquisition sensitivity. The test shows that the acquisition module has a variety of working modes in acquisition speed and sensitivity, is compatible with signal processing of a variety of symbol modulation modes, and is suitable for a variety of application scenarios.
Foreground Detection Using an Attention Module and a Video Encoding

Foreground detection is the task of labelling the foreground or background pixels in the video sequence and it depends on the context of the scene. For many years, methods based on background model have been the most used approaches for detecting foreground; however, their methods are sensitive to error propagation from the first background model estimations. To address this problem, we proposed a U-net based architecture with an attention module, where the encoding of the entire video sequence is used as attention context to get features related to the background model. We tested our network on sixteen scenes from the CDnet2014 dataset, with an average F-measure of 88.42. The results also show that our model outperforms traditional and neural networks methods. Thus, we demonstrated that an attention module on a U-net based architecture can deal with the foreground detection challenges.
Evaluation Method of Posture Balance of Wushu Athletes Based on Higuchi Analysis

In martial arts, the correct recognition and correction of the wrong posture of martial arts athletes can improve the quality of their daily training. In the process of posture recognition and correction, the affine deformation of human body is easy to occur in the process of martial arts, which leads to the appearance of action feature points with low brightness. However, the traditional method is to extract these feature points and compare them with the correct posture to realize the recognition and correction of posture. As a result, the wrong posture of athletes can not be detected and corrected in real time. In order to accurately evaluate the balance of the control of specific movements and postures of Wushu athletes, a balance evaluation method based on Higuchi fractal dimension analysis is proposed. The fractal dimension (FD) of the time series in the front to back and inside to outside directions is calculated by Higuchi algorithm. It can realize the tracking and monitoring of athletes’ movements and complete the detection and recognition of Wushu postures, High accuracy and stability.
A Joint Approach Based on Matrix Factorization for Multi-view Clustering

In recent years, multiple view clustering tasks has attracted sustained concern as numerous realistic data are composed of distinct expressions. The point is to adequately integrate knowledge from multiple perspectives for facilitating the clustering tasks. The non-negative matrix factorization (NMF) technique is extensively applied in multiple perspective clustering on account of possessing the ability of dimensionality reduction. This paper introduces an innovative NMF multi-view clustering approach, which can extract complementary and compatible information presented in multiple perspective data. Besides, for solving the non-convex optimal issue of the objective function presented in this paper, an available iterative renewing approach is introduced. Experiments on two standard data sets indicate the advantages of our raised algorithm contrasted with basic methods.
Research on Image Quality Enhancement Algorithm Using Hessian Matrix

The Hessian matrix has a wide range of applications in image processing, such as edge detection, feature point detection, etc. This paper proposes an image enhancement algorithm based on the Hessian matrix. First, the Hessian matrix is obtained by convolving the derivative of the Gaussian function. Then use the Hessian matrix to enhance the linear structure in the image. Experimental results show that the method proposed in this paper has strong robustness and accuracy.