Reviewerly/exHarmony huggingface dataset:
A Method of Remote Sensing Image Object Detection Based on Attention

Remote sensing image object detection is an important and challenging task in the field of computer vision. Remote sensing image object detection has important applications in agriculture, meteorology, military and other fields. The traditional method of remote sensing image object detection and recognition is difficult to deal with a large amount of data, and its feature expression relies on artificial design. This paper uses a remote sensing image object detection method based on attention mechanism, uses SSD object detection model with resnet50 as feature extraction network, and further improves the accuracy by adding attention module in the feature extraction network, The results compared with the SSD algorithm without attention module, the proposed model has higher recognition accuracy for remote sensing image objects, and the average accuracy is 92.1% on the RSOD dataset.
Topic Models with Neural Variational Inference for Discussion Analysis in Social Networks

Topic models and their extensions are widely used to represent documents using generative probabilistic models. Many models were developed and applied to analyze the content of news articles, scientific papers, and other sources of textual data. However, these classic algorithms have severe problems when dealing with short texts, processing multilingual, and noisy data. These problems are most prevalent in the task of analyzing corpora collected from social networks. Authors consider different topic models for users’ discussion analysis in social networks. Authors evaluate topic modeling results of these approaches using a modified NPMI measure. The standard news20 dataset was used as a test collection as well as two user discussions on Twitter in both Russian and English. The experiment showed that a combination of ETM and pre-trained embeddings based on ELMO vastly outperforms traditional topic models such as BTM while analyzing short and unbalanced texts such as Twitter messages.
Python in Thermo

This essay will illustrate how you can use python, create your tools for use in mathematics, thermodynamics, organic chemistry, and so on. make the most out of available resources that are made using this programming language. If you do not have experience in programming, don’t worry – this essay will show guide you through understanding the basic concept of programming and navigating python code. The main purpose of this essay is to reduce the need of using chemical engineering charts and hands books, convert them to both practical and comfortable codes. Whenever you face to THERMO.EXP in this article you would learn a new program and would see the result.
Implement and Manage FSLogix

In this chapter, you will learn about FSLogix, which is used to store user profiles on remote storage. It is important to understand the FSLogix options so that you can select an appropriate option for your requirements. In this chapter, you will be planning, installing, and configuring FSLogix for pooled desktops and all the profile options such as containers and cloud caches to store the user profile on remote storage.
Possible Application of Generalized Nets in Telemedicine Screening of Corona Virus Disease 2019 (COVID-19)

The purpose of the present paper is to present possible application of generalized nets with the development of a reduced GN-model as a telemedicine screening tool for COVID-19. The model describes a possible algorithm for remote symptom screening and the different transitions of the model are representing respectively the different criteria of the evaluation process.
Object-Oriented Programming

Python is object oriented to its core. Every item—variable, function, operator, exception, signal, even a string literal—is an object. MATLAB also supports object-oriented programming, in fact with greater rigor than Python, but with less fluidity as well.
Improving Evolutionary Generative Adversarial Networks

Generative adversarial network (GAN) is a powerful method to reproduce the distribution of a given data set. It is widely used for generating photo-realistic images or data collections that appear real. Evolutionary GAN (E-GAN) is one of state-of-the-art GAN variations. E-GAN combines population based search and evolutionary operators from evolutionary algorithms with GAN to enhance diversity and search performance. In this study we aim to improve E-GAN by adding transfer learning and crossover which is a key evolutionary operator that is commonly used in evolutionary algorithms, but not in E-GAN.
Beyond the <i>AJR</i>: One Step Closer to Generating Realistic Artificial Mammograms

Beyond the AJR: One Step Closer to Generating Realistic Artificial MammogramsAnton S. Becker, MD, PhD1Audio Available | Share
Appraising Cloud Services and Climate Change

We’ve all got our heads stuck in the clouds somewhere. From something as straightforward as using Microsoft of Google email, or OneDrive or Dropbox file backup and sync, to systems such as Teams or Zoom for chat and collaboration, we’re using cloud services that are powered by huge data centers, and we have been doing so now for a couple of decades at least.
UAV Target Tracking Based on Adaptive Spatio-Temporal Regularization

Aiming at the problem of fast motion and motion blur in UAV target tracking process, an adaptive spatiotemporal regularization correlation filtering target tracking algorithm is proposed. Firstly, in order to meet the real-time requirements of UAV target tracking, a fast correlation filtering target tracking algorithm framework is adopted. Secondly, based on the correlation filtering target tracking algorithm, a temporal regularization and spatial regularization method is proposed to deal with the problem of fast-moving target and solve the boundary effect problem of correlation filtering. Then, the optimal solution of the constructed ridge regression function is obtained. In order to solve the problem quickly, the alternating direction multiplier (ADMM) iterative method is come up and used as the solution for the optimal correlation filter template to ensure the target tracking in the subsequent frames. Finally, the fast motion challenge of target is tested on three different datasets: OTB100, UAV123 and DTB70. The experimental results show that the algorithm proposed in this paper can better deal with the boundary effect caused by the rapid movement of the target compared with other relevant filtering algorithms.
Trustworthy Collaborative Trajectory Privacy Scheme for Continuous LBS

With the high demand of using location-based services (LBSs) in our daily lives, the privacy protection of users’ trajectories has become a significant concern. When users utilize LBSs, their location and trajectory information may expose their identities in continuous LBSs. Using the spatial and temporal correspondences on users’ trajectories, adversaries can easily gather their private information. Using collaboration between users instead of location service providers (LSPs) reduces the chance of revealing private information to adversaries. However, there is an assumption of a trusting relationship between peers. In this paper, we propose the trustworthy collaborative query-trajectory privacy-preserving (TCQTPP) scheme, which anonymizes users’ trajectories and resolves the untrustworthy relationship between users based on peer-to-region LBSs. Moreover, the TCQTPP scheme provides query content preservation based on a fake query concept in which we conceal the user’s actual query among a set of queries. The results of several experiments with different conditions confirm that our proposed scheme can protect users’ trajectory privacy successfully in a trustworthy and efficient manner. .
Guidelines for the Sustainable Development of Computing Technology

When referring to sustainability, the expression must be understood in terms of environmental, social and economic factors, with an emphasis on the breadth of its scope. Computational solutions involve consumer goods and hence it should be responsibility of the software and hardware industry, (as well as its designers and developers), to ensure that there is an awareness of the need for sustainability. The literature includes several works that address the question of sustainable design. However, there are few studies that deal with sustainability in terms of design and its Computing technology is still in its early stages. In light of this, this paper has formalized a set of recommendations to guide the designers in the creation of computational solutions, and thus allow the sustainability factors in design to be considered. The guidelines were applied in an academic scenario and three specialists evaluated the solutions made with and without them. The results suggest that the guidelines supported the sustainable development of Computing technologies.
Lateral chromatic aberration: a tool for retrieving the fringes phase

In this work, the possibility of using the inherent lateral chromatic aberration (LCA) of the lenses to calculate the phase of fringe patterns is reported. It is shown that a single fringe pattern can be decomposed into three phase shifted images and that the phase can be retrieved successfully from those images. Experiments are carried out to retrieve the phase of some fringes, and the results are compared with those obtained using standard temporal and spatial phase shifting algorithms.
Artificially Intelligent and Almost Robotic Extremists

A self-organized and self-similar scenario exists on the horizon of repetitive service workers and little intellectual capital, as there is a huge black cloud of unemployment, a storm closer and closer that is being generated for robotics systems.
Parameter analysis and performance simulation of satellite-earth quantum link under the condition of stratus form snowfall

In order to study the communication performance and parameter analysis of the satellite-to-earth link of the quantum satellite under the condition of forming snowfall in the stratosphere, firstly, according to the distribution function and extinction characteristics of the concentric spherical ice water condensation nuclei in the stratosphere and the snowfall, according to the Mie theory, the system environment is obtained. The extinction efficiency factor establishes the relationship between the quantum signal transmission distance and link attenuation under different system environmental levels. Further studies have been done to establish the relationship between channel capacity, survival function, bit error rate, transmission distance, and system environment level under the condition of long-distance transmission of optical quantum signals at a moderate proportion of stratus cloud and snowfall. The simulation shows that as the transmission distance and environmental level increase, the link attenuation gradually increases, the channel capacity and survival function are greatly reduced, and the bit error rate increases. Therefore, when performing satellite-to-earth link communication, the relevant parameters of quantum communication should be adjusted adaptively to reduce the influence of snowfall caused by the formation of stratus clouds and improve the transmission performance of quantum communication.
The Weighted Bootstrap

In this chapter, (weighted) bootstrap methods for testing statistical functionals are presented. The relationship to dependency is that Efron’s bootstrap involves multinominal weights which are correlated. Some modifications of these weights are proposed to address this issue.
METHOD OF COLLECTING TEXTS FROM RELIGIOUS PAGES ON TWITTER SOCIAL NETWORK

Currently, social networks are a platform for a large amount of information. With the exception of useful information, social networks have become a convenient platform for illegal actions. Suspicious activity often remains in the shadows due to the lack of threat detection and analysis systems in social networks. The article provides a brief overview of approaches for analyzing information from a user profile.
An e-learning System for Evaluating Analysis Results Based on Experimental Data

We have developed an e-learning system and are using it for student experiments and lectures in science and engineering. The e-learning system can evaluate several types of student answers, including numerical values, algebraic formulas and short movies. In addition, it is possible to evaluate the numerical values calculated from experimental data by creating a Maxima program, which is a characteristic feature of the system. By presenting typical e-learning contents, we will show you useful functions of the system in detail.
Principal Colors Extraction Using Topological Data Analysis

Computer vision often struggles with extracting principal colors from an image. When the expected principal color count is known, this problem is solved successfully using traditional computer vision and machine learning techniques. For instance, there’s image quantization by representing pixels as an octree structure and color space clustering with the K-Means algorithm. However, these methods would not solve the problem of a custom count of desired extracted principal colors. In this paper, we propose an approach to extract principal colors from an image, relying only on a priori image without information about the desired color count. To realize the proposed method, we use neural networks to warp colors space to a special space and then transform this obtained space by extracting topological features.
Multiple UAVs Path Planning Based on Optimized Water Drops Algorithm

In order to overcome the disadvantages such as high range cost and long calculation time of traditional water drops algorithm for path planning, an optimized water drops algorithm is proposed for multiple UAVs path planning. The set of waypoints to be selected on the grid map is determined based on the calculation step length and heading angle, and the heuristic term is introduced into the probability calculation of choosing the waypoints from the set. The selection of the next waypoint is more flexible. It does not need to limit the waypoint to locate at the center or edge line of the adjacent grid, thus reduces the data amount of soil content information which need to be updated. Compared with the traditional water drops algorithm, ant colony algorithm based on Voronoi map and ant colony algorithm based on grid map, the proposed algorithm shortens the planning path range by 3.2%, 14.4% and 4.9% respectively, and the running time of the algorithm is reduced by 52.9%, 35.1% and 47.6% respectively.
Billing System and 5G Network Slicing Service

By referring to the international mainstream operational support models such as eTOM (Enhanced Telecom Operations Map) and NGOSS (Next Generation Operations System and Software), combined with the iterative development of large domestic projects and the experience of the new system construction of emerging telecom carriers, the enterprise service model and data model are transformed into the enterprise system architecture for the telecom full-service. Through the summary of the requirements of the system in 5G application scenarios, the transformation scheme and data structure design are proposed under the 4G deployment network. In the development of the new system, problems are solved such as the 5G service-launch for users, 4G VoLTE (Voice over Long-Term Evolution) and NSA (Non-Standalone) users service-launch in the SA (Standalone) area, the support of 5G slice scene, and the automatic service-launch of SA by default.
Language Pitfalls

Neither MATLAB nor Python are ideal languages. Both have annoying and downright dangerous aspects—at least for novice programmers unfamiliar with more subtle aspects of the languages’ designs. Detractors of one or the other language are keen to point out the other’s flaws while brushing off criticism of their preferred language.
Review of Zottola (2021): Transgender Identities in the Press: A Corpus-Based Discourse Analysis

Abstract This article reviews Transgender Identities in the Press: A Corpus-Based Discourse Analysis
Research on Application of Target Detection Network Based on SSD in Underground Coal Mine

Aiming at the problems of poor underground coal mine environment, poor illumination, mixed background, blurred pedestrians, multi-scale pedestrians, etc., and traditional target detection usually extracts features manually, which requires a large workload, cumbersome detection steps, low detection accuracy, and serious missed detection. Based on the theory of deep learning, this paper proposes a method for identifying people underground in coal mines based on Single Shot MultiBox Detector (SSD). According to the self-made downhole worker data set based on the VOC data set, the labelme labeling tool is used to manually label the downhole personnel as "person", and the SSD-VGG16 network model is trained. Experiments show that the model can effectively detect people underground in coal mines. When the IOU threshold is 0.5, the SSD model has an average accuracy of 97.13% and a recall rate of 87.53%. The model can better detect underground personnel and has a certain The universal applicability of the mine is of reference significance for the intelligent monitoring system of coal mines.
Comparing the Performance of Classical and Deep Learning Methods on Small Image Datasets

In this work, we present the results of an experimental study of the problem of image classification under the condition of small image datasets. The performance of both traditional and CNN-based methods is examined and compared based on two benchmark image datasets. The first dataset consisted of 12,000 routine hematoxylin-eosin stained histological images depicting normal and tumor areas of breast cancer. The second dataset includes 10,800 axial 2D slices of CT images representing 3 characteristic anatomical classes. Color co-occurrence matrices of an adaptive 32-color palette were used in the case of the traditional approach of histology image classification whereas CT image co-occurrence utilized suitably scaled Hounsfield CT image intensities. The classification itself was done using commonly known SVM, Random Forests, and kNN classifiers. The Deep Learning approach employs 5 Convolutional Neural Networks including a simple 3-layer VGG-like one as well as NASNet Mobile, MobileNet v3, EfficientNet-B2, and reasonably heavy BiT-S R50×1. Classification of CT images was performed using some testing framework resembling the Content-Based Image Retrieval scenario with the Top N = 24 most similar images. As a result, it was found that traditional methods outperform the CNN-based image classification technique on the training sets comprised of less than 840 images. Also, among the 5 examined CNN architectures, the BiT-S R50x1 neural network provides the best and the most consistent results.
Inception Based Medical Image Registration

Biological tissue has strong absorbability and strong scattering, which may lead to edge blur, low signal-to-noise ratio and so on, which affects the registration of medical images. This chapter simulates and makes the biological tissue imitation image data set under the above conditions, cleverly designs a regression structure embedded in the U-net Inception module, and forms an unsupervised deep learning medical image registration method. It is applied to the biological tissue imitation data set for the first time. The experimental results show that the image registration method based on SIFT, ORB, BRISK, AKAZE and so on can not successfully complete the registration work because it can not find a sufficient number of effective key points on the data set, SURF although it can complete the registration work. But the effect is weaker than the proposed method. Based on the medical image registration method proposed by Inception, this chapter can effectively solve the problem of biological tissue image registration with strong absorption and strong scattering.
Packaged Functions

R has over 18,000 packages, most of which contain functions. Functions are at the heart of R and provide R with R’s great versatility. Functions are R objects, and they are of the closure, builtin, or special type, the function mode, and the function class. Packaged functions are functions that have been created as a part of an R package. On the computer, packages are stored in a library (i.e., in a folder) and are placed in the library (folder) when installed.
Design and Implementation of Relay Nodes in SBC Multi-display System

Multi-display systems use virtualization technology to create single large screens that allow multiple people to view high-resolution videos and images simultaneously. As such, they are often used in conjunction with high-performance computer systems to visualize simulation results. However, multi-display systems also tend to be expensive to construct because they generally require high-performance computers. To overcome this limitation, our previous study proposed a multi-display system consisting of one head node and multiple display nodes constructed using single-board computers (SBCs). Unfortunately, increasing the number of displays in a multi-display system also increases the processing load on the head node, which eventually produces a bottleneck that slows the whole system. Therefore, this study proposes the introduction of relay nodes between the head and display nodes as a way to distribute and parallelize the image frame processing task. Herein, we show that our method can reduce the head node’s central processing unit (CPU) usage while also providing sufficient margin in the relay node CPU utilization rates. These evaluation results demonstrate that system bottlenecks can be eliminated by implementing relay nodes and performing distributed parallel processing in multi-display systems.
Principal and Minor Eigenvectors

In Chapter 4, I discussed adaptive algorithms for the computation of the principal eigenvector of the online correlation matrix Ak∈ℜnXn. However, in some applications, it is not enough to just compute the principal eigenvector; we also need to compute the minor eigenvectors of Ak. One such application is multi-dimensional data compression or data dimensionality reduction in multimedia video transmission [Le Gall 91]. For example, in still video compression by the JPEG technique, the image is divided into 8X8 blocks. This high-dimensional video data can be reduced to lower dimensions by projecting it onto the principal eigenvector subspace of its online correlation matrix. The process of data projection onto the eigenvector subspace by a linear transform is known as principal component analysis (PCA) and is closely related to the Karhunen-Loeve Transform (KLT) [Fukunaga 90]. We approximate the eigenvectors by fixed transform vectors given by the discrete cosine transform (DCT), which is the central compression method of the MPEG standard [Le Gall 91]. It can be shown that DCT is asymptotically equivalent to PCA for signals coming from a first-order Markov model, which is a reasonable model for digital images.
Joint Accuracy and Resource Allocation for Green Federated Learning Networks

This paper studies the energy and time resource optimization of federated learning (FL) in wireless communication networks. In the considered network model, each client uses local data for model training, and then sends the trained FL model to the central server. However, the energy budget of the local computing and transmission process is limited. Therefore, reducing energy consumption should be given priority when we consider the FL efficiency and accuracy. We invest a green communication joint learning issue and expressed as an optimization problem. To minimize the energy consumption under the condition that the overall FL time is constrained, we propose an iterative algorithm based on Lyapunov optimization. Our algorithm selects the clients participating in each round and allocates the different bandwidth to each client. At the same time, the connection between local training and communication process is considered so that we can get the optimal client local calculation force.