Reviewerly/exHarmony huggingface dataset:
Research of Target Detection Algorithm Based on the Remote Sensing Image

This paper proposes a method using the deep neural network for satellite remote sensing image detection. This paper improves its network structure base on the YOLO-v3 network, which includes reshaping multi-scale feature maps, improving the network structure, improving the detection ability of weak targets in remote sensing images, reducing the number of convolution channels in CNN, and improves the detection speed. In this paper, the target database is established based on satellite remote sensing images such as GF-2 and ZY-3. The results of this paper show that the proposed algorithm has excellent performance in target detection accuracy and efficiency.
Remote Sensing Image Enhancement Technology of UAV Based on Improved GAN

In view of the problem that the remote sensing image of UAV is greatly affected by the weather, this paper proposes a UAV remote sensing image enhancement technology based on improved GAN. Firstly, the noise feature is extracted by the noise feature extraction network based on convolutional neural network (CNN). Secondly, using the noise features extracted by CNNs, the generation of GAN for image enhancement is constructed. For the problem of large storage overhead, low computational efficiency and pixel block size limiting the size of the perceptual region, this paper introduces a full CNN model (FCN) as a generation network. Finally, a data set for training is constructed using image enhancement techniques based on damaged sample learning. The experimental results show that the enhanced algorithm in this paper shows excellent performance in remote sensing images of UAV.
Text Generation Using Long Short-Term Memory Networks

The domain of natural language processing has lately achieved exceptional breakthroughs especially after the origination of the deep neural networks. This has enabled the machine learning engineers to develop such deep models that are capable of performing high-level automation, empowering computer systems to interact with the humans in a competent manner. With the usage of special types of deep neural networks known as recurrent neural networks, it is possible to accomplish various applications in the domain of natural language processing including sentiment analysis, part-of-speech tagging, machine translation, and even text generation. This paper presents a deep, stacked long short-term memory network, an advanced form of recurrent neural network model which can generate text from a random input seed. This paper discusses the shortcomings of a conventional recurrent neural network hence bringing forward the concept of long short-term memory networks along with its architecture and methodologies being adopted.
Face recognition using selected topographical features

This paper represents a new features selection method to improve an existed feature type. Topographical (TGH) features provide large set of features by assigning each image pixel to the related feature depending on image gradient and Hessian matrix. Such type of features was handled by a proposed features selection method. A face recognition feature selector (FRFS) method is presented to inspect TGH features. FRFS depends in its main concept on linear discriminant analysis (LDA) technique, which is used in evaluating features efficiency. FRFS studies feature behavior over a dataset of images to determine the level of its performance. At the end, each feature is assigned to its related level of performance with different levels of performance over the whole image. Depending on a chosen threshold, the highest set of features is selected to be classified by SVM classifier
Analysis of Queuing Model-Based Cloud Data Centers

Cloud computing is the process of allocating the network access admission to a group of selected users having advanced and smart pattern of computing facilities on the plan of usefulness of the network permission for accessing the network resources whenever there is a demand for the facility to be provided from the cloud. It may be a customary term and thus the regular service that was being delivering the required services to the hosts among net. Here, the cloud computing mechanism is employed for describing each list of platforms that were out there to the users for operating and additionally the many styles of applications which will be processed. The current technique was being thought about by most of the analyzers because of the most potential and therefore the most helpful space for the analysis and also for analysis in academe like universities and major research laboratories. Performance analysis of many connected applications and their sub-elements were being thought about in the concert of the helpful and principally used analysis space within the recent years. This method and its services were being employed principally for the suppliers of the cloud and its connected spaces, and therefore, the beneficiaries of this method of area were each supplier of the cloud and therefore the customers associated with the cloud. Solely few notable works are revealed with regard to performance analysis in cloud computing. In General, the analytical models were geared towards the models that use the cloud and its services through the performance of the model, and the current model was analyzed and evaluated for various configurations and assumptions. These assumptions were based on the queuing theory, and its accuracy is verified with numerical calculations and simulations. The present paper deals with the performance evaluation in terms of steady-state parameters of a small cloud server farm using single- and multi-server queuing models. Single-server model includes M/M/1 and M/Er/1. Multi-server model considered includes M/M/c and M/M/c/c. A comparison among the steady-state parameters evaluated for the above queuing models with respect to traffic intensity is also presented.
An Improved Pigeon-Inspired Optimization Combining Adaptive Inertia Weight with a One-Dimension Modification Mechanism

In recent years, many population-based swarm intelligence (SI) algorithms have been developed for solving optimization problems. The pigeon-inspired optimization (PIO) algorithm is one new method that is considered to be a balanced combination of global and local search by the map and compass operator and the landmark operator. In this paper, we propose a novel method of adaptive nonlinear inertia weight along with the velocity in order to improve the convergence speed. Additionally, the one-dimension modification mechanism is introduced during the iterative process, which aims to avoid the loss of good partial solutions because of interference phenomena among the dimensions. For separable functions, the one-dimension modification mechanism is more effective for the search performance. Our approach effectively combines the method of adaptive inertia weight with the strategy of one-dimension modification, enhancing the ability to explore the search space. Comprehensive experimental results indicates that the proposed PIO outperforms the basic PIO, the other improved PIO and other improved SI methods in terms of the quality of the solution.
Two-Stage Training Method of RetinaNet for Bird’s Nest Detection

Common nesting materials such as branches, straws, and wires fall on high-voltage power lines causing short-circuit faults. In recent years, neural network has developed rapidly in the field of objects detection. Through the shooting of the drone and the base station camera, the neural network is used to identify and locate the bird’s nest in the image, which has great use prospects in the intelligent detection of the transmission system. RetinaNet is currently a representative objects detection network, using the focal loss to adjust the imbalance between foreground and background. In this paper, we apply RetinaNet to the bird’s nest detection of transmission systems. Due to the complex environment of the transmission system, the detector obtained by the single-stage training recognize the line equipment or other objects as the nest easily. Combining the experimental results of single-stage training, we propose a two-stage training method driven by false detection samples, which improves the performance of the detector.
Hierarchical multimodal attention for end-to-end audio-visual scene-aware dialogue response generation

This work is extended from our participation in the 7th Dialogue System Technology Challenge (DSTC7), where we participated in the Audio Visual Scene-aware Dialogue System (AVSD) track. The AVSD track evaluates how dialogue systems understand video scenes and responds to users about the video visual and audio content. We propose a hierarchical attention approach on user queries, video caption, audio and visual features that contribute to improved evaluation results. We also apply a nonlinear feature fusion approach to combine the visual and audio features for better knowledge representation. Our proposed model shows superior performance in terms of both objective evaluation and human rating as compared to the baselines. In this extended work, we also provide a more extensive review of the related work, conduct additional experiments with word-level and context-level pretrained embeddings, and investigate different qualitative aspects of the generated responses.
LL(1) as a Property Is not Enough for Obtaining Proper Parsing

The LL(1) is a top-down technique. This technique manipulates with any set of strings to decide which one belongs to the language that deals with. This technique uses a set of properties of two main functions: first and follow. These properties are the main constrains of using LL(1). This research answers the following question: Is the LL(1) property for a language sufficient for applying parsing method? And if not, what would be the alternative?
Optimal measures, nonzero projections, and irreducible representations

This brief note considers the problem of maximizing the probability of detection for a finite number of mixed quantum states. It is shown that for linearly independent density matrices, an optimal measure that is simple and unique contains nonzero operators that are orthogonal projections on subspaces associated to irreducible representations (irreps) of a finite-dimensional Hilbert space.
Quasi-Classical Description of the “Quantum Bottleneck” Effect for Thermal Relaxation of an Atom in a Resonator

We consider the escape of a photon from a single-mode optical cavity with a controlled variable intensity. The source of the photon is a relaxing two-level atom. The quantum bottleneck effect involves a counterintuitive decrease of the probability of photon escape from the cavity with the increase of its escape intensity. Numerical simulations of the process were carried out using the basic Lindblad quantum equation for the Jaynes-Cummings model with thermal relaxation. A quasi-classical description of the bottleneck mechanism is presented, similar to the Zeno effect. The counterintuitive behavior described plays an important role in the description of the exchange of single photons in nanosystems and in molecular complexes converting solar energy in bacteria.
End-to-End Image Super-Resolution via Generative Adversarial Network

Abstract Image super-resolution is to extract information from single or more low- resolution images and use this information to get the corresponding high-resolution image. The ability to capture high texture details of low-resolution images is one of the impressive advantages of generative adversarial networks (GANs). This paper mainly studies the image reconstruction method based on a single low-resolution image, and builds an end-to-end image Super-Resolution method via generative adversarial networks to improve the image resolution. Convolution neural networks use the mean square error (MSE) as loss function. For such loss function, structural similarity (SSIM) and peak signal to noise ratio (PSNR) can be obtained, but high texture details of original images are often lost. The generative adversarial network mainly contains two parts: generator network and discriminator network. Adversarial loss uses a discriminator network to push our solution to a natural image manifold, which is trained to distinguish super-resolution images from photo-realistic images. Additionally, content loss of our network was indicated by perceptual similarity rather than similarity of pixel-wised space.
Attentive recurrent text categorisation models using word-category cross embedding on financial comments

User-oriented comments in web forums carry rich information that helps people to find answers about a topic or subject. However, real-world data often contain redundant or irrelevant content, which leads to the introduction of noise in text classification tasks. In this paper, we propose an attentive recurrent neural network model for financial text categorisation based on a cross-embedding input representation. Our cross-embedding approach combines contextual information from words in a comment and contextual information from labels in a category set. Besides the proposed model, we also built an annotated real-world dataset containing complex comments posted by different users on a financial website. Experimental results compare our approach with the state-of-the-art baselines based on Recurrent Neural Network (RNN) extensions. The best configuration shows an increase of almost 12% in precision and 8% in F1-score when compared to the second best-performing baselines.
Comparative analysis of results of numerical simulation of cyber-physical biosensor systems on the basis of lattice differential equations

The article deals with the comparative analysis of the results of numerical modeling of mathematical models of cyber-physical biosensor systems on hexagonal and rectangular lattices using lattice differential equations.The main attention is given to the mathematical description of the discrete population dynamics in combination with the dynamic logic of the studied models.The lattice differential equations with delay are proposed to simulate antigen-antibody interaction within hexagonal and rectangular biopixels.Appropriate spatial operators have been used to model the interaction between biopixels similar to the phenomenon of diffusion.The paper presents the results of numerical simulations in the form of phase plane images and lattice images of the probability of antigen to antibody binding in the biopixels of cyberphysical biosensor systems for antibody populations relative to antigen populations.The obtained experimental results make it possible to carry out a comparative analysis of the stability of mathematical models of cyberphysical immunosensory systems on hexagonal and rectangular lattices using lattice differential equations.It is concluded that at a constant delay value ) 25 .0 , 0 [ for the model on the hexagonal lattice and ] 22 .0 , 0 [when using a rectangular lattice, respectively, the solutions of the mathematical models studied tend to non-identical endemic states, which in this case are stable foci.The results of the phase diagrams of antigen populations, antibodies and lattice images of the likelihood of antigen binding to antibodies in the biopixels of cyberphysical biosensor systems conclude that at a constant delay value 25 , 0 (in the case of a hexagonal lattice) and 23 .0 (in the case of a rectangular lattice), Hopf bifurcation occurs and all subsequent trajectories correspond to stable boundary cycles for all pixels.The obtained experimental results make it possible to perform a comparative analysis of the stability of mathematical models of cyberphysical biosensor systems on hexagonal and rectangular lattices using lattice differential equations.
Review on the Improvement and Application of Ant Colony Algorithm

The ant colony optimization algorithm is an approximation algorithm and it is also a probabilistic algorithm for finding optimized paths. Many combinatorial optimization problems have been solved by the ant colony optimization algorithm. Firstly, the basic principles of ant colony algorithm are first introduced by this reference. Secondly, it briefs several improvements method of ant colony algorithm and the application in solving practical problems, including the improvement of ant colony algorithm, parameter combination tuning and the application of ant colony algorithm in combination optimization problem. Finally, the problems existing in the ant colony algorithm are summarized and forecasted in this article.
Can we fight COVID-19 without resorting to mass surveillance?

Governments across the world are doing everything they can to fight the COVID-19 virus. Used correctly, data collected through mobile phones could help monitor the effectiveness of lockdown measures and track contacts of people who have been tested positive. We've had many people reaching out to ask if the data could be collected and used effectively without enabling mass surveillance. We thought we'd share our response
Predictive Vector Quantized Variational AutoEncoder for Spectral Envelope Quantization

The Predictive Vector Quantized Variational AutoEncoder is proposed to improve the reconstruction error of the conventional VQ-VAE. The proposed model can predict the current data from the previous data. The performance of the quantized spectral envelope parameters of the high-quality 48 kHz WORLD vocoder is evaluated. The results indicate that the Predictive Vector Quantized Variational AutoEncoder has a lower distortion with four target bitrates in term of log-spectral distortion, compared with the conventional VQ-VAE.
Comparison of algorithm Support Vector Machine and C4.5 for identification of pests and diseases in chili plants

Abstract Data from the Central Bureau of Statistics of the population working in the agricultural sector continued to decline from 39.22 million in 2013 to 38.97 million in 2014, the number dropped back to 37.75 million in 2015. According to the MIT G-Lab Team (global entrepreneurship program) concludes five factors that make it difficult to raise agricultural productivity to compete in the domestic market, namely the low education of farmers in dealing with pests, the difficulty of access to finance for rural areas, lack of skills, lack of access to information and lack of application of agricultural technology. Chili plants are plants that are very susceptible to pests so BPS noted a decrease in chili production reaching 25%. Information about chili pests is collected so that it becomes a database that can be used to identify disease pests using the data mining method. The use of data mining algorithms is expected to help in the identification of pests and diseases in chili plants. In this study comparing the performance classification techniques of Support Vector Machine (SVM) and C4.5 algorithms. The attributes used consist of Leaves, Stems, and Fruits. By using each training data and testing data as many as 30 data. The results of the study were conducted, based on the accuracy of SVM, which was 82.33% and C4.5 89.29 %%. The final result of this study was that the accuracy of the C4.5 method was better.
A French to English Language Translator Using Recurrent Neural Network with Attention Mechanism

In today's world there are many people who are facing the problem of language translator for ex: talking to a person who only knows a language which you do not understand or you have some information in a language like French and you only Know English, etc. This type of problem can be overcome by a technology called machine translation. This paper proposed machine translation using the recurrent neural network with attention mechanism, where the recurrent neural network (RNN) are types of neural networks designed for capturing information from sequence and time series data. RNN is useful to learn pattern in a given set of data as the human language is one big complex pattern or a complicated pattern. In machine translation, two recurrent neural networks work together for the transformation of one sequence to the other sequence. An encoder network will change an input sequence in a vector and on the other hand the decoder network will change the vector in a new sequence. To improve the above-stated RNN model we will use the attention mechanism which helps to focus on the specific range of the input sequences.
The development of e-training system for government officers Bangkok metropolitan administration, Thailand

The purpose of this research were:
Multilevel Image Thresholding Based on Adaptive Particle Swarm Optimization

In this paper, an adaptive particle swarm optimization (APSO) algorithm is utilized to search the optimal multilevel threshold value using Otsu as its objective function. The PSNR is adopted to evaluate the effect of image segmentation. The simulation results proved that the APSO is suitable for multilevel thresholding image segmentation.
Application of security reference architecture to Big Data ecosystems in an industrial scenario

Summary Big Data environments are typically very complex ecosystems; this means that implementing them is complicated. One possible technique with which to address this complexity is the use of abstraction. Reference architecture (RA) can be useful for an improved understanding of the main components of Big Data. Herein, we propose a security RA that includes the management of security concerns and provides the main elements of a Big Data ecosystem. Application of this architecture to real‐world scenarios facilitates its refinement and improves its usefulness. In this article, we present a case study of a real‐world Big Data ecosystem implemented in a banking environment. This ecosystem was developed by everis, an NTT company with which we collaborated for this study. To conduct this validation case study, a map was established between the elements of the Big Data ecosystem implemented and our proposal. Consequently, a series of valuable lessons that can improve both our architecture and the security of the Big Data environment were obtained. These include recommendations for a set of best practices such as the use of security patterns.
Potential Detection of Lentigo Maligna Melanoma on Solar Lentigines Image Based on Android

Solar Lentigines is a skin disease caused by frequent exposure to direct sunlight. Appearance in solar lentigines can resemble Lentigo Malignant Melanoma cancer at an early stage. solar lentigines a disease that is not dangerous and does not require special treatment, but if there are significant changes such as asymmetrical wounds, obscure borders, non-homogeneous colors, diameters exceeding 6 millimeters, solar lentigines are suspected as lentigo malignant early stage melanoma. lentigo malignant melanoma is a rare but dangerous type of skin cancer if it is not treated immediately with asymmetrical, unclear boundaries, non-homogeneous colors, diameters exceeding 6 millimeters. This research aims to help detect the potential of lentigo malignant melanoma disease by using the image of solar lentigines. This application uses the ABCD method for feature extraction to scratch the input image and decision tree for classification. ABCD method is a medical method used to detect cancer in terms of asymmetry, obscure borders, color, diameter. The data of this research were obtained from one hospital in Bandung and the data was presented in table form and explained informally. The result of the application is a diagnosis of the potential for disease. The accuracy value of this application is 97.5% from 60 training data.