Reviewerly/exHarmony huggingface dataset:
Versatile CAPTCHA Generation Using Machine Learning and Image Processing

Due to the significant increase in the size of the internet and the number of users on this platform there has been a tremendous increase in load on various websites and web-based applications. This load is from the user end which causes unforeseen conditions which leads to unacceptable consequences such as crash or a data loss scenario at the webserver end. Therefore, there is a need to reduce the load on the server as well as the chances of network attacks that increase with the increased user base. The undue consequences such as data loss and server crash are caused due to two main reasons: the first one being an overload of users and the second due to an increased number of automatic programs or robots. A technique can be utilized to overcome this scenario by introducing a delay in the operation speed on the user end through the use of a CAPTCHA mechanism. Most of the classical approaches use a single method for the generation of the CAPTCHA, to overcome this proposed model uses the versatile image CAPTCHA generation mechanism. We have introduced a system that utilizes manualbased, face detection-based, colour based and random object insertion technique to generate 4 different random types of CAPTCHA. The proposed methodology implements a region of interest and convolutional neural networks to achieve the generation of the CAPTCHA effectively.
The $[46, 9, 20]_2$ code is unique

<p style='text-indent:20px;'>The minimum distance of all binary linear codes with dimension at most eight is known. The smallest open case for dimension nine is length <inline-formula><tex-math id="M1">\begin{document}$ n = 46 $\end{document}</tex-math></inline-formula> with known bounds <inline-formula><tex-math id="M2">\begin{document}$ 19\le d\le 20 $\end{document}</tex-math></inline-formula>. Here we present a <inline-formula><tex-math id="M3">\begin{document}$ [46,9,20]_2 $\end{document}</tex-math></inline-formula> code and show its uniqueness. Interestingly enough, this unique optimal code is asymmetric, i.e., it has a trivial automorphism group. Additionally, we show the non-existence of <inline-formula><tex-math id="M4">\begin{document}$ [47,10,20]_2 $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M5">\begin{document}$ [85,9,40]_2 $\end{document}</tex-math></inline-formula> codes.
Exploration on the Generation of Chinese Palindrome Poetry

Recently, Chinese poetry generation gains many significant achievement with the development of deep learning. However, existing methods can not generate Chinese palindrome poetry. Besides, there is no public dataset of Chinese palindrome poetry. In this paper, we propose a novel Chinese palindrome poetry generation model, named Chinese Palindrome Poetry Generation Model (CPPGM), based on the universal seq2seq model and language model with specific beam search algorithms. In addition, the proposed model is the first to generate Chinese palindrome poetry automatically, and is applicable to other palindromes, such as palindrome couplets. Compared with several methods we propose, the experimental results demonstrate the superiority of CPPGM with machine evaluation as well as human judgment.
Channel-Wise Part-Based Weighting Aggregation for Image Retrieval

The part-based weighting aggregation (PWA) method is a simple and straightforward way for creating powerful image representations in image retrieval. In this paper we analyze the burstiness among the responses of the aggregated channels in the PWA method, and propose a channel-wise weights utilizing the sum-pooling aggregation of the deep features' responses to regulate the burstiness among the feature maps. Firstly, we apply the channel weights to the aggregation stage of PWA. The regulation of the burstiness among the aggregated channels boosts the PWA performance. Secondly, we use the channel weights to the part selection stage, namely, selecting the part detectors based on the channel weighted feature maps to obtain more discriminate parts to further improve the PWA performance. Comprehensive experiments on four standard datasets have demonstrated the effects of the proposed method.
WITHDRAWN: Blockchain financial development based on FPGA and Convolutional Neural Network

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
Word-Level Error Correction in Non-autoregressive Neural Machine Translation

Non-Autoregressive neural machine translation (NAT) not only achieves rapid training but also actualizes fast decoding. However, the implementation of parallel decoding is at the expense of quality. Due to the increase of speed, the dependence on the context of the target side is discarded which resulting in the loss of the translation contextual position perception ability. In this paper, we improve the model by adding capsule network layers to extract positional information more effectively and comprehensively, that is, relying on vector neurons to compensate for the defects of traditional scalar neurons to store the position information of a single segment. Besides, word-level error correction on the output of NAT model is used to optimize generated translation. Experiments show that our model is superior to the previous model, with a BLEU score of 26.12 on the WMT2014 En-De task and a BLEU score of 31.93 on the WMT16 Ro-En, and the speed is even more than six times faster than the autoregressive model.
A Comparative Study on the Development Status of Environmental Education in the United States, the United Kingdom, and Australia

Abstract With the rapid development of the world economy, the global environment and development problems faced by mankind have become increasingly prominent. At the expense of the environment, people have created unprecedented material wealth and promoted the rapid development of the world’s material civilization. This leads to environmental deterioration, resource consumption, and serious ecological destruction. The realization of environmentally sustainable development has become a major issue of common concern for all mankind. Environmental education is a comprehensive lifelong education with the relationship between humans and the environment as the core. According to agenda 21, “education is the decisive factor in implementing sustainable development strategies and empowering people to solve environmental and development problems. Educational behavior plays an important role in sustainable development. Its main purpose is to train people to consciously protect the environment and care for the environment by using education. Environmental protection is an important part of sustainable development, and environmental education is the decisive factor to realize environmental protection. It arouses citizens’ awareness of environmental protection, improves the quality of the whole people’s environmental protection, and finally realizes sustainable development. his paper makes a comparative study on the development status of environmental education in the United States, the United Kingdom, and Australia. By analyzing and discussing the current problems of environmental education, this paper tries to conclude the far-reaching significance of the future development of environmental education.
Update-Based Machine Learning Classification of Hierarchical Symbols in a Slowly Varying Two-Way Relay Channel

This paper presents a stochastic inference problem suited to a classification approach in a time-varying observation model with continuous-valued unknown parameterization. The utilization of an artificial neural network (ANN)-based classifier is considered, and the concept of a training process via the backpropagation algorithm is used. The main objective is the minimization of resources required for the training of the classifier in the parametric observation model. To reach this, it is proposed that the weights of the ANN classifier vary continuously with the change of the observation model parameters. This behavior is then used in an update-based backpropagation algorithm. This proposed idea is demonstrated on several procedures, which re-use previously trained weights as prior information when updating the classifier after a channel phase change. This approach successfully saves resources needed for re-training the ANN. The new approach is verified via a simulation on an example communication system with the two-way relay slowly fading channel.
Full Explicit Consistency Constraints in Uncalibrated Multiple Homography Estimation

We reveal a complete set of constraints that need to be imposed on a set of 3-by-3 matrices to ensure that the matrices represent genuine homographies associated with multiple planes between two views. We also show how to exploit the constraints to obtain more accurate estimates of homography matrices between two views. Our study resolves a long-standing research question and provides a fresh perspective and a more in-depth understanding of the multiple homography estimation task.
Graph-Based Locality-Sensitive Circuit Sketch Recognizer

The understanding of circuit diagram is very important for the study of electrical engineering. Existing circuit diagram simulation tools are mostly based on GUI interface and rely on users to click or drag icons with mouse, which requires them to be familiar with the software and distracts a great deal of their attention from the circuit diagram itself. Although a lot of previous works have devoted to designing algorithmic solution to recognize hand-drawn circuit diagrams automatically, there still exists strict constraints on users' drawing habits and stroke orders. In order to address these inconveniences, this paper proposes a novel sketch recognition algorithm named LS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> D. It uses graph to model the input strokes and their relationships, and leverages cycles by local strokes to detect some circuit components. Theoretical derivations have demonstrated that LS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> D can efficiently recognize diagrams with different drawing styles and arbitrary stroke orders. To furthermore illustrate the practical value of the proposed approach, we construct a prototype of pen-based circuit diagram system based on LS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> D , which enables users to draw circuit diagrams directly on the digital screen without any other restriction. An experiment of 158 samples collected from 17 users is conducted on the designed platform. Our approach has achieved 93.04% recognition accuracy and overall 4.53 from a 5-scale user satisfaction rating, which outperforms previous state-of-the-art methods numerically. It is shown that the same approach can also be generalized to many other sketch recognition applications with minor modifications. To facilitate future researches and applications, we publish our source code, model, and training data at https://github.com/Huage001/ Graph-Based-Circuit-Painter.
Efficient Electronic Document Access Control Management using Natural Language Processing

Electronic Document Access Control and Management has been challenging as large scale organizations and various governments are transitioning towards an electronic media platform. The electronic medium includes the majority of the electronic documents that are utilized for their ease in sharing and storage. The increasing number of documents that are being stored are becoming highly difficult to manually store and manage. The risk of sensitive documents being accessed by an unauthorized person is also remaining as a looming threat that needs to be eliminated. There has been a considerable amount of research has been performed for enabling an effective Document Management System. But most of them fall short of the expectations due to their inability to accurately perform automatic access control. Therefore, this paper outlines an effective and secure technique for the maintenance of an access control mechanism through a machine learning approach. The presented technique implements Term Frequency - Inverse Document Frequency (TF-IDF), Entropy Evaluation along with the Decision tree to achieve an accurate and reliable Document Management System. The automatic electronic document access control relieves the manual calibration of the Document Management system significantly. The proposed research experiment demonstrates a comparison with the traditional document management system, where the proposed EDACM system yields a Root Mean Square Error score of 1.48, which is significantly less and achieved an accuracy of 85.71%, denotes a better fit model. Thus, the proposed approach exclusively boosts the performance of Document Access Control Management.
How image capturing setups influence the quality of SfM reconstructions for wind turbine blade inspection

Increased leading edge roughness (LER) is one of the main causes for wind turbine blade performance degradation. To ensure a consistently high energy output, the surface erosion of wind turbine blades, needs to be monitored regularly, so preventive measurements can be done. Capturing 3D surface data is becoming a more and more popular way to extract and quantify roughness on a micro level. In this paper we want to test the possibility of using Structure from Motion (SfM) 3D reconstructions for extracting surface roughness information from wind turbine blades. We test various capturing scenarios with varying horizontal and vertical image overlap, as well as varying distances to the blade, using a real blade in outdoor conditions. We analyze the quality of the reconstructions and provide a benchmark, as well as guidelines to what are the best possible capturing conditions for ensuring a high quality and noise free 3D surface results. All data from the experiments is made publicly accessible.
Constrained Multiobjective Optimization for the Design of Energy-Efficient Context Recognition Systems

Context recognition (CR) systems infer the user’s context, such as their physical activity, from sensor data obtained, for example, with smartphone sensors. Designing an energy-efficient CR system, however, is a complex optimization problem involving conflicting objectives and several constraints arising from real-world limitations and designers’ preferences. To address this task, we propose a constrained multiobjective formulation of the CR design problem. Unlike most studies in this domain, we use a true multiobjective approach in solving it. Specifically, we apply a multiobjective evolutionary algorithm equipped with two different constraint handling techniques. Their performance is demonstrated in optimizing six CR systems of various complexity. The proposed problem formulation and the optimization results make it possible to better understand the CR systems operation and provide valuable information to the designers.
Research on the Robot Uncalibrated Visual Servo Method Based on the Kalman Filter with Optimized Parameters

Considering the complex calibration of the robot visual servo system, this paper proposed an uncalibrated visual servo method, which is based on the Kalman filter optimized by the simultaneous perturbation stochastic approximation algorithm. In the proposed method, the Kalman filter is applied to the identification of the robot image Jacobi matrix, and the initial values of filter parameters are optimized with the simultaneous perturbation stochastic approximation algorithm. Estimating the image Jacobi matrix and designing the control rate of robots with this method can avoid the complex calibration process. The simulation experiment on the visual servo system of eye-in-hand 6-DOF robots shows that the proposed method can realize the localization in the visual servo system of full-DOF robots accurately without calibrating the camera parameters and the coordinate relations of robots.
A Particle Swarm Optimization Based Joint Geometrical and Statistical Alignment Approach with Laplacian Regularization

Transfer Learning or Domain Adaptation is an emerging sub-field of Machine learning in which the source domain carrying ample amount of labeled data is employed to classify a diverse but inter-related target domain data. However, in primitive machine learning algorithms, there is a pre-assumption that training and test data belong to the same set of distributions. But in a real-world scenario, the source (or training data), as well as the target domain (or test data), has a diverse distribution of data. Also, the source domain is equipped with the labeled data information while the target domain has a scarcity of labeled data or has completely unlabelled data information. Although the existing transfer learning algorithms like Joint Geometrical and Statistical Alignment (JGSA) takes into account several objectives to reduce the geometrical shift and distribution shift simultaneously, still they lack in fulfilling objectives like Laplacian regularization and degenerate feature elimination. So, we proposed a novel approach called Particle Swarm Optimization based Joint Geometrical and Statistical Alignment approach with Laplacian regularization (PSO-JGSAL) which incorporates new objective with JGSA and also utilizes the PSO approach for selection of an optimum set of features for classification purposes. Rigorous experiments have been done on Office-Caltech datasets (with SURF Features and Decaf Features) and our proposed PSO-JGSAL shows promising results as compared to already existing primitive and domain adaptation methods.
The Optical Method of Multi-angle Phase Triangulation for Measuring the Three-dimensional Geometry of Convex and Extended Object

Abstract The paper presents a multi-angle phase triangulation method for measuring the three-dimensional geometry of convex and extended objects. The method is based on the use of two photodetectors and a light source. Measurements are performed based on the analysis of a stereo pair images obtained from photodetectors. Structured illumination is used as the analyzed texture. The proposed method improves the accuracy of measurements and makes it possible to measure the full three-dimensional geometry of convex and extended objects in an optical manner based on the triangulation principle and structured lighting.
Application of machine learning to improving numerical weather prediction

In this paper are presented a brief overview of trends in numerical weather prediction, difficulties and the nature of their occurrence, the existing and perspective ways to overcome them. The neural network architecture is proposed as a promising approach to increase the accuracy of the 2m temperature forecast by COSMO regional model. This architecture allows predicting errors of the atmospheric model forecasts with their further corrections. Experiments were conducted with different prehistories of regional model errors. The number of epochs was determined after which the increase of the so-called retraining of the network had place. It is shown that the proposed architecture makes it possible to achieve an improvement of 2m temperature forecast in approximately 50 % of cases.
Combining Filter Bank and KSH for Image Retrieval in Bone Scintigraphy

Bone scintigraphy is widely used to diagnose bone tumor and metastasis. Accurate bone scan image retrieval is of great importance for tumor metastasis diagnosis. In this paper, we propose a framework to retrieve images by integrating the techniques of texture feature extraction and supervised hashing with kernels (KSH). We first use a filter bank to extract the texture features. Then KSH is used to train a set of hashing functions with constructed features, which can convert images to hashing codes. We can obtain the most similar retrieval images by comparing Hamming distance of these hashing codes. We evaluate the proposed framework quantitatively on the testing dataset and compare it with other methods.
Towards Continuous User Authentication Using Personalised Touch-Based Behaviour

In this paper, we present an empirical evaluation of 30 features used in touch-based continuous authentication. It is essential to identify the most significant features for each user, as behaviour is different amongst humans. Thus, a fixed feature set cannot be applied to all models. We highlight this importance by selecting features accordingly using our approach, seeking to individually select and empirically test the discriminative power of a range of features as well as feature interaction in the context of individual users. We test five different feature selection techniques: Mutual Information, Sequential Forward Selection, Sequential Floating Forward Selection, Sequential Backwards Selection, and Sequential Floating Backwards Selection. Our results show that a unique set of features can be selected for each user, while increasing or maintaining performance, i.e. up to 27 out of 30 features were removed for one user without affecting performance. We also show that distinctive features should be evaluated on a user basis, as particular features may be significant for some, while redundant for others. Moreover, for each user, the same features are selected for horizontal and vertical strokes while performance persists when using a horizontal model to predict vertical behaviour and vice versa.
Towards High-efficient QoT Prediction of Wide-area Optical Backbone Network: A Reservoir Computing View

Wide-area optical backbone network provides crucial support for the fifth-generation (5G) development. Precise quality of transmission (QoT) prediction is conducive to assisting in the automatic low-margin network configurations to meet the demands of the 5G network, such as high reliability, high efficiency, and low latency. At present, machine learning has been developed to capture the valuable information hidden in the QoT data, whereas it is still a challenging problem for these existing models to simultaneously ensure prediction accuracy and training efficiency. To address this issue, a novel QoT prediction method based on reservoir computing is proposed, considering echo state network and its variants. The basic idea is that nonlinearity can be dealt by means of linear regression in the high-dimension reservoir state space. On real-world QoT time series benchmark, the experimental results show that our proposals significantly outperform the state-of-the-art models for single-channel QoT prediction. Besides, their superiorities are reconfirmed by statistical analysis.