Reviewerly/exHarmony huggingface dataset:
Super Resolution Enhancement of Satellite Remote Sensing Images of Transmission Tower Based on Multi-map Residual Network and Wavelet Transform

Existing satellite remote sensing images are often used to observe the fuzzy phenomenon of transmission line bodies. It is necessary to enhance super-resolution, but traditional superresolution technology is difficult to obtain rich details and edge information of transmission towers. This paper proposes a multiscale edge enhancement method combining multi-map residual convolutional neural network and wavelet transform to solve these problems. Specifically, we first use a multi-map residual convolutional neural network to directly take the low-resolution image as the initial input of the network, and then use a convolutional layer to extract features. Secondly, a multi-mapping network is established by residual learning, and a batch normalization layer is added to optimize the network to enrich the feature information needed for high-resolution image aggregation. Finally, we use deconvolution layers to complete image upsampling and output high-resolution images, so the initial image can directly complete the end-to-end mapping relationship between low-resolution images and high-resolution images without performing preprocessing. On this basis, multiscale edge enhancement is performed on the transmission tower based on wavelet transform to obtain the final super-resolution enhancement result. Experiments on different benchmark data sets show that the proposed method is superior to the existing methods in the four quantitative indicators of peak signal-to-noise ratio, structural similarity, entropy and image detail enhancement.
NOTE-TAKING AS A METHODOLOGICAL TECHNIQUE FOR RATIONALIZATION AND REPRODUCTION OF SPECIALIZED TEXTS IN FOREIGN STUDENTS’ EDUCATION

The paper presents one of the most important moments in foreign language teaching and learning taking into accounts the relevance of the European language policy and the importance of foreign languages in education. Habit formation of taking notes on lectures and specialized subject literature is one of the primary tasks in teaching and training in a technical school. Questions concerning methodologically correct organization of note-taking learning process, increasing the level of listening habits for further successful involvement of foreign language students in the complete learning process, require attention and development of specialized training models, which can support students’ better perception of greater amount of audio materials, seminars, lectures, practical classes, etc. Particular attention should be paid to the deliberate, purposeful and well-organized students’ independent work, which promotes the acquisition of habits. Autonomous learning requires responsible attitude and becomes an effective tool for shaping their professional competence. The author considers some aspects concerning the peculiarities of foreign language teaching to overseas students in a polytechnic university. Described are the stages of developing the skills for note-taking during lectures and during reading reference books on specialized disciplines. The author suggests methodological ideas for solving this language problem. Key words: polytechnic university, обучение на foreign students, methodology for teaching Bulgarian language, note-taking, specialized vocabulary.
Teacher Attendance Monitoring System Teaching with QR-Code and Geo Location using Android Platform

Abstract The world of education has a good level of reputation if it has a professional educator staff and with high integrity, one of the examples is that educators must be hardened on time and not truant to teach students at school. So that the stigma of students and the community does not give a negative value. The role of the principal is also very important to build high integrity, a principal must always monitor every day the teachers who enter the class. But the limitations and the many teaching hours of teachers that must be monitored make the principal difficult because of limited time and other busyness. So the need to design and build an Android platform-based application that is intended to monitor the schedule, and the presence of teachers in real-time called Teacher Attendance Monitoring System Teaching with QR-Code and Geo-Location. SoftwareThe software is intended to make it easier for principals to monitor teachers who teach in class in realtime. Black box testing results show 100% for functional testing of systems and for testing the application interface has a 100% success rate. Then the software is feasible to use.
Robust Graph Regularized Non-negative Matrix Factorization for Image Clustering

Non-negative matrix factorization and its variants have been utilized for computer vision and machine learning, however, they fail to achieve robust factorization when the dataset is corrupted by outliers and noise. In this paper, we propose a roust graph regularized non-negative matrix factorization method (RGRNMF) for image clustering. To improve the clustering effect on the image dataset contaminated by outliers and noise, we propose a weighted constraint on the noise matrix and impose manifold learning into the low-dimensional representation. Experimental results demonstrate that RGRNMF can achieve better clustering performances on the face dataset corrupted by Salt and Pepper noise and Contiguous Occlusion.
Detection of Malaria Parasites in Thin Blood Smears Using CNN-Based Approach

Mukherjee, Sabyasachi Chatterjee, Srinjoy Bandyopadhyay, Oishila Biswas, ArindamEarly and accurate detection of malaria is the main key in controlling and eradicating this deadly disease. An efficient automated tool for examining stained blood slides can be very helpful in this regard. This paper proposes an approach for automatic detection of malarial parasites from thin blood smears. Texture- and intensity-based analyses are performed to detect the blood particles in the pre-processed image. Extracted red blood cell particles are then sent to a convolutional neural network for further probing. The proposed CNN architecture is trained with a publicly available dataset along with some Giemsa-stained blood smear images collected from a hospital. The performance of malaria detection process gives a satisfactory dice score of 0.95.
A Highly Parallel Constant-Time Almost-Inverse Algorithm

In this paper, we present a highly parallel and area-efficient constant-time inversion algorithm over the r-th degree polynomial ring, derived from Schroeppel's Almost Inverse algorithm. We propose a first constant time version, from which we derive a highly-parallel and a faster algorithm, while still preserving the constant-time property. This constitutes an alternative and relatively unexplored approach to inversion, compared to the more common multiplicative approach by Itoh and Tsuji, and has extensive application in algorithms such as the BIKE proposal for quantumresistant cryptography. Our approach is extremely area-efficient, with a constant area with respect to the polynomial degree r.
Experiment Research on Mobile Terminal Image Scene Recognition Based on optimization

Convolutional neural network (CNN), the network structure of weight sharing, which significantly reduces the complexity of the model and the number of weights, is a research hotspot in the field of speech analysis and image recognition. However, in most cases, insufficient data or small classification differences will result in poor training results: translation invariance and pooling layer. In this paper, the Cross-Entropy overfitting and sample unbalance problems were adjusted by parameter equalization, and the accuracy increased from 51.5% to 87.3%. At the same time, it is verified that in the same datasets, the identification error of the simplified model on the mobile is less than 2%.
Quantum-Inspired Algorithm with Evolution Strategy

Quantum-inspired algorithms are efficient for solving global search optimization problems. Nevertheless, their application is limited by two main requirements: a knowledge of a cost function and a big computational effort. To address both limitations, this paper presents a global optimization algorithm mixing a Quantum Diffusion Monte Carlo (DMC) method and an Evolution Strategy (ES). The proposed approach takes advantage of quantum models for efficiently finding solutions close the global optimum by applying the DMC. However, the DMC needs to be adapted to an optimization process. In order to improve relatively slow convergence rate of the DMC, we integrate a step size control for a diffusion displacement process of the DMC. Then, an (1+1)-ES is applied to find the minimum value of the cost function. Experimentation shows that the proposed approach can find global minima with fewer objective function evaluations than Quantum Particle Swarm Optimization and Quantum Annealing. Thus, it seems to be promising for solving black-box problems, where a reliable trade-off between exploration and exploitation is required.
Research on the Sharing Mode of Digital Party School Information and Data Resources

This paper focuses on the grid of digital Party school data resources to get a grid platform suitable for mining. At the same time, we design a data mining algorithm suitable for the digital Party school platform. With the help of the grid model of resource scheduling, the mining model is established. According to the characteristics of digital Party school resources, the service-based distributed data mining method is used to merge and prune the original decision tree, and the decision tree algorithm is optimized from attribute reduction and pruning. Then the optimized decision tree algorithm is applied to the digital Party school information resources to verify the effectiveness of the algorithm. It is proved that the method is helpful to form an effective and reasonable digital information resource system with the characteristics of the Party school, to realize the mutual benefit among the party schools at all levels of the Party school system, and to provide full play to the value of the information resource itself.
Improving the robustness of LSTMs for word classification using stressed word endings in dual-state word-beam search

In recent years, long short-term memory neural networks (LSTMs) followed by a connectionist temporal classification (CTC) have shown strength in solving handwritten text recognition problems. Such networks can handle not only sequence variability but also geometric variation by using a convolutional front end, at the input side. Although different approaches have been introduced for decoding activations in the CTC output layer, only limited consideration is given to the use of proper label-coding schemes. In this paper, we use a limited-size ensemble of end-to-end convolutional LSTM Neural Networks to evaluate four label-coding schemes. Additionally, we evaluate two CTC search techniques: Best-path search vs dual-state word-beam search (DSWBS). The classifiers in the ensemble have comparable architectures but variable numbers of hidden units. We tested the coding and search approaches on three datasets: A standard benchmark IAM dataset (English) and two more difficult historical handwritten datasets (diaries and field notes, highly multilingual). Results show that stressing the word endings in the label-coding scheme yields a higher performance, especially for DSWBS. However, stressing the start-of-word shapes with a token appears to be disadvantageous.
Auxiliary Attribute Aided Few-shot Representation Learning for Gun Image Retrieval

Few-shot representation learning is one of the most challenging tasks in machine learning research field. The related applications including gun image retrieval usually achieve limited performance due to the lack of learning samples. In this paper, We propose a flexible and conceptually straightforward framework for few-shot gun image retrieval. We use ResNet as backbone network and design a hierarchical loss system based on auxiliary attributes extracted from different layers. Enhanced by a series of auxiliary attributes, discriminative features are learned efficiently. Experiments on a gun image dataset demonstrate the effectiveness of the proposed approach. In addition, it is worth noting that our framework can be easily extended to other few-shot learning tasks.
Intelligent Test Paper Generation Based on Dynamic Programming Algorithm

Abstract This paper describes the problem of intelligent paper grouping and its mathematical model. By optimizing and improving the traditional dynamic programming algorithm, its space complexity is reduced from O(nb) to O(b). At the same time, the flexibility of dynamic programming algorithm is increased by using marker function and tracking algorithm, and the result composition is tracked to obtain the optimal solution. Finally, through several experiments, the improved dynamic programming algorithm is compared with the greedy algorithm and brute force algorithm, and it is found that the improved dynamic programming algorithm has a very good result and is with high efficiency when applied to the simple test paper. It is the most recommended algorithm among the two algorithms compared in this paper.
Research On Path Planning Of Hull Decontamination Robot Based On Q-Learning

Aiming at the problems of monitoring the running environment of the underwater decontamination robot and receiving data in real time, it is inevitable to develop a human-computer interaction interface with remote monitoring function. The design addresses the problem of remote monitoring of hull decontamination robots. Taking STM32 microcontroller as the control core, and the operation monitoring interface written in python language is used to monitor the working environment and robot posture of the decontamination robot in real time. The remote operation robot has the characteristics of simple structure, convenient operation and intuitive display effect. In addition, it studies the path planning of a hull decontamination robot in a multi-obstacle environment, uses the Q-Learning algorithm to learn the collision avoidance behavior of the robot, and conducts simulation experiments.
An Improved Online Testing Technique For Reversible Circuits

The emerging technology of reversible circuits offers a potential solution to the synthesis of ultra low-power quantum computing systems. A reversible circuit can be envisaged as a cascade of reversible gates only, such as Toffoli gate, which has two components: k control bits and a target bit (k-CNOT), k ≥1. While analyzing testability issues in a reversible circuit, the missing-gate fault model is often used for modeling physical defects in k-CNOT gates. In this paper, we propose online design-for-testability (DFT) technique. The proposed method is an improved version of an earlier work by Kole et. al. Our method yields less overhead in terms of quantum cost as compared to the original approach. The method is advantageous for circuits where consecutive gates occur frequently with the same set of controls.
Mining Portuguese Comparative Sentences in Online Reviews

The constant expansion of e-commerce, recently boosted due to the coronavirus pandemic, has led to a huge increase in online shopping. More and more, customers demand online reviews of products and comments on the Web to make decisions about buying a product over another. In this context, sentiment analysis techniques constitute the traditional way to summarize user's opinions that criticizes or highlights the positive aspects of a product. Sentiment analysis of reviews usually relies on extracting positive and negative aspects of products, neglecting comparative opinions. Such opinions do not directly express a positive or negative view but contrast aspects of products from different competitors. In this paper, we present the first effort towards detecting comparative sentences in Portuguese. Identifying comparative sentences is a key task for companies to know how users are comparing a product with their competitors and is essential for developing sentiment summarization applications for the end user. In addition, we present a supervised approach to automatically detect Portuguese comparative sentences, classifying them into five distinct groups: (1) Non-Comparative, (2) Non-Equal Gradable, (3) Equative, (4) Superlative e (5) Non-Gradable. To that end, this paper provides three main contributions: (1) a Portuguese lexicon list with words used to make comparisons; (2) two new Portuguese datasets with comparative sentences; and (3) a hierarchical approach for detecting multiple comparisons and classify the sentences in different groups by using state-of-art classification algorithms, reaching an accuracy of 87%.
Bridging Art and Design teaching generations

This study describes the design and development of an online educational platform with the aim of sharing the legacy of individual knowledge from retired teachers and researchers in Art and Design. This work is an integral part of the "Wisdom Transfer" project which aims to rescue and protect the inheritance of knowledge and educational experience provided by this generation of disappearing scholars. This group of art and design teachers and researchers is responsible for the genesis of Higher Education Design teaching, in Northern Portugal. In this regard, the platform designed in this study aims to serve as a privileged environment for sharing knowledge with younger generations, namely Design students. It is intended, through the design of an infographic interface, to convert abundant and complex information into a clear and engaging visual communication system for a younger audience, privileging access to knowledge through interactive digital media. This study led to the compilation and sharing of information gathered from the collected experience of 30 academics on the online platform.
Authentication and Key Agreement Based Binary Tree for D2D Group Communication

Emerging device-to-device (D2D) communication in 5th generation (5G) mobile communication networks and internet of things (loTs) provides many benefits in improving network capabilities such as energy consumption, communication delay and spectrum efficiency. D2D group communication has the potential for improving group-based services including group games and group discussions. Providing security in D2D group communication is the main challenge to make their wide usage possible. Nevertheless, the issue of security and privacy of D2D group communication has been less addressed in recent research work. In this paper, we propose an authentication and key agreement tree group-based (AKATGB) protocol to realize a secure and anonymous D2D group communication. In our protocol, a group of D2D users are first organized in a tree structure, authenticating each other without disclosing their identities and without any privacy violation. Then, D2D users negotiate to set a common group key for establishing a secure communication among themselves. Security analysis and performance evaluation of the proposed protocol show that it is effective and secure.
중학생 청소년의 인터넷활용실태에 대한 연구

The main purpose of this paper is to propose the direction to middle school student in order to improve their ability in utilizing the information from the computer and research on the actual condition is based on the Question and Answer toward the middle school students in the Bondang Area. In terms of education purpose of using computer, students usually surf the web to find the information to accomplish their study and assignment. As to educational effect, we can not deny that the internet is utilized as a tool to get useful information but it is still skeptic if we can expect the other effect such as improvement of scholastic performance, development of thinking power, increased ability to face and solve problem and increase self-control ability.
Towards a Crypto Asset Taxonomy

There are over 1900 cryptocurrencies trading in the market as of September 2020 and the number is rapidly growing. In the current crypto scene, cryptocurrencies are seen as investment vehicles by many, yet every crypto asset is designed to operate in a specific sector within a pre-defined business model. In addition to sector, there are various characteristics and factors that differentiate one crypto asset from another. Crypto investors can leverage these factors and characteristics and use these indicators to create different trading strategies. In our work, in order to guide the decision-making process for investors and to help them analyse crypto assets in a holistic manner, we classify the crypto assets under various characteristics, aiming towards a crypto asset taxonomy. In this paper, we focus on automated annotation of the cryptocurrencies in terms of sector, transaction anonymity and asset type through the public information. Though the information we utilise is public, it is scattered around quite a vast number of sources in different formats. Therefore, we generated an annotated dataset by collecting information from various sources. We utilised several supervised learning algorithms, including both traditional ones and more recent neural models, and analyzed the classification performance for the three aspects.
A New image encryption technology implementing a big random S-Box

This paper traces the implementation of a new chaos-based color image encryption system using three different substitution matrices as a profound improvement of Vigenère's classical method. After vectorizing the clear image and computing an initialization value to modify the value of the first pixel and start the encryption process, each pixel is successively confused by the three S-boxes generated from the chaotic maps used, a chaining is established between the encrypted pixel and the next clear pixel, to increase the impact of the avalanche effect and to protect the system from known differential attacks. Simulations performed by our algorithm on a large database of images of different sizes and formats show that our approach is immune to known attacks.
Autoencoder-based Image Companding

This paper presents a deep learning-based method for effective image companding. The autoencoder inherits the effectiveness of Convolutional Neural Networks (CNN) and residual learning framework to transform High Dynamic Range (HDR) images to Low Dynamic Range (LDR) and its reverse process. Since, the image companding task involves the nondifferentiable operation, thus the encoder and decoder networks are alternately trained using an iterative approach. The experimental result clearly reveals that the proposed method yields a promising result.
Identification of Opinion Makers on Twitter

Galeshchuk, Svitlana Qiu, JuTwitter is a social platform that helps share ideas quickly and concisely. Although the network offers equal rights to post short texts, the attention these messages attract frequently depends on a user’s status in the real world. Thus the tweets of real life high-profile opinion makers will a priori have a higher probability of spurring the interest of society than the messages from the so-called grassroots. The paper elaborates on the developed classifier that detects automatically such opinion makers on Twitter. The approach exploits the Mixed Effect Random Forests method combined with the features engineered from the Twitter data. The accuracy and the sensitivity of the proposed technique outperform the results of the other machine learning classifiers on the out-of-sample data.