Reviewerly/exHarmony huggingface dataset:
Extending the GLS endomorphism to speed up GHS Weil descent using Magma

Let q=2n, and let E/Fqℓ be a generalized Galbraith–Lin–Scott (GLS) binary curve, with ℓ≥2 and (ℓ,n)=1. We show that the GLS endomorphism on E/Fqℓ induces an efficient endomorphism on the Jacobian JacH(Fq) of the genus-g hyperelliptic curve H corresponding to the image of the GHS Weil-descent attack applied to E/Fqℓ, and that this endomorphism yields a factor-n speedup when using standard index-calculus procedures for solving the Discrete Logarithm Problem (DLP) on JacH(Fq). Our analysis is backed up by the explicit computation of a discrete logarithm defined on a prime-order subgroup of a GLS elliptic curve over the field F25⋅31. A Magma implementation of our algorithm finds the aforementioned discrete logarithm in about 1,035 CPU-days.
A Parallel Backbone Networks Structure for Scene Text Detection

Abstract Text detection in complex scenes is very hard realize by the diversification of text distribution, direction, and typesetting. This paper proposes one scene text detection method with end-to-end structure with parallel backbone network and region segmentation. With multiple deformable convolutions and extracting features of multi-dimensional text regions, multiple candidate regions of different sizes are generated and corresponding states are further given. Experiments show that compared with baseline, this method can further adapt to the problem that the different shapes and angles of the target in the image lead to the decrease of accuracy.
Campus Bullying Detection Algorithm Based on Surveillance Camera Image

In recent years, the phenomenon of campus violence has gradually come into people’s attention. Detection of campus bullying based on surveillance camera image has become a research hotspot. This paper designs a campus violence detection algorithm with a 3D convolutional neural network. The detection process includes three parts: video image preprocessing, feature extraction, and classification algorithm design. 80% of the samples in the video database are used to train the classification model, 20% of the samples are used as the testing set, and a fivefold cross-validation method is used to evaluate the performance of the classification algorithm. The simulation results show that the average classification accuracy of the proposed algorithm is 95.02%, indicating that the violence detection algorithm has good performance.
Dense Residual Network for Multispectral Image Compression

Considering that multispectral images have a mass of complex features, to extract intact feature data, a compression framework for multispectral images based on dense residual network (DRN) is proposed in this paper. The multispectral images are first fed into the encoder, and residual dense block in it can read all the information learned from the preceding block via a contiguous memory mechanism, then preserves the features adaptively. Then the data is compressed by down-sampling and converted to bit stream by quantization and entropy encoding. With that, global dense features can be extracted completely from the original image. Additionally, rate distortion optimization is used to make the data more compact. Then, we reconstruct the image via entropy decoding, inverse quantization, up-sampling, and deconvolution. The experimental result shows that the proposed method outperforms JPEG2000 at the same bit rate.
Influence of Data Pre-Processing on Genetic Algorithm Based Clustering

Clustering technique is used to partition or group a given dataset or pattern into disjoint clusters. In this paper, for data clustering, k-medians based clustering algorithm along with a Genetic Algorithm is used to cluster the data points. In distance-based partitioning, a major problem is that features with higher ranges have more influence on the distance measure resulting in poor clustering. To overcome this problem, in this paper, we have pre-processed the dataset using min-max scaling to bring all features into the same range. This has helped to achieve the expected clustering accuracy as the dataset has been smoothed. We have worked with Iris and Wine datasets. We have observed that clustering accuracy of our proposed method is improved by 1.124% for Iris dataset and 38.043% for Wine dataset compared to the clustering accuracy of the modified k-means method published earlier.
Sharing geotagged pictures for an Emotion-based Recommender System

Recommender systems are prominently used for movie or app recommendation or in e-commerce by considering profiles, past preferences and increasingly also further personalized measures. We designed and implemented an emotion-based recommender system for city visitors that takes into account user emotion and user location for the recommendation process. We conducted a comparative study between the emotion-based recommender system and recommender systems based on traditional measures. Our evaluation study involved 28 participators and the experiments showed that the emotion-based recommender system increased the average rating of the recommendation by almost 19%. We conclude that the use of emotion can significantly improve the results and especially their level of personalization.
Research on the Application of Foreign Literature Based on Computer Retrieval

Abstract With the popularization of the utilization of foreign literature in China, the relevant literature researchers and readers put forward higher requirements for the utilization of literature, especially strengthen the research and analysis of the role of foreign literature corpus. Based on this, this paper first analyses the concept and connotation of computer retrieval, then studies the concept and connotation of computer retrieval, and finally gives the utilization of foreign literature based on computer retrieval.
Crisis of «digital transformation» of Russian education

Thirty years of reforms in secondary and higher education in Russia have shown the fallacy of the course taken. This course was aimed at training workers for one of the «raw material donors» of the modern world and a passive incompetent population. Recent innovations are the digital economy, the introduction of artificial intelligence, and the digital transformation of the Russian education covid19 pandemic, which is ongoing, and has demonstrated the failure of these plans. The revival of Russia should begin with futureoriented reforms. Many of them are directly related to education. The directions of some of them are discussed in the report.
Temporal Multi-Agent’s Logic, Knowledge, Uncertainty, and Plausibility

The paper studies temporal logic implementing multi-agent's approach and formalizing knowledge and uncertainty. We consider non-transitive linear time and multi-valued models—the ones using separate valuations \(V_j\) for the agent's knowledge of facts and summarized (agreed) valuation together with rules for computation truth values for compound formulas. The basic mathematical problems we study here are decidability and decidability w.r.t. admissible rules. First, we study the general case—the logic with non-uniform intransitivity—and solve its decidability problem. Also, we consider a modification of this logic—temporal logic with uniform non-transitivity—and solve the problem of recognizing admissibility in this logic. The conclusion contains a discussion and a list of open problems.
OVERVIEW OF FRAMEWORKS FOR WORKING WITH BLENDER FILES, CREATING WEBGL OBJECTS

The aim of the study is to compare the best libraries to use for exporting models from a graphics editor and create WebGL objects from them, defining principles of their use and the distinctive features of each. For this, the following empirical methods were used: observing the development process using the studied tools, comparing the complexity of algorithms and describing the features of each tool. The article discusses options for placing ready-made 3D models created using the Blender graphics editor on a website and the possibility of subsequent work with them. The relevance of using the following libraries is analyzed: Blend4Web, Three.js. The use of a relatively new engine Verge3D,which works on these libraries, is alsodescribed, replacing all the programmer’s actions with an interface that is understandable for a 3D artist.
DEVELOPMENT OF DECISION SUPPORT INFORMATION SYSTEM FOR POST-EARTHQUAKE FUNCTIONALITY OF RC BUILDING

We developed an information gathering and judgment system that supports building facility managers who do not specialize in architectural structural mechanics to decide continuous use of damaged buildings immediately after a major earthquake. In the development system, it is necessary for the structural engineer to set up major structural members for confirming the damage situation beforehand. After the earthquake, the facility managers register the visual confirmation result of the structural members, thereby presenting the information of building continuous use. We conducted experiment for verification of the system for building facility managers and extracted improvements and considerations for the system.
Ddper: Decentralized Distributed Prioritized Experience Replay

In off-policy reinforcement learning, prioritized experience replay plays an important role. However, the centralized prioritized experience replay becomes the bottleneck for efficient training. We propose to approximate the centralized prioritized experience replay in a distributed and decentralized way under certain mild assumptions. To be specific, each actor stores samples in its local replay in the same way as prioritized experience replay, the learner fetches a batch of samples from these replays following a certain strategy. We implement a Deep Q-Learning off-policy algorithm upon the proposed framework. The comparison experiments are performed on a commonly used subset of the Atari-57 learning environment. The experimental results show that the proposed framework speeds up training as the number of actors increases. With the same algorithm and hyper-parameter settings, the proposed framework with 16 actors achieves superior performance that Ape-X with 32 and even more actors does.
Implementation of FSOS requirements through the organization out-of-audit work

The article is devoted to the organization of the pedagogical process, contributing to the formation of professional and general competencies in students. The importance of extracurricular work, including project work, is emphasized. The article presents the experience of joint activities of teachers and students.
Auto Defect Detection Using Customer Reviews for Product Recall Insurance Analysis

The challenge for Product Recall Insurance companies and their policyholders to manually explore their customer product’s defects from online customer reviews (OCR) delays product risk analysis and product recall recovery processes. In today's product life cycle, product recall events happen almost every day and there is no practical method to automatically transfer the massive amount of valuable online customer reviews, such as defect information, performance issue, and serviceability feedback, to the Product Recall Insurance team as well as their policyholders’ engineers to analyze the product risk and evaluate their premium. This lack of early risk analysis and defect detection mechanism often increases the risks of a product recall and cost of claims for both the insurers and policyholder, potentially causing billions of dollars in economic loss, liability resulting from the bodily injury, and loss of company credibility. This research explores two different kinds of Recurrent Neural Network (RNN) models and one Latent Dirichlet Allocation (LDA) topic model to extract product defect information from OCRs. This research also proposes a novel approach, combined with RNN and LDA models, to provide the insurers and the policyholders with an early view of product defects. The proposed approach first employs the RNN models for sentiment analysis on customer reviews to identify negative reviews and reviews that mention product defects, then applies the LDA model to retrieve a summary of key defect insight words from these reviews. Results of this research show that both the insurers and the policyholders can discover early signs of potential defects and opportunities for improvement when using this novel approach on eight of the bestselling Amazon home furnishing products. This combined approach can locate the keywords of these products’ defects and issues that customers mentioned the most in their OCRs, which allows the insurers and the policyholders to take required mitigation actions earlier, proactively stop the diffusion of the detective products, and hence lower the cost of claim and premium.
Modeling Ornaments in Carnatic Music Signals via Wavelets

Gamakas are musical elements that refer to various forms of inflexions, such as shakes and quivers over musical notes in South Indian classical music. Gamakas can be broadly regarded as ornamentations to musical notes in South Indian classical music. The identity of a melody referred to as the rāga is characterized using associated notes of the scale and gamakas. We perform analysis of Carnatic music gamakas using wavelets. This provides a compact mathematical representation of the isolated ornaments in the time domain. We reconstruct the gamakas using wavelet synthesis and show high correlation to recorded waveforms.
Camera Bias in a Fine Grained Classification Task

We show that correlations between the camera used to acquire an image and the class label of that image can be exploited by convolutional neural networks (CNN), resulting in a model that “cheats” at an image classification task by recognizing which camera took the image and inferring the class label from the camera. We show that models trained on a dataset with camera / label correlations do not generalize well to images in which those correlations are absent, nor to images from unencountered cameras. Furthermore, we investigate which visual features they are exploiting for camera recognition. Our experiments present evidence against the importance of global color statistics, lens deformation and chromatic aberration, and in favor of high frequency features, which may be introduced by image processing algorithms built into the cameras.
Contemporary Human Activity Recognition Based Predictions by Sensors Using Random Forest Classifier

The task of recognizing human activities directs extensive divergence of various functions and applications. Despite analysing the intricate activity it endures demanding requirements in contemporary field of research. A subject performs a definite task at a particular time by determining the activity by using sensor data. In this research task we appraise a unique way by using data with supervised learning techniques by placing sensors on the human body by contingent upon classification process at different stages. The State-of-art machine learning approach random forests are widely discussed in terms of covering practical and theoretical aspects of body sensing. The eventual target is the superior rate of accurate predictions effecting Human Activity Recognition further effective for behavioural monitoring, medical and healthcare sectors. Classification processes are deployed for pairs of activities that are distracted often and this work attempts to analyse the essential sensors for the improved prediction. The results shows the best accuracy scores and the remaining of our findings we expose the outline, exhibiting the degree of distraction between features of ranking and human activities which renders back to sensor ranking.
The application of statistical methods in the development of Cyrillic-Latin converter for Tatar language

This article deals with the problem of development of Cyrillic-Latin converter for Tatar language which will be able to convert a text written in Tatar to Latin using Cyrillic graphics. In addition, the article describes some aspects of Cyrillic graphics to Latin for the Tatar language. The authors worked in two directions: various statistical methods necessary for the Converter operation were considered, as well as the speed and accuracy of the conversion algorithms were analyzed.We created an algorithm and developed software modules that allow converting messages written in the Tatar Cyrillic alphabet to the Tatar Latin alphabet.According to the local law acts, a verbal and an algorithmic model of conversion was constructed. In the process of development, it turned out that the process of a Tatar word conversion depends on its origin. Native Tatar words are converted according to the phonetic principle (κeJIaM - qäläm), the borrowed words are converted according to the rules of transliteration. The main problem of the study is the problem of a word origin recognition. In order to solve this problem, the authors propose various algorithmic models. Software tools based on the statistical processing of linguistic data are considered and developed in the work: combined bigram analysis, naive Bayesian classification and a brute-force direct search. Each of these algorithms is used to determine the etymology of a word, on which depends the application of certain rules of conversion from Cyrillic to Latin.Thus, the result of our work is a developed software product that can perform the process of converting Cyrillic graphics into Latin for Tatar language. Further research in this area is related to the development of the software tool and its use in educational activities.
AEG: An Adversarial Attack Algorithm Based on Structured Data and Threshold Constraint

Although machine learning models are very effective in practical applications, many researches have shown that most algorithms are vulnerable to attack with well-designed adversarial examples and adversarial attack algorithms. But the current studies are mainly based on images, which are not suitable for models trained on structured data. In this paper, to expand the research on adversarial examples applicable to structured data, a new adversarial attack algorithm called AEG is proposed with a modified gradient descent method. Real-world structured data and trained load prediction models are applied for attack experiments. Experiments show that the algorithm has achieved significant attack performance while evading the anomaly detection mechanism.
Systems, Services, Solutions of the Public Cloud

With the public cloud, organizations have access to an extensive, diverse range of new and emerging technologies as well as well-established ones. Their support staff may combine these technologies in a number of novel ways to form solutions that do more and cost less while meeting the business requirements of organizations. The public cloud bewilders, even confuses the average user, as it is an area of increasing technical complexity that is undergoing rapid change in terms of both a diversification of available services and the growth in underlying contingent technologies. This chapter discusses the public cloud in terms of the three basic service models of infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS) and then discusses the public cloud in the context of the four deployment models of private cloud, public cloud, hybrid cloud, and community cloud. It concludes with the risks and challenges associated with security and the need of organizations to make use of of multiple public cloud providers.
Fracture Behavior of Modified Polymer Materials by Digital Image Correlation Method

Polymer materials play an important role in today's social development, and become important materials in industry, agriculture, national defense and other fields. With the further development of modern engineering material technology, polymer materials, together with information technology and biotechnology, promote the progress and development of society. The performance and quality of modified polymer materials have also entered the research field of vision of various industries and become the focus of exploration and analysis. The following is the research on the fracture behavior of modified polymer materials with the help of digital image correlation method. The purpose of this paper is to clarify the basic fracture work, shaping work and other fracture work parameters of various modified polymer materials, and grasp the root cause of fracture work parameters of modified polymer materials, so as to clarify the modification processing parameter means of these polymer materials.
Formal Software Requirement Elicitation based on Semantic Algebra and Cognitive Computing

Autonomous software requirement analysis and generation are a persistent challenge to theories and technologies of software engineering. A cognitive system is demanded to automatically elicit and rigorously refine informal software requirements in natural language descriptions into formal specifications. This paper presents a novel software requirements elicitation methodology based on latest advances in software science and denotational mathematics such as semantic algebra and concept algebra. It is found that user requirements for a software system in natural language may be either expressed in to-be sentences for software structures or to-do sentences for software behaviors. Thus, formal software requirements may be elicited by two sets of structural and functional models. This approach is implemented by a tool for Formal Requirement Elicitation and Analysis (FREA). Experimental results demonstrate that the FREA tool may rigorously elicit and generate formal requirements for arbitrary software systems specified in real-time process algebra (RTPA) or equivalent notations. This technology paves a way towards autonomous code generation in software engineering.
Geografis Information System of Determining the High School (SMA) Zonation in Merauke City in Receiving New Students (PPDB) Using the Buffering Method

Abstract Education is one of the bases in creating an atmosphere, learning processes in increasing the knowledge of skills and character development of each person. The Education and Culture Office of the Merauke Regency based on the Permendikbud Republic of Indonesia No. 51 of 2018 and Permendikbud No. 20 of 2019 concerning zoning determination, if students want to register to be accepted at the school, they must pay attention to the distance between their place of residence and the distance of the closest school, so that there is an even distribution of the number of students, as well as the composition of knowledge from students nothing is too dominating as a whole but evenly. This research is carried out by utilizing the Geographic Information System (GIS) in determining to zone for students who will register to Senior High School (SHS) in the Merauke city area, to obtain information about the nearest school, based on Government regulations. the parameter used is distance radius. the method applied in this research is the buffering analysis model. This analysis model can identify locations to obtain coverage that can be used in identifying objects based on the radius, thus producing polygons around objects. The results of this study are in the form of GIS to SHS and can map and determine SHS zonation in Merauke City, as well as displaying the distribution of SHS in Merauke city.
Bitcoin Prediction Using Ensemble Modelling

Bitcoin is one of the recent phases for the cryptocurrency front. It uses peer to peer technology for operation with no authority or bank making note or controlling it any for or factor. Its transactions are managed and it is issued out collectively by the network in place. Bitcoin is open-source – Its layout is public i.e. nobody owns or controls bitcoin but everyone can participate. What is awe inspiring is the exciting use bitcoin provides unlike any of its predecessors are the Faster peer to peer Transactions, the Worldwide acceptance and the Lowest processing rate in the market, the main goal of the work is to find a better methodology on which the traders can depend on while investing in the bitcoins, so that they can be sure of conformed returns and in turn increase the overall investment in the cryptocurrency as a whole.