Reviewerly/exHarmony huggingface dataset:
Legal Practice Mode Based on Computer Technology

Abstract With the rapid development of computer science and technology, virtual reality technology emerges as the times require, and is valued and favored by all walks of life. Law education has always been based on theoretical teaching, through a large number of theoretical recitation and contact, to improve students’ legal professional performance, but this teaching mode often leads to the plight of students with sufficient theoretical knowledge but lack of practical ability. This paper mainly designs and tests a VR education platform based on computer technology, and carries out a comparative experiment on law major classes in Colleges and universities in this city. The test data show that the response speed of the platform is less than 3 seconds, and the compressive capacity meets the requirements of this experiment. Through the analysis of the experimental data for one month, VR education platform based on computer technology can cultivate students’ practical ability, and effectively solve the dilemma of law students’ emphasis on theory but not on practice.
FPGA Implementations of Algorithms for Preprocessing of High Frame Rate and High Resolution Image Streams in Real Time

This paper presents FPGA implementations of image filtering and image averaging – two widely applied image preprocessing algorithms. The implementations are targeted for real time processing of high frame rate and high resolution image streams. The developed implementations are evaluated in terms of resource usage, power consumption, and achievable frame rates. For the evaluation, Microsemi’s Smartfusion2 Advanced Development Kit is used. It includes a SmartFusion2 M2S150 SoC FPGA. The performance of the developed implementation of image filtering algorithm is compared to a solution provided by MATLAB’s Vision HDL Toolbox, which is evaluated on the same platform. The performance of the developed implementations are also compared with FPGA implementations found in existing publications, although those are evaluated on different FPGA platforms. Difficulties with performance comparison between implementations on different platforms are addressed and limitations of processing image streams with FPGA platforms discussed.
Clustering Introductory Computer Science Exercises Using Topic Modeling Methods

Manually determining concepts present in a group of questions is a challenging and time-consuming process. However, the process is an essential step while modeling a virtual learning environment since a mapping between concepts and questions using mastery level assessment and recommendation engines is required. In this article, we investigated unsupervised semantic models (known as topic modeling techniques) to assist computer science teachers in this task and propose a method to transform Computer Science 1 teacher-provided code solutions into representative text documents, including the code structure information. By applying nonnegative matrix factorization and latent Dirichlet allocation techniques, we extract the underlying relationship between questions and validate the results using an external dataset. We consider the interpretability of the learned concepts using 14 university professors' data, and the results confirm six semantically coherent clusters using the current dataset. Moreover, the six topics comprise the main concepts present in the test dataset, achieving 0.75 in the normalized pointwise mutual information metric. The metric correlates with human ratings, making the proposed method useful and providing semantics for large amounts of unannotated code.
An Analysis for Motivating Sketching Practice with Augmented Reality in Da Vinci Eye

Aside from sketching with naked eyes, Augmented Reality (AR) allows users to sketch a wide variety of items in an innovative approach. And this research aims to explore how this technology helps users learn better with higher intrinsic motivation. In order to investigate how this type of apps help users learn and practice sketching, we chose Da Vinci Eye as our tool in this research. We recruited 16 participants whose ages are between 20 and 25 as our participants. 8 of them never had the intensive training on sketching; and the others had majored in art-related professions. Results showed that tracing the virtual silhouette increased the motivation of beginners to practice sketching; on the other hand, this assistance didn't help too much for those who had several years of sketching experience. Our findings provided initial confirmation that AR application has the potential to improve novices' motivation and performance in sketching.
Integration of Students’ Academic and Extracurricular Activities by Creating Conditions for Business-Oriented Design and Practical Activities at the University

A humanistic approach to education has been established in modern Russian and foreign pedagogy, based on the recognition of the value of students’ unique creative abilities and aimed at open interaction and identification of their potential opportunities. At the same time, the educational environment should create all the conditions for students to form their own trajectory of individual development.
Low-Hardware Digit-Serial Sequential Polynomial Basis Finite Field GF(2$$^m$$) Multiplier for Trinomials

Finite field GF(2\(^m\)) multipliers are employed in practical applications such as elliptic curve cryptography (ECC) and Reed-Solomon encoders. Digit-level finite field multipliers are best suitable for applications that require low-hardware implementation while operating at speeds that conform to today’s high data rates. With the emergence of Internet of Things (IoT), many resource-constrained devices such as IoT edge devices came into proliferate usage. To secure these constrained devices, ECC must be implemented in these devices with low-hardware complexity. Hence, it requires to design efficient digit-serial finite field multipliers since the performance of ECC greatly depends on the performance of the finite field multiplier employed. Many efficient designs for digit-serial finite field multipliers are presented in the literature to achieve better area and time complexities. In this paper, we present an area-efficient sequential digit-serial finite field multiplier for trinomials. The hardware and time complexities of the proposed multiplier are estimated for GF(2\(^{409}\)) and compared with the similar multipliers available in the literature. The comparison shows that the proposed multiplier achieves lower hardware complexity. Therefore, the proposed multiplier is attractive for cost-effective high-speed applications such as IoT edge devices.
Remote sensing image registration based on full convolution neural network and k-nearest neighbor ratio algorithm

Abstract Aiming at the problem of low accuracy of remote sensing image registration caused by the negative effects of noise and imaging in some traditional algorithms, an effective remote sensing image registration method based on depth feature from coarse to fine is proposed. In the coarse registration stage, the full convolution neural network is used to extract the features of the input image, then the nearest neighbor distance ratio algorithm is used to coarse match the features and finally an approximate transformation matrix is obtained. In the stage of fine registration, firstly, the image features are extracted by the improved convolutional neural network based on shortcut connection, then the affine transform coefficients are obtained by the combination of approximate transform matrix and k-nearest neighbor ratio algorithm. Finally, the image to be registered can be transformed according to the coefficients to achieve the purpose of registration. Experimental results show that, compared with the comparison method, the proposed method can increase the correct matching correspondence, so as to improve the accuracy of registration.
Review of Machine Learning methods for Identification of Cyberbullying in Social Media

In the modern era, the usage of internet has increased tremendously which in turn has led to the evolution of large amount of data. Cyber world has its own pros and cons. One of the alarming situations in web 4.0 is cyber bullying a type of cyber-crime. When the bullying occurs on line with the aid of technology it is known as cyber bullying. This research paper have surveyed the work done by 30 different researchers on cyber bullying, and elaborated on different methodologies adopted by them for the detection of bullying, and how you protect the society from online evil act of cyber bullying.
Analysis of Metaheuristics Feature Selection Algorithm for Classification

Classification is a very vital task that is performed in machine learning. A technique used for classification is trained on various instances to foresee the class labels of hidden instances, and this is known as testing instances. The technique used for classification is able to find the connection between the class and instances due to the aid from the training process known as attributes. Redundant and non-relevant data are eradicated from the dataset with feature selection technique and these gives room for enhancement of the classification performance through feature selection. This research displays the feature selection techniques performances and are divided into wrapped-based metaheuristics algorithm and filter-based algorithms using two educational datasets. Four different classification techniques were used on the datasets and the outcome shows that Decision Tree (DT) gave the best performance on the datasets. Furthermore, the result shows that the proposed CHPSO-DE outshined other feature selection algorithms in that it obtained the best classification performance by using fewer features. The result of the various feature selection and classification technique will help researchers in getting the most efficient of feature selection algorithms and classification techniques.
High Speed FIR Filter Using Radix-2r Multiplier and Its Application for Denoising EOG Signal

In this paper, a modified finite impulse response (FIR) filter design has been proposed for the denoising bio-electrical signals like Electrooculography(EOG). The proposed filter architecture uses modified multiplier block, which is implemented using modified Radix-[Formula: see text] arithmetic-based representation for minimizing the multiple constant multiplication and conventional ripple carry adders are replaced with [Formula: see text] compressors. This proposed architecture is implemented by using Radix-[Formula: see text]-based multiplier and [Formula: see text] compressor architectures for achieving better improvement in the critical path delay. The Radix-[Formula: see text]-based arithmetic bit recording is used in order to reduce the design complexity of the multiplication. The proposed architecture significantly reduced the delay when compared to existing and conventional architectures.
Seq2seq Automatic Question Answering System of Medical Guide Station Based on Background Information

Automatic question answering technology brings great convenience to doctor-patient communication. Generally, a sequence-to-sequence (Seq2Seq) framework is used to build a question and answer model, but the model cannot make full use of the text information in the relevant context, and some answers generated are relatively simple. To this end, this paper combines medical background information with the Seq2Seq question and answer model, selects the independent recurrent neural network (IndRNN) as the codec of the question and answer model, and establishes an automatic question and answer system for the medical guidance station. Experiments have proved that the response generated by the Q&A model of the medical guidance platform that introduces medical background information is more rich and flexible, with higher accuracy, and close to the real medical guidance response.
Robust Neural Text Classification and Entailment via Mixup Regularized Adversarial Training

Recent studies show that neural models for natural language processing are usually fragile under adversarial attacks (e.g., character-level insertion and word-level synonym substitution), which exposes the lack of robustness. Most defense techniques are tailored to specific semantic level attacks and do not possess the ability to mitigate multi-level attack simultaneously. Adversarial training has been shown the effectiveness of increasing model robustness. However, it often suffers from degradation on normal data, especially when the proportion of adversarial examples increase. To address this, we propose mixup regularized adversarial training (MRAT) against multi-level attack. Our method can utilize multiple adversarial examples to increase model intrinsic robustness without sacrificing the performance on normal data. We evaluate our method on text classification and entailment tasks. Experimental results on different text encoders (BERT, LSTM and CNN) with multi-level attack show that our method outperforms adversarial training consistently.
Toll-based access vs pirate access: a webometric study of academic publishers

Purpose The purpose of this paper is to draw a comparison of the Web traffic ranking, usage and popularity of websites of databases of reputed publishers, namely, ScienceDirect and Emerald Insight, that provide access on subscription basis with Sci-Hub, on the basis of data obtained from Alexa databank ( www.alexa.com ). Sci-Hub is a website that provides pirated open-access to the research literature, where piracy, according to The Economic Times (2020), refers to the unauthorized duplication of copyrighted content. Design/methodology/approach Under present study, the quantitative study of the collected data was carried out with help of descriptive research methodology. The Alexa databank was singled out as the source of data. This study crawled through Alexa databank on 01.12.2019 and collected relevant data regarding Sci-Hub, ScienceDirect and Emerald Insight using the search terms Sci-hub.tw, Sciencedirect.com and Emeraldinsight.com sequentially. Different criteria were taken into consideration, which include global traffic rank, the average number of page views per user, time taken for uploading, bounce rate, percentage of users, the number of in-links and daily time spent on the site. Findings The results of this study showed that ScienceDirect has the highest traffic rank and in-linking sites among the surveyed databases. But highest number of page visits were recorded for Sci-Hub with fastest downloading speed. It has also been observed that the users spent less time on ScienceDirect and Emerald Insight as compared to Sci-Hub. This study further observed that Sci-Hub has the lowest bounce rate. Users from both the developing and developed economies use the Sci-Hub, though the highest number of visitors belongs to the developing nations. Originality/value This study provides an overview of the performance of toll-based publishing databases with pirated database based on different criteria through World Wide Web. Though, this study in no way supports or endorses the unauthorized and illegal access to knowledge, but such data helps in depicting and analyzing how much a particular database is accessed by its users all over the globe and also determines and illustrates the time spent by users while accessing a specific database, thus, providing the user preferences in information seeking activities. This study provides an overall view of adoption of open resources.
A Study on the Utilization of Drone Education in the Fourth Industrial Revolution

Abstract In the era of the Fourth Industrial Revolution, various high-tech technologies are developed. Among these technologies, drone field is currently very burgeoning and important. Especially when drones become cheaper, drone technology gets more popular. Therefore, research on the methodology of drone education is very necessary. And in future societies, the demand for creative talent increases. However, there is a lack of relevant research compared to the importance of drone education. So this study is about the methodology of drone education that is practically available in educational institutions. The results of this study can enhance learners’ understanding of drones and the opportunity to use drones in various fields. In addition, the learners will be able to improve their problem-solving skills and creativity. And these findings can be used in future drone projects. In addition, the result of the study can be a theoretical basis for future education.
Two people, one graph: the effect of rotated viewpoints on accessibility of data visualizations

Abstract In co-located, multi-user settings such as multi-touch tables, user interfaces need to be accessible from multiple viewpoints. In this project, we investigated how this goal can be achieved for depictions of data in bar graphs. We designed a laboratory task in which participants answered simple questions based on information depicted in bar graphs presented from differently rotated points of view. As the dependent variable, we measured differences in response onsets relative to the standard viewpoint (i.e., upright graphs). In Experiment 1, we manipulated graph and label orientation independently of each other. We observed that rotations of the labels rather than rotations of the graph itself pose a challenge for accessing depicted information from rotated viewpoints. In Experiment 2, we studied whether replacing word labels with pictographs could overcome the detrimental effects of rotated labels. Rotated pictographs were less detrimental than rotated word labels, but performance was still worse than in the unrotated baseline condition. In Experiment 3, we studied whether color coding could overcome the detrimental effects of rotated labels. Indeed, for multicolored labels, the detrimental effect of label rotation was in the negligible range. We discuss the implications of our findings for the underlying psychological theory as well as for the design of depicted statistical information in multi-user settings.
Image Restoration Network Under Complex Meteorological Environment: GRASPP-GAN

The color and contrast of objects in the image will be affected by meteorological factors, especially rain and snow will block part of the image, which will change the information contained in the image. Image restoration under bad weather conditions has practical application value. At present, most of the research focuses on the removal of fog, and the research on complex rain and snow is relatively less. Rain has more prominent features in gradient domain, and it is more distinct from non-rain image texture. In this paper, Generative Adversarial Networks is used to combine the information of image in gradient domain and spatial domain to get better performance of rain removal. Gradient aided coding is used in the generator to generate depth features that are more conducive to rain removal. In the discriminator, the gradient is used as an additional input to provide more recognizable rain and non-rain information, which enhances the discriminator’s ability to distinguish the image generated by the generator and the ground truth. By modifying the network structure of the expanded spatial pyramid pooling (ASPP), the abnormal rain removal results produced by the generator are reduced. Experimental results show that the proposed method improves the performance of rain removal and the visual quality of the generated image.
Selection of Potential Sites for Sustainable Development of Solar Photovoltaic Plants in Northeastern Brazil Using GIS and Multi-Criteria Analysis

Brazil has one of the highest percentages of solar radiation in the world and which it is a favorable condition to generate electricity using solar photovoltaic systems. The construction of photovoltaic plants depends on the site selection that influences the electricity generation capacity and the socio-economic benefits that can be derived from it in the future. This article proposes to combine Spatial analysis that involves the acquisition and management of spatial data using a Geographic Information System (GIS), and the fuzzy multi-criteria AHP-TOPSIS model to identify potential locations for the installation of solar photovoltaic systems in the northeastern region of Brazil, State of Pernambuco. The combination of GIS and fuzzy AHP-TOPSIS offers the user the possibility of assigning merit categories to the mapping according to multiple assessment criteria. The GIS include factors of the restrictions and criteria. The restrictions are inserted into the GIS using layers defined from current legislation (urban areas, undeveloped land, community sites, infrastructure, etc.), which reduce the study area by eliminating zones in which PV installation is not permitted. The results show that 22 and 40% of the area of the state of Pernambuco has the very high and high potential, respectively, for the implantation of PV.
Machine Learning Approach to Efficient Hyperparameters Search for Video Streams Semantic Analysis Algorithms

Computer vision algorithms have complex set of parameters that significantly influence resulting quality. On the one hand, such a big amount of parameters that can be varied results in algorithm ability to fit for particular usage circumstances. On the other hand, fine-tuning of even several parameters may require a lot of human resources unless this task is automated. An approach to such automatization is provided considering a machine learning problem with several peculiarities. First, some parameters are restricted to have integer values. Second, multiple metrics describing algorithm quality are restricted to have integer values as well. Third, there are no analytical formulae describing dependences between parameters and metrics values. Mentioned features make gradient-descent-like methods widely used for neural networks training inapplicable for solving that optimization problem. As result, automatically tuned parameters provides better quality than manually selected ones.
Construction of College English Teaching Model in the Era of Artificial Intelligence

Abstract The development of artificial intelligence (AI) technology provides new thoughts and ideas for the reform of English teaching(ET) mode. This article understands the related knowledge of AI technology and ecological pedagogy, and studies and analyses the current situation of college ET in my country, and discusses how to use AI technology to reform my country’s ET model. Relying on data mining technology and deep learning algorithms in AI, the ET model gradually develops in a better direction, making it a virtuous circle.
Toward a Comparison of Classical and New Privacy Mechanism

In the last decades, the development of interconnectivity, pervasive systems, citizen sensors, and Big Data technologies allowed us to gather many data from different sources worldwide. This phenomenon has raised privacy concerns around the globe, compelling states to enforce data protection laws. In parallel, privacy-enhancing techniques have emerged to meet regulation requirements allowing companies and researchers to exploit individual data in a privacy-aware way. Thus, data curators need to find the most suitable algorithms to meet a required trade-off between utility and privacy. This crucial task could take a lot of time since there is a lack of benchmarks on privacy techniques. To fill this gap, we compare classical approaches of privacy techniques like Statistical Disclosure Control and Differential Privacy techniques to more recent techniques such as Generative Adversarial Networks and Machine Learning Copies using an entire commercial database in the current effort. The obtained results allow us to show the evolution of privacy techniques and depict new uses of the privacy-aware Machine Learning techniques.