Reviewerly/exHarmony huggingface dataset:
Answer Selection Based on Aligned Local Composite Features and Global Features

With the rapid increase of text information on the Internet, intelligent question answering has attracted considerable attention in many different domains. Many studies use intelligent question answering as an answer selection task and use local or global features to encode sentence representation, which has been proven to be very effective. However, due to the different characteristics of the corpora in different domains or languages, these methods usually suffer a great reduction in performance and cannot produce a sentence representation with good qualities. In this paper, we introduce Hybrid Model combining Local Composite features and Global features into a Siamese network (HM-LCGS) to alleviate the issue. The framework consists of a novel convolution neural network like architecture called local composite features convolution neural network to extract sufficient semantic information of the text from different granularity, shortcut connection to combine local composite features into pre-trained embeddings, alignment layer to mine the correlation between question and answer and bidirectional Long Short-Term Memory to encode the final sentence representation. The experimental results on InsuranceQA and cMedQA datasets show that with suitable granularity selection and embedding method, our proposed model can achieve competitive performance compared with other state-of-the-art models.
Automated Breast Cancer Diagnosis Based on Neural Network Algorithms

Breast cancer, the most common among the women, is primarily examined visually by starting with initial screening of breast and lymph nodes in armpit, dermoscopic analysis, histopathological tests and a biopsy. In India as per the ratings, 25–35% women are affected with breast cancer in every city in India. Being in a developing country like India, majority of women are dying due to breast cancer and for every 4 min women is affected with the same. Women are suffering a lot due to this curse, masses or micro calcification are the symptoms a woman can have at a previous stage, where microcalcification is mineral which gets into the breast tissue. It’s quite hard to examine due to ultra-fine shapes and uncertain edges. It is being categorized into three main categories normal, malignant and Benign, which can be detected using proposed system. In proposed scheme, input images are being compared with large collection of databases with the replacement of sigmoid activation function. Probabilistic neural network is used to describe nonlinear statement limits which further leads to Bayes optimal and also all the function which bear same properties as well. Any input data or algorithm can be pointed with the four layer neural network. Standard PNN consists of input layer of N nodes, a pattern layer of m nodes, a summation layer of k node and it consists of pseudo layer L nodes, which is used for decision making which is also known as decision layer for producing output.
Performance of ESI Dominant Disciplines – a Case Study of 21 Scientific Research Institutions in Yunnan Province

In the big data environment, in the face of the weak competitiveness of disciplines in Southwest China, this study uses ESI and incites database, combined with bibliometric analysis method to conduct multi-dimensional benchmarking analysis on the comprehensive performance of ESI dominant disciplines of 21 scientific research institutions in Yunnan Province, so as to strengthen the interdisciplinary cooperation of various scientific research institutions in Yunnan Province, and improve the global average level of comprehensive cited performance To promote the coordinated development among disciplines of various scientific research institutions in Yunnan Province, enhance the international competitiveness of advantageous disciplines, and promote the development potential of potential disciplines and weak disciplines.
Self-assessment of entrepreneurial competencies of students of higher education

Entrepreneurship has become an important academic field leading to a growing interest of scientists in the study, development and evaluation of entrepreneurial competencies of young people. Business based aspects of entrepreneurial competencies relate to the development of personality traits, student skills that influence one’s personal development for successful entrepreneurs. The main purpose of this paper is to examine the entrepreneurial competencies of students during their studies and how they relate to their socio-demographic characteristics. The research was conducted in November 2019 on a sample of 172 respondents and included first-year undergraduate students of business economics and undergraduate studies of digital marketing at institution of higher education. The results of the research show a positive statistically significant correlation between all entrepreneurial competencies of students. Statistically significant differences in entrepreneurial competencies according to the gender of students were found. And finally, statistically significant differences in entrepreneurial competencies between undergraduate students of business economics and undergraduate studies in digital marketing were shown. It is expected of students with a higher degree of management competencies attending entrepreneurial education more capable of becoming future entrepreneurs. The primary contribution of this paper is to point out the need to increase students’ awareness of the positive impact of entrepreneurial education programs that contribute to the development of such competencies.
Finite Fields of Characteristic 2

In this chapter we extend beyond integers modulo primes to consider finite fields of characteristic 2. For a more extensive presentation of finite fields, the reader should consult Lidl and Niederreiter [1]. For a different presentation of finite fields of characteristic 2, the reader could consult Golomb [2]. Finite field arithmetic in characteristic 2 is used in the Advanced Encryption Standard (AES). It can be preferable in other cryptosystems, because computer hardware works in binary, and thus the underlying arithmetic operations needed to encrypt and decrypt can be very fast.
A Study into the Setting and Teaching Methods of “Chinese Classroom Teaching Method” for MTCSOL Course

The “Full-time MTCSOL Professional Degree Graduate Instructive Training Program Training Program” stipulates that there are three types of MTCSOL curriculum settings: core courses, extended courses, and training courses. In the MTCSOL curriculum system of Yunnan Normal University, the Chinese classroom teaching method is the core compulsory course of the degree and has a very important position. However, there are very few special studies on this course at present, so it is necessary to conduct in-depth research on this. This paper investigates and analyzes the Chinese classroom teaching method from the perspective of course evaluation, adopts the course evaluation method that combines quantitative evaluation and qualitative evaluation, and uses questionnaire survey method, classroom observation method, and interview method as research methods. First, design the scale, analyze the reliability and validity, and then discover the problems in the curriculum based on the results of statistical analysis. After that, we combine classroom observations and interviews to explore the causes of the problems, and then propose solutions and suggestions for curriculum improvement, in order to help improve the curriculum.
Sentiment Analysis for Modern Standard Dialect Using Genetic Folding Algorithm

This paper presents an approach based on the Genetic Folding (GF) algorithm to discriminate between positive and negative Arabic reviews in social media. Sentiment analysis is a set of techniques for collecting subjective opinions from a document. Sentiment analysis in Arabic is difficult due to rich language morphology. In addition, it is further complicated if the assignment is applied to very informal and noisy Twitter data. This paper aims to extract and evaluate Twitter data written in the Saudi Arabic dialect. The Twitter dataset is labeled for subjectivity and sentiment analysis at the sentence level. The model uses TF-IDF representations and applies three different kernels for support vector machines to compare with the GF algorithm, intending to extract users’ sentiment from written text. Experimental results showed that the GF algorithm leads to substantially better performance and showed that the mean square error was minimal, and the GF algorithm was the most accurate in predicting topic polarity. Best results were achieved using the generated GF kernel with an overall accuracy of 100% over 96% of the best other kernels.
Research on Color Adaptation of Automobile Head-up Display Interface

The purpose of this paper is to study the color display problem of the car head-up display interface. The color recognition effect of the existing car head-up display interface differs greatly in different environments, and it cannot guarantee that the driver will always make a clear recognition. Based on the color coding theory and color adaptation theory of the digital interface, this paper redesigned the color coding method of the car's head-up display interface to ensure that its colors can adapt to different background colors and ensure the efficiency of reading. Through the overall simplification algorithm and the selective simplification algorithm, the main colors of the background environment are extracted, and the corresponding head-up display interface colors are matched according to the color coding theory to establish experimental materials. Then, using reaction time as a performance indicator, the participants' subjective feelings were tested and the driver's ability to recognize interface information in different driving environments was evaluated. The experimental conclusions show that the cognitive effect of the HUD interface optimized by the color adaptive theory is better than the existing interface, and it has made a positive contribution to improving the design of the automobile head-up display interface interface.
Blending of Cryptography and HLSB Steganography Techniques for Digital Images

Advancement in the use of internet facility has led to find how secret images can be shared confidentially from one part of the world to other. Applying only cryptographic encryption algorithm to digital images has many limitations. To make it more secure we merge steganography with cryptographic technique. In the proposed method we use RC4 stream cipher algorithm along with HLSB steganography technique. Use of Hash function in HLSB steganography technique makes the algorithm more secure. High level of similarity exists between secret image and extracted image.
Smart Events in Behavior of Non-player Characters in Computer Games

This work contains a solution improvement for Smart Events, which are one of the ways to guide the behavior of NPCs in computer games. The improvement consists of three aspects: introducing the possibility of group actions by agents, i.e. cooperation between them, extending the SE with the possibility of handling ordinary events not only emergency, and introducing the possibility of taking random (but predetermined) actions as part of participation in the event. In addition, two event scenarios were presented that allowed the Smart Events operation to be examined. The study consists of comparing the performance of the SE with another well-known algorithm (FSM) and of comparing different runs of the same event determined by the improved algorithm. Comparing the performance required proposing measures that would allow for the presentation of quantitative differences between the runs of different algorithms or the same algorithm in different runs. Three were proposed: time needed by the AI subsystem in one simulation frame, the number of decisions in the frame, and the number of frames per second of simulation.
Collaborative Knowledge Construction Process Model Based on Internet+

With the rapid development of computer technology, human society is entering the era of knowledge society and information technology, so education is facing great challenges. The concept and practice of education need to be innovated, the knowledge construction in learning needs to be strengthened, and the focus of teaching should be shifted from “activity centered” to “thought centered”. The goal of collaborative knowledge construction is to form valuable public knowledge for learning groups, rather than simply improving the contents of learning individuals’ minds. It focuses on the construction and improvement of group knowledge. Through literature research, the topic form based on the Wiki network environment may help the construction of collaborative knowledge. On this basis, the topic collaborative knowledge construction model of the Internet + network environment is further discussed.
USING ARDUINO IN STUDYING THE SECTION "ALGORITHMIZATION AND PROGRAMMING" OF THE SCHOOL INFORMATICS COURSE AS A PREPARATION FOR LEARNING THE BASICS OF ENGINEERING

The article discusses the possibility of preparing schoolchildren for the study of the basics of engineering in the framework of training in the section "Algorithmization and programming" of the informatics course based on the Arduino platform. Such training can be organized by including in the material under study practical work on the creation of electronic controlled devices: a weather station, a "smart" night light, a traffic light, a music box, etc. A simple assembly system allows using the Arduino kit for 40-minute lessons: 10 to 20 minutes depending on the complexity of the project. Also, in the classroom, it is proposed to use Tinkercad, a free 3D modeling program that allows you to assemble an electronic device online, write a code, test it, make sure it works, and download a sketch code for further use. The article presents a methodology for carrying out work in the 10th grade to create a "music box" device in Arduino. The purpose of this work is to generalize and systematize the topic "One-dimensional arrays of integers". The practical part of the lesson involves working with light, sound, control, and rotation of the figure. Thus, the concept of "array", algorithmic structures "loop" and "branching" are repeated. This work can also be carried out as an extracurricular event, for example, within the framework of a subject week.
An Overview of Current Deep Learned Rendering Technologies

This paper focuses on various real-time and deep learning rendering technologies used in the rendering of images. Real-time techniques such as anti-aliasing and superresolution have been discussed. Two deep learning rendering models, namely DLSS and NSS, have also been discussed. Their merits and demerits have been listed. Current challenges have been mentioned.
Investigation and Reflection on the Cultivation of Application-oriented Talents of Agricultural Sciences in Universities

The application-oriented talents are the needs of the society. Strengthening the cultivation of practical ability is an important part of the transformation of colleges and universities into application-oriented ones. The cultivation of applied talents should pay more attention to the improvement and development of students' practical ability. Based on the questionnaire survey on practical ability of students majoring in agricultural sciences in Shenyang Institute of Technology, the author put forward relevant thinking from the perspective of applied talents training.
Teaching Underachieving Algebra Students to Construct Models Using a Simple Intelligent Tutoring System

An algebraic model uses a set of algebraic equations to describe a situation. Constructing such models is a fundamental skill, but many students still lack the skill, even after taking several algebra courses in high school and college. For underachieving college students, we developed a tutoring system that taught students to decompose the to-be-modelled situation into schema applications, where a schema represents a simple relationship such as distance-rate-time or part-whole. However, when a model consists of multiple schema applications, it needs some connection among them, usually represented by letting the same variable appear in the slots of two or more schemas. Students in our studies seemed to have more trouble identifying connections among schemas than identifying the schema applications themselves. This paper describes a newly designed tutoring system that emphasizes such connections. An evaluation was conducted using a regression discontinuity design. It produced a marginally reliable positive effect of moderate size (d = 0.4).
Considering Filter Importance and Irreplaceability In Filter Pruning

Deep convolutional neural network (CNNs) have gained a great success in computer vision tasks. However, the computation and parameter storage costs of CNNs are very large, thus a large number of studies have tried to reduce the computation and parameters of CNNs. Quantization and pruning are the usual strategies for model compression. Previous filter pruning works usually prune filters with small norm because the common viewpoint is that the smaller norm of filters, the less contribution of filters. However, above strategy ignores that features extracted by those filters with large norm may be redundancy and the features extracted by small norm filters are irreplaceable. In this paper, in light of above findings, we propose a novel filter pruning method called Importance Diversity Filter Pruning (IDFP), which considers both filter contribution and filter irreplaceability. We conduct extensive experiments on CIFAR-10 and CIFAR-100 datasets. The results illustrate the effectiveness of our method.
A Framework for Filtering Step of Number Field Sieve and Function Field Sieve

Public key cryptography is based on two popular mathematical hard problems namely the integer factorization and discrete logarithm problem. Number Field Sieve (NFS) and Function Field Sieve (FFS) are the most efficient and widely used algorithms to solve integer factorization and discrete logarithm problems. The main phases of these algorithms are polynomial selection, relation collection and linear algebra. Among these steps, relation collection and linear algebra are the most computationally costly. The filtering step is used in between relation collection and linear algebra step, which employs several strategies to reduce the coefficient matrix size and makes the matrix suitable for linear algebra, so that linear algebra takes comparatively lesser time. This paper presents a complete framework for the filtering step in the NFS class of algorithms. In this paper, the filtering step is considered as two main sub-phases. The two phases are namely matrix construction and matrix reduction. In the first sub-phase, the relations collected from the relation collection step are structured to represent in a matrix form. The main goal of this phase is to construct a relation from the smooth elements obtained in the relation collection phase along with the other necessary inputs with respect to NFS class of algorithms. The second sub-phase deals with matrix reduction in which various strategies such as duplicate removal, singleton removal, clique removal and merge are applied to reduce the overall matrix size preserving the sparsity at the same time. Algorithms for the first and second phases are designed and analyzed in the paper. The experiments are conducted on the relations collected from standard tool CADO-NFS and the results are reported. From the results, it is shown that the filter map structure presented in the current work for matrix representation helps to improve the overall performance of filtering module. Specifically, the method to handle duplicates in the matrix instead of the traditional method improves the duplicate removal module of filtering. Additionally, an optimized clique removal method of filtering which is introduced in the present work further improves the performance of filtering.
Enhanced SURF- and Wavelet-Based Underwater Image Stitching

Underwater images are used in numerous scientific applications in the fields of marine geology, archaeology, military reconnaissance, finding underwater resources, detection of temporal changes under the sea, environment damage assessment, etc. Stitching these underwater images to obtain a clear view is a challenging and interesting problem for researchers. Several advances have been made to stitch normal images, but the problem of stitching underwater images has been poorly exploded because underwater images suffer from poor visibility conditions. Since underwater images are captured by unmanned underwater vehicles (UAVs), the orientation of the images obtained also introduces an difficult problem. An effective underwater image stitching technique is proposed in this paper. The images obtained from a particular location are oriented in correct angle using self-organizing map (SOM). The features of the oriented images are obtained with the help of speeded-up robust feature (SURF) registration technique. Hessian matrix plays the role of obtaining the feature points because it augments the number of feature points. From the obtained feature points, the overlapping regions in the images are identified. These regions are then eliminated using random sample consensus (RANSAC) algorithm. Finally, the pre-processed images are fused to obtain the overview of that particular area, thus provides a helping hand for researchers in various fields.
Is Simple English Wikipedia As Simple And Easy-to-Understand As We Expect It To Be?

Conceptual complexity of a written text plays an important role in maintaining reader's interest in reading it. Therefore, automatic text simplification systems should, apart from considering lexical and syntactic complexity of a text, also consider the conceptual complexity. In this study, we analyze and compare two widely used English text simplification corpora, one professionally produced (Newsela) and the other collaboratively made by amateurs and enthusiasts (English Wikipedia–Simple English Wikipedia), focusing on 19 conceptual complexity features. The results indicated that simplification operations made during the production of Simple English Wikipedia in many cases do not follow the patterns of the professionally simplified corpora, thus casting doubts on adequacy of using Simple English Wikipedia as training material for automatic text simplification systems.
Graph-in-Graph Contrastive Learning for Semi-Supervised Adaptation

Semi-supervised domain adaptation (SSDA) aims to adapt the model from the labeled source domain to the target domain including few labeled data. Extracting the general features is important to solve SSDA, which is beneficial to promote the model to adapt to the target domain. To this end, in this paper, we propose a novel framework to enhance the generalization of the model which improves the accuracy in the target domain. Particularly, we construct a new graph-in-graph component to model the internal relationship of the input feature, which is helpful for extracting rich and general features. In addition, for large amounts of unlabeled data in the target domain, we use the contrastive loss to optimize the network and extract general representations. We evaluate our framework on three benchmark datasets including Domain-Net, Office-Home, and Office. The extensive experimental results demonstrate the proposed method achieves state-of-the-art performance.