Reviewerly/exHarmony huggingface dataset:
A Comparative Study of Deep and Shallow Parsing Approaches to Automated Grammaticality Evaluation

The concept of automated grammar evaluation of natural language texts is one that has attracted significant interests in the natural language processing community. It is the examination of natural language text for grammatical accuracy using computer software. The current work is a comparative study of different deep and shallow parsing techniques that have been applied to lexical analysis and grammaticality evaluation of natural language texts. The comparative analysis was based on data gathered from numerous related works. Shallow parsing using induced grammars was first examined along with its two main sub-categories, the probabilistic statistical parsers and the connectionist approach using neural networks. Deep parsing using handcrafted grammar was subsequently examined along with several of it‟s subcategories including Transformational Grammars, Feature Based Grammars, Lexical Functional Grammar (LFG), Definite Clause Grammar (DCG), Property Grammar (PG), Categorial Grammar (CG), Generalized Phrase Structure Grammar (GPSG), and Head-driven Phrase Structure Grammar (HPSG). Based on facts gathered from literature on the different aforementioned formalisms, a comparative analysis of the deep and shallow parsing techniques was performed. The comparative analysis showed among other things that while the shallow parsing approach was usually domain dependent, influenced by sentence length and lexical frequency and employed machine learning to induce grammar rules, the deep parsing approach were not domain dependent, not influenced by sentence length nor lexical frequency, and they made use of well spelt out set of precise linguistic rules. The deep parsing techniques proved to be a more labour intensive approach while the induced grammar rules were usually faster and reliability increased with size, accuracy and coverage of training data. The shallow parsing approach has gained immense popularity owing to availability of large corpora for different languages, and has therefore become the most accepted and adopted approach in recent times.&#x0D; Keywords: Grammaticality, Natural language processing, Deep parsing, Shallow parsing, Handcrafted grammar, Precision grammar, Induced grammar, Automated scoring, Computational linguistics, Comparative study.
Ethical analysis of biometric data collection in collegiate athletics

Technology has become an integral part of all aspects of life, especially for health and wellness. The most familiar use of technology within health and wellness is a device that tracks biometric data such as a Fitbit ® , Apple Watch ® , and other health monitoring services. Applying health data to overall wellness and performance has also been implemented into athletics as a means to track performance and analyze risk of injury. As new technology is developed and implemented into athletics, rules and regulations are warranted to ensure these devices are used effectively and responsibly. In the U.S., major sports leagues such as the National Basketball Association (NBA), National Hockey League (NHL), and National Football League (NFL) have made provisions about wearable technology and established protocols for the collection, analysis, and management of biometric data. Many of these challenges at the professional level have been outlined in Barbara Osborne’s article, “Legal and Ethical Implications of Athletes’ Biometric Data Collection in Professional Sport”. 3 Similar to professional sports organizations, technology for performance and injury risk analyses is being considered for college sports. College athletes however do not have the same rights and responsibilities as professionals. At the college level, there are unique potential legal and ethical challenges in regard to data collection and usage.
기업의 정보보호 공시가 기업가치에 미치는 영향

Although Firms have been increasing their information security significantly to handle increased security risks, the effects of information security were not well understood. This study aims to investigate the market value of information security by employing the event study methodology. Our research also explores how market responses vary depending on the type of information security announcements. We collected 177 firm-level information security announcements between 2001 and 2017 in South Korea. For all samples, our results indicate that the stock market positively reacts to information security announcements. We also conducted subsample analysis and found that while information security certification announcement has a positive impact on the stock market, information security activities (e.g. award, information security system) announcement had no impact on the stock market. Our study adopted a novel approach (i.e. event study) for investigating the effects of information security and found that information security investment positively affects firm value. Our results allow managers to measure the effects of information security investment and help them make right decisions on information security investment.
A method for sentiment analysis of film reviews based on deep learning and natural language processi

A method for sentiment analysis of film reviews based on deep learning and natural language processing is disclosed. The method for analyzing emotions of film reviews by deep learning includes: getting film reviews text data and marking positive and negative emotions in film reviews; preprocessing the film reviews by removing redundant information; vectorizing film reviews text according to the bag-of-words model; splitting the vectorized film reviews into training sets and test sets; setting up the initial deep learning model of film reviews sentiment analysis, which connects and integrates four convolution neural network layers, two pooling layers, and two full connected layers; training the initial deep learning model by training data set to generate the final deep learning model, using the final deep learning model to detect the film reviews test set and output the detection results. The invention can accurately distinguish positive and negative emotions of film reviews, and the deep learning model has a simple structure and a small amount of calculation, thereby improving the speed of emotion analysis of film reviews. a raw review -Remove HTML, review text -n n-leters- letters only lowercase & split into individual ones Join a clean review 4-the words- meaningfulwords * Remove - words together stopwords Figure 1 [sentense] John likes to watch movies. Mary likes too. John also likes to watch football game. mokes 52 to:3 John likes to watchmovies.Marylikestoo. Johnalso likes to watch football game. [1, 1, 1, 1, 0, 1, 1, 1, 0, 0] Figure 2
Application of Ear Detection in Non-frontal Face Recognition

Abstract Face gesture recognition is a research hotspot in the field of machine vision and pattern recognition.In this paper, we use the Haar-Like feature-based Gentle Adaboost algorithm to detect the ear of non-frontal face. An ear sample library for ear detection of non-frontal face images was constructed. Through zoom detection window and determining the ROI area, it can successfully detect ear and work better than the basic Adaboost algorithm. The detection rate of the algorithm in the experiment reached 82%. It is suitable for most of the non-frontal faces we photographed.It still has a good detection effect for images with a small amount of hair or eyeglass frame interference.Based on the contour information of the ear, an approximate method for determining the lower end of the ear based on edge detection and a head and neck boundary determination algorithm based on the lower end of the human ear are proposed, which is more complete than the previous algorithm based on the determination of the concave points on both sides of the neck. The face area is segmented to make the face contour finally applied on the fitted ellipse more accurate.
Mathematical Modelling of Students’ Academic Performance Evaluation Using Fuzzy Logic

In recent years, the concept of fuzzy logic techniques is applied for evaluating performance in the teaching-learning process. In this paper, we have proposed a novel fuzzy inference model for students’ academic performance evaluation for multi-input variables. The Triangular membership function, Trapezoidal membership function and Gaussian membership function are used to obtain the degree of satisfaction of each student evaluation based on marks. The proposed model applied fuzzification, fuzzy inference and defuzzification taking into consideration the difficulty and intricacy of the problem. The transparency, objectivity and easy execution of the proposed fuzzy inference model provide a useful way to evaluate students’ achievement more reasonably and impartially. Experimental results are compared for three membership functions and also with the MATLAB solution. The results of MATLAB are proved the validity of the proposed techniques. We have also compared the results with the existing statistical method.
Flipping the Classroom to Stimulate Active Learning in Higher Education Students

The objective of this research is to implement the flipped classroom model as a teaching strategy for active learning. The approach here takes advantage information and communication technology potentials, as an essential factor in stimulating and improving significant learning undergraduate students in Industrial Economics. The analysis included a sample of 30 undergraduate students in Industrial Economics studying theory econometrics in their sixth semester during the academic year 2017-2018. The students are from the National School of Higher Studies Leon Unit of the Universidad Nacional Autonoma de Mexico. The results reveal significant improvements in the learning of the students that followed the flipped classroom methodology.
Why We Must Fight Transphobia

Probably the fastest growing area of hatred online is Transphobia. This prejudice is directed to both the hatred of both people who correct their gender ...
Identification of Safest Path using Spatial Analysis

Safety is of critical importance to an individual while travelling. The goal of this project is to provide an effective solution to the masses to ensure their safety when travelling from one place to another. The proposed system is developed by building a robust model by using police FIR data and crime news articles scraped from the web. Techniques from Natural Language Processing(NLP) have been used to convert the unstructured to a form that can be applied to train a machine learning model. The location of a crime is extracted from these articles by using a technique called geo-tagging. The locations extracted from the articles are used map the location of the crime on a map and the occurrences of the crimes at there respective locations are stored. This problem can be now visualized as a graph search problem where the locations of crimes are the vertices and the crime intensities are the edges connecting the vertices. Finally, a shortest path algorithm is applied to find the safest route between two locations. Keywords: Natural Language Processing, Web scraping, Naive Bayes, Parts of Speech Tagging, Geo- tagging, Safest Path, Word Embeddings. Cite this Article Aaditya Naik, Pritesh Parmar, Subhash Mudaliar, Monali Deshmukh. Identification of Safest Path using Spatial Analysis. Journal of Communication Engineering & Systems. 2020; 10(2): 17–24p.
Controlling All-Optical Helicity-Dependent Switching in Engineered Rare-Earth Free Synthetic Ferrimagnets.

All-optical helicity-dependent switching in ferromagnetic layers has revealed an unprecedented route to manipulate magnetic configurations by circularly polarized femtosecond laser pulses. In this work, rare-earth free synthetic ferrimagnetic heterostructures made from two antiferromagnetically exchange coupled ferromagnetic layers are studied. Experimental results, supported by numerical simulations, show that the designed structures enable all-optical switching which is controlled, not only by light helicity, but also by the relative Curie temperature of each ferromagnetic layer. Indeed, through the antiferromagnetic exchange coupling, the layer with the larger Curie temperature determines the final orientation of the other layer and so the synthetic ferrimagnet. For similar Curie temperatures, helicity-independent back switching is observed and the final magnetic configuration is solely determined by the initial magnetic state. This demonstration of electrically-detected, optical control of engineered rare-earth free heterostructures opens a novel route toward practical opto-spintronics.
Combinations of statistical and semantic approaches applied to scientific digital libraries for the promotion of multidisciplinary research

The knowledge of all science domains is now available on digital libraries. The problem is that the papers belonging to different research communities do not use the same vocabulary to talk about the same subject. Access to relevant documents with information retrieval tools, search engines or research-paper recommender systems will fail if these methods do not consider this linguistic variability. In this work, we present strategies for using artificial intelligence technologies to successfully expand the literature search to bring diversity to the recommended results, thereby promoting multidisciplinary research.
Research and Design of Chinese Question Answer System for Heart Disease

This article is aimed at the current common users to obtain information on heart disease online, citing the current popular question and answer system. this article introduces a reading comprehension model to find answers that match the questions. In order to make the reading comprehension model have better application in the question answering system, the following work is done in this article: (1) Quoted word vectors are used to process Chinese text. Since Chinese is more complicated than English, quoted word vectors are currently a better method for processing Chinese text. (2) Construct a question answering system, which can be used to get answers to questions in a paragraph.
The Use of Billing Peer Support in the Formation of the Level of Competence in the Field of Programming of Future Teachers of Computer Science

In this article, an interpretation of the problems of preparing future teachers of Computer Science for the upcoming changes in the teaching of programming languages at present, that is, in general secondary schools of our country and the use of cognitive concomitant in this place has been made. These situations come with the help of specific topics and issues.
Accelerating the Forward Convnets with Software Pipeline

We present a new approach to accelerate the forward convolutional neural netWorks (ConvNets). Instead of taking data parallelism into account for faster inference, We speed up ConvNets With the technology of software pipeline. Since a ConvNet is a chain-like model, Which means that the output of current layer Would become the input of next layer, it is natural to accelerate it With the use of software pipeline. Firstly, the forward propagation in each layer, treated as a minimum unit, is timed during the inference. Then, according to the computation time in each layer, one or several units Would compose a working part With an allocation algorithm. Finally, these Working parts Would be executed in different computing resource simultaneously, so that the ConvNet could be operated by software pipeline. This is a conceptually simple, flexible and general Way to accelerate the forward ConvNet, We named it pipeline ConvNet (PipeCNN). Experiments show that the method could obtain good speedup and improve the throughput of the networks.
SVM Kernel and Genetic Feature Selection Based Automated Diagnosis of Breast Cancer

Background: Breast cancer is the development of a malignant tumor in the breast of human beings (especially females). If not detected at the initial stages, it can substantially lead to an inoperable construct. It is a reason for majority of cancer-related deaths throughout the world. Objectives: The main aim of our study is to diagnose the breast cancer at early stage so that required treatment can be provided for survival. The tumor is classified as malignant or benign accurately at early stage using a novel approach that includes an ensemble of Genetic Algorithm for feature selection and kernel selection for SVM-Classifier. Methods: The proposed GA-SVM (Genetic Algorithm – Support Vector Machine) algorithm in this paper optimally selects the most appropriate features for training with the SVM classifier. Genetic Programming is used to select the features and the kernel for the SVM classifier. Genetic Algorithm operates by exploring the optimal layout of features for breast cancer, thus, subjugating the problems faced in exponentially immense feature space. Results: The proposed approach accounts for a mean accuracy of 98.82% by using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset available on UCI with the training and testing ratio being 50:50 respectively. Conclusion: The results prove that our proposed model outperforms the previously designed models for breast cancer diagnosis. The outcome assures that the GA-SVM model may be used as an effective tool in assisting the doctors for treating the patients. Alternatively, it may be utilized as an alternate opinion in their eventual diagnosis.
Do Wearable Devices Change Behavior? A Study of Smart Fitness Trackers

PurposeThe study focuses on the physical activity behavior change effect of smart wristband, which is the most popular type of fitness tracker nowadays. The purpose of the research is to investigate how people''s workout behavior may change after wearing a smart band and examine what kind of role persuasive design plays in behavior change.Design/Methodology/ApproachThis research employed an experimental study to examine whether the user''s workout behaviors changed after using wristband from the “Behavior Wizard” perspective. A representative smart wristband from a major vendor was selected as the objects of experimental study. In the experiment, by comparing users’ workout behavior before and after using the wristband, behavior changes of all the experiment participants were classified into one of the 15 behavior change types. Users perceived persuasive design characteristics were measured and group differences were tested among different behavior change groups.FindingsThis research found that nearly half of the participants changed their workout behavior while half retained their workout status or no exercise status. Half of the participants who did not do exercise in their spare time started walking in the experiment. Results also showed that participants who started working out perceived higher levels of persuasive design devised into the smart band than participants who preserved no exercise status, except for facilitation and reward strategies. Participants who retained workout and those who increased workout frequency perceived no difference in smart band persuasive design.
INVERSION FORMULA FOR THE EIGENFUNCTION WAVELET TRANSFORM

Adv. Math. Sci. J. Vol. 9, No. 9 Open Access INVERSION FORMULA FOR THE EIGENFUNCTION WAVELET TRANSFORM M. M. Dixit, C. P. Pandey and D. Das DOI: https://doi.org/10.37418/amsj.9.9.5 Full Text Abstract Abstract In this paper, we accomplished the concept of convolution of Eigenfunction transform for the study of Continuous Eigenfunction wavelet transform and … page AMSJ9-N9-5 Read More »
Formal Analysis of Improved and Secure Architecture of E-voting System using Hierarchical Coloured Petri Nets

Casting a vote is the fundamental right of any citizen. Pakistan follows the traditional manual voting system and there are number of factors influencing in such a way that put a question as if the vote has been casted correctly and should be considered towards final count. Manual counting process of vote do not provide the security measures minimizing the attempts of frauds like increase or decrease in the vote count. Therefore, the idea of secure and improved E-voting system is proposed to overcome such flaws in current voting system in Pakistan. In this manuscript, an improved component-based architecture, security features and future recommendations are presented. Different techniques linked with voter’s authentication, confidentiality and integrity are suggested to minimize the flaws and maximize the benefits of voting system. Further, the proposed architecture is formally modelled through ‘coloured Petri nets’ to validate it. Formal analysis is also performed based on the state space generated to simulate the hierarchical coloured Petri net based formal model of proposed E-voting system.
Illuminating the complex behaviour of particles in optical traps with machine learning

Computationally accurate methods for simulating optical tweezers tend to be prohibitively slow, limiting their use to only very simple problems. Simplified models, such as the harmonic model, enable larger simulations by trading off accuracy for speed. In this presentation, I will demonstrate how training an artificial neural network to predict optical force combines the speed of a harmonic model with the accuracy of a semi-analytical method. Artificial neural networks not only enable more extensive and accurate dynamics simulations, but also collaboration through sharing of pre-trained models which can easily be distributed and used on mobile devices and in web browsers, as I will demonstrate.
Subject Approach to Information Retrieval with Special Reference to Bengali Documents: A Critical Study

The library provides its services to satisfy the user’s approach. This study is done to assess users’ attitudes towards the retrieval of the Bengali document in OPAC through subject headings. It also traces users’ perceptions regarding subject access highlighting the problems in the retrieval of Bengali documents by subject in OPACs. It can be inferred that some features are required to be incorporated to improve the functionality of the OPACs and thereby to satisfy users’ requirements. Results revealed that search experience using subject headings is not satisfactory. Keywords search rather performs better but due to redundancy of search, it is hard to retrieve required documents in the least possible time. It is also revealed that fewer perform known-item searches and their success rates for these searches are generally good. Thus, the findings demand standard principles and rules for the construction of Bengali Subject Headings. As per the need of new-age users, an efficient OPAC search facility can be developed where there will be a chance for ranking information retrieval.
Affordable Security or Big Guy vs Small Guy (Transcript of Discussion)

Our talk today is about “Affordable Security” and we dub it “Big Guy versus Small Guy” in the sense that Big Guys are the one who have a lot of money and Small Guys are the poor guys.
Cost–Benefits Analysis for Computer-Based Tutoring Systems

In a research and development environment, the decisions to undertake, fund, continue, and cancel projects are constantly reviewed. Under ideal conditions, decisions such as these are always made after carefully evaluating the costs and benefits involved. This chapter offers a worked-through illustration of how a cost–benefits analysis might be performed to decide among strategies for implementing an intelligent tutoring system. Cost–benefits analysis provides a financial basis for selecting among a set of actions or decisions. At the most simple level, cost–benefits analysis involves tabulating a set of costs and benefits associated with a set of competing options. The chapter provides some examples of how to conduct a cost–benefits analysis when considering the adoption of computer-based tutoring technologies. It examines some of the major costs and benefits to be considered when evaluating tutoring technologies. Finally, two hypothetical scenarios based on the capabilities of intelligent tutoring are provided to illustrate the cost–benefits analysis.