Reviewerly/exHarmony huggingface dataset:
RGCL at SemEval-2020 Task 6: Neural Approaches to DefinitionExtraction

This paper presents the RGCL team submission to SemEval 2020 Task 6: DeftEval, subtasks 1 and 2. The system classifies definitions at the sentence and token levels.It utilises state-ofthe-art neural network architectures, which have some task-specific adaptations, including an automatically extended training set.Overall, the approach achieves acceptable evaluation scores, while maintaining flexibility in architecture selection.
The Problem with ‘the Background Logic Problem’

Anti-exceptionalism is the view that logic is, among other things, responsive to a posteriori evidence.This brings three questions to the fore: i) what is evidence in logic?ii) how to determine when evidence favours a particular logic, and iii) what kind of logic we use in such disputes?We argue that problem iii), when added to the thesis that natural language does have a specific logic, makes the whole project of logical theory choice untenable.The reason, we argue, is that the logic we are supposed to have vitiates any characterization of evidence, making a change of logic unlikely.We do that by considering two case studies involving adoption of paraconsistent systems, where the very idea that we have a logic operating in natural language prevent any possibility of change.We then go on and suggest, in broad lines, a version of anti-exceptionalism without the natural language logic hypothesis.
A fast, accurate and dense feature matching algorithm for aerial images

Three-dimensional (3D) reconstruction based on aerial images has broad prospects, and feature matching is an important step of it.However, for high-resolution aerial images, there are usually problems such as long time, mismatching and sparse feature pairs using traditional algorithms.Therefore, an algorithm is proposed to realize fast, accurate and dense feature matching.The algorithm consists of four steps.Firstly, we achieve a balance between the feature matching time and the number of matching pairs by appropriately reducing the image resolution.Secondly, to realize further screening of the mismatches, a feature screening algorithm based on similarity judgment or local optimization is proposed.Thirdly, to make the algorithm more widely applicable, we combine the results of different algorithms to get dense results.Finally, all matching feature pairs in the low-resolution images are restored to the original images.Comparisons between the original algorithms and our algorithm show that the proposed algorithm can effectively reduce the matching time, screen out the mismatches, and improve the number of matches.
Feature Extraction and Fusion for Face Recognition Systems using Pre-Trained Convolutional Neural Networks

Recently, face recognition applications achieved promising results by using Convolutional Neural Network (CNN).CNN has the capability to extract features automatically from images and does not need to extract hand-crafted features as traditional algorithms.Feature fusion aims to provide improvements of data validity for both traditional algorithms and deep learning algorithms.In this paper we propose a feature fusion approach for face recognition, the approach performs fusion at the feature level by applying two pre-trained CNNs AlexNet and ResNet-50.Firstly, extracting the feature from both pre-trained CNN AlexNet and ResNet-50 separately.Secondly, fuse the feature maps learned from AlexNet and ResNet-50.Finally, a Support Vector Machine (SVM) classier is used for the classification task.Experiments are conducted on the following datasets: FEI face, GTAV face, ORL, F_LFW, Georgia Tec Face, LFW, DB_Collection, demonstrate the effectiveness of the proposed approach.In addition, the fusion of the two CNN based models AlexNet and ResNet-50 lead to significant performance improvement.In particular, the fusion approach achieves accuracy in range (96.21%-100%) on all datasets.
Analysis of Cybersecurity Standard and Framework Components

Satisfactory cybersecurity protection, encompassing all data security solutions, can only be achieved by adopting a cybersecurity framework that provides a structure and methodology for protecting critical digital assets. In addition, security experts recommend using cybersecurity standards which consist of a collection of best practices to protect organizations from cyber threats. However, many organizations, companies and governments lack experienced personnel in the cybersecurity domain, so they have difficulty adopting a standard approach or cybersecurity framework. Protecting organizations from cyber threats while demonstrating compliance with laws and standards is seen as extremely complex due to the difficulty on choosing the appropriate standard to be used. Moreover, lack of knowledge on the elements needed that offered by the standard is lead to the problem on identifying the started point where the protection will be began.  Therefore, in this paper, a literature and the analysis is presented in identifying the elements of cybersecurity standard and framework that can be facilitate the organization or government on choosing the appropriate standard and framework to be used and utilized. The literature review was carried out to understand the various types of cybersecurity standards and frameworks and the analysis is conducted to identify the elements in each of them. In this paper, eight steps are presented and include the types of international standards, which are general, local regulation, as well as specific standards used in the industrial sector, to conclude the findings of the analysis. Furthermore, a relation map is presented using Writing a Literature Review release 2.0 approach to show the relationship between the literature review and future research.
Computer Aided Classification of Benign and Malignant Breast Lesions using Maximum Response 8 Filter Bank and Genetic Algorithm

Breast Cancer has very low survival rate when detected at later stages. Hence, Computer Aided Diagnostic Models can assist the medical practitioners by diagnosing reports without human intervention. In this paper the sample size considered is 401(200 benign and 201 malignant images) and has been acquired from the Digital Database for Screening Mammography. This paper propose a novel method to detect breast malignancies using texton based analysis. The filter bank that has been used here to obtain the texton based response is the Maximum Response 8 Filter Bank. Further Haralick's features from the Gray Level Co-occurence Matrix , histogram based features from Local Binary Pattern and statistical features namely skewness and kurtosis are extracted from each filter response. Genetic Algorithm and Linear Discriminant Analysis has been used for feature selection and feature reduction respectively. Classification is performed using three classifiers namely Naive bayes, Logistic Regression and Linear SVM. The proposed algorthm exhibit an Accuracy of 87.5% and Area under Curve of 0.95 using Logistic Regression classifier.
Sentiment Analysis for Emotional Speech Synthesis in a News Dialogue System

As smart speakers and conversational robots become ubiquitous, the demand for expressive speech synthesis has increased.In this paper, to control the emotional parameters of the speech synthesis according to certain dialogue contents, we construct a news dataset with emotion labels ("positive," "negative," or "neutral") annotated for each sentence.We then propose a method to identify emotion labels using a model combining BERT and BiLSTM-CRF, and evaluate its effectiveness using the constructed dataset.The results showed that the classification model performance can be efficiently improved by preferentially annotating news articles with low confidence in the human-in-the-loop machine learning framework.
The Effect of Cryptocurrency Returns Volatility on Stock Prices and Exchange Rate Returns Volatility in Nigeria

The global usage and acceptability of bitcoin and other forms of cryptocurrencies as another means of payment have attracted the attention of financial and economic experts in recent times, but research on these means of payment and their relationship with economic and financial variables are scanty in Nigeria. This study, therefore, examined the nexus between the two key economic and financial variables (exchange rate and stock market price) and the most traded cryptocurrency (Bitcoin and Etherum) in Nigeria. The study used monthly data between August 2015 and December 2019 and employed the Generalized Autoregressive Conditional Heteroscedasticity (GARCH 1,1), Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH 1,1), and Granger causality technique to estimate the reaction of the volatility of exchange rates and stock market prices to volatility in cryptocurrency prices. The result shows that the stock market price is more influenced by the instability of bitcoin and ethereum prices than the exchange rate in Nigeria. Further, there is evidence of a one-way causality from bitcoin and ethereum to all share index. Given these findings, there is a need for the stock market investors in Nigeria to pay rapped attention to the movement of cryptocurrency prices.
The Chan-Vese Model With Elastica and Landmark Constraints for Image Segmentation

In order to completely separate objects with large sections of occluded boundaries in an image, we devise a new variational level set model for image segmentation combining the Chan-Vese model with elastica and landmark constraints. For computational efficiency, we design its Augmented Lagrangian Method (ALM) or Alternating Direction Method of Multiplier (ADMM) method by introducing some auxiliary variables, Lagrange multipliers, and penalty parameters. In each loop of alternating iterative optimization, the sub-problems of minimization can be easily solved via the Gauss-Seidel iterative method and generalized soft thresholding formulas with projection, respectively. Numerical experiments show that the proposed model can not only recover larger broken boundaries but can also improve segmentation efficiency, as well as decrease the dependence of segmentation on parameter tuning and initialization.
Pengembangan E-Modul Berbasis Software FlipBook maker Materi Peninggalan Megalitik Di Pasemah Dalam Mata Kuliah Sejarah Indonesia Kuno Di FKIP Universitas Sriwijaya

This scientific work titled E-modules Development Software-based FlipBook maker megalithic relics in Pasemah in the course of ancient Indonesian history in FKIP Sriwijaya University. Development research uses ALESSI and Trollip development models consisting of three stages, namely planning stage, design stage and development stage. Next after going through the planning, design and development phases, the researchers conducted an alpha test of E-module Software-based FlipBook Maker to the material experts, learning design experts, and media experts with declared valid and decent results Piloted. The practicality of E-module based on Flipbookmaker Software is obtained based on the results of beta test of 3 (three) students with a percentage of 89.0, 96.0, and 94.55 which means to be in a very practical category. The effectiveness of E-module Software based FlipBook Maker is obtained from the results of the field trials, there was an increase in student learning outcomes with the acquisition of the N-gain score of 0.76 with high category. Based on the results of this study it can be concluded that the E-module Software based flipbook maker on megalithic relics in Pasemah in ancient Indonesia history courses developed proved valid, practical, and effective to use In learning.
SimpleNLG-TI: Adapting SimpleNLG to Tibetan

Surface realisation is the last but not the least phase of Natural Language Generation, which aims to produce high-quality natural language text based on meaning representations. In this article, we present our work on SimpleNLG-TI, a Tibetan surface realiser, which follows the design paradigm of SimpleNLG-EN. SimpleNLG-TI is built up by our investigation of the core features of Tibetan morphology and syntax. Through this work, we provide a robust and flexible surface realiser for Tibetan generation systems.
Research on Intangible Cultural Heritage Education and Inheritance in Universities

This paper first analyzes the advantages and problems of intangible cultural heritage education and inheritance in Chinese universities, and then points out the key points of local universities in constructing the education system of intangible cultural heritage in terms of curriculum setting, textbook preparation and pedagogical reform, and perfecting teacher deployment and teaching conditions.
Volumetric End-to-End Optimized Compression for Brain Images

The amount of volumetric brain image increases rapidly, which requires a vast amount of resources for storage and transmission, so it's urgent to explore an efficient volumetric compression method. Recent years have witnessed the progress of deep learning-based approaches for two-dimensional (2D) natural image compression, but the field of learned volumetric image compression still remains unexplored. In this paper, we propose the first end-to-end learning framework for volumetric image compression by extending the advanced techniques of 2D image compression to volumetric images. Specifically, a convolutional autoencoder is used to compress 3D image cubes, and the non-local attention models are embedded in the convolutional autoencoder to jointly capture local and global correlations. Both hyperprior and autoregressive models are used to perform the conditional probability estimation in entropy coding. To reduce model complexity, we introduce a convolutional long short-term memory network for the autoregressive model based on channel-wise prediction. Experimental results on volumetric mouse brain images show that the proposed method outperforms JPEG2000-3D, HEVC and state-of-the-art 2D methods.
FPAI at SemEval-2020 Task 10: A Query Enhanced Model with RoBERTa for Emphasis Selection

This paper describes the model we apply in the SemEval-2020 Task 10.We formalize the task of emphasis selection as a simplified query-based machine reading comprehension (MRC) task, i.e. answering a fixed question of "Find candidates for emphasis".We propose our subword puzzle encoding mechanism and subword fusion layer to align and fuse subwords.By introducing the semantic prior knowledge of the informative query and some other techniques, we attain the 7th place during the evaluation phase and the first place during train phase.
Building longitudinal medication dose data using medication information extracted from clinical notes in electronic health records

Abstract Objective To develop an algorithm for building longitudinal medication dose datasets using information extracted from clinical notes in electronic health records (EHRs). Materials and Methods We developed an algorithm that converts medication information extracted using natural language processing (NLP) into a usable format and builds longitudinal medication dose datasets. We evaluated the algorithm on 2 medications extracted from clinical notes of Vanderbilt’s EHR and externally validated the algorithm using clinical notes from the MIMIC-III clinical care database. Results For the evaluation using Vanderbilt’s EHR data, the performance of our algorithm was excellent; F1-measures were ≥0.98 for both dose intake and daily dose. For the external validation using MIMIC-III, the algorithm achieved F1-measures ≥0.85 for dose intake and ≥0.82 for daily dose. Discussion Our algorithm addresses the challenge of building longitudinal medication dose data using information extracted from clinical notes. Overall performance was excellent, but the algorithm can perform poorly when incorrect information is extracted by NLP systems. Although it performed reasonably well when applied to the external data source, its performance was worse due to differences in the way the drug information was written. The algorithm is implemented in the R package, “EHR,” and the extracted data from Vanderbilt’s EHRs along with the gold standards are provided so that users can reproduce the results and help improve the algorithm. Conclusion Our algorithm for building longitudinal dose data provides a straightforward way to use EHR data for medication-based studies. The external validation results suggest its potential for applicability to other systems.
PEDAGOGICAL WORKSHOP AS A FORM OF DEVELOPMENT OF ESTIMATED COMPETENCE OF A MATHEMATICS TEACHER

The article actualizes the problem of the formation of the teacher's evaluative competence.The material for the research was the generalization of the experience of the development of this competence of mathematics teachers through their participation in the work in pedagogical workshops.A pedagogical workshop is a form of advanced training, the activities of teachers in which are built in stages: practical immersion in the problem; group reflection; determination of ways to solve the problem (search workshop); comprehension and development of experience.The effectiveness of pedagogical workshops in the direction of the development of the assessment competence of teachers is due to: the orientation of the work to the specific needs of the workshop participants; focus on maintaining professional motivation and aspirations of teachers for self-development; a cognitive interaction of participants that allows educators to learn from each other.The study confirmed the positive impact of this form of advanced training on improving the assessment competence of mathematics teachers.Participants in pedagogical workshops indicated the development of their readiness to develop tasks for summative and formative assessment, focused on the assessment of high-order skills; the ability to overcome stereotypes, creative application of techniques and assessment techniques, noted the enrichment of professional experience in the integrative use of summative and formative assessment practices.
Global convergence and geometric characterization of slow to fast weight evolution in neural network training for classifying linearly non-separable data

In this paper, we study the dynamics of gradient descent in learning neural networks for classification problems. Unlike in existing works, we consider the linearly non-separable case where the training data of different classes lie in orthogonal subspaces. We show that when the network has sufficient (but not exceedingly large) number of neurons, (1) the corresponding minimization problem has a desirable landscape where all critical points are global minima with perfect classification; (2) gradient descent is guaranteed to converge to the global minima. Moreover, we discovered a geometric condition on the network weights so that when it is satisfied, the weight evolution transitions from a slow phase of weight direction spreading to a fast phase of weight convergence. The geometric condition says that the convex hull of the weights projected on the unit sphere contains the origin.
Efficient message transmission via twisted Edwards curves

Abstract In this paper, we suggest a novel public key scheme by incorporating the twisted Edwards model of elliptic curves. The security of the proposed encryption scheme depends on the hardness of solving elliptic curve version of discrete logarithm problem and Diffie-Hellman problem. It then ensures secure message transmission by having the property of one-wayness, indistinguishability under chosen-plaintext attack (IND-CPA) and indistinguishability under chosen-ciphertext attack (IND-CCA). Moreover, we introduce a variant of Nyberg-Rueppel digital signature algorithm with message recovery using the proposed encryption scheme and give some countermeasures to resist some wellknown forgery attacks.
The Subjective Well-Being of Students and its Factors: Theoretical Analysis

The article defines subjective well-being and describes its structure. The features of young people in the student period associated with their age and social status are shown in this article. It is also shown that the factors that determine subjective well-being and its components are not only the social status and age of students, but also different indicators: external (related to the environment) and internal (describing a person).
PENAL CHARACTERISTICS OF CONVICTED WOMEN

The results of studies of female crime, identification of its sources, reasons for criminal behavior of women are of considerable scientific interest in the modern conditions. It is caused by the fact that female crime is quite negatively affecting the society, its institutions, especially family, its moral and psychological atmosphere. Female crime is a ratio of the moral health of the society, its spirituality, attitude to basic universal human values.
The Classification of Documents in Malay and Indonesian Using the Naive Bayesian Method Uses Words and Phrases as a Training Set

Malay Language and Indonesian Language are two closely related languages, sharing a lot in common in the meanings of words and grammar. Classifying the two languages automatically using a tool is a challenge because the two languages are very similar. The classification method that is widely used today is the Naive Bayesian method. This method needs to be implemented in a particular way to increase the level of classification accuracy. In this study, a new method was used, by using a training set in the form of words and phrases instead of just using a training set in the form of words only. With this method, the level of classification accuracy of the two languages is increased.
On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages

While recurrent models have been effective in NLP tasks, their performance on context-free languages (CFLs) has been found to be quite weak.Given that CFLs are believed to capture important phenomena such as hierarchical structure in natural languages, this discrepancy in performance calls for an explanation.We study the performance of recurrent models on Dyck-n languages, a particularly important and well-studied class of CFLs.We find that while recurrent models generalize nearly perfectly if the lengths of the training and test strings are from the same range, they perform poorly if the test strings are longer.At the same time, we observe that recurrent models are expressive enough to recognize Dyck words of arbitrary lengths in finite precision if their depths are bounded.Hence, we evaluate our models on samples generated from Dyck languages with bounded depth and find that they are indeed able to generalize to much higher lengths.Since natural language datasets have nested dependencies of bounded depth, this may help explain why they perform well in modeling hierarchical dependencies in natural language data despite prior works indicating poor generalization performance on Dyck languages.We perform probing studies to support our results and provide comparisons with Transformers.
Reinforcement Learning-based Resource Allocation in Quantum Key Distribution Networks

Efficient utilization of key resources is one of the key problems in quantum key distribution (QKD) networks. This paper verifies the effectiveness of using reinforcement learning to realize resource allocation in QKD networks.