Reviewerly/exHarmony huggingface dataset:
When BERT meets Bilbo: a learning curve analysis of pretrained language model on disease classification

Abstract Background Natural language processing (NLP) tasks in the health domain often deal with limited amount of labeled data due to high annotation costs and naturally rare observations. To compensate for the lack of training data, health NLP researchers often have to leverage knowledge and resources external to a task at hand. Recently, pretrained large-scale language models such as the Bidirectional Encoder Representations from Transformers (BERT) have been proven to be a powerful way of learning rich linguistic knowledge from massive unlabeled text and transferring that knowledge to downstream tasks. However, previous downstream tasks often used training data at such a large scale that is unlikely to obtain in the health domain. In this work, we aim to study whether BERT can still benefit downstream tasks when training data are relatively small in the context of health NLP. Method We conducted a learning curve analysis to study the behavior of BERT and baseline models as training data size increases. We observed the classification performance of these models on two disease diagnosis data sets, where some diseases are naturally rare and have very limited observations (fewer than 2 out of 10,000). The baselines included commonly used text classification models such as sparse and dense bag-of-words models, long short-term memory networks, and their variants that leveraged external knowledge. To obtain learning curves, we incremented the amount of training examples per disease from small to large, and measured the classification performance in macro-averaged $$F_{1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>F</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math> score. Results On the task of classifying all diseases, the learning curves of BERT were consistently above all baselines, significantly outperforming them across the spectrum of training data sizes. But under extreme situations where only one or two training documents per disease were available, BERT was outperformed by linear classifiers with carefully engineered bag-of-words features. Conclusion As long as the amount of training documents is not extremely few, fine-tuning a pretrained BERT model is a highly effective approach to health NLP tasks like disease classification. However, in extreme cases where each class has only one or two training documents and no more will be available, simple linear models using bag-of-words features shall be considered.
VIDEO-BASED TABLE TENNIS TRACKING AND TRAJECTORY PREDICTION USING CONVOLUTIONAL NEURAL NETWORKS

One of the fascinating aspects of sports rivalry is that anything can happen. The significant difficulty is that computer-aided systems must address how to record and analyze many game events, and fractal AI plays an essential role in dealing with complex structures, allowing effective solutions. In table tennis, we primarily concentrate on two issues: ball tracking and trajectory prediction. Based on these two components, we can get ball parameters such as velocity and spin, perform data analysis, and even create a ping-pong robot application based on fractals. However, most existing systems rely on a traditional method based on physical analysis and a non-machine learning tracking algorithm, which can be complex and inflexible. As mentioned earlier, to overcome the problem, we proposed an automatic table tennis-aided system based on fractal AI that allows solving complex issues and high structural complexity of object tracking and trajectory prediction. For object tracking, our proposed algorithm is based on structured output Convolutional Neural Network (CNN) based on deep learning approaches and a trajectory prediction model based on Long Short-Term Memory (LSTM) and Mixture Density Networks (MDN). These models are intuitive and straightforward and can be optimized by training iteratively on a large amount of data. Moreover, we construct a table tennis auxiliary system based on these models currently in practice.
Stable Label-Specific Features Generation for Multi-Label Learning via Mixture-Based Clustering Ensemble

Multi-label learning deals with objects associated with multiple class labels, and aims to induce a predictive model which can assign a set of relevant class labels for an unseen instance. Since each class might possess its own characteristics, the strategy of extracting label-specific features has been widely employed to improve the discrimination process in multi-label learning, where the predictive model is induced based on tailored features specific to each class label instead of the identical instance representations. As a representative approach, LIFT generates label-specific features by conducting clustering analysis. However, its performance may be degraded due to the inherent instability of the single clustering algorithm. To improve this, a novel multi-label learning approach named SENCE (stable label-Specific features gENeration for multi-label learning via mixture-based Clustering Ensemble) is proposed, which stabilizes the generation process of label-specific features via clustering ensemble techniques. Specifically, more stable clustering results are obtained by firstly augmenting the original instance representation with cluster assignments from base clusters and then fitting a mixture model via the expectation-maximization (EM) algorithm. Extensive experiments on eighteen benchmark data sets show that SENCE performs better than LIFT and other well-established multi-label learning algorithms.
On optimisation of web crawler system on Scrapy framework

With the continuous development of internet technology, life is accompanied by data at all times. However, network data is so complicated and confusing that it is difficult for users to find valuable information. Therefore, being able to acquire data from a vast data ocean has become an essential skill for today's business development. In this paper, a web crawler system based on the Scrapy framework is optimised to further enhance the crawler efficiency, increase the crawler speed, and break the crawler limit.
A Higher-Order N-gram Model to Enhance Automatic Word Prediction for Assamese Sentences Containing Ambiguous Words

Word prediction is a technique which tries to suggest the users’ words after knowing the few input letters of the user. This predictive model also tries to generate the future words or next words of a sentence by observing earlier words of the sentence. In this research, two problems are combined, one is word prediction and the next is handling of ambiguous words. A word prediction model predicts the future words of a sentence by using n-gram based model. In general, predictive models use unigram, bigram or trigram models to predict the next words. In case of sentences consisting of ambiguous words, the predictive model by using only bigram or trigram cannot perform well to predict the next words. To enhance this prediction for ambiguous words, maximum of six previous input words are observed and try to predict almost the exact words after the ambiguous words in those particular contexts. Different level of experiments are done and the results are compared for modified or enhanced prediction model with the traditional prediction model, improvement on accuracy and failure rate are found in the enhanced model. The accuracy of the Traditional Model is 60.68% on the hand the accuracy of the Enhanced Model is 66.88%. The failure rate of the Traditional Model is 32.35% and the Enhanced Model is 29.17%
A novel fuzzy convolutional neural network for recognition of handwritten Marathi numerals

Pattern classification is the approach of designing a method to map the inputs to the matching output classes. A novel fuzzy convolutional neural network (FCNN) is proposed in this paper for recognition of handwritten Marathi numerals. FCNN uses fuzzy set hypersphere as a pattern classifier to map inputs to classes represented by the combination of the fuzzy set hypersphere. Given labelled classes, the model designed proved efficient with 100% accuracy on the training set. The two major factors which improve the learning algorithm of FCNN are: 1) extract the dominant features from numeral image patterns using customised convolutional neural network (CCNN); 2) supervised clustering is used to create a new fuzzy hypersphere based on its distance measurement learning rules of fuzzy hypersphere neural network (FHSNN) and pattern classification done by the fuzzy membership function. Performance evaluation of model is done on Marathi numerals large datasets and its performance is found to be superior to traditional convolutional neural network (CNN) model. The obtained results demonstrate the fact that FCNN learning rules can be used as a useful representation for different classification pattern problems.
Gesture To Speech Conversion using Flex sensors, MPU6050 and Python

Communicating through hand gestures is one of the most common forms of non-verbal and visual communication adopted by speech impaired population all around the world. The problem existing at the moment is that most of the people are not able to comprehend hand gestures or convert them to the spoken language quick enough for the listener to understand. A large fraction of India’s population is speech impaired. In addition to this communication to sign language is not a very easy task. This problem demands a better solution which can assist speech impaired population conversation without any difficulties. As a result, reducing the communication gap for the speech impaired. This paper proposes an idea which will assist in removing or at least reducing this gap between the speech impaired and normal people. The research going on this area mostly focuses on image processing approaches. However, a cheaper and user-friendly approach has been used in this paper. The idea is to make a glove that can be worn by the speech impaired people which will further be used to convert the sign language into speech and text. Our prototype involves Arduino Uno as a microcontroller which is interfaced with flex sensors and accelerometer, gyroscopic sensor for reading the hand gestures. Furthermore, to perform better execution, we have incorporated an algorithm for better interpretation of data and therefore producing more accurate result. Thereafter, we use python to interface Arduino Uno with a microprocessor and finally converting into speech. The prototype has been calibrated in accordance with the ASL (American Sign Language)
Music Computer Education Concept for Teachers: Raising the Problem

The beginning of the 21th century was marked by the introduction of computer and communication technologies in all spheres of human activity. Global changes have occurred in the way information is transmitted and presented. Digital technology has penetrated into music and education. The achievements of sound recording, the technology of creating musical compositions combined with the new mass media possibilities have defined the previously non-existent areas of development and distribution of music, and require such knowledge that musicians who have received a classic music education do not have.The article describes the basic, which is devoted to development of actual theoretical and practical basis of raising knowledge of students in music department with usage of music computer technologies
Stateful RORI-based countermeasure selection using hypergraphs

Cost-sensitive metrics have been widely used during the past years as financial metrics that quantify the monetary costs and benefits of security investments, assess risks, and select countermeasures accordingly. However, due to the complexity of current attacks, and the level of dynamicity required in the estimation of the parameters composing the metrics, the use of a novel approach that considers restrictions, inter-dependency, as well as, the previous and current state at which the system is exposed to, is required. We propose in this article a Stateful Return on Response Investment (denoted by StRORI) that uses hypergraphs to model actions that have been previously deployed (e.g., at state ST0) while the current state of the system (e.g., ST1) is under analysis. As a result, StRORI is a dynamic tool that considers the changes of the system in terms of number of active devices, previously deployed countermeasures, the cost of adding a new countermeasure or suppressing a previously deployed one, and the effectiveness of a group of security measures due to the implementation of a given action. A case study is presented about the integration of the StRORI index with hypergraph models to assess countermeasures against cyber attacks.
Comparative Evaluation of Viewing and Self-Representation on Passability Affordances to a Realistic Sliding Doorway in Real and Immersive Virtual Environments

As Virtual Reality (VR) devices become more accessible, a multitude of VR applications engage users in highly immersive virtual environments that feature realistic graphics, real-life scenarios, and self-avatars. Many of these simulations require users to make spontaneous affordance judgments such as stepping over obstacles, passing through gaps, etc. which are shown to be affected by the nature of our self-representation in the virtual world. As the technology for creating self-avatars becomes more widely available, it is important to explore how various affordance judgments are affected by the presence of self-avatars. In this work, we investigate the effects of body-scaled self-avatars on the affordance of passability in a natural setting. We implemented a gender-matched body-scaled self-avatar using HTC Vive trackers and evaluated how passability judgments for a sliding doorway in VR, with and without an avatar, compared to the real world judgments. The results suggest that passability judgments are more conservative in VR as compared to the real world. However, the presence of a self-avatar does not significantly affect passability judgments made in VR. This does not align with previous findings which show that having a self-avatar improves judgments and estimates.
Forward feature extraction from imbalanced microarray datasets using wrapper based incremental genetic algorithm

Learning from imbalanced datasets is a critical challenge confronting researchers. Unequal distribution of classes in the imbalanced datasets lead to biased classification especially in microarray gene expression analysis. Since all features in the dataset will not contribute to the analysis, only prominent and significant features need to be identified. The paper addresses both these issues by proposing wrapper based incremental genetic algorithm (IGA) which incrementally evaluates and adds attributes into the genetic algorithm process rather than evaluation of all attributes thereby reducing the computational complexity and number of features used and improving the measures like classification accuracy, GMean, F1 measure, precision and recall. The experiments are conducted on 8 microarray gene expression datasets and the results show that performance of IGA is encouraging and superior to existing methods that are compared.
A knowledge management model for enhancing quality and performance of higher education institutions: insights from Oman

The quality and performance of higher education institutions (HEIs) are crucial to the success of HEIs in the short and long run. In this paper, a knowledge management (KM) model for HEIs is developed and proposed as an appraising mechanism that can be used to identify and monitor the quality and performance areas that are important to HEIs. The conceptualisation and operationalisation of the KM model is also articulated extensively in this paper and tested rigorously using a sample of HEIs in the form of College of Applied Sciences (CASs) in Oman. The findings from the study, which produces important implications for the Ministry of Higher Education (MoHE) as well as for HEIs, generally supports the KM model as an appraising mechanism for identifying and monitoring the quality and performance of HEIs.
Using Text Mining Tools to Inform Search Term Generation: An Introduction for Librarians

The use of text mining tools can help librarians improve the precision of searches, increase search sensitivity, and translate search strategies across multiple research databases. When combined with the intuitive approaches that librarians commonly use, text mining tools help reduce biases by improving the objectivity, transparency, and reproducibility of search strategies. This paper seeks to boost librarian confidence by providing a step-by-step guide for conducting text analyses with two free text mining tools, Voyant Tools and the statistical programming language R. It provides a brief introduction to concepts related to the use of text mining tools for search term generation, then details processes for using Voyant Tools and R and RStudio as investigative instruments.
An overview of Quantum Cryptography and Shor’s Algorithm

The paper aims to examine the mechanisms of quantum cryptography and review the relationship between quantum and classical encryption schemes.A brief introduction of quantum computation is provided, with a simplified explanation of Shor's Algorithm showcasing the potentials of quantum computation.Related literature such as books, journals, proceedings, lecture notes and webpages on quantum cryptography were reviewed and were sourced from prominent databases like IEEE Xplore, ScienceDirect, and JSTOR.This gave a clearer picture on the mechanisms of quantum cryptography and Shor's algorithm.The authors were able to succinctly describe quantum cryptography, show how encryption is achieved by exploiting the properties of quantum particles, and demonstrated with examples the intricacies with Shor's algorithm.It is expected that interested researchers will be more informed on current research in quantum cryptography and influence potential cryptography scholars to explore further the mechanisms of quantum cryptography, quantum computation, and other principles of the quantum theory.
Research on Face Recognition Based on Deep Learning

In order to reduce the occurrence of identity theft and promote the fairness of clocking in, face recognition has been attracting increasing attention. In this paper, on the basis of analyzing the advantages and disadvantages of common face recognition methods (facial geometric feature based method, template matching-based method, wavelet feature-based method and PCA-based method), the factors affecting face recognition such as illumination and age are fully considered. By adopting the Convolutional Neural Networks (CNN) model, a face recognition system is designed and implemented. The system test shows that the system can call the camera to recognize human face, and output the name of the recognized person accurately by loading the trained model.
Approaches to Investigating Complex Dynamical Systems

This chapter reports on a short Caron, F. experiment conducted in Canada to explore the potential, and feasibility, of introducing complex dynamical systemsDynamical systems into mathematics curricula, both in schools and in university undergraduate programs. In particular it aimed to identify those mathematical habits of mind that are (or could be) developed in schools, universities and outside these traditional learning environments through exploring complex systemsComplex systems when approaching real life situations. The use of game design to engage members of the Canadian mathematics education community in the modelling of a real-life ecosystem brought to light different mathematical habits of mind and provided a snapshot of where we are with respect to modelling in mathematics education. Put in perspective with current lines of inquiry in the modelling of complex dynamical systems, in integrating modelling in mathematics education, and in developing computational thinkingComputational thinking , the experiment opened a reflection on the possibilities and feasibility of helping tackle the complexity of our world’s most pressing challenges through mathematics education.
Introduction to Machine Learning

Machine learning refers to the changes in systems that perform tasks associated with artificial intelligence. This chapter presents introduction types and application of machine learning. This chapter also presents the basic concepts related to feature selection techniques such as filter, wrapper and hybrid methods and various machine learning techniques such as artificial neural network, Naive Bayes classifier, support vector machine, k-nearest-neighbor, decision trees, bagging, boosting, random subspace method, random forests, k-means clustering and deep learning. In the last the performance measure of the classifier is presented.
Exploring the Data Processing Practices of Cloud ERP—A Case Study

ABSTRACT Although cloud enterprise resource planning (ERP) systems are becoming popular, there are still firms that have yet to migrate to the cloud because of data confidentiality and data security concerns. Firms that plan to adopt cloud ERP systems are expected to first understand cloud ERP's data processing practices. This study proposes a case study method with a systematic content analysis of cloud ERP providers' data processing agreements. The study will explore the data disclosure, data security, data sub-processing, and data retention and deletion practices of cloud ERP providers. In addition, this study investigates the linguistic characteristics of cloud ERP providers' data processing agreements including readability, litigious language, and uncertainty language. The findings of this study can help organizations to have a better understanding of the data process practices of cloud ERP providers, as well as the shared responsibilities between cloud ERP providers and users in maintaining data security.
Beyond VIAF

This paper aims to investigate the reciprocal relationship between VIAF® and Wikidata and their possible roles in the semantic web environment. It deals with their data, their approach, their domain, and their stakeholders, with particular attention to identification as a fundamental goal of Universal Bibliographic Control. After examining interrelationships among VIAF, Wikidata, libraries and other GLAM institutions, a double approach is used to compare VIAF and Wikidata: first, a quantitative analysis of VIAF and Wikidata data on personal entities, presented in eight tables; and second, a qualitative comparison of several general characteristics, such as purpose, scope, organizational and theoretical approach, data harvesting and management (shown in table 9). Quantitative data and qualitative comparison show that VIAF and Wikidata are quite different in their purpose, scope, organizational and theoretical approach, data harvesting, and management. The study highlights the reciprocal role of VIAF and Wikidata and its helpfulness in the worldwide bibliographical context and in the semantic web environment and outlines new perspectives for research and cooperation.