COVID-19 datasets : a brief overview

The outbreak of the COVID-19 pandemic affects lives and social-economic development around the world. The affecting of the pandemic has motivated researchers from different domains to find effective solutions to diagnose, prevent, and estimate the pandemic and relieve its adverse effects. Numerous COVID-19 datasets are built from these studies and are available to the public. These datasets can be used for disease diagnosis and case prediction, speeding up solving problems caused by the pandemic. To meet the needs of researchers to understand various COVID-19 datasets, we examine and provide an overview of them. We organise the majority of these datasets into three categories based on the category of ap-plications, i.e., time-series, knowledge base, and media-based datasets. Organising COVID-19 datasets into appropriate categories can help researchers hold their focus on methodology rather than the datasets. In addition, applications and COVID-19 datasets suffer from a series of problems, such as privacy and quality. We discuss these issues as well as potentials of COVID-19 datasets. © 2022, ComSIS Consortium. All rights reserved

MML inference of hierarchical probabilistic finite state machine

A Finite State Machine (FSM) is a mathematical model of computation which can effectively model a sequence of words or tokens. A grammar representing a collection of tokens in a finite alphabet might contain regularities that are not fully captured by a deterministic formal grammar. Therefore, the simple FSM model is extended to include some probabilistic structure in the grammar which is now termed as Probabilistic Finite State Machine (PFSM). We extend earlier work on inferring PFSMs using the Bayesian informationtheoretic Minimum Message Length (MML) principle to the case of inferring hierarchical PFSMs (HPFSMs). HPFSMs consist of an outer PFSM whose states can internally contain a PFSM (or, recursively, an HPFSM). The alphabet of each such internally contained PFSM can be smaller than the complete HPFSM. HPFSMs can often represent the behaviour of a PFSM more concisely, and MML's ability to deal with both discrete structures and continuous probabilities renders MML well suited to this more general inference. We empirically compare on pseudo-random data-sets. © 2019 IEEE

Learning and Inference of Probabilistic Finite State Machines using MML and Applications to Classification Problem

This thesis examines the problem of learning Probabilistic Finite State Machines from text data and applies it to text classification. Probabilistic Finite State Machines capture regularities and patterns in the text data very effectively and this feature is combined with the ability to compress using the Minimum Message Length principle. Different approaches are developed and are applied on a two-class classification scenario like, classifying spam and non-spam emails on the Enron spam datasets and prediction of individuals in the Activities of Daily Living datasets. The approaches produce significant results and outperform the existing methods of classification

Statistical compression-based models for text classification

Text classification is the task of assigning predefined categories to text documents. It is a common machine learning problem. Statistical text classification that makes use of machine learning methods to learn classification rules are particularly known to be successful in this regard. In this research project we are trying to re-invent the text classification problem with a sound methodology based on statistical data compression technique-the Minimum Message Length (MML) principle. To model the data sequence we have used the Probabilistic Finite State Automata (PFSAs). We propose two approaches for text classification using the MML-PFSAs. We have tested both the approaches with the Enron spam dataset and the results of our empirical evaluation has been recorded in terms of the well known classification measures i.e. recall, precision, accuracy and error. The results indicate good classification accuracy that can be compared with the state of art classifiers. © 2016 IEEE

MML inference of Finite State Automata for probabilistic spam detection

Abstract—MML (Minimum Message Length) has emerged as a powerful tool in inductive inference of discrete, continuous and hybrid structures. The Probabilistic Finite State Automaton (PFSA) is one such discrete structure that needs to be inferred for classes of problems in the field of Computer Science including artificial intelligence, pattern recognition and data mining. MML has also served as a viable tool in many classes of problems in the field of Machine Learning including both supervised and unsupervised learning. The classification problem is the most common among them. This research is a two-fold solution to a problem where one part focusses on the best inferred PFSA using MML and the second part focusses on the classification problem of Spam Detection. Using the best PFSA inferred in part 1, the Spam Detection theory has been tested using MML on a publicly available Enron Spam dataset. The filter was evaluated on various performance parameters like precision and recall. The evaluation was also done taking into consideration the cost of misclassification in terms of weighted accuracy rate and weighted error rate. The results of our empirical evaluation indicate the classification accuracy to be around 93%, which outperforms well-known established spam filters

Physics-informed graph learning

An expeditious development of graph learning in recent years has found innumerable applications in several di-versified fields. Of the main associated challenges are the volume and complexity of graph data. The graph learning models suffer from the inability to efficiently learn graph information. In order to indemnify this inefficacy, physics-informed graph learning (PIGL) is emerging. PIGL incorporates physics rules while performing graph learning, which has enormous benefits. This paper presents a systematic review of PIGL methods. We begin with introducing a unified framework of graph learning models followed by examining existing PIGL methods in relation to the unified framework. We also discuss several future challenges for PIGL. This survey paper is expected to stimulate innovative research and development activities pertaining to PIGL. © 2022 IEEE

In your face : sentiment analysis of metaphor with facial expressive features

Metaphor plays an important role in human communication, which often conveys and evokes sentiments. Numerous approaches to sentiment analysis of metaphors have thus gained attention in natural language processing (NLP). The primary focus of these approaches is on linguistic features and text rather than other modal information and data. However, visual features such as facial expressions also play an important role in expressing sentiments. In this paper, we present a novel neural network approach to sentiment analysis of metaphorical expressions that combines both linguistic and visual features and refer to it as the multimodal model approach. For this, we create a Chinese dataset, containing textual data from metaphorical sentences along with visual data on synchronized facial images. The experimental results indicate that our multimodal model outperforms several other linguistic and visual models, and also outperforms the state-of-the-art methods. The contribution is realized in terms of novelty of the approach and creation of a new, sizeable, and scarce dataset with linguistic and synchronized facial expressive image data. The dataset is particularly useful in languages other than English and the approach addresses one of the most challenging NLP issue: sentiment analysis in metaphor. © 2021 IEEE

Deep learning model to empower student engagement in online synchronous learning environment

Following the start of the pandemic, online synchronous learning has grown significantly. The higher education sector is searching for new creative ways to provide the information online because of the switch from face-to-face to online synchronous course delivery. Students are also becoming accustomed to studying online, and research has shown that synchronous online learning has a variety of effects on student engagement. For instance, according to statistics from the National Survey of Student Engagement, students are less likely to participate in collaborative learning, studentfaculty interactions, and conversations when learning online if they use quantitative reasoning during face-to-face instruction. Additionally, studies suggest that because they depend on their devices to take online classes, students feel more alienated from their lecturers. This has been linked to a drop in contacts with peers and teachers as a result. By using a cutting-edge deep learning model to predict learner engagement behaviour in a synchronous teaching environment, our research intends to improve online engagement. The model with a clever trigger will encourage the disengaged pupils to communicate with the teachers online. Smart triggers will be built around factors that have been found, focusing on disengaged students to engage them in real-time with automatic, personalized feedback. © 2022 IEEE