TEMPORAL DATA EXTRACTION AND QUERY SYSTEM FOR EPILEPSY SIGNAL ANALYSIS

The 2016 Epilepsy Innovation Institute (Ei2) community survey reported that unpredictability is the most challenging aspect of seizure management. Effective and precise detection, prediction, and localization of epileptic seizures is a fundamental computational challenge. Utilizing epilepsy data from multiple epilepsy monitoring units can enhance the quantity and diversity of datasets, which can lead to more robust epilepsy data analysis tools. The contributions of this dissertation are two-fold. One is the implementation of a temporal query for epilepsy data; the other is the machine learning approach for seizure detection, seizure prediction, and seizure localization. The three key components of our temporal query interface are: 1) A pipeline for automatically extract European Data Format (EDF) information and epilepsy annotation data from cross-site sources; 2) Data quantity monitoring for Epilepsy temporal data; 3) A web-based annotation query interface for preliminary research and building customized epilepsy datasets. The system extracted and stored about 450,000 epilepsy-related events of more than 2,497 subjects from seven institutes up to September 2019. Leveraging the epilepsy temporal events query system, we developed machine learning models for seizure detection, prediction, and localization. Using 135 extracted features from EEG signals, we trained a channel-based eXtreme Gradient Boosting model to detect seizures on 8-second EEG segments. A long-term EEG recording evaluation shows that the model can detect about 90.34% seizures on an existing EEG dataset with 961 hours of data. The model achieved 89.88% accuracy, 92.32% sensitivity, and 84.76% AUC based on the segments evaluation. We also introduced a transfer learning approach consisting of 1) a base deep learning model pre-trained by ImageNet dataset and 2) customized fully connected layers, to train the patient-specific pre-ictal and inter-ictal data from our database. Two convolutional neural network architectures were evaluated using 53 pre-ictal segments and 265 continuous hours of inter-ictal EEG data. The evaluation shows that our model reached 86.79% sensitivity and 3.38% false-positive rate. Another transfer learning model for seizure localization uses a pre-trained ResNext50 structure and was trained with an image augmentation dataset labeling by fingerprint. Our model achieved 88.22% accuracy, 34.99% sensitivity, 1.02% false-positive rate, and 34.3% positive likelihood rate

Towards a breakthrough Speaker Identification approach for Law Enforcement Agencies: SIIP

This paper describes SIIP (Speaker Identification Integrated Project) a high performance innovative and sustainable Speaker Identification (SID) solution, running over large voice samples database. The solution is based on development, integration and fusion of a series of speech analytic algorithms which includes speaker model recognition, gender identification, age identification, language and accent identification, keyword and taxonomy spotting. A full integrated system is proposed ensuring multisource data management, advanced voice analysis, information sharing and efficient and consistent man-machine interactions

Towards a breakthrough speaker identification approach for law enforcement agencies

This paper describes a high performance innovative and sustainable Speaker Identification (SID) solution, running over large voice samples database. The solution is based on development, integration and fusion of a series of speech analytic algorithms which includes speaker model recognition, gender identification, age identification, language and accent identification, keyword and taxonomy spotting. A full integrated system is proposed ensuring multisource data management, advanced voice analysis, information sharing and efficient and consistent man-machine interactions

Extracting fine-grained economic events from business news

Based on a recently developed fine-grained event extraction dataset for the economic domain, we present in a pilot study for supervised economic event extraction. We investigate how a state-of-the-art model for event extraction performs on the trigger and argument identification and classification. While F1-scores of above 50{%} are obtained on the task of trigger identification, we observe a large gap in performance compared to results on the benchmark ACE05 dataset. We show that single-token triggers do not provide sufficient discriminative information for a fine-grained event detection setup in a closed domain such as economics, since many classes have a large degree of lexico-semantic and contextual overlap

A review of natural language processing in contact centre automation

Contact centres have been highly valued by organizations for a long time. However, the COVID-19 pandemic has highlighted their critical importance in ensuring business continuity, economic activity, and quality customer support. The pandemic has led to an increase in customer inquiries related to payment extensions, cancellations, and stock inquiries, each with varying degrees of urgency. To address this challenge, organizations have taken the opportunity to re-evaluate the function of contact centres and explore innovative solutions. Next-generation platforms that incorporate machine learning techniques and natural language processing, such as self-service voice portals and chatbots, are being implemented to enhance customer service. These platforms offer robust features that equip customer agents with the necessary tools to provide exceptional customer support. Through an extensive review of existing literature, this paper aims to uncover research gaps and explore the advantages of transitioning to a contact centre that utilizes natural language solutions as the norm. Additionally, we will examine the major challenges faced by contact centre organizations and offer reco

Development of deep learning applications for the automated extraction of chemical information from scientific literature

This dissertation focuses on developing deep learning applications for extracting chemical information from scientific literature, particularly targeting the automated recognition of molecular structures in images. DECIMER Segmentation, a novel application, employs a Mask Region-based Convolutional Neural Network (MRCNN) model to segment chemical structures in documents, aided by a mask expansion algorithm, marking a significant advancement in processing chemical literature. The Optical Chemical Structure Recognition (OCSR) tool DECIMER Image Transformer uses an encoder-decoder architecture to convert chemical structure depictions into the machine-readable SMILES format. The model has been trained on over 450 million pairs of images and SMILES representations. Its ability to interpret various depiction styles, including hand-drawn structures, sets a new standard in OCSR. To artificially generate large and diverse OCSR training datasets using multiple cheminformatics toolkits, RanDepict was developed. The diversification of training data ensures robust model generalisation across different chemical structure depictions. A unique dataset of hand-drawn molecule images was created to evaluate the model's performance in interpreting these challenging depictions. This dataset further contributes to the understanding of automated structure recognition from diverse styles. The integration of these technologies led to the creation of DECIMER.ai, an open-source web application that combines segmentation and interpretation tools, allowing users to extract and process chemical information from literature efficiently. The work concludes with a discussion on the significance of open data in advancing molecular informatics, highlighting the potential to broader chemical research domains. By adhering to FAIR data standards and open-source principles, the tools developed for this dissertation are designed for adaptability and future development within the community

Integrating Sustainability in Asian Business Schools

This chapter discusses the ProSPER.Net initiative to integrate sustainability into business schools in the Asia-Pacific region. The project deserves scrutiny, as it led to positive change within each of the partner universities, involving innovative research, curricular transformation and the development of regional case studies. As such, the project is an instructive case in its own right, highlighting the successes, as well as the obstacles encountered, in introducing and coordinating education for sustainable development (ESD) in diverse organizations. The experience of the project clearly shows that whereas there is a recognized need to promote ESD in business schools throughout Asia, the process of achieving this is difficult and complex. Through a consideration of the achievements and frustrations of the project, it is hoped that this chapter will contribute to a better understanding of these issues, and will help in subsequent efforts to integrate sustainability in business education

Speech technologies for the audiovisual and multimedia interaction environments

361 p

Protectbot: A Chatbot to Protect Children on Gaming Platforms

Online gaming no longer has limited access, as it has become available to a high percentage of children in recent years. Consequently, children are exposed to multifaceted threats, such as cyberbullying, grooming, and sexting. The online gaming industry is taking concerted measures to create a safe environment for children to play and interact with, such efforts remain inadequate and fragmented. Different approaches utilizing machine learning (ML) techniques to detect child predatory behavior have been designed to provide potential detection and protection in this context. After analyzing the available AI tools and solutions it was observed that the available solutions are limited to the identification of predatory behavior in chat logs which is not enough to avert the multifaceted threats. In this thesis, we developed a chatbot Protectbot to interact with the suspect on the gaming platform. Protectbot leveraged the dialogue generative pre-trained transformer (DialoGPT) model which is based on Generative Pre-trained Transformer 2 (GPT-2). To analyze the suspect\u27s behavior, we developed a text classifier based on natural language processing that can classify the chats as predatory and non-predatory. The developed classifier is trained and tested on Pan 12 dataset. To convert the text into numerical vectors we utilized fastText. The best results are obtained by using non-linear SVM on sentence vectors obtained from fastText. We got a recall of 0.99 and an F_0.5-score of 0.99 which is better than the state-of-the-art methods. We also built a new dataset containing 71 predatory full chats retrieved from Perverted Justice. Using sentence vectors generated by fastText and KNN classifier, 66 chats out of 71 were correctly classified as predatory chats