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Full-Text Articles in Engineering
Analysis Of Rule-Based And Shallow Statistical Models For Covid-19 Cough Detection For A Preliminary Diagnosis, Arshia Arif, Eisa Alanazi, Ayesha Zeb, Waqar Shahid Qureshi
Analysis Of Rule-Based And Shallow Statistical Models For Covid-19 Cough Detection For A Preliminary Diagnosis, Arshia Arif, Eisa Alanazi, Ayesha Zeb, Waqar Shahid Qureshi
Conference papers
Coronavirus pandemic that has spread all over the world, is one of its kind in the recent past, that has mobilized researchers in areas such as (not limited to) pre-screening solutions, contact tracing, vaccine developments, and crowd estimation. Pre-screening using symptoms identification, cough classification, and contact tracing mobile applications gained significant popularity during the initial outbreak of the pandemic. Audio recordings of coughing individuals are one of the sources that can help in the pre-screening of COVID-19 patients. This research focuses on quantitative analysis of covid cough classification using audio recordings of coughing individuals. For analysis, we used three different …
Moving Targets: Addressing Concept Drift In Supervised Models For Hacker Communication Detection, Susan Mckeever, Brian Keegan, Andrei Quieroz
Moving Targets: Addressing Concept Drift In Supervised Models For Hacker Communication Detection, Susan Mckeever, Brian Keegan, Andrei Quieroz
Conference papers
Abstract—In this paper, we are investigating the presence of concept drift in machine learning models for detection of hacker communications posted in social media and hacker forums. The supervised models in this experiment are analysed in terms of performance over time by different sources of data (Surface web and Deep web). Additionally, to simulate real-world situations, these models are evaluated using time-stamped messages from our datasets, posted over time on social media platforms. We have found that models applied to hacker forums (deep web) presents an accuracy deterioration in less than a 1-year period, whereas models applied to Twitter (surface …
Active Learning For Auditory Hierarchy, William Coleman, Sarah Jane Delany, Charlie Cullen, Ming Yan
Active Learning For Auditory Hierarchy, William Coleman, Sarah Jane Delany, Charlie Cullen, Ming Yan
Conference papers
Much audio content today is rendered as a static stereo mix: fundamentally a fixed single entity. Object-based audio envisages the delivery of sound content using a collection of individual sound ‘objects’ controlled by accompanying metadata. This offers potential for audio to be delivered in a dynamic manner providing enhanced audio for consumers. One example of such treatment is the concept of applying varying levels of data compression to sound objects thereby reducing the volume of data to be transmitted in limited bandwidth situations. This application motivates the ability to accurately classify objects in terms of their ‘hierarchy’. That is, whether …
A Machine Learning Management Model For Qoe Enhancement In Next-Generation Wireless Ecosystems, Eva Ibarrola, Mark Davis, Camille Voisin, Ciara Close, Leire Cristobo
A Machine Learning Management Model For Qoe Enhancement In Next-Generation Wireless Ecosystems, Eva Ibarrola, Mark Davis, Camille Voisin, Ciara Close, Leire Cristobo
Conference papers
Next-generation wireless ecosystems are expected to comprise heterogeneous technologies and diverse deployment scenarios. Ensuring a good quality of service (QoS) will be one of the major challenges of next-generation wireless systems on account of a variety of factors that are beyond the control of network and service providers. In this context, ITU-T is working on updating the various Recommendations related to QoS and users' quality of experience (QoE). Considering the ITU-T QoS framework, we propose a methodology to develop a global QoS management model for next-generation wireless ecosystems taking advantage of big data and machine learning. The results from a …
Examining A Hate Speech Corpus For Hate Speech Detection And Popularity Prediction, Filip Klubicka, Raquel Fernandez
Examining A Hate Speech Corpus For Hate Speech Detection And Popularity Prediction, Filip Klubicka, Raquel Fernandez
Conference papers
As research on hate speech becomes more and more relevant every day, most of it is still focused on hate speech detection. By attempting to replicate a hate speech detection experiment performed on an existing Twitter corpus annotated for hate speech, we highlight some issues that arise from doing research in the field of hate speech, which is essentially still in its infancy. We take a critical look at the training corpus in order to understand its biases, while also using it to venture beyond hate speech detection and investigate whether it can be used to shed light on other …
Robustness And Prediction Accuracy Of Machine Learning For Objective Visual Quality Assessment, Andrew Hines, Paul Kendrick, Adriaan Barri, Manish Narwaria, Judith A. Redi
Robustness And Prediction Accuracy Of Machine Learning For Objective Visual Quality Assessment, Andrew Hines, Paul Kendrick, Adriaan Barri, Manish Narwaria, Judith A. Redi
Conference papers
Machine Learning (ML) is a powerful tool to support the development of objective visual quality assessment metrics, serving as a substitute model for the perceptual mechanisms acting in visual quality appreciation. Nevertheless, the reliability of ML-based techniques within objective quality assessment metrics is often questioned. In this study, the robustness of ML in supporting objective quality assessment is investigated, specifically when the feature set adopted for prediction is suboptimal. A Principal Component Regression based algorithm and a Feed Forward Neural Network are compared when pooling the Structural Similarity Index (SSIM) features perturbed with noise. The neural network adapts better with …
Ecue: A Spam Filter That Uses Machine Learning To Track Concept Drift, Sarah Jane Delany, Padraig Cunningham, Barry Smyth
Ecue: A Spam Filter That Uses Machine Learning To Track Concept Drift, Sarah Jane Delany, Padraig Cunningham, Barry Smyth
Conference papers
While text classification has been identified for some time as a promising application area for Artificial Intelligence, so far few deployed applications have been described. In this paper we present a spam filtering system that uses example-based machine learning techniques to train a classifier from examples of spam and legitimate email. This approach has the advantage that it can personalise to the specifics of the user’s filtering preferences. This classifier can also automatically adjust over time to account for the changing nature of spam (and indeed changes in the profile of legitimate email). A significant software engineering challenge in developing …