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Full-Text Articles in Computer Engineering
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 …
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 …