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Social and Behavioral Sciences Commons™
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Full-Text Articles in Social and Behavioral Sciences
Towards A More Effective Bidirectional Lstm-Based Learning Model For Human-Bacterium Protein-Protein Interactions, Huaming Chen, Jun Shen, Lei Wang, Yaochu Jin
Towards A More Effective Bidirectional Lstm-Based Learning Model For Human-Bacterium Protein-Protein Interactions, Huaming Chen, Jun Shen, Lei Wang, Yaochu Jin
Faculty of Engineering and Information Sciences - Papers: Part B
The identification of protein-protein interaction (PPI) is one of the most important tasks to understand the biological functions and disease mechanisms. Although numerous databases of biological interactions have been published in debt to advanced high-throughput technology, the study of inter-species protein-protein interactions, especially between human and bacterium pathogens, remains an active yet challenging topic to harness computational models tackling the complex analysis and prediction tasks. In this paper, we comprehensively revisit the prediction task of human-bacterium protein-protein interactions (HB-PPI), which is a first ever endeavour to report an empirical evaluation in learning and predicting HB-PPI based on machine learning models. …
Utilizing Qr Codes To Verify The Visual Fidelity Of Image Datasets For Machine Learning, Yang-Wai Chow, Willy Susilo, Jianfang Wang, Richard Buckland, Joon Sang Baek, Jongkil Kim, Nan Li
Utilizing Qr Codes To Verify The Visual Fidelity Of Image Datasets For Machine Learning, Yang-Wai Chow, Willy Susilo, Jianfang Wang, Richard Buckland, Joon Sang Baek, Jongkil Kim, Nan Li
Faculty of Engineering and Information Sciences - Papers: Part B
Machine learning is becoming increasingly popular in modern technology and has been adopted in various application areas. However, researchers have demonstrated that machine learning models are vulnerable to adversarial examples in their inputs, which has given rise to a field of research known as adversarial machine learning. Potential adversarial attacks include methods of poisoning datasets by perturbing input samples to mislead machine learning models into producing undesirable results. While such perturbations are often subtle and imperceptible from the perspective of a human, they can greatly affect the performance of machine learning models. This paper presents two methods of verifying the …
Learning To Read Equine Agency: Sense And Sensitivity At The Intersection Of Scientific, Tacit And Situated Knowledges, Sanna Karkulehto, Nora Schuurman
Learning To Read Equine Agency: Sense And Sensitivity At The Intersection Of Scientific, Tacit And Situated Knowledges, Sanna Karkulehto, Nora Schuurman
Animal Studies Journal
The aim of this essay is to address the challenges and problems in communicating with horses and interpreting their communication in everyday handling and training situations. We seek ways to learn more about equine communication and agency in the prevention of cruelty against animals and in enhancing animal welfare. We ask how it would be possible to learn to read the subtle signs of equine communication and agency in a sensible, sensitive, and ethical way to increase the health and wellbeing of horses that humans interact with. We have placed this theoretical examination in a multidisciplinary framework that consists of …