Gilles Fauconnier & Mark Turner, " The way we think: conceptual blending and the mind's hidden complexities"

The COVID-19 infection is increasing at a rapid rate, with the availability of limited number of testing kits. Therefore, the development of COVID-19 testing kits is still an open area of research. Recently, many studies have shown that chest Computed Tomography (CT) images can be used for COVID-19 testing, as chest CT images show a bilateral change in COVID-19 infected patients. However, the classification of COVID-19 patients from chest CT images is not an easy task as predicting the bilateral change is defined as an ill-posed problem. Therefore, in this paper, a deep transfer learning technique is used to classify COVID-19 infected patients. Additionally, a top-2 smooth loss function with cost-sensitive attributes is also utilized to handle noisy and imbalanced COVID-19 dataset kind of problems. Experimental results reveal that the proposed deep transfer learning-based COVID-19 classification model provides efficient results as compared to the other supervised learning models.

Deep Transfer Learning Based Classification Model for COVID-19 Disease.

A Whole New Mind: Why Right-Brainers Will Rule the Future

Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score. 

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Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

This paper presents the architecture of Almond, an open, crowdsourced, privacy-preserving and programmable virtual assistant for online services and the Internet of Things (IoT). Included in Almond is Thingpedia, a crowdsourced public knowledge base of natural language interfaces and open APIs. Our proposal addresses four challenges in virtual assistant technology: generality, interoperability, privacy, and usability. Generality is addressed by crowdsourcing Thingpedia, while interoperability is provided by ThingTalk, a high-level domain-specific language that connects multiple devices or services via open APIs. For privacy, user credentials and user data are managed by our open-source ThingSystem, which can be run on personal phones or home servers. Finally, we address usability by providing a natural language interface, whose capability can be extended via training with the help of a menu-driven interface. We have created a fully working prototype, and crowdsourced a set of 187 functions across 45 different kinds of devices. Almond is the first virtual assistant that lets users specify trigger-action tasks in natural language. Despite the lack of real usage data, our experiment suggests that Almond can understand about 40% of the complex tasks when uttered by a user familiar with its capability.

Almond: The Architecture of an Open, Crowdsourced, Privacy-Preserving, Programmable Virtual Assistant

Analysis of tuberculosis severity levels from CT pulmonary images based on enhanced residual deep learning architecture

Effective student support can be seen as a key element in enhancing students’ learning experience and has the potential to improve retention, progression and student achievement. The purpose of this research was to investigate student responses to the idea of using an avatar-based virtual support system and to develop such a system for piloting in a specific university service. A survey was conducted to establish the perceived need for such a system. A prototype system was then implemented in a limited domain, namely student employability and a further survey was conducted to obtain feedback from potential users as to its efficacy. This was generally positive and our intention is now to roll out the system across other university services.

Enhancing Student Support with a Virtual Assistant

We present our approach to teaching functional programming to First Year Computer Science students at Middlesex University through projects in robotics. A holistic approach is taken to the curriculum, emphasising the connections between different subject areas. A key part of the students' learning is through practical projects that draw upon and integrate the taught material. To support these, we developed the Middlesex Robotic plaTfOrm (MIRTO), an open-source platform built using Raspberry Pi, Arduino, HUB-ee wheels and running Racket (a LISP dialect). In this paper we present the motivations for our choices and explain how a number of concepts of functional programming may be employed when programming robotic applications. We present some students' work with robotics projects: we consider the use of robotics projects to have been a success, both for their value in reinforcing students' understanding of programming concepts and for their value in motivating the students.

Teaching Functional Patterns through Robotic Applications

Student support is an important function in all universities. Most students expect access to support 24/7, but support staff cannot be available at all times of day. This paper addresses this problem, describing the development of an avatar-based system to guide students through the materials provided by a university student employability service. Firstly, students and staff were surveyed to establish the demand for such a system. The system was then constructed. Finally, the system was evaluated by students and staff, which led to a clearer understanding of the optimal role for avatar-based systems and consequent improvements to the system’s functionality.

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An investigation into the efficacy of avatar-based systems for student advice

The use of audio feedback is becoming more prevalent and it would be possible to use avatars for this purpose. When audio feedback is recorded by a human tutor, the recording contains not only the text of the feedback, but also additional information associated with the intonation and manner of delivery of the voice. Experiments were conducted to investigate student’s responses to the use of audio in comparison with other forms of feedback. Students were generally positive about audio feedback; results also indicated that the conveyed emotion or intent is significant and that it is perceived by the student as an important part of the feedback. We also explore this in the context of strategies for the deployment of virtual agents in the provision of feedback.

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Edward Currie

Papers

Analysis of tuberculosis severity levels from CT pulmonary images based on enhanced residual deep learning architecture

Enhancing Student Support with a Virtual Assistant

Teaching Functional Patterns through Robotic Applications

An investigation into the efficacy of avatar-based systems for student advice

Smart Feedback and the Challenges of Virtualisation