SOA services in higher education

Service Oriented Architecture (SOA) is a recent architectural framework for distributed software system development in which software components are packaged as Services. It has become increasingly popular in academia and in industry, but has been principally used in the business domain. However, in higher education, SOA has rarely been applied or investigated. In this paper, we propose the idea of applying SOA technologies in the education domain, to increase both interoperability and flexibility within the e-learning environment. We expect that both students and teachers in higher educational institutions can benefit from this approach. We also describe a number of possible SOA services, along with a high level service roadmap to support a university's learning and teaching activities

Opportunities and challenges in using AI Chatbots in Higher Education

Artificial intelligence (AI) conversational chatbots have gained popularity over time, and have been widely used in the fields of e-commerce, online banking, and digital healthcare and well-being, among others. The technology has the potential to provide personalised service to a range of consumers. However, the use of chatbots within educational settings is still limited. In this paper, we present three chatbot prototypes, the Warwick Manufacturing Group, University of Warwick, are currently developing, and discuss the potential opportunities and technical challenges we face when considering AI chatbots to support our daily activities within the department. Three AI virtual agents are under development: 1) to support the delivery of a taught Master's course simulation game; 2) to support the training and use of a newly introduced educational application; 3) to improve the processing of helpdesk requests within a university department. We hope this paper is informative to those interested in using chatbots in the educational domain. We also aim to improve awareness among those within the chatbot development industry, in particular the chatbot engine providers, about the educational and operational needs within educational institutes, which may differ from those in other domains

Does science need computer science?

IBM Hursley Talks Series 3An afternoon of talks, to be held on Wednesday March 10 from 2:30pm in Bldg 35 Lecture Room A, arranged by the School of Chemistry in conjunction with IBM Hursley and the Combechem e-Science Project.The talks are aimed at science students (undergraduate and post-graduate) from across the faculty. This is the third series of talks we have organized, but the first time we have put them together in an afternoon. The talks are general in nature and knowledge of computer science is certainly not necessary. After the talks there will be an opportunity for a discussion with the lecturers from IBM.Does Science Need Computer Science?Chair and Moderator - Jeremy Frey, School of Chemistry.- 14:00 "Computer games for fun and profit" (*) - Andrew Reynolds - 14:45 "Anyone for tennis? The science behind WIBMledon" (*) - Matt Roberts - 15:30 Tea (Chemistry Foyer, Bldg 29 opposite bldg 35) - 15:45 "Disk Drive physics from grandmothers to gigabytes" (*) - Steve Legg - 16:35 "What could happen to your data?" (*) - Nick Jones - 17:20 Panel Session, comprising the four IBM speakers and May Glover-Gunn (IBM) - 18:00 Receptio

Relative depth estimation from single monocular images with deep convolutional network

Field of study: Computer science.Dr. Grant Scott, Thesis Supervisor."December 2017."Depth estimation from single monocular images is a theoretical challenge in computer vision as well as a computational challenge in practice. This thesis addresses the problem of depth estimation from single monocular images using a deep convolutional neural fields framework; which consists of convolutional feature extraction, superpixel dimensionality reduction, and depth inference. Data were collected using a stereo vision camera, which generated depth maps though triangulation that are paired with visual images. The visual image (input) and computed depth map (desired output) are used to train the model, which has achieved 83 percent test accuracy at the standard 25 percent tolerance. The problem has been formulated as depth regression for superpixels and our technique is superior to existing state-of-the-art approaches based on its demonstrated its generalization ability, high prediction accuracy, and real-time processing capability. We utilize the VGG-16 deep convolutional network as feature extractor and conditional random fields depth inference. We have leveraged a multi-phase training protocol that includes transfer learning and network fine-tuning lead to high performance accuracy. Our framework has a robust modular nature with capability of replacing each component with different implementations for maximum extensibility. Additionally, our GPU-accelerated implementation of superpixel pooling has further facilitated this extensibility by allowing incorporation of feature tensors with exible shapes and has provided both space and time optimization. Based on our novel contributions and high-performance computing methodologies, the model achieves a minimal and optimized design. It is capable of operating at 30 fps; which is a critical step towards empowering real-world applications such as autonomous vehicle with passive relative depth perception using single camera vision-based obstacle avoidance, environment mapping, etc.Includes bibliographical references (pages 61-65)

Research in computer science

Several short summaries of the work performed during this reporting period are presented. Topics discussed in this document include: (1) resilient seeded errors via simple techniques; (2) knowledge representation for engineering design; (3) analysis of faults in a multiversion software experiment; (4) implementation of parallel programming environment; (5) symbolic execution of concurrent programs; (6) two computer graphics systems for visualization of pressure distribution and convective density particles; (7) design of a source code management system; (8) vectorizing incomplete conjugate gradient on the Cyber 203/205; (9) extensions of domain testing theory and; (10) performance analyzer for the pisces system