Visual learning often occurs in a specific context, where an agent acquires skills through exploration and tracking of its location in a consistent environment. The historical spatial context of the agent provides a similarity signal for self-supervised contrastive learning. We present a unique approach, termed Environmental Spatial Similarity (ESS), that complements existing contrastive learning methods. Using images from simulated, photorealistic environments as an experimental setting, we demonstrate that ESS outperforms traditional instance discrimination approaches. Moreover, sampling additional data from the same environment substantially improves accuracy and provides new augmentations. ESS allows remarkable proficiency in room classification and spatial prediction tasks, especially in unfamiliar environments. This learning paradigm has the potential to enable rapid visual learning in agents operating in new environments with unique visual characteristics. Potentially transformative applications span from robotics to space exploration. Our proof of concept demonstrates improved efficiency over methods that rely on extensive, disconnected datasets.

In what seems like a scenario from a sci-fi movie, a UK university will soon be projecting guest lessons from professors halfway across the globe. Loughborough University in Leicestershire, England has begun beaming in lecturers from the Massachusetts Institute of Technology (MIT) using holographic technology, the Guardian reports. The lecturers, specializing in sports science, will teach fashion students how to create "immersive shows," according to the publisher. In addition, management students will also be taught. Loughborough University's pro-vice-chancellor Professor Rachel Thomson told the Guardian that the strategy could help the university with its sustainability goals, particularly when it comes to flying in guest lecturers from around the world.

The TECHNGI research project at Loughborough University invite you to our Artificial Intelligence and Next Generation Insurance Services Conference 2021/22, supported by the Willis Research Network. The TECHNGI project at Loughborough University TECHNGI – Technology driven Next Generation Insurance has been conducting research on artificial intelligence (AI) in insurance for the past two and half years. Our project has been examining insurance industry experience with employing AI, building a core base of knowledge on the broader challenges of adoption, including the implication for business models and the organisational and public policy challenges. This online conference will be delivered across four separate themed sessions. We will draw on the insights of the project, bringing together a range of perspectives on the business application of Artificial Intelligence (AI) and its role in the ongoing digital transformation of the insurance industry.

WASHINGTON, DC, October 4, 2021 (ENS) – A new international project is creating advanced artificial intelligence, AI, programs that will enable machines to learn progressively over a lifetime and share those experiences with each other. Uses of this new technology could include co-operating self-learning autonomous vehicles such as self-driving cars, robotic rescue and exploration systems, distributed monitoring systems to detect emergencies, or cybersecurity systems of agents that monitor large networks. Researchers hope the technology will allow machines to reuse information, adapt quickly to new conditions and collaborate by sharing information. The project is part of the initiative Shared-Experience Lifelong Learning, or ShELL, a program funded by the Defense Advanced Research Projects Agency, DARPA. This U.S. government military agency is credited with some of the biggest technological advances in recent history such as the Internet, the miniaturization of GPS, Siri, and the computer mouse.

The aim behind a new international project is to develop advanced AI programs that will allow machines to learn gradually over a lifetime and share that input with each other. Scientists are optimistic that the technology will enable machines to reuse data, adapt rapidly to new conditions and work in partnership by sharing data. The project comes under the initiative known as Shared-Experience Lifelong Learning (ShELL), a program financially supported by the Defense Advanced Research Projects Agency (DARPA) -- a U.S. government agency known for some major technological developments in recent history such as the Internet, Siri, the miniaturization of GPS and the computer mouse. It began this month and is being headed by Dr. Andrea Soltoggio of Loughborough's Computer Science department, in partnership with Dr. Soheil Kolouri at Vanderbilt University and Dr. Cong Liu at the University of Texas at Dallas, both in the United States. The idea behind this project is to gain a deep understanding of how and what an AI system learns when dealing with a new task, so that we can exploit task similarities and share information to create fast, reliable, and collaborating learning agents.

Computer scientists at Loughborough University have teamed up with multi-disciplinary engineering consultancy, Cundall, to create an artificial intelligence system that can predict building emissions rates (BER) – an important value used to calculate building energy performance – of non-domestic buildings. Current methods can take hours to days to produce BERs and are generated by manually inputting hundreds of variables. Dr Georgina Cosma and postgraduate student Kareem Ahmed, of the School of Science, have designed and trained an AI model to predict BER values with 27 inputs with little loss in accuracy. Better yet, the proposed AI model – which was created with the support of Cundall's Head of Research and Innovation, Edwin Wealend, and trained using large-scale data obtained from UK government energy performance assessments – can generate a BER value almost instantly. Dr Cosma says the research "is an important first step towards the use of machine learning tools for energy prediction in the UK" and it shows how data can "improve current processes in the construction industry".

A'cyber seed' that grows'like a plant' to design structures using material from the local environment has been developed by researchers. The'seed' is composed of hundreds of pieces of information, digitally encoded, that includes data on necessary materials, properties, and other parameters such as weight, height, colour, and density. Simple seeds could have 50 lines of information, with six pieces of information per line. This seed, algorithmically, then attempts to grow into a particular design set out by researchers from Queen's University Belfast, Loughborough University and the University of York. Starting off from a single cell in a CAD (computer-aided design) program, the seed will grow in a certain direction until it reaches the limit of the parameter it has been programmed with.

Most of the two-dimensional (2D) hydraulic/hydrodynamic models are still computationally too demanding for real-time applications. In this paper, an innovative modelling approach based on a deep convolutional neural network (CNN) method is presented for rapid prediction of fluvial flood inundation. The CNN model is trained using outputs from a 2D hydraulic model (i.e. LISFLOOD-FP) to predict water depths. The pre-trained model is then applied to simulate the January 2005 and December 2015 floods in Carlisle, UK. The CNN predictions are compared favourably with the outputs produced by LISFLOOD-FP. The performance of the CNN model is further confirmed by benchmarking against a support vector regression (SVR) method. The results show that the CNN model outperforms SVR by a large margin. The CNN model is highly accurate in capturing flooded cells as indicated by several quantitative assessment matrices. The estimated error for reproducing maximum flood depth is 0 ~ 0.2 meters for the 2005 event and 0 ~ 0.5 meters for the 2015 event at over 99% of the cells covering the computational domain. The proposed CNN method offers great potential for real-time flood modelling/forecasting considering its simplicity, superior performance and computational efficiency.

Educational Data Mining (EDM) is a developing discipline, concerned with expanding the classical Data Mining (DM) methods and developing new methods for discovering the data that originate from educational systems. Student attendance in higher education has always been dealt with in a classical way, educators rely on counting the occurrence of attendance or absence building their knowledge about students as well as modules based on this count. This method is neither credible nor does it necessarily provide a real indication of a student performance. This study tries to formulate the extracted knowledge in a way that guarantees achieving accurate and credible results. Student attendance data, gathered from the educational system, were first cleaned in order to remove any randomness and noise, then various attributes were studied so as to highlight the most significant ones that affect the real attendance of students. The next step was to derive an equation that measures the Student Attendance Credibility (SAC) considering the attributes chosen in the previous step. The reliability of the newly developed measure was then evaluated in order to examine its consistency. Finally, the J48 DM classification technique was utilized in order to classify modules based on the strength of their SAC values. Results of this study were promising, and credibility values achieved using the newly derived formula gave accurate, credible, and real indicators of student attendance, as well as accurate classification of modules based on the credibility of student attendance on those modules.

Various studies have shown that students tend to get higher marks when assessed through coursework based assessment methods which include either modules that are fully assessed through coursework or a mixture of coursework and examinations than assessed by examination alone. There are a large number of educational data mining studies that preprocess data through conventional data mining processes including data preparation process, but they are using transcript data as they stand without looking at examination and coursework results weighting which could affect prediction accuracy. This paper proposes a different data preparation process through investigating more than 230000 student records in order to prepare students marks based on the assessment methods of enrolled modules. The data have been processed through different stages in order to extract a categorical factor through which students module marks are refined during the data preparation process. The results of this work show that students final marks should not be isolated from the nature of the enrolled modules assessment methods. They must rather be investigated thoroughly and considered during EDMs data preprocessing phases. More generally, it is concluded that educational data should not be prepared in the same way as other data types due to differences as data sources, applications, and types of errors in them. Therefore, an attribute, coursework assessment ratio, is proposed to be used in order to take the different modules assessment methods into account while preparing student transcript data. The effect of CAR on prediction process using the random forest classification technique has been investigated. It is shown that considering CAR as an attribute increases the accuracy of predicting students second year averages based on their first year results.