In this work, our goals are two fold: large-vocabulary continuous sign language recognition (CSLR), and sign language retrieval. To this end, we introduce a multi-task Transformer model, CSLR2, that is able to ingest a signing sequence and output in a joint embedding space between signed language and spoken language text. To enable CSLR evaluation in the large-vocabulary setting, we introduce new dataset annotations that have been manually collected. These provide continuous sign-level annotations for six hours of test videos, and will be made publicly available. We demonstrate that by a careful choice of loss functions, training the model for both the CSLR and retrieval tasks is mutually beneficial in terms of performance -- retrieval improves CSLR performance by providing context, while CSLR improves retrieval with more fine-grained supervision. We further show the benefits of leveraging weak and noisy supervision from large-vocabulary datasets such as BOBSL, namely sign-level pseudo-labels, and English subtitles. Our model significantly outperforms the previous state of the art on both tasks.

Although Large Language Models (LLMs) excel at addressing straightforward reasoning tasks, they frequently struggle with difficulties when confronted by more complex multi-step reasoning due to a range of factors. Firstly, natural language often encompasses complex relationships among entities, making it challenging to maintain a clear reasoning chain over longer spans. Secondly, the abundance of linguistic diversity means that the same entities and relationships can be expressed using different terminologies and structures, complicating the task of identifying and establishing connections between multiple pieces of information. Graphs provide an effective solution to represent data rich in relational information and capture long-term dependencies among entities. To harness the potential of graphs, our paper introduces Structure Guided Prompt, an innovative three-stage task-agnostic prompting framework designed to improve the multi-step reasoning capabilities of LLMs in a zero-shot setting. This framework explicitly converts unstructured text into a graph via LLMs and instructs them to navigate this graph using task-specific strategies to formulate responses. By effectively organizing information and guiding navigation, it enables LLMs to provide more accurate and context-aware responses. Our experiments show that this framework significantly enhances the reasoning capabilities of LLMs, enabling them to excel in a broader spectrum of natural language scenarios.

We introduce a novel Dual Input Stream Transformer (DIST) for the challenging problem of assigning fixation points from eye-tracking data collected during passage reading to the line of text that the reader was actually focused on. This post-processing step is crucial for analysis of the reading data due to the presence of noise in the form of vertical drift. We evaluate DIST against nine classical approaches on a comprehensive suite of nine diverse datasets, and demonstrate DIST's superiority. By combining multiple instances of the DIST model in an ensemble we achieve an average accuracy of 98.5\% across all datasets. Our approach presents a significant step towards addressing the bottleneck of manual line assignment in reading research. Through extensive model analysis and ablation studies, we identify key factors that contribute to DIST's success, including the incorporation of line overlap features and the use of a second input stream. Through evaluation on a set of diverse datasets we demonstrate that DIST is robust to various experimental setups, making it a safe first choice for practitioners in the field.

Reassured Ltd is the largest non-advised life insurance intermediary in the UK with established locations in Basingstoke, Portsmouth, Southampton, Manchester, Bristol, Bournemouth and Chester. We launched our new Advised business offering in 2021 and continue to grow our sales and support teams across the entire business this year. We arrange over 15,000 new customer life insurance policies per month with leading sales conversions and customer satisfaction metrics. The business is outbound, B2C sales from internet generated customer enquiries. As a Senior Data Engineer you will be the lead in the team and your role will be to metamorphose the raw data into something viable and readable before its presentation to the stakeholders.

Skeleton-based human action recognition is a longstanding challenge due to its complex dynamics. Some fine-grain details of the dynamics play a vital role in classification. The existing work largely focuses on designing incremental neural networks with more complicated adjacent matrices to capture the details of joints relationships. However, they still have difficulties distinguishing actions that have broadly similar motion patterns but belong to different categories. Interestingly, we found that the subtle differences in motion patterns can be significantly amplified and become easy for audience to distinct through specified view directions, where this property haven't been fully explored before. Drastically different from previous work, we boost the performance by proposing a conceptually simple yet effective Multi-view strategy that recognizes actions from a collection of dynamic view features. Specifically, we design a novel Skeleton-Anchor Proposal (SAP) module which contains a Multi-head structure to learn a set of views. For feature learning of different views, we introduce a novel Angle Representation to transform the actions under different views and feed the transformations into the baseline model. Our module can work seamlessly with the existing action classification model. Incorporated with baseline models, our SAP module exhibits clear performance gains on many challenging benchmarks. Moreover, comprehensive experiments show that our model consistently beats down the state-of-the-art and remains effective and robust especially when dealing with corrupted data. Related code will be available on https://github.com/ideal-idea/SAP .

Co-Op is facing a legal challenge to its'Orwellian' and'unlawful' use of facial recognition cameras. Privacy rights group Big Brother Watch claimed supermarket staff could add people to a secret'blacklist' without them knowing. But Co-Op says it is using the Facewatch system in shops with a history of crime, so it can protect its staff. Big Brother Watch said the independent grocery chain had installed the surveillance technology in 35 stores across Portsmouth, Bournemouth, Bristol, Brighton and Hove, Chichester, Southampton and London. It claimed staff could add individuals to a watch-list where their biometric information is kept for up to two years.

Oscar-winning creative studio Framestore and Bournemouth University are seeking two research fellows to help drive forward the future of visual effects. Joining the Faculty of Media and Communications' Centre for Applied Creative Technologies (CfACTs) and gaining access to Framestore's world-leading teams, tech and software, the selected candidates will embark on two-year research programmes to help solve key problems facing the VFX industry. Manne Öhrström, Framestore's Global Head of Software VFX, said: "Framestore's Technology & Research Team comprises a diverse melting pot of computer scientists, engineers and physicists who are always striving for innovative solutions to take the company's work to the next level. This is a group of gifted technologists wholly focused on the industry's future, and the work they do impacts every aspect of Framestore's business. The potential for using machine learning in areas like lighting and rendering is huge, and we can't wait to welcome two new research fellows to the team – we're sure that their work will prove absolutely invaluable."

A coastal survey using drones in Dorset has laid bare the scale of the UK's litter problem. The drones flew over beaches in Bournemouth, Christchurch and Poole across seven days in the May half term this year. Eighteen sites along the seafront in the region were monitored between May 27 and June 2, covering an overall area of 475,000 square metres. The technology found more than 1.5 tonnes of rubbish left behind by visitors – a third of which were glass bottles when measured by volume. In all, more than 123,000 items were identified, up from 22,266 in a drone survey of the same areas during the March lockdown – marking an astonishing 454 per cent increase due to relaxing lockdown measures.

A pilot scheme in Bournemouth, Christchurch and Poole (BCP) will see drones used to help councils reduce litter. BCP Council is partnering with the environmental charity Hubbub, startup Ellipsis Earth and fast food brand McDonald's – which is funding the trial – to use drone data to inform the placement of bins, street cleaning schedules and behaviour change campaigns around litter. The partners have called the pilot "the most scientifically robust litter survey ever undertaken in the UK". Drone imagery is processed by Ellipsis Earth software to automatically and rapidly detect discarded litter items and quantify them by type and brand to create litter heatmaps. This data, along with expert analysis and recommendations, will be shared with BCP council, Hubbub and McDonald's, to help them better understand and prevent littering.

Real-world problems often involve the optimisation of multiple conflicting objectives. These problems, referred to as multi-objective optimisation problems, are especially challenging when more than three objectives are considered simultaneously. This paper proposes an algorithm to address this class of problems. The proposed algorithm is an evolutionary algorithm based on an evolution strategy framework, and more specifically, on the Covariance Matrix Adaptation Pareto Archived Evolution Strategy (CMA-PAES). A novel selection mechanism is introduced and integrated within the framework.