SPARQL query rewriting is a fundamental mechanism for uniformly querying heterogeneous ontologies in the Linked Data Web. However, the complexity of ontology alignments, particularly rich correspondences (c: c), makes this process challenging. Existing approaches primarily focus on simple (s: s) and par tially complex (s: c) alignments, thereby overlooking the challenges posed by more expressive alignments. Moreover, the intricate syntax of SPARQL presents a barrier for non - expert users seeking to fully exploit the knowledge encapsulated in ontologies. T his article proposes an innovative approach for the automatic rewriting of SPARQL queries from a source ontology to a target ontology, based on a user's need expressed in natural language. It leverages the principles of equivalence transitivity as well as the advanced capabilities of large language models such as GPT - 4 . By integrating these elements, this approach stands out for its ability to efficiently handle complex alignments, particularly (c: c) correspondences, by fully exploiting their expressivene ss. Additionally, it facilitates access to aligned ontologies for users unfamiliar with SPARQL, providing a flexible solution for querying heterogeneous data. I n the Linked Data Web, aligned ontologies play a crucial role in facilitating interoperability between different data sources.

Action recognition models have achieved promising results in understanding instructional videos. However, they often rely on dominant, dataset-specific action sequences rather than true video comprehension, a problem that we define as ordinal bias. To address this issue, we propose two effective video manipulation methods: Action Masking, which masks frames of frequently co-occurring actions, and Sequence Shuffling, which randomizes the order of action segments. Through comprehensive experiments, we demonstrate that current models exhibit significant performance drops when confronted with nonstandard action sequences, underscoring their vulnerability to ordinal bias. Our findings emphasize the importance of rethinking evaluation strategies and developing models capable of generalizing beyond fixed action patterns in diverse instructional videos. Due to the dominant action pair'Take-Background', the model fails to predict the action'Open.' Action recognition in instructional videos has witnessed remarkable progress, primarily driven by models that excel in curated benchmark datasets (Farha & Gall, 2019; Ishikawa et al., 2021; Li et al., 2020; Yi et al., 2021).

Improving energy efficiency in residential buildings is critical to combating climate change and reducing greenhouse gas emissions. Retrofitting existing buildings, which contribute a significant share of energy use, is therefore a key priority, especially in regions with outdated building stock. Artificial Intelligence (AI) and Machine Learning (ML) can automate retrofit decision-making and find retrofit strategies. However, their use faces challenges of data availability, model transparency, and compliance with national and EU AI regulations including the AI act, ethics guidelines and the ALTAI. This paper presents a trustworthy-by-design ML-based decision support framework that recommends energy efficiency strategies for residential buildings using minimal user-accessible inputs. The framework merges Conditional Tabular Generative Adversarial Networks (CTGAN) to augment limited and imbalanced data with a neural network-based multi-label classifier that predicts potential combinations of retrofit actions. To support explanation and trustworthiness, an Explainable AI (XAI) layer using SHapley Additive exPlanations (SHAP) clarifies the rationale behind recommendations and guides feature engineering. Two case studies validate performance and generalization: the first leveraging a well-established, large EPC dataset for England and Wales; the second using a small, imbalanced post-retrofit dataset from Latvia (RETROFIT-LAT). Results show that the framework can handle diverse data conditions and improve performance up to 53% compared to the baseline. Overall, the proposed framework provides a feasible, interpretable, and trustworthy AI system for building retrofit decision support through assured performance, usability, and transparency to aid stakeholders in prioritizing effective energy investments and support regulation-compliant, data-driven innovation in sustainable energy transition.

We propose Point-PNG, a novel self-supervised learning framework that generates conditional pseudo-negatives in the latent space to learn point cloud representations that are both discriminative and transformation-sensitive. Conventional self-supervised learning methods focus on achieving invariance, discarding transformation-specific information. Recent approaches incorporate transformation sensitivity by explicitly modeling relationships between original and transformed inputs. However, they often suffer from an invariant-collapse phenomenon, where the predictor degenerates into identity mappings, resulting in latent representations with limited variation across transformations. To address this, we propose Point-PNG that explicitly penalizes invariant collapse through pseudo-negatives generation, enabling the network to capture richer transformation cues while preserving discriminative representations. To this end, we introduce a parametric network, COnditional Pseudo-Negatives Embedding (COPE), which learns localized displacements induced by transformations within the latent space. A key challenge arises when jointly training COPE with the MAE, as it tends to converge to trivial identity mappings. To overcome this, we design a loss function based on pseudo-negatives conditioned on the transformation, which penalizes such trivial invariant solutions and enforces meaningful representation learning. We validate Point-PNG on shape classification and relative pose estimation tasks, showing competitive performance on ModelNet40 and ScanObjectNN under challenging evaluation protocols, and achieving superior accuracy in relative pose estimation compared to supervised baselines.

The government says some 50,000 young people are expected to benefit from a programme to expand apprenticeships as it looks to tackle youth unemployment. The £725 million package, which was earmarked in the Budget and covers the next three years, will be used to create apprenticeships in sectors including AI, hospitality and engineering. Apprenticeships for people under the age of 25 at small and medium-sized businesses will be fully funded as part of the package, removing the 5% that they currently have to pay. The government is aiming to reverse a decline in the number of young people starting apprenticeships, which has fallen by almost 40% in the past decade. The funding also includes £140m for a pilot that the Department for Work and Pensions says will allow local mayors to connect young people with employers and apprenticeship opportunities, although it is unclear exactly how the money will be used.

Ramzan Kadyrov vows retaliation against Ukraine following drone attack on Grozny building, while Ukrainian officials say Zelenskyy remains unafraid of threats.

Kevin Sinfield has completed seven ultramarathons in seven days to raise money and awareness for motor neurone disease (MND). The rugby league legend ran about 300km (185 miles) throughout the week, starting at Bury St Edmunds Rugby Club and ending at Leeds Rhinos home ground, Headingley Stadium. The 45-year-old completed an ultramarathon of at least 45km (27.9 miles) each day of his challenge, in bursts of 7km (4.3 miles). On Sunday he crossed the finish line in front of hundreds of supporters, who had gathered in the stadium's North and West stands to cheer him on. He said: To the MND Community and the people we've met on route, all through the last week, all through the past five years, to everybody we've met - it's an absolutely beautiful community.

Science Archaeology Ancient'dirty dishes' may have led archaeologists astray for decades A new study questions if Bronze Age dishes really do have traces of olive oil. Breakthroughs, discoveries, and DIY tips sent every weekday. As far as kitchen staples, you don't really get much better than olive oil . It can do it all--jazz up a salad, sauté vegetables, add a nice crisp to some noodles, and more. Humans have been using olive oil for about 8,000 years, so archeologists often report olive oil residue on excavated pottery.

An ensemble of about 10 robots - which will not be androids but ground vehicles between 40cm and 2m tall - will work with humans to learn how to be funny. An ensemble of about 10 robots - which will not be androids but ground vehicles between 40cm and 2m tall - will work with humans to learn how to be funny. A robot walks into a bar: can a Melbourne researcher get AI to do comedy? Robots can make humans laugh - mostly when they fall over - but a new research project is looking at whether robots using AI could ever be genuinely funny. If you ask ChatGPT for a funny joke, it will serve you up something that belongs in a Christmas cracker: "Why don't skeletons fight each other? Because they don't have the guts."

Prince William reveals keeping smartphones away from Prince George, Charlotte and Louis creates 'tense issue,' citing concerns children access harmful online content.