Adverts for Nike, Superdry and Lacoste have been banned for making misleading claims about their green credentials. The UK's advertising watchdog challenged the brands over the use of the word sustainable in paid-for Google ads which were not backed up by evidence of their sustainability. The Advertising Standards Authority (ASA) identified three adverts from the retailers promising customers sustainable materials, sustainable style and sustainable clothing. The UK's advertising code states that the basis of claims about environmental sustainability must be clear and supported by a high level of substantiation. In each case, it asked the companies for evidence to back up the claims about the sustainability of the products.

What is in the 28-point US plan for Ukraine? 'Ukraine is running out of men, money and time' Can the US get all sides to end the war? Why is Europe opposing Trump's peace plan? Here's where things stand on Wednesday, December 3: Russian forces attacked Ukraine's Kherson region, using "rocket launchers, mortars and drones", killing a 76-year-old woman and injuring at least two other people, the Kherson Regional Prosecutor's Office said in a post on Telegram. A Russian drone attack killed one person and injured five people in the eastern Ukrainian city of Kramatorsk, the head of the city's military administration, Oleksandr Honcharenko, wrote on Facebook.

Increasing climate change and habitat loss are driving unprecedented shifts in species distributions. Conservation professionals urgently need timely, high-resolution predictions of biodiversity risks, especially in ecologically diverse regions like Africa. We propose EcoCast, a spatio-temporal model designed for continual biodiversity and climate risk forecasting. Utilizing multisource satellite imagery, climate data, and citizen science occurrence records, EcoCast predicts near-term (monthly to seasonal) shifts in species distributions through sequence-based transformers that model spatio-temporal environmental dependencies. The architecture is designed with support for continual learning to enable future operational deployment with new data streams. Our pilot study in Africa shows promising improvements in forecasting distributions of selected bird species compared to a Random Forest baseline, highlighting EcoCast's potential to inform targeted conservation policies. By demonstrating an end-to-end pipeline from multi-modal data ingestion to operational forecasting, EcoCast bridges the gap between cutting-edge machine learning and biodiversity management, ultimately guiding data-driven strategies for climate resilience and ecosystem conservation throughout Africa.

Low-resource African languages remain underrepresented in sentiment analysis research, resulting in limited lexical resources and reduced model performance in multilingual applications. This gap restricts equitable access to Natural Language Processing (NLP) technologies and hinders downstream tasks such as public-health monitoring, digital governance, and financial inclusion. To address this challenge, this paper introduces TriLex, a three-stage retrieval-augmented framework that integrates corpus-based extraction, cross-lingual mapping, and Retrieval-Augmented Generation (RAG) driven lexicon refinement for scalable sentiment lexicon expansion in low-resource languages. Using an expanded lexicon, we evaluate two leading African language models (AfroXLMR and AfriBERTa) across multiple case studies. Results show that AfroXLMR consistently achieves the strongest performance, with F1-scores exceeding 80% for isiXhosa and isiZulu, aligning with previously reported ranges (71-75%), and demonstrating high multilingual stability with narrow confidence intervals. AfriBERTa, despite lacking pre-training on the target languages, attains moderate but reliable F1-scores around 64%, confirming its effectiveness under constrained computational settings. Comparative analysis shows that both models outperform traditional machine learning baselines, while ensemble evaluation combining AfroXLMR variants indicates complementary improvements in precision and overall stability. These findings confirm that the TriLex framework, together with AfroXLMR and AfriBERTa, provides a robust and scalable approach for sentiment lexicon development and multilingual sentiment analysis in low-resource South African languages.

We introduce Bin2Vec, a new framework that helps compare software programs in a clear and explainable way. Instead of focusing only on one type of information, Bin2Vec combines what a program looks like (its built-in functions, imports, and exports) with how it behaves when it runs (its instructions and memory usage). This gives a more complete picture when deciding whether two programs are similar or not. Bin2Vec represents these different types of information as views that can be inspected separately using easy-to-read charts, and then brings them together into an overall similarity score. Bin2Vec acts as a bridge between binary representations and machine learning techniques by generating feature representations that can be efficiently processed by machine-learning models. We tested Bin2Vec on multiple versions of two well-known Windows programs, PuTTY and 7-Zip. The primary results strongly confirmed that our method compute an optimal and visualization-friendly representation of the analyzed software. For example, PuTTY versions showed more complex behavior and memory activity, while 7-Zip versions focused more on performance-related patterns. Overall, Bin2Vec provides decisions that are both reliable and explainable to humans. Because it is modular and easy to extend, it can be applied to tasks like auditing, verifying software origins, or quickly screening large numbers of programs in cybersecurity and reverse-engineering work.

Focus group discussions generate rich qualitative data but their analysis traditionally relies on labor-intensive manual coding that limits scalability and reproducibility. We present a systematic framework for applying BERTopic to focus group transcripts using data from ten focus groups exploring HPV vaccine perceptions in Tunisia (1,075 utterances). We conducted comprehensive hyperparameter exploration across 27 configurations, evaluating each through bootstrap stability analysis, performance metrics, and comparison with LDA baseline. Bootstrap analysis revealed that stability metrics (NMI and ARI) exhibited strong disagreement (r = -0.691) and showed divergent relationships with coherence, demonstrating that stability is multifaceted rather than monolithic. Our multi-criteria selection framework yielded a 7-topic model achieving 18\% higher coherence than optimized LDA (0.573 vs. 0.486) with interpretable topics validated through independent human evaluation (ICC = 0.700, weighted Cohen's kappa = 0.678). These findings demonstrate that transformer-based topic modeling can extract interpretable themes from small focus group transcript corpora when systematically configured and validated, while revealing that quality metrics capture distinct, sometimes conflicting constructs requiring multi-criteria evaluation. We provide complete documentation and code to support reproducibility.

Stereotypes are known to have very harmful effects, making their detection critically important. However, current research predominantly focuses on detecting and evaluating stereotypical biases, thereby leaving the study of stereotypes in its early stages. Our study revealed that many works have failed to clearly distinguish between stereotypes and stereotypical biases, which has significantly slowed progress in advancing research in this area. Stereotype and Anti-stereotype detection is a problem that requires social knowledge; hence, it is one of the most difficult areas in Responsible AI. This work investigates this task, where we propose a five-tuple definition and provide precise terminologies disentangling stereotypes, anti-stereotypes, stereotypical bias, and general bias. We provide a conceptual framework grounded in social psychology for reliable detection. We identify key shortcomings in existing benchmarks for this task of stereotype and anti-stereotype detection. To address these gaps, we developed StereoDetect, a well curated, definition-aligned benchmark dataset designed for this task. We show that sub-10B language models and GPT-4o frequently misclassify anti-stereotypes and fail to recognize neutral overgeneralizations. We demonstrate StereoDetect's effectiveness through multiple qualitative and quantitative comparisons with existing benchmarks and models fine-tuned on them. The dataset and code is available at https://github.com/KaustubhShejole/StereoDetect.

Haaland joins Premier League 100 club - who else is in it? Erling Haaland has become the 35th footballer to join the Premier League's '100 club' by reaching a century of goals in the competition with the opener in Manchester City's fixture at Fulham. The Norwegian striker has also become the fastest player to reach the milestone, with his 100th goal coming in his 111th game - beating Alan Shearer's previous record, set in 1995, by 13 appearances. Haaland's total goals-per-game rate in the Premier League is now 0.90, and he may eventually challenge for Shearer's overall goals record of 260 in the division, which launched in 1992-93 when it broke away from the English Football League. Haaland's City contract - a record nine-and-a-half-year deal announced in January - runs through to the end of the 2033-34 season.

Global growth is holding up better than expected as an artificial intelligence (AI) investment boom helps offset some of the shock from United States tariff hikes, according to the Organisation for Economic Co-operation and Development (OECD). The Paris-based organisation, however, warned on Tuesday that global growth was vulnerable to any new outbreak of trade tensions, while investor optimism about AI could trigger a stock market correction if expectations are not met. It predicted a rebound to 3.1 percent in 2027. OECD head Mathias Cormann said the trade shocks triggered by US President Donald Trump's tariff hikes had so far proved relatively mild, but added their costs were likely to rise. "The full effects of those higher tariffs since the start of the year will become clearer as firms run down the inventories that they built up," he told a press conference.

Artificial intelligence risks increasing inequality between developed and developing countries, a United Nations report has warned. The report, titled "The Next Great Divergence" and released by the United Nations Development Programme's Asia and Pacific regional bureau on Tuesday, calls for urgent, coordinated policy action to manage the impact of the technology. "We think that AI is heralding a new era of rising inequality between countries, following years of convergence in the last 50 years," Philip Schellekens, the bureau's chief economist, told a briefing in Geneva, according to the Reuters news agency. The report argues that AI, like the Industrial Revolution before it, has the potential to unlock unprecedented opportunities or deepen existing divides, across a global landscape marked by vast gaps in wealth, skills, and digital access. Even wealthier countries would suffer if poorer states were left behind by the AI revolution, said Schellekens. "If inequality continues to rise, the spillover effects of that in terms of the security agenda, in terms of undocumented forms of migration, will also become more daunting," he worries.