This paper presents our approach to multi-label emotion detection in Hausa, a low-resource African language, for SemEval Track A. We fine-tuned AfriBERTa, a transformer-based model pre-trained on African languages, to classify Hausa text into six emotions: anger, disgust, fear, joy, sadness, and surprise. Our methodology involved data preprocessing, tokenization, and model fine-tuning using the Hugging Face Trainer API. The system achieved a validation accuracy of 74.00%, with an F1-score of 73.50%, demonstrating the effectiveness of transformer-based models for emotion detection in low-resource languages.

We present the QuranMorph corpus, a morphologically annotated corpus for the Quran (77,429 tokens). Each token in the QuranMorph was manually lemmatized and tagged with its part-of-speech by three expert linguists. The lemmatization process utilized lemmas from Qabas, an Arabic lexicographic database linked with 110 lexicons and corpora of 2 million tokens. The part-of-speech tagging was performed using the fine-grained SAMA/Qabas tagset, which encompasses 40 tags. As shown in this paper, this rich lemmatization and POS tagset enabled the QuranMorph corpus to be inter-linked with many linguistic resources. The corpus is open-source and publicly available as part of the SinaLab resources at (https://sina.birzeit.edu/quran)

As e-commerce rapidly integrates artificial intelligence for content creation and product recommendations, these technologies offer significant benefits in personalization and efficiency. AI-driven systems automate product descriptions, generate dynamic advertisements, and deliver tailored recommendations based on consumer behavior, as seen in major platforms like Amazon and Shopify. However, the widespread use of AI in e-commerce raises crucial ethical challenges, particularly around data privacy, algorithmic bias, and consumer autonomy. Bias -- whether cultural, gender-based, or socioeconomic -- can be inadvertently embedded in AI models, leading to inequitable product recommendations and reinforcing harmful stereotypes. This paper examines the ethical implications of AI-driven content creation and product recommendations, emphasizing the need for frameworks to ensure fairness, transparency, and need for more established and robust ethical standards. We propose actionable best practices to remove bias and ensure inclusivity, such as conducting regular audits of algorithms, diversifying training data, and incorporating fairness metrics into AI models. Additionally, we discuss frameworks for ethical conformance that focus on safeguarding consumer data privacy, promoting transparency in decision-making processes, and enhancing consumer autonomy. By addressing these issues, we provide guidelines for responsibly utilizing AI in e-commerce applications for content creation and product recommendations, ensuring that these technologies are both effective and ethically sound.

Kyiv, Ukraine – There is a Persian word millions of Ukrainians fear. Shahed – also spelled as Shaheed or Shahid, originally a Quranic term for "martyr" or "witness" – is the name given to the triangular, explosives-laden, Iranian-designed drones that became a harrowing part of daily life and death in wartime Ukraine. These days, they are assembled in the Volga-region Russian city of Yelabuga and undergo constant modifications to make them faster, smarter and deadlier during each air raid that involves hundreds of drones. Their latest Russian versions shot down in Ukraine earlier this month have artificial intelligence modules to better recognise targets, video cameras and two-way radio communication with human operators. "The word'Shahed' will forever be cursed in Ukrainian next to'Moscow' and'Putin'," said Denys Kovalenko, referring to Russian President Vladimir Putin. Kovalenko's face and arms were cut by glass shards after a Shahed exploded above his northern Kyiv neighbourhood in 2023.

In the span of just a few years, drones have become instrumental in warfare. Conflicts in Ukraine, Iran, Nagorno-Karabakh, Sudan, and elsewhere have shown how autonomous vehicles have become a quintessential part of modern combat. It's a fact that Taiwan knows all too well. The island nation, fearing imminent invasion from China, has both the need, know-how, and industry necessary to build a robust and advanced drone program. Yet Taiwan, which has set an ambitious target of producing 180,000 drones per year by 2028, is struggling to create this industry from scratch.

Four killed in Kyiv in new Russian aerial attack 12 minutes agoShareSaveJaroslav LukivBBC NewsShareSaveUkraine's emergencies service DSNSRescuers from Ukraine's emergencies service DSNS tackle fire in a residential building destroyed in the latest Russian attack on Kyiv At least four people have been killed in an overnight Russian missile and drone attack on Ukraine's capital Kyiv, the interior minister says. In a post on social media, Ihor Klymenko says residential areas, hospitals and sports infrastructure were hit. "An entire section of a residential high-rise building was destroyed" in the worst-hit Shevchenkivskyi district, he says, adding that some people are trapped under the rubble. In the Kyiv region, a woman was killed and another two people injured in the Russian aerial attack, regional head Mykola Kalashnyk says. The Russian military has not commented on the issue.

Recent advances in large language models (LLMs) have led to remarkable progress across domains, yet their capabilities in the humanities, particularly history, remain underexplored. Historical reasoning poses unique challenges for AI, involving multimodal source interpretation, temporal inference, and cross-linguistic analysis. While general-purpose agents perform well on many existing benchmarks, they lack the domain-specific expertise required to engage with historical materials and questions. To address this gap, we introduce HistBench, a new benchmark of 414 high-quality questions designed to evaluate AI's capacity for historical reasoning and authored by more than 40 expert contributors. The tasks span a wide range of historical problems-from factual retrieval based on primary sources to interpretive analysis of manuscripts and images, to interdisciplinary challenges involving archaeology, linguistics, or cultural history. Furthermore, the benchmark dataset spans 29 ancient and modern languages and covers a wide range of historical periods and world regions. Finding the poor performance of LLMs and other agents on HistBench, we further present HistAgent, a history-specific agent equipped with carefully designed tools for OCR, translation, archival search, and image understanding in History. On HistBench, HistAgent based on GPT-4o achieves an accuracy of 27.54% pass@1 and 36.47% pass@2, significantly outperforming LLMs with online search and generalist agents, including GPT-4o (18.60%), DeepSeek-R1(14.49%) and Open Deep Research-smolagents(20.29% pass@1 and 25.12% pass@2). These results highlight the limitations of existing LLMs and generalist agents and demonstrate the advantages of HistAgent for historical reasoning.

Predictive Process Monitoring (PPM) often uses deep learning models to predict the future behavior of ongoing processes, such as predicting process outcomes. While these models achieve high accuracy, their lack of interpretability undermines user trust and adoption. Explainable AI (XAI) aims to address this challenge by providing the reasoning behind the predictions. However, current evaluations of XAI in PPM focus primarily on functional metrics (such as fidelity), overlooking user-centered aspects such as their effect on task performance and decision-making. This study investigates the effects of explanation styles (feature importance, rule-based, and counterfactual) and perceived AI accuracy (low or high) on decision-making in PPM. We conducted a decision-making experiment, where users were presented with the AI predictions, perceived accuracy levels, and explanations of different styles. Users' decisions were measured both before and after receiving explanations, allowing the assessment of objective metrics (Task Performance and Agreement) and subjective metrics (Decision Confidence). Our findings show that perceived accuracy and explanation style have a significant effect.

Image generation models are revolutionizing many domains, and urban analysis and design is no exception. While such models are widely adopted, there is a limited literature exploring their geographic knowledge, along with the biases they embed. In this work, we generated 150 synthetic images for each state in the USA and related capitals using FLUX 1 and Stable Diffusion 3.5, two state-of-the-art models for image generation. We embed each image using DINO-v2 ViT-S/14 and the Fréchet Inception Distances to measure the similarity between the generated images. We found that while these models have implicitly learned aspects of USA geography, if we prompt the models to generate an image for "United States" instead of specific cities or states, the models exhibit a strong representative bias toward metropolis-like areas, excluding rural states and smaller cities. {\color{black} In addition, we found that models systematically exhibit some entity-disambiguation issues with European-sounding names like Frankfort or Devon.

Predictive artificial intelligence (AI) offers an opportunity to improve clinical practice and patient outcomes, but risks perpetuating biases if fairness is inadequately addressed. However, the definition of "fairness" remains unclear. We conducted a scoping review to identify and critically appraise fairness metrics for clinical predictive AI. We defined a "fairness metric" as a measure quantifying whether a model discriminates (societally) against individuals or groups defined by sensitive attributes. We searched five databases (2014-2024), screening 820 records, to include 41 studies, and extracted 62 fairness metrics. Metrics were classified by performance-dependency, model output level, and base performance metric, revealing a fragmented landscape with limited clinical validation and overreliance on threshold-dependent measures. Eighteen metrics were explicitly developed for healthcare, including only one clinical utility metric. Our findings highlight conceptual challenges in defining and quantifying fairness and identify gaps in uncertainty quantification, intersectionality, and real-world applicability.