Accurate prediction of Remaining Useful Life (RUL) in aero-engines is vital for predictive maintenance, improved operational reliability, and reduced lifecycle costs. While deep learning approaches have demonstrated strong potential in this area, most existing methods focus primarily on model architecture design and treat input features uniformly, often neglecting the influence of data preprocessing. In this work, we propose a novel preprocessing pipeline that enhances RUL prediction by improving data quality and temporal representation before model training. Our approach leverages complete temporal sequences and generates RUL estimates at each timestep, enabling the model to capture finegrained degradation dynamics and deliver continuous prognostic insights throughout the engine's operational life. To validate the effectiveness of the proposed pipeline, we conduct experiments on the NASA C-MAPSS dataset. Comparative evaluations against a suite of state-of-the-art neural models including CNN, RNN, LSTM, DCNN, TCN, BiGRUTSAM, AGCNN, and ATCN, demonstrate that our approach consistently achieves superior accuracy and robustness in aeroengine RUL prediction. These results highlight the critical role of preprocessing in maximizing the effectiveness of neural prognostic models.

Pretrained language models are integral part of AI applications, but their high computational cost for training limits accessibility. Initiatives such as Bloom and StarCoder aim to democratize access to pretrained models for collaborative community development. Despite these efforts, such models encounter challenges such as limited multilingual capabilities, risks of catastrophic forgetting during continual pretraining, and the high costs of training models from scratch, alongside the need to align with AI safety standards and regulatory frameworks. This paper presents Aurora-M, a 15B parameter multilingual open-source model trained on English, Finnish, Hindi, Japanese, Vietnamese, and code. Continually pretrained from StarCoderPlus on 435B additional tokens, Aurora-M surpasses 2T tokens in total training token count. It is the first open-source multilingual model fine-tuned on human-reviewed safety instructions, thus aligning its development not only with conventional red-teaming considerations, but also with the specific concerns articulated in the Biden-Harris Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. We evaluate Aurora-M across a wide range of tasks and languages, showcasing its robustness against catastrophic forgetting and its superior performance in multilingual settings, particularly in safety evaluations. We open-source Aurora-M and its variants to encourage responsible open-source development of large language models at https://huggingface.co/aurora-m.

This article presents an intervention study on the effects of the combined methods of 1) the Socratic method, 2) chain-of-thought (CoT) reasoning, 3) simplified gamification, and 4) formative feedback on university students’ math learning driven by large language models (LLMs). We call our approach Mathematics Explanations through Games by AI LLMs (MEGA). Some students struggle with math, and as a result, avoid math-related disciplines or subjects despite the importance of math across many fields, including signal processing. Oftentimes, students’ math difficulties stem from suboptimal pedagogy. We compared the MEGA method to the traditional step-by-step (CoT) method to ascertain which is better by using a within-group design after randomly assigning questions for the participants, who are university students. Samples (n=60) were randomly drawn from each of the two test sets of the Grade School Math 8 K (GSM8K) and Mathematics Aptitude Test of Heuristics (MATH) datasets, based on an error margin of 11%, a confidence level of 90%, and a manageable number of samples for the student evaluators. These samples were used to evaluate two capable LLMs at length [Generative Pretrained Transformer 4o (GPT4o) and Claude 3.5 Sonnet] out of the initial six that were tested for capability. The results showed that students agree in more instances that the MEGA method is experienced as better for learning for both datasets. It is even much better than the CoT (47.5% compared to 26.67%) in the more difficult MATH dataset, indicating that MEGA is better at explaining difficult math problems. We also calculated the accuracies of the two LLMs and showed that model accuracies differ for the methods. MEGA appears to expose the hallucination challenge that still exists with these LLMs better than CoT. We provide public access to the MEGA app, the preset instructions that we created, and the annotations by the students for transparency.

We introduce a novel writing method called Probing Chain-of-Thought, which potentially prevents students from cheating using a large language model while enhancing their critical thinking. large language models have disrupted education and many other fields. For fear of students cheating, many educationists have resorted to banning their use. We conduct studies in two different courses with 65 students using qualitative research design primarily (i.e. phenomenological) and quantitative methods. The students in each course were asked to prompt a large language model of their choice with one question from a set of four (random) questions and required to affirm or refute statements in the large language model output by using peer-reviewed references as evidence. In addition, the rubric for assessing the students writing included 5 more criteria: focus, logic, content, style and correctness. The average success rate of the writing of students based on the criteria for the two cases is 79.49% (±12.82%). The results of the rubric assessment show two things: (1) Probing Chain-of-Thought stimulates critical thinking and writing of students through engagement with large language models when we compare the large language models-only output to Probing Chain-of-Thought output and (2) Probing Chain-of-Thought may prevent cheating because of clear limitations in the concerned large language models when we compare students’ Probing Chain-of-Thought output to large language models’ Probing Chain-of-Thought output. In quantitative analysis, we also discover that most students prefer to give answers in fewer words than large language models, which are typically verbose. The average word counts for students in the first course, ChatGPT 3.5, and Phind (v8) are 208, 391 and 383, respectively, while it is 405, 356, and 315 for students, ChatGPT 3.5, and BingAI, respectively, in the second course, where we enforced a minimum word-count of 300 for the students. We provide access to the outputs for possible assessments (available after review).

This paper addresses the critical challenge of vehicle detection in the harsh winter conditions in the Nordic regions, characterized by heavy snowfall, reduced visibility, and low lighting. Due to their susceptibility to environmental distortions and occlusions, traditional vehicle detection methods have struggled in these adverse conditions. The advanced proposed deep learning architectures brought promise, yet the unique difficulties of detecting vehicles in Nordic winters remain inadequately addressed. This study uses the Nordic Vehicle Dataset (NVD), which contains UAV (unmanned aerial vehicle) images from northern Sweden, to evaluate the performance of state-of-the-art vehicle detection algorithms under challenging weather conditions. Our methodology includes a comprehensive evaluation of single-stage, two-stage, segmentation-based, and transformer-based detectors against the NVD. We propose a series of enhancements tailored to each detection framework, including data augmentation, hyperparameter tuning, transfer learning, and Specifically implementing and enhancing the Detection Transformer (DETR). A novel architecture is proposed that leverages self-attention mechanisms with the help of MSER (maximally stable extremal regions) and RST (Rough Set Theory) to identify and filter the region that model long-range dependencies and complex scene contexts. Our findings not only highlight the limitations of current detection systems in the Nordic environment but also offer promising directions for enhancing these algorithms for improved robustness and accuracy in vehicle detection amidst the complexities of winter landscapes. The code and the dataset are available at https://nvd.ltu-ai.dev.

Recent advances in speech synthesis have enabled many useful applications like audio directions in Google Maps, screen readers, and automated content generation on platforms like TikTok. However, these systems are mostly dominated by voices sourced from data-rich geographies with personas representative of their source data. Although 3000 of the world's languages are domiciled in Africa, African voices and personas are under-represented in these systems. As speech synthesis becomes increasingly democratized, it is desirable to increase the representation of African English accents. We present Afro-TTS, the first pan-African accented English speech synthesis system able to generate speech in 86 African accents, with 1000 personas representing the rich phonological diversity across the continent for downstream application in Education, Public Health, and Automated Content Creation. Speaker interpolation retains naturalness and accentedness, enabling the creation of new voices.

Despite the recent progress on scaling multilingual machine translation (MT) to severalunder-resourced African languages, accuratelymeasuring this progress remains challenging,since evaluation is often performed on n-grammatching metrics such as BLEU, which typically show a weaker correlation with humanjudgments. Learned metrics such as COMEThave higher correlation; however, the lack ofevaluation data with human ratings for underresourced languages, complexity of annotationguidelines like Multidimensional Quality Metrics (MQM), and limited language coverageof multilingual encoders have hampered theirapplicability to African languages. In this paper, we address these challenges by creatinghigh-quality human evaluation data with simplified MQM guidelines for error detection and direct assessment (DA) scoring for 13 typologically diverse African languages. Furthermore, we develop AFRICOMET: COMETevaluation metrics for African languages byleveraging DA data from well-resourced languages and an African-centric multilingual encoder (AfroXLM-R) to create the state-of-theart MT evaluation metrics for African languages with respect to Spearman-rank correlation with human judgments (0.441).

We reflect on the experiences in organizing and implementing a high-quality Blended Intensive Programme (BIP) as a joint international event. A BIP is a short programme that combines physical mobility with a virtual part. The 6-day event, titled “DigiHealth-AI: Practice, Research, Ethics, and Regulation”, was organized in collaboration with partners from five European nations and support from the EU’s ERASMUS+ programme in November 2023. We introduced a new learning method called ProCoT, involving large language models (LLMs), for preventing cheating by students in writing. We designed an online survey of key questions, which was conducted at the beginning and the end of the BIP. The highlights of the survey are as follows: By the end of the BIP, 84% of the respondents agreed that the intended learning outcomes (ILOs) were fulfilled, 100% strongly agreed that artificial intelligence (AI) benefits the healthcare sector, 62% disagree that they are concerned about AI potentially eliminating jobs in the healthcare sector (compared to 57% initially), 60% were concerned about their privacy when using AI, and 56% could identify, at least, two known sources of bias in AI systems (compared to only 43% prior to the BIP). A total of 541 votes were cast by 40 students, who were the respondents. The minimum and maximum numbers of students who answered any particular survey question at a given period are 25 and 40, respectively.