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).

This comprehensive systematic review critically analyzes the current progress and challenges in automating transtibial prosthesis alignment. The manual identification of alignment changes in prostheses has been found to lack reliability, necessitating the development of automated processes. Through a rigorous systematic search across major electronic databases, this review includes the highly relevant studies out of an initial pool of 2111 records. The findings highlight the urgent need for automated alignment systems in individuals with transtibial amputation. The selected studies represent cutting-edge research, employing diverse approaches such as advanced machine learning algorithms and innovative alignment tools, to automate the detection and adjustment of prosthesis alignment. Collectively, this review emphasizes the immense potential of automated transtibial prosthesis alignment systems to enhance alignment accuracy and significantly reduce human error. Furthermore, it identifies important limitations in the reviewed studies, serving as a catalyst for future research to address these gaps and explore alternative machine learning algorithms. The insights derived from this systematic review provide valuable guidance for researchers, clinicians, and developers aiming to propel the field of automated transtibial prosthesis alignment forward.

This paper investigates the application of large language models (LLMs) in the educational field, specifically focusing on roles like "Teacher Assistant" and "Study Assistant" to enhance personalized and adaptive learning. The significance of integrating AI in educational frameworks is underscored, given the shift towards AI-powered educational tools. The methodology of this research is structured and multifaceted, examining the dynamics between prompt engineering, methodological approaches, and LLM outputs with the help of indexed documents. The study bifurcates its approach into prompt structuring and advanced prompt engineering techniques. Initial investigations revolve around persona and template prompts to evaluate their individual and collective effects on LLM outputs. Advanced techniques, including few-shot and chain-of-thought prompting, are analyzed for their potential to elevate the quality and specificity of LLM responses. The "Study Assistant" aspect of the study involves applying these techniques to educational content across disciplines such as biology, mathematics, and physics. Findings from this research are poised to contribute significantly to the evolution of AI in education, offering insights into the variables that enhance LLM performance. This paper not only enriches the academic discourse on LLMs but also provides actionable insights for the development of sophisticated AI-based educational tools. As the educational landscape continues to evolve, this research underscores the imperative for continuous exploration and refinement in the application of AI to fully realize its benefits in education.

In the pursuit of enhancing smart city infrastructure, computer vision serves as a pivotal element for traffic management, scene understanding, and security applications. This research investigates the performance of the YOLO v9-c and YOLO v9-e object detection models in identifying vehicles under snowy weather conditions, leveraging various data augmentation techniques. The study highlights that, historically, object detection relied on complex, handcrafted features, but deep learning advancements have enabled more efficient and accurate end-to-end learning directly from raw data. Despite these advancements, detecting objects in adverse weather conditions like snow remains challenging, affecting the safety and effectiveness of autonomous systems. The study examines the performance of YOLO v9-c and YOLO v9-e under four different scenarios: no augmentation, snow accumulation, snow overlay, and snow depth mapping. Results indicate that both models achieve their highest precision without augmentation, with YOLO v9-c and YOLO v9-e reaching precisions of 82% and 80%, respectively. However, the snow accumulation method severely impacts detection accuracy, with precision dropping to 36% for YOLO v9-c and 43% for YOLO v9-e. Snow overlay augmentation shows better adaptability, with YOLO v9-c achieving 68% and YOLO v9-e 76% precision. Snow depth mapping results in moderate impacts, with precisions of 59% for YOLO v9-c and 61% for YOLO v9-e. The findings emphasize the importance of careful selection and tuning of augmentation techniques to improve object detection models’ robustness under snowy weather conditions, thereby enhancing the safety and efficiency of autonomous systems. The study suggests a tunned augmentation that helps YOLO v9-c and YOLO v9-e reach precisions of 85% and 83%. Future research should focus more on optimizing augmentation parameters, diversifying training data, and employing domain randomization to further enhance the robustness and generalization capabilities of these models. This approach aims to ensure more reliable performance of autonomous systems in real-world conditions where adverse weather is a common occurrence. The code and the dataset will be available at https://nvd.Itu-ai.dev/

This paper addresses the critical need for advanced real-time vehicle detection methodologies in Vehicle Intelligence Systems (VIS), especially in the context of using Unmanned Aerial Vehicles (UAVs) for data acquisition in severe weather conditions, such as heavy snowfall typical of the Nordic region. Traditional vehicle detection techniques, which often rely on custom-engineered features and deterministic algorithms, fall short in adapting to diverse environmental challenges, leading to a demand for more precise and sophisticated methods. The limitations of current architectures, particularly when deployed in real-time on edge devices with restricted computational capabilities, are highlighted as significant hurdles in the development of efficient vehicle detection systems. To bridge this gap, our research focuses on the formulation of an innovative approach that combines the fractional B-spline wavelet transform with a tailored U-Net architecture, operational on a Raspberry Pi 4. This method aims to enhance vehicle detection and localization by leveraging the unique attributes of the NVD dataset, which comprises drone-captured imagery under the harsh winter conditions of northern Sweden. The dataset, featuring 8450 annotated frames with 26,313 vehicles, serves as the foundation for evaluating the proposed technique. The comparative analysis of the proposed method against state-of-the-art detectors, such as YOLO and Faster RCNN, in both accuracy and efficiency on constrained devices, emphasizes the capability of our method to balance the trade-off between speed and accuracy, thereby broadening its utility across various domains.

Handwritten signatures in biometric authentication leverage unique individual characteristics for identification, offering high specificity through dynamic and static properties. However, this modality faces significant challenges from sophisticated forgery attempts, underscoring the need for enhanced security measures in common applications. To address forgery in signature-based biometric systems, integrating a forgery-resistant modality, namely, noninvasive electroencephalography (EEG), which captures unique brain activity patterns, can significantly enhance system robustness by leveraging multimodality’s strengths. By combining EEG, a physiological modality, with handwritten signatures, a behavioral modality, our approach capitalizes on the strengths of both, significantly fortifying the robustness of biometric systems through this multimodal integration. In addition, EEG’s resistance to replication offers a high-security level, making it a robust addition to user identification and verification. This study presents a new multimodal SignEEG v1.0 dataset based on EEG and hand-drawn signatures from 70 subjects. EEG signals and hand-drawn signatures have been collected with Emotiv Insight and Wacom One sensors, respectively. The multimodal data consists of three paradigms based on mental, & motor imagery, and physical execution: i) thinking of the signature’s image, (ii) drawing the signature mentally, and (iii) drawing a signature physically. Extensive experiments have been conducted to establish a baseline with machine learning classifiers. The results demonstrate that multimodality in biometric systems significantly enhances robustness, achieving high reliability even with limited sample sizes. We release the raw, pre-processed data and easy-to-follow implementation details.