AI is employed in education to personalize learning experiences, provide adaptive feedback, and automate administrative tasks. It utilizes machine learning algorithms, natural language processing, and data analytics to enhance student engagement, optimize teaching methods, and streamline educational processes, leading to more effective and personalized education.
This paper unveils FaceNet-MMAR, an advanced facial recognition model tailored for intelligent university libraries. By optimizing traditional FaceNet algorithms with innovative features, including mobilenet, mish activation, attention module, and receptive field module, the model showcases superior accuracy and efficiency, garnering high satisfaction rates from both teachers and students in real-world applications.
Researchers from the USA leverage Large Language Models (LLMs) to automatically extract social determinants of health (SDoH) from clinical narratives, addressing challenges in healthcare data. Their innovative approach, combining Flan-T5 models and synthetic data augmentation, showcases remarkable efficiency, emphasizing the potential to bridge gaps in understanding and addressing crucial factors influencing patients' well-being.
In this study, researchers from Valley Children's Hospital leverage artificial intelligence and data visualization to harness clinical genetic data for estimating genetic disorder prevalence and mapping variants to local geographies. The groundbreaking methodology, exemplified in a five-year analysis, offers a blueprint for healthcare systems to translate genetic testing data into actionable insights for tailored population health management.
The article emphasizes the pivotal role of Human Factors and Ergonomics (HFE) in addressing challenges and debates surrounding trust in automation, ethical considerations, user interface design, human-AI collaboration, and the psychological and behavioral aspects of human-robot interaction. Understanding knowledge gaps and ongoing debates is crucial for shaping the future development of HFE in the context of emerging technologies.
Researchers from the University of Tuscia, Italy, introduced a machine learning (ML)-based classification model to offer tailored support tools and learning strategies for university students with dyslexia. The model, trained on a self-evaluation questionnaire from over 1200 dyslexic students, demonstrated high accuracy in predicting effective methodologies, providing a personalized approach to enhance learning outcomes and well-being. The study emphasizes the potential applications in education, psychology, and tool/strategy development, encouraging future research directions and student involvement in the design process.
Researchers discuss the transformative role of Multimodal Large Language Models (MLLMs) in science education. Focusing on content creation, learning support, assessment, and feedback, the study demonstrates how MLLMs provide adaptive, personalized, and multimodal learning experiences, illustrating their potential in various educational settings beyond science.
Researchers from Nanjing University of Science and Technology present a novel scheme, Spatial Variation-Dependent Verification (SVV), utilizing convolutional neural networks and textural features for handwriting identification and verification. The scheme outperforms existing methods, achieving 95.587% accuracy, providing a robust solution for secure handwriting recognition and authentication in diverse applications, including security, forensics, banking, education, and healthcare.
This research explores end-user programming of collaborative robots through kinesthetic teaching. The study compares self-guided practice with curriculum-based training and finds no significant difference in programming proficiency. While both approaches offer insights into factors affecting success, the study underscores the need for refined learning interventions in end-user robot programming to enhance skills and perceptions effectively.
This research, published in PLOS One, investigates the protective feature preferences of the adult Danish population in various AI decision-making scenarios. With a focus on both public and commercial sectors, the study explores the nuanced interplay of demographic factors, societal expectations, and trust in shaping preferences for features such as AI knowledge, human responsibility, non-discrimination, human explainability, and system performance.
This research investigates the determinants of earthquake insurance uptake in Oklahoma post-2011 seismic events. Through supervised machine learning, it identifies influential factors including age, gender, ethnicity, political affiliation, tenure, housing status, education, income, earthquake experience, and environmental attitudes. The study emphasizes the significance of awareness and advanced machine learning tools for predictive modeling in managing environmental risks and advocates for informed disaster management strategies.
Despite prior positive notions, this study on augmented reality (AR) in a Chinese vocational college setting challenges its efficacy. In a three-stage experiment on architectural education, AR did not significantly improve academic performance, revealing diverse impacts across genders and grades. The study emphasizes the nuanced relationship between AR and learning outcomes, urging a cautious approach and tailored educational strategies based on individual student characteristics.
This research delves into the synergy of Artificial Intelligence (AI) and Internet of Things (IoT) security. The study evaluates and compares various AI algorithms, including machine learning (ML) and deep learning (DL), for classifying and detecting IoT attacks. It introduces a novel taxonomy of AI methodologies for IoT security and identifies LSTM as the top-performing algorithm, emphasizing its potential applications in diverse fields.
This study delves into the influence of exposure to social bots on individuals' perceptions and policy preferences regarding these automated accounts on popular platforms like Twitter, Facebook, and Instagram. The research reveals that even minimal exposure distorts perceptions of bot prevalence and self-efficacy, triggering reactive policy sentiments among social media users.
In this study, the authors delve into psychological factors influencing consumer attitudes toward AI systems. They categorize barriers into opacity, emotionlessness, rigidity, autonomy, and non-humanness, examining how each relates to human cognition. The study emphasizes the need for targeted, context-specific interventions based on system capabilities and user inclinations, providing insights for improving acceptance and mitigating risks in the adoption of AI technologies.
The AI-driven transformation of the labor market demands a nuanced approach to skill measurement. Researchers, in a recent AISeL publication, illuminate the dynamic nature of AI skills and propose a novel dynamic co-occurrence method, addressing limitations in static approaches. Leveraging empirical analysis, they reveal the evolving landscape of AI skills and demonstrate the superior performance of the dynamic method in capturing emergence and evolution.
Researchers introduce a pioneering framework leveraging IoT and wearable technology to enhance the adaptability of AR glasses in the aviation industry. The multi-modal data processing system, employing kernel theory-based design and machine learning, classifies performance, offering a dynamic and adaptive approach for tailored AR information provision.
This study investigates the widespread usage of AI tools, particularly ChatGPT and GPT-4, among German students across disciplines. With nearly two-thirds of students utilizing these tools, the research emphasizes the need for further exploration into usage patterns, perceptions, and potential implications for teaching and learning.
Researchers introduce LLaVA-Interactive, an innovative multimodal human and artificial intelligence (AI) interaction prototype. This system enables users to engage in multi-turn dialogues and interact through visual and language prompts, offering diverse applications, from content creation to culinary guidance, and inspiring future research in multimodal interactive systems.
This paper presents a comprehensive survey of large language model (LLM) evaluation across various dimensions, including knowledge, reasoning, alignment, safety, and specialized domains. It covers a wide range of evaluation benchmarks and methodologies to assess the capabilities, ethical considerations, robustness, and application-specific performance of LLMs, aiming to guide the development of LLMs that are beneficial and trustworthy.
This study delves into customer preferences for automated parcel delivery modes, including autonomous vehicles, drones, sidewalk robots, and bipedal robots, in the context of last-mile logistics. Using an Integrated Nested Choice and Correlated Latent Variable model, the research reveals that cost and time performance significantly influence the acceptability of technology, with a growing willingness to explore novel delivery automation when cost and time align.
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