Deep Reinforcement Learning Approaches for Technology Enhanced Learning

Artificial Intelligence (AI) has advanced significantly in recent years, transforming various industries and domains. Its ability to extract patterns and insights from large volumes of data has revolutionised areas such as image recognition, natural language processing, and autonomous systems. As AI systems become increasingly integrated into daily human life, there is a growing need for meaningful collaboration and mutual engagement between humans and AI, known as Human-AI Collaboration. This collaboration involves combining AI with human workflows to achieve shared objectives. In the current educational landscape, the integration of AI methods in Technology Enhanced Learning (TEL) has become crucial for providing high-quality education and facilitating lifelong learning. Human-AI Collaboration also plays a vital role in the field of Technology Enhanced Learning (TEL), particularly in Intelligent Tutoring Systems (ITS). The COVID-19 pandemic has further emphasised the need for effective educational technologies to support remote learning and bridge the gap between traditional classrooms and online platforms. To maximise the performance of ITS while minimising the input and interaction required from students, it is essential to design collaborative systems that effectively leverage the capabilities of AI and foster effective collaboration between students and ITS. However, there are several challenges that need to be addressed in this context. One challenge is the lack of clear guidance on designing and building user-friendly systems that facilitate collaboration between humans and AI. This challenge is relevant not only to education researchers but also to Human-Computer Interaction (HCI) researchers and developers. Another challenge is the scarcity of interaction data in the early stages of ITS development, which hampers the accurate modelling of students' knowledge states and learning trajectories, known as the cold start problem. Moreover, the effectiveness of Intelligent Tutoring Systems (ITS) in delivering personalised instruction is hindered by the limitations of existing Knowledge Tracing (KT) models, which often struggle to provide accurate predictions. Therefore, addressing these challenges is crucial for enhancing the collaborative process between humans and AI in the development of ITS. This thesis aims to address these challenges and improve the collaborative process between students and ITS in TEL. It proposes innovative approaches to generate simulated student behavioural data and enhance the performance of KT models. The thesis starts with a comprehensive survey of human-AI collaborative systems, identifying key challenges and opportunities. It then presents a structured framework for the student-ITS collaborative process, providing insights into designing user-friendly and efficient systems. To overcome the challenge of data scarcity in ITS development, the thesis proposes two student modelling approaches: Sim-GAIL and SimStu. SimStu leverages a deep learning method, the Decision Transformer, to simulate student interactions and enhance ITS training. Sim-GAIL utilises a reinforcement learning method, Generative Adversarial Imitation Learning (GAIL), to generate high-fidelity and diverse simulated student behavioural data, addressing the cold start problem in ITS training. Furthermore, the thesis focuses on improving the performance of KT models. It introduces the MLFBKT model, which integrates multiple features and mines latent relations in student interaction data, aiming to improve the accuracy and efficiency of KT models. Additionally, the thesis proposes the LBKT model, which combines the strengths of the BERT model and LSTM to process long sequence data in KT models effectively. Overall, this thesis contributes to the field of Human-AI collaboration in TEL by addressing key challenges and proposing innovative approaches to enhance ITS training and KT model performance. The findings have the potential to improve the learning experiences and outcomes of students in educational settings

A Massera Type Criterion for Linear Functional Differential Equations with Advance and Delay

AbstractIn this note, a Massera type criterion for the existence of periodic solutions for linear functional differential equations with advance and delay is established. Because of the presence of an advanced argument, the definition of the fundamental solution operator seems unknown. Hence a method different from the usual one is employed. Applications to periodic problems for nonlinear equations are also given

Hand gesture recognition for user-defined textual inputs and gestures

Despite recent progress, hand gesture recognition, a highly regarded method of human computer interaction, still faces considerable challenges. In this paper, we address the problem of individual user style variation, which can significantly affect system performance. While previous work only supports the manual inclusion of customized hand gestures in the context of very specific application settings, here, an effective, adaptable graphical interface, supporting user-defined hand gestures is introduced. In our system, hand gestures are personalized by training a camera-based hand gesture recognition model for a particular user, using data just from that user. We employ a lightweight Multilayer Perceptron architecture based on contrastive learning, reducing the size of the data needed and the training timeframes compared to previous recognition models that require massive training datasets. Experimental results demonstrate rapid convergence and satisfactory accuracy of the recognition model, while a user study collects and analyses some initial user feedback on the system in deployment

Soil–plant–pollinator relationships in urban grass and meadow habitats: competing benefits and demands of tall flowering plants on soil and pollinator diversity

Urban green spaces can be important habitats for soil, plant, and pollinator diversity and the complementary ecosystem functions they confer. Most studies tend to investigate the relationships between plant diversity with either soil or pollinator diversity, but establishing their relationship across habitat types could be important for optimising ecosystem service provision via alternative management (for instance, urban meadows in place of short amenity grass). Here, we investigate soil–plant–pollinator relationships across urban grass and meadow habitats through a range of measured biodiversity (soil mesofauna and macrofauna, plants, aboveground invertebrates, and pollinators) and edaphic variables. We found significant effects of habitat type on available nutrients (plant and soil C:N ratios) but less clear relationships were observed between habitat type and diversity metrics. Soil–plant–pollinator interactions across habitat types and sites showed an interconnection, whereby flowering plant abundance increased alongside soil macrofauna abundance. Site characteristics that showed strong effects on plant and invertebrate diversity metrics were C:N ratios (plant and soil) and soil pH, suggesting a potential role of nutrient availability on soil–plant–pollinator associations. Our results suggest that a combination of short-mown grass, tall grass, and sown flowers can provide greater benefits for soil and pollination services as each habitat type benefits different taxa due to differing sensitivities to management practices. For example, pollinators benefit from sown flowers but soil fauna are sensitive to annual sowing. Our results also indicate that sown flowers may not optimise overall biodiversity as expected due to disturbance and the depleting role of tall, flowering plants on soil nutrient availability. Future research across a greater range of sites in urban landscapes would resolve the potential role of nutrient availability in modulating soil–plant–pollinator interactions in urban green spaces.Natural Environment Research Council (NERC

Asiaticoside Mitigates Alzheimer’s Disease Pathology by Attenuating Inflammation and Enhancing Synaptic Function

Alzheimer’s disease (AD) is a prevalent neurodegenerative disorder, hallmarked by the accumulation of amyloid-β (Aβ) plaques and neurofibrillary tangles. Due to the uncertainty of the pathogenesis of AD, strategies aimed at suppressing neuroinflammation and fostering synaptic repair are eagerly sought. Asiaticoside (AS), a natural triterpenoid derivative derived from Centella asiatica, is known for its anti-inflammatory, antioxidant, and wound-healing properties; however, its neuroprotective function in AD remains unclear. Our current study reveals that AS, when administered (40 mg/kg) in vivo, can mitigate cognitive dysfunction and attenuate neuroinflammation by inhibiting the activation of microglia and proinflammatory factors in Aβ1-42-induced AD mice. Further mechanistic investigation suggests that AS may ameliorate cognitive impairment by inhibiting the activation of the p38 MAPK pathway and promoting synaptic repair. Our findings propose that AS could be a promising candidate for AD treatment, offering neuroinflammation inhibition and enhancement of synaptic function

An attitude determination method based on convected Euler angle error model for SINS/CNS integrated system

The attitude determination method plays an important role in SINS/CNS integrated system for spacecraft. Since the misalignment angels are indirect measurements, the misalignment angle model used in the existing attitude determination method can cause transformation errors. To solve the problem, an attitude determination method based on convected Euler angle error model for SINS/CNS integrated system is proposed. The attitude error propagation is analyzed, and the convected Euler angle error model is derived. Furthermore, the state equation of SINS/CNS integrated system is established. The Kalman filter estimates and compensates the Euler angle errors. Finally, simulation results verified that the proposed method can improve the attitude accuracy compared to the conventional misalignment angle method

Liver Imaging Reporting and Data System (LI-RADS) v2018: differential diagnostic value of ADC values for benign and malignant nodules with moderate probability (LR-3)

ObjectiveTo evaluate the usefulness of the apparent diffusion coefficient (ADC) in differentiating between benign and malignant LR-3 lesions classified by Liver Imaging Reporting and Data System 2018 (LI-RADS v2018).MethodsRetrospectively analyzed 88 patients with liver nodules confirmed by pathology and classified as LR-3 by LI-RADS. All patients underwent preoperative contrast-enhanced MR examination, and the following patient-related imaging features were collected: tumor size,nonrim APHE, nonperipheral “washout”, enhancing “capsule”, mild-moderate T2 hyperintensity, fat in mass, restricted diffusion, and nodule-in-nodule architecture. We performed ROC analysis and calculated the sensitivity and specificity.ResultsA total of 122 lesions were found in 88 patients, with 68 benign and 54 malignant lesions. The mean ADC value for malignant and benign lesions were 1.01 ± 0.15 × 103 mm2/s and 1.41 ± 0.31 × 103 mm2/s, respectively. The ADC value of malignant lesions was significantly lower than that of benign lesions, p < 0.0001. Compared with other imaging features, ADC values had the highest AUC (AUC = 0.909), with a sensitivity of 92.6% and a specificity of 74.1% for the differentiation of benign and malignant lesions.ConclusionsADC values are useful for differentiating between benign and malignant liver nodules in LR-3 classification, it improves the sensitivity of LI-RADS in the diagnosis of HCC while maintaining high specificity, and we recommend including ADC values in the standard interpretation of LI-RADSv2018