This systematic review focuses on anomaly detection for connected and autonomous vehicles. The initial database search identified 2160 articles, of which 203 were included in this review after rigorous screening and assessment. This study revealed that the most commonly used Artificial Intelligence (AI) algorithms employed in anomaly detection are neural networks like LSTM, CNN, and autoencoders, alongside one-class SVM. Most anomaly-based models were trained using real-world operational vehicle data, although anomalies, such as attacks and faults, were often injected artificially into the datasets. These models were evaluated mostly using five key evaluation metrics: recall, accuracy, precision, F1-score, and false positive rate. The most frequently used selection of evaluation metrics used for anomaly detection models were accuracy, precision, recall, and F1-score. This systematic review presents several recommendations. First, there is a need to incorporate multiple evaluation metrics to provide a comprehensive assessment of the anomaly detection models. Second, only a small proportion of the studies have made their models open source, indicating a need to share models publicly to facilitate collaboration within the research community, and to validate and compare findings effectively. Third, there is a need for benchmarking datasets with predefined anomalies or cyberattacks to test and improve the effectiveness of the proposed anomaly-based detection models. Furthermore, there is a need for future research to investigate the deployment of anomaly detection to a vehicle to assess its performance on the road. There is a notable lack of research done on intrusion detection systems using different protocols to CAN, such as Ethernet and FlexRay.

Narratives serve as fundamental frameworks in our understanding of the world and play a crucial role in collaborative sensemaking, providing a versatile foundation for sensemaking. Framing is a subtle yet potent mechanism that influences public perception through specific word choices, shaping interpretations of reported news events. Despite the recognized importance of narratives and framing, a significant gap exists in the literature with regard to the explicit consideration of framing within the context of computational extraction and representation. This article explores the capabilities of a specific narrative extraction and representation approach -- narrative maps -- to capture framing information from news data. The research addresses two key questions: (1) Does the narrative extraction method capture the framing distribution of the data set? (2) Does it produce a representation with consistent framing? Our results indicate that while the algorithm captures framing distributions, achieving consistent framing across various starting and ending events poses challenges. Our results highlight the potential of narrative maps to provide users with insights into the intricate framing dynamics within news narratives. However, we note that directly leveraging framing information in the computational narrative extraction process remains an open challenge.

Policy-based methods have achieved remarkable success in solving challenging reinforcement learning problems. Among these methods, off-policy policy gradient methods are particularly important due to that they can benefit from off-policy data. However, these methods suffer from the high variance of the off-policy policy gradient (OPPG) estimator, which results in poor sample efficiency during training. In this paper, we propose an off-policy policy gradient method with the optimal action-dependent baseline (Off-OAB) to mitigate this variance issue. Specifically, this baseline maintains the OPPG estimator's unbiasedness while theoretically minimizing its variance. To enhance practical computational efficiency, we design an approximated version of this optimal baseline. Utilizing this approximation, our method (Off-OAB) aims to decrease the OPPG estimator's variance during policy optimization. We evaluate the proposed Off-OAB method on six representative tasks from OpenAI Gym and MuJoCo, where it demonstrably surpasses state-of-the-art methods on the majority of these tasks.

Guidance commands of flight vehicles are a series of data sets with fixed time intervals, thus guidance design constitutes a sequential decision problem and satisfies the basic conditions for using deep reinforcement learning (DRL). In this paper, we consider the scenario where the escape flight vehicle (EFV) generates guidance commands based on DRL and the pursuit flight vehicle (PFV) generates guidance commands based on the proportional navigation method. For the EFV, the objective of the guidance design entails progressively maximizing the residual velocity, subject to the constraint imposed by the given evasion distance. Thus an irregular dynamic max-min problem of extremely large-scale is formulated, where the time instant when the optimal solution can be attained is uncertain and the optimum solution depends on all the intermediate guidance commands generated before. For solving this problem, a two-step strategy is conceived. In the first step, we use the proximal policy optimization (PPO) algorithm to generate the guidance commands of the EFV. The results obtained by PPO in the global search space are coarse, despite the fact that the reward function, the neural network parameters and the learning rate are designed elaborately. Therefore, in the second step, we propose to invoke the evolution strategy (ES) based algorithm, which uses the result of PPO as the initial value, to further improve the quality of the solution by searching in the local space. Simulation results demonstrate that the proposed guidance design method based on the PPO algorithm is capable of achieving a residual velocity of 67.24 m/s, higher than the residual velocities achieved by the benchmark soft actor-critic and deep deterministic policy gradient algorithms. Furthermore, the proposed ES-enhanced PPO algorithm outperforms the PPO algorithm by 2.7\%, achieving a residual velocity of 69.04 m/s.

As generative artificial intelligence (AI), particularly Large Language Models (LLMs), continues to permeate healthcare, it remains crucial to supplement traditional automated evaluations with human expert evaluation. Understanding and evaluating the generated texts is vital for ensuring safety, reliability, and effectiveness. However, the cumbersome, time-consuming, and non-standardized nature of human evaluation presents significant obstacles to the widespread adoption of LLMs in practice. This study reviews existing literature on human evaluation methodologies for LLMs within healthcare. We highlight a notable need for a standardized and consistent human evaluation approach. Our extensive literature search, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, spans publications from January 2018 to February 2024. This review provides a comprehensive overview of the human evaluation approaches used in diverse healthcare applications.This analysis examines the human evaluation of LLMs across various medical specialties, addressing factors such as evaluation dimensions, sample types, and sizes, the selection and recruitment of evaluators, frameworks and metrics, the evaluation process, and statistical analysis of the results. Drawing from diverse evaluation strategies highlighted in these studies, we propose a comprehensive and practical framework for human evaluation of generative LLMs, named QUEST: Quality of Information, Understanding and Reasoning, Expression Style and Persona, Safety and Harm, and Trust and Confidence. This framework aims to improve the reliability, generalizability, and applicability of human evaluation of generative LLMs in different healthcare applications by defining clear evaluation dimensions and offering detailed guidelines.

Autonomous robots for gathering information on objects of interest has numerous real-world applications because of they improve efficiency, performance and safety. Realizing autonomy demands online planning algorithms to solve sequential decision making problems under uncertainty; because, objects of interest are often dynamic, object state, such as location is not directly observable and are obtained from noisy measurements. Such planning problems are notoriously difficult due to the combinatorial nature of predicting the future to make optimal decisions. For information theoretic planning algorithms, we develop a computationally efficient and effective approximation for the difficult problem of predicting the likely sensor measurements from uncertain belief states}. The approach more accurately predicts information gain from information gathering actions. Our theoretical analysis proves the proposed formulation achieves a lower prediction error than the current efficient-method. We demonstrate improved performance gains in radio-source tracking and localization problems using extensive simulated and field experiments with a multirotor aerial robot.

While humans excel at continual learning (CL), deep neural networks (DNNs) exhibit catastrophic forgetting. A salient feature of the brain that allows effective CL is that it utilizes multiple modalities for learning and inference, which is underexplored in DNNs. Therefore, we study the role and interactions of multiple modalities in mitigating forgetting and introduce a benchmark for multimodal continual learning. Our findings demonstrate that leveraging multiple views and complementary information from multiple modalities enables the model to learn more accurate and robust representations. This makes the model less vulnerable to modality-specific regularities and considerably mitigates forgetting. Furthermore, we observe that individual modalities exhibit varying degrees of robustness to distribution shift. Finally, we propose a method for integrating and aligning the information from different modalities by utilizing the relational structural similarities between the data points in each modality. Our method sets a strong baseline that enables both single-and multimodal inference. Our study provides a promising case for further exploring the role of multiple modalities in enabling CL and provides a standard benchmark for future research. Lifelong learning requires the learning agent to continuously adapt to new data while retaining and consolidating previously learned knowledge. This ability is essential for the deployment of deep neural networks (DNNs) in numerous real-world applications. However, one critical issue in enabling continual learning (CL) in DNNs is catastrophic forgetting, whereby the model drastically forgets previously acquired knowledge when required to learn new tasks in sequence (McCloskey & Cohen, 1989).

Analytical models developed in offline settings with pre-prepared data are typically used to predict students' performance. However, when data are available over time, this learning method is not suitable anymore. Online learning is increasingly used to update the online models from stream data. A rehearsal technique is typically used, which entails re-training the model on a small training set that is updated each time new data is received. The main challenge in this regard is the construction of the training set with appropriate data samples to maintain good model performance. Typically, a random selection of samples is made, which can deteriorate the model's performance. In this paper, we propose a memory-based online incremental learning approach for updating an online classifier that predicts student performance using stream data. The approach is based on the use of the genetic algorithm heuristic while respecting the memory space constraints as well as the balance of class labels. In contrast to random selection, our approach improves the stability of the analytical model by promoting diversity when creating the training set. As a proof of concept, we applied it to the open dataset OULAD. Our approach achieves a notable improvement in model accuracy, with an enhancement of nearly 10% compared to the current state-of-the-art, while maintaining a relatively low standard deviation in accuracy, ranging from 1% to 2.1%.

The field of AIED, as defined by the work conducted under the auspices of the International Society of Artificial Intelligence in Education, has been built on big and well-intentioned ambitions to understand, devise and scale-up best learning and teaching practices to as many students as possible. This ambition has been bolstered most notably by the Bloom (1984) studies, which are still routinely cited throughout the AIED literature as a key justification and motivation for the field. This ambition had bootstrapped much of the work within the field and it has spurred in-depth research examining how specific populations of students learn, what are the prerequisites (cognitive, affective, and pedagogic) for successful learning, and how AIED technologies might be designed to help develop and capitalise on such learning prerequisites. Personalisation through adaptivity of assessment and feedback (for the purpose of this article used in the broad sense of pedagogical support) remains at the heart of the work conducted by AIED researchers, regardless of their specific areas of specialisation, or their philosophical or epistemological perspectives. This is why, to date, the AIED community repeatedly voted to retain its long-debated connection with the wider field of AI - a domain like AIED insofar as its central paradigm of adaptive agent technologies, but unlike AIED as far as its aim to emulate human capacities only to the extent that it is useful to a given application's success in achieving its specific goals.

In this paper, we present a novel approach for text independent phone-to-audio alignment based on phoneme recognition, representation learning and knowledge transfer. Our method leverages a self-supervised model (wav2vec2) fine-tuned for phoneme recognition using a Connectionist Temporal Classification (CTC) loss, a dimension reduction model and a frame-level phoneme classifier trained thanks to forced-alignment labels (using Montreal Forced Aligner) to produce multi-lingual phonetic representations, thus requiring minimal additional training. We evaluate our model using synthetic native data from the TIMIT dataset and the SCRIBE dataset for American and British English, respectively. Our proposed model outperforms the state-of-the-art (charsiu) in statistical metrics and has applications in language learning and speech processing systems. We leave experiments on other languages for future work but the design of the system makes it easily adaptable to other languages.