The hype around self-driving cars has been growing over the past years and has sparked much research. Several modules in self-driving cars are thoroughly investigated to ensure safety, comfort, and efficiency, among which the controller is crucial. The controller module can be categorized into longitudinal and lateral controllers in which the task of the former is to follow the reference velocity, and the latter is to reduce the lateral displacement error from the reference path. Generally, a tuned controller is not sufficient to perform in all environments. Thus, a controller that can adapt to changing conditions is necessary for autonomous driving. Furthermore, these controllers often depend on vehicle models that also need to adapt over time due to varying environments. This paper uses graphs to present novel techniques to learn the vehicle model and the lateral controller online. First, a heterogeneous graph is presented depicting the current states of and inputs to the vehicle. The vehicle model is then learned online using known physical constraints in conjunction with the processing of the graph through a Graph Neural Network structure. Next, another heterogeneous graph - depicting the transition from current to desired states - is processed through another Graph Neural Network structure to generate the steering command on the fly. Finally, the performance of this self-learning model-based lateral controller is evaluated and shown to be satisfactory on an open-source autonomous driving platform called CARLA.

In this paper, we discuss learning algorithms and their importance in different types of applications which includes training to identify important patterns and features in a straightforward, easy-to-understand manner. We will review the main concepts of artificial intelligence (AI), machine learning (ML), deep learning (DL), and hybrid models. Some important subsets of Machine Learning algorithms such as supervised, unsupervised, and reinforcement learning are also discussed in this paper. These techniques can be used for some important tasks like prediction, classification, and segmentation. Convolutional Neural Networks (CNNs) are used for image and video processing and many more applications. We dive into the architecture of CNNs and how to integrate CNNs with ML algorithms to build hybrid models. This paper explores the vulnerability of learning algorithms to noise, leading to misclassification. We further discuss the integration of learning algorithms with Large Language Models (LLM) to generate coherent responses applicable to many domains such as healthcare, marketing, and finance by learning important patterns from large volumes of data. Furthermore, we discuss the next generation of learning algorithms and how we may have an unified Adaptive and Dynamic Network to perform important tasks. Overall, this article provides brief overview of learning algorithms, exploring their current state, applications and future direction.

Knowledge Distillation is a commonly used Deep Neural Network compression method, which often maintains overall generalization performance. However, we show that even for balanced image classification datasets, such as CIFAR-100, Tiny ImageNet and ImageNet, as many as 41% of the classes are statistically significantly affected by distillation when comparing class-wise accuracy (i.e. class bias) between a teacher/distilled student or distilled student/non-distilled student model. Changes in class bias are not necessarily an undesirable outcome when considered outside of the context of a model's usage. Using two common fairness metrics, Demographic Parity Difference (DPD) and Equalized Odds Difference (EOD) on models trained with the CelebA, Trifeature, and HateXplain datasets, our results suggest that increasing the distillation temperature improves the distilled student model's fairness -- for DPD, the distilled student even surpasses the fairness of the teacher model at high temperatures. This study highlights the uneven effects of Knowledge Distillation on certain classes and its potentially significant role in fairness, emphasizing that caution is warranted when using distilled models for sensitive application domains.

Software engineering (SE) chatbots are increasingly gaining attention for their role in enhancing development processes. At the core of chatbots are the Natural Language Understanding platforms (NLUs), which enable them to comprehend and respond to user queries. Before deploying NLUs, there is a need to train them with labeled data. However, acquiring such labeled data for SE chatbots is challenging due to the scarcity of high-quality datasets. This challenge arises because training SE chatbots requires specialized vocabulary and phrases not found in typical language datasets. Consequently, chatbot developers often resort to manually annotating user queries to gather the data necessary for training effective chatbots, a process that is both time-consuming and resource-intensive. Previous studies propose approaches to support chatbot practitioners in annotating users' posed queries. However, these approaches require human intervention to generate rules, called labeling functions (LFs), that identify and categorize user queries based on specific patterns in the data. To address this issue, we propose an approach to automatically generate LFs by extracting patterns from labeled user queries. We evaluate the effectiveness of our approach by applying it to the queries of four diverse SE datasets (namely AskGit, MSA, Ask Ubuntu, and Stack Overflow) and measure the performance improvement gained from training the NLU on the queries labeled by the generated LFs. We find that the generated LFs effectively label data with AUC scores of up to 85.3%, and NLU's performance improvement of up to 27.2% across the studied datasets. Furthermore, our results show that the number of LFs used to generate LFs affects the labeling performance. We believe that our approach can save time and resources in labeling users' queries, allowing practitioners to focus on core chatbot functionalities.

Approximately half of the world's population is multilingual, making multilingual ASR (MASR) essential. Deploying multiple monolingual models is challenging when the ground-truth language is unknown in advance. This motivates research efforts on configurable multilingual MASR models that can be prompted manually or adapted automatically to recognise specific languages. In this paper, we present the Configurable MASR model with Summary Vector (csvMASR), a novel architecture designed to enhance configurability. Our approach leverages adapters and introduces speech summary vector representations, inspired by conversational summary representations in speech diarization, to combine outputs from language-specific components at the utterance level. We also incorporate an auxiliary language classification loss to enhance configurability. Using data from 7 languages in the Multilingual Librispeech (MLS) dataset, csvMASR outperforms existing MASR models and reduces the word error rate (WER) from 10.33\% to 9.95\% when compared with the baseline. Additionally, csvMASR demonstrates superior performance in language classification and prompting tasks.

Reinforcement learning (RL) in partially observable settings is challenging because the agent's observations are not Markov. Recently proposed methods can learn variable-order Markov models of the underlying process but have steep memory requirements and are sensitive to aliasing between observation histories due to sensor noise. This paper proposes dynamic-depth context tree weighting (D2-CTW), a model-learning method that addresses these limitations. D2-CTW dynamically expands a suffix tree while ensuring that the size of the model, but not its depth, remains bounded. We show that D2-CTW approximately matches the performance of state-of-the-art alternatives at stochastic time-series prediction while using at least an order of magnitude less memory. We also apply D2-CTW to model-based RL, showing that, on tasks that require memory of past observations, D2-CTW can learn without prior knowledge of a good state representation, or even the length of history upon which such a representation should depend.

Diabetic foot ulcers (DFUs) are a leading cause of hospitalizations and lower limb amputations, placing a substantial burden on patients and healthcare systems. Early detection and accurate classification of DFUs are critical for preventing serious complications, yet many patients experience delays in receiving care due to limited access to specialized services. Telehealth has emerged as a promising solution, improving access to care and reducing the need for in-person visits. The integration of artificial intelligence and pattern recognition into telemedicine has further enhanced DFU management by enabling automatic detection, classification, and monitoring from images. Despite advancements in artificial intelligence-driven approaches for DFU image analysis, the application of large language models for DFU image transcription has not yet been explored. To address this gap, we introduce UlcerGPT, a novel multimodal approach leveraging large language and vision models for DFU image transcription. This framework combines advanced vision and language models, such as Large Language and Vision Assistant and Chat Generative Pre-trained Transformer, to transcribe DFU images by jointly detecting, classifying, and localizing regions of interest. Through detailed experiments on a public dataset, evaluated by expert clinicians, UlcerGPT demonstrates promising results in the accuracy and efficiency of DFU transcription, offering potential support for clinicians in delivering timely care via telemedicine.

Code generation aims to automatically generate code from input requirements, significantly enhancing development efficiency. Recent large language models (LLMs) based approaches have shown promising results and revolutionized code generation task. Despite the promising performance, LLMs often generate contents with hallucinations, especially for the code generation scenario requiring the handling of complex contextual dependencies in practical development process. Although previous study has analyzed hallucinations in LLM-powered code generation, the study is limited to standalone function generation. In this paper, we conduct an empirical study to study the phenomena, mechanism, and mitigation of LLM hallucinations within more practical and complex development contexts in repository-level generation scenario. First, we manually examine the code generation results from six mainstream LLMs to establish a hallucination taxonomy of LLM-generated code. Next, we elaborate on the phenomenon of hallucinations, analyze their distribution across different models. We then analyze causes of hallucinations and identify four potential factors contributing to hallucinations. Finally, we propose an RAG-based mitigation method, which demonstrates consistent effectiveness in all studied LLMs. The replication package including code, data, and experimental results is available at https://github.com/DeepSoftwareAnalytics/LLMCodingHallucination

Effective patient-provider communication is crucial in clinical care, directly impacting patient outcomes and quality of life. Traditional evaluation methods, such as human ratings, patient feedback, and provider self-assessments, are often limited by high costs and scalability issues. Although existing natural language processing (NLP) techniques show promise, they struggle with the nuances of clinical communication and require sensitive clinical data for training, reducing their effectiveness in real-world applications. Emerging large language models (LLMs) offer a new approach to assessing complex communication metrics, with the potential to advance the field through integration into passive sensing and just-in-time intervention systems. This study explores LLMs as evaluators of palliative care communication quality, leveraging their linguistic, in-context learning, and reasoning capabilities. Specifically, using simulated scripts crafted and labeled by healthcare professionals, we test proprietary models (e.g., GPT-4) and fine-tune open-source LLMs (e.g., LLaMA2) with a synthetic dataset generated by GPT-4 to evaluate clinical conversations, to identify key metrics such as `understanding' and `empathy'. Our findings demonstrated LLMs' superior performance in evaluating clinical communication, providing actionable feedback with reasoning, and demonstrating the feasibility and practical viability of developing in-house LLMs. This research highlights LLMs' potential to enhance patient-provider interactions and lays the groundwork for downstream steps in developing LLM-empowered clinical health systems.

This paper introduces a novel method for effectively removing artifacts from EEG signals by combining the Empirical Mode Decomposition (EMD) method with a machine learning architecture. The proposed method addresses the limitations of existing artifact removal techniques by enhancing the EMD method through interpolation of the upper and lower. For conventional artifact removal methods, the EMD technique is commonly employed. However, the challenge lies in accurately interpolating the missing components of the signal while preserving its inherent frequency components. To overcome this limitation, we incorporated machine learning technique, which enables us to carefully handle the interpolation process without directly manipulating the data. The key advantage of our approach lies in the preservation of the natural characteristics of the EEG signal during artifact removal. By utilizing machine learning for interpolation, we ensure that the average component obtained through the EMD method retains the crucial frequency components of the original signal. This preservation is essential for maintaining the integrity and fidelity of the EEG data, allowing for accurate analysis and interpretation. The results obtained from our evaluation serve to validate the effectiveness of our approach and pave the way for further advancements in EEG signal processing and analysis.