Objective: The reading level of health educational materials significantly influences information understandability and accessibility, particularly for minoritized populations. Many patient educational resources surpass the reading level and complexity of widely accepted standards. There is a critical need for high-performing text simplification models in health information to enhance dissemination and literacy. This need is particularly acute in cancer education, where effective prevention and screening education can substantially reduce morbidity and mortality. Methods: We introduce Simplified Digestive Cancer (SimpleDC), a parallel corpus of cancer education materials tailored for health text simplification research. Utilizing SimpleDC alongside the existing Med-EASi corpus, we explore Large Language Model (LLM)-based simplification methods, including fine-tuning, reinforcement learning (RL), reinforcement learning with human feedback (RLHF), domain adaptation, and prompt-based approaches. Our experimentation encompasses Llama 2 and GPT-4. A novel RLHF reward function is introduced, featuring a lightweight model adept at distinguishing between original and simplified texts, thereby enhancing the model's effectiveness with unlabeled data. Results: Fine-tuned Llama 2 models demonstrated high performance across various metrics. Our innovative RLHF reward function surpassed existing RL text simplification reward functions in effectiveness. The results underscore that RL/RLHF can augment fine-tuning, facilitating model training on unlabeled text and improving performance. Additionally, these methods effectively adapt out-of-domain text simplification models to targeted domains.

User-specific future activity prediction in the healthcare domain based on previous activities can drastically improve the services provided by the nurses. It is challenging because, unlike other domains, activities in healthcare involve both nurses and patients, and they also vary from hour to hour. In this paper, we employ various data processing techniques to organize and modify the data structure and an LSTM-based multi-label classifier for a novel 2-stage training approach (user-agnostic pre-training and user-specific fine-tuning). Our experiment achieves a validation accuracy of 31.58\%, precision 57.94%, recall 68.31%, and F1 score 60.38%. We concluded that proper data pre-processing and a 2-stage training process resulted in better performance. This experiment is a part of the "Fourth Nurse Care Activity Recognition Challenge" by our team "Not A Fan of Local Minima".

Automatic License Plate Recognition systems aim to provide an end-to-end solution towards detecting, localizing, and recognizing license plate characters from vehicles appearing in video frames. However, deploying such systems in the real world requires real-time performance in low-resource environments. In our paper, we propose a novel two-stage detection pipeline paired with Vision API that aims to provide real-time inference speed along with consistently accurate detection and recognition performance. We used a haar-cascade classifier as a filter on top of our backbone MobileNet SSDv2 detection model. This reduces inference time by only focusing on high confidence detections and using them for recognition. We also impose a temporal frame separation strategy to identify multiple vehicle license plates in the same clip. Furthermore, there are no publicly available Bangla license plate datasets, for which we created an image dataset and a video dataset containing license plates in the wild. We trained our models on the image dataset and achieved an AP(0.5) score of 86% and tested our pipeline on the video dataset and observed reasonable detection and recognition performance (82.7% detection rate, and 60.8% OCR F1 score) with real-time processing speed (27.2 frames per second).

In the history of conversational AI agents, ELIZA [2], [3], one In this era of artificial intelligence (AI), chatbots are becoming of the first rule-based chatbots, took it upon itself to pass the more and more popular every day for their versatility, easy famous Turing Test and pioneer the path of guided computer accessibility, personalizing features, and, more importantly, responses. Even though it failed to pass the test completely, it their ability to generate automated responses. Specifically for surely did not come short in paving the way for other artificial these purposes, we now see an uprise of chatbots everywhere - chatbots, which ranged from responding emotionally (PARRY) from personal to organizational, to business websites or other [2]-[4] to simply having fun conversations by running pattern online platforms, for which it can be trained on suitable data matching (Jabberwacky) [2]. Later, this field got more to make it, in a broader sense, a virtual assistant representative matured with the inception of AI-powered chatbots, namely of the said entities. Dr. Sbaitso [3] and A.L.I.C.E (Artificial Linguistic Internet In the light of this newly emerging scope, we explore the Computer Entity) [2], [4]- which was able to mimic humans possibilities of how these conversational AI agents can be when chatting online or answering questions. From there, integrated properly and thus be an immensely useful tool to it was not long before Smarterchild, Siri, Google Assistant, maintain business activities. To better understand the concurrent and other personalized assistant-like chatbots or conversational chatbots and find possible modifications in them and AI agents came into existence. With conversational AI, now, for further and more customized improvements, we choose a anyone can build, integrate, and use message-based or speechbased trendy chatbot platform Rasa as our study subject.