Deep Convolutional Neural Networks for MultilabelPrediction Using RGBD Data

Robotics relies heavily on the system's ability to perceive the world around the robot accurately and quickly. In a narrow setting as in manufacturing this goal is relatively simple. To make robotics feasible in more dynamic settings we must handle more objects, more attributes, and events that may be out of the scope of what a system has been exposed to previously. To this end, the present work focuses on automatic feature formation from RGB-D data, using deep convolutional neural networks, in order to recognize, not only objects but also attributes which are more applicable across objects, including those objects which have not been seen previously. Progress is shown in relation to more standard systems and near real-time classification of multiple targets is achieved

Using Large Language Models to Automate Category and Trend Analysis of Scientific Articles: An Application in Ophthalmology

Purpose: In this paper, we present an automated method for article classification, leveraging the power of Large Language Models (LLM). The primary focus is on the field of ophthalmology, but the model is extendable to other fields. Methods: We have developed a model based on Natural Language Processing (NLP) techniques, including advanced LLMs, to process and analyze the textual content of scientific papers. Specifically, we have employed zero-shot learning (ZSL) LLM models and compared against Bidirectional and Auto-Regressive Transformers (BART) and its variants, and Bidirectional Encoder Representations from Transformers (BERT), and its variant such as distilBERT, SciBERT, PubmedBERT, BioBERT. Results: The classification results demonstrate the effectiveness of LLMs in categorizing large number of ophthalmology papers without human intervention. Results: To evalute the LLMs, we compiled a dataset (RenD) of 1000 ocular disease-related articles, which were expertly annotated by a panel of six specialists into 15 distinct categories. The model achieved mean accuracy of 0.86 and mean F1 of 0.85 based on the RenD dataset. Conclusion: The proposed framework achieves notable improvements in both accuracy and efficiency. Its application in the domain of ophthalmology showcases its potential for knowledge organization and retrieval in other domains too. We performed trend analysis that enables the researchers and clinicians to easily categorize and retrieve relevant papers, saving time and effort in literature review and information gathering as well as identification of emerging scientific trends within different disciplines. Moreover, the extendibility of the model to other scientific fields broadens its impact in facilitating research and trend analysis across diverse disciplines

Question classification in the medical domain using convolutional neural networks and word embeddings

Question Answering Systems have become important due to the necessity to retrieveanswers from questions stated in Natural Language, without the need of queryingstructured data sources. One of the key steps of these systems is the question clas-sification process, where a label with the type of the

Kernel and Classifier Level Fusion for Image Classification.

Automatic understanding of visual information is one of the main requirements for a complete artificial intelligence system and an essential component of autonomous robots. State-of-the-art image recognition approaches are based on different local descriptors, each capturing some properties of the image such as intensity, color and texture. Each set of local descriptors is represented by a codebook and gives rise to a separate feature channel. For classification the feature channels are combined by using multiple kernel learning (MKL), early fusion or classifier level fusion approaches. Due to the importance of complementary information in fusion techniques, there is an increasing demand for diverse feature channels. The first part of the thesis focuses on the ways to encode information from images that is complementary to the state-of-the-art local features. To address this issue we present a novel image representation which can encode the structure of an object and propose three descriptors based on this representation. In the state-of-the-art recognition system the kernels are often computed independently of each other and thus may be highly informative yet redundant. Proper selection and fusion of the kernels is, therefore, crucial to maximize the performance and to address the efficiency issues in visual recognition applications. We address this issue in second part of the thesis where, we propose novel techniques to fuse feature channels for object and pattern recognition. We present an extensive evaluation of the fusion methods on four object recognition datasets and achieve state-of-the-art results on all of them. We also present results on four bioinformatics datasets to demonstrate that the proposed fusion methods work for a variety of pattern recognition problems, provided that we have multiple feature channels

NoRBERT: Transfer Learning for Requirements Classification

Classifying requirements is crucial for automatically handling natural language requirements. The performance of existing automatic classification approaches diminishes when applied to unseen projects because requirements usually vary in wording and style. The main problem is poor generalization. We propose NoRBERT that fine-tunes BERT, a language model that has proven useful for transfer learning. We apply our approach to different tasks in the domain of requirements classification. We achieve similar or better results (F$_1$-scores of up to 94%) on both seen and unseen projects for classifying functional and non-functional requirements on the PROMISE NFR dataset. NoRBERT outperforms recent approaches at classifying nonfunctional requirements subclasses. The most frequent classes are classified with an average F$_1$-score of 87%. In an unseen project setup on a relabeled PROMISE NFR dataset, our approach achieves an improvement of ten percentage points in average F$_1$- score compared to recent approaches. Additionally, we propose to classify functional requirements according to the included concerns, i.e., function, data, and behavior. We labeled the functional requirements in the PROMISE NFR dataset and applied our approach. NoRBERT achieves an F$_1$-score of up to 92%. Overall, NoRBERT improves requirements classification and can be applied to unseen projects with convincing results