Easy Data Augmentation in Sentiment Analysis of Cyberbullying

Instagram, a social media platform, has in the vicinity of 2 billion active users in 2023. The platform allows users to post photos and videos with one another. However, cyberbullying remains a significant problem for about 50% of young Indonesians. To address this issue, sentiment analysis for comment filtering uses a Support Vector Machine (SVM) and Easy Data Augmentation (EDA). EDA will augment the dataset, enabling robust prediction and analysis of cyberbullying by introducing more variation. Based on the tests, SVM combination with EDA results in a 2.52% increase in the k-Fold Cross Validation score. Our proposed approach shows an improved accuracy of 92.5%, 2.5% higher than that of the existing state-of-the-art method. To maintain the reproducibility and replicability of this research, the source code can be accessed at uns.id/eda_svm

Few-Shot Learning for Clinical Natural Language Processing Using Siamese Neural Networks

Clinical Natural Language Processing (NLP) has become an emerging technology in healthcare that leverages a large amount of free-text data in electronic health records (EHRs) to improve patient care, support clinical decisions, and facilitate clinical and translational science research. Recently, deep learning has achieved state-of-the-art performance in many clinical NLP tasks. However, training deep learning models usually requires large annotated datasets, which are normally not publicly available and can be time-consuming to build in clinical domains. Working with smaller annotated datasets is typical in clinical NLP and therefore, ensuring that deep learning models perform well is crucial for the models to be used in real-world applications. A widely adopted approach is fine-tuning existing Pre-trained Language Models (PLMs), but these attempts fall short when the training dataset contains only a few annotated samples. Few-Shot Learning (FSL) has recently been investigated to tackle this problem. Siamese Neural Network (SNN) has been widely utilized as an FSL approach in computer vision, but has not been studied well in NLP. Furthermore, the literature on its applications in clinical domains is scarce. In this paper, we propose two SNN-based FSL approaches for clinical NLP, including Pre-Trained SNN (PT-SNN) and SNN with Second-Order Embeddings (SOE-SNN). We evaluated the proposed approaches on two clinical tasks, namely clinical text classification and clinical named entity recognition. We tested three few-shot settings including 4-shot, 8-shot, and 16-shot learning. Both clinical NLP tasks were benchmarked using three PLMs, including BERT,BioBERT, and BioClinicalBERT. The experimental results verified the effectiveness of the proposed SNN-based FSL approaches in both NLP tasks

A Framework to Create a Deep Learning Detector from a Small Dataset: A Case of Parawood Pith Estimation

A deep learning-based object detector has been successfully applied to all application areas. It has high immunity to variations in illumination and deviations among objects. One weakness of the detector is that it requires a huge, undefinable dataset for training the detector to avoid overtraining and make it deployable. This research proposes a framework to create a deep learning-based object detector with a limited-sized dataset. The framework is based on training the detector with the regions surrounding an object that typically contain various features over a more extensive area than the object itself. Our proposed algorithm further post-processes the detection results to identify the object. The framework is applied to the problem of wood pith approximation. The YOLO v3 framework was employed to create the detector with all default hyperparameters based on the transfer learning approach. A wood pith dataset with only 150 images is used to create the detector with a ratio between training to testing of 90:10. Several experiments were performed to compare the detection results from different approaches to preparing the regions surrounding a pith, i.e., all regions, only close neighbors, and only diagonal neighbors around a pith. The best experiment result shows that the framework outperforms the typical approach to create the detector with approximately twice the detection precision at a relative average error. Doi: 10.28991/ESJ-2023-07-01-017 Full Text: PD

Regularising disparity estimation via multi task learning with structured light reconstruction

3D reconstruction is a useful tool for surgical planning and guidance. However, the lack of available medical data stunts research and development in this field, as supervised deep learning methods for accurate disparity estimation rely heavily on large datasets containing ground truth information. Alternative approaches to supervision have been explored, such as self-supervision, which can reduce or remove entirely the need for ground truth. However, no proposed alternatives have demonstrated performance capabilities close to what would be expected from a supervised setup. This work aims to alleviate this issue. In this paper, we investigate the learning of structured light projections to enhance the development of direct disparity estimation networks. We show for the first time that it is possible to accurately learn the projection of structured light on a scene, implicitly learning disparity. Secondly, we \textcolor{black}{explore the use of a multi task learning (MTL) framework for the joint training of structured light and disparity. We present results which show that MTL with structured light improves disparity training; without increasing the number of model parameters. Our MTL setup outperformed the single task learning (STL) network in every validation test. Notably, in the medical generalisation test, the STL error was 1.4 times worse than that of the best MTL performance. The benefit of using MTL is emphasised when the training data is limited.} A dataset containing stereoscopic images, disparity maps and structured light projections on medical phantoms and ex vivo tissue was created for evaluation together with virtual scenes. This dataset will be made publicly available in the future

Transfer Learning Using Infrared and Optical Full Motion Video Data for Gender Classification

This work is a review and extension of our ongoing research in human recognition analysis using multimodality motion sensor data. We review our work on hand crafted feature engineering for motion capture skeleton (MoCap) data, from the Air Force Research Lab for human gender followed by depth scan based skeleton extraction using LIDAR data from the Army Night Vision Lab for person identification. We then build on these works to demonstrate a transfer learning sensor fusion approach for using the larger MoCap and smaller LIDAR data for gender classification

Siamese Basis Function Networks for Data-Efficient Defect Classification in Technical Domains

Training deep learning models in technical domains is often accompanied by the challenge that although the task is clear, insufficient data for training is available. In this work, we propose a novel approach based on the combination of Siamese networks and radial basis function networks to perform data-efficient classification without pretraining by measuring the distance between images in semantic space in a data-efficient manner. We develop the models using three technical datasets, the NEU dataset, the BSD dataset, and the TEX dataset. In addition to the technical domain, we show the general applicability to classical datasets (cifar10 and MNIST) as well. The approach is tested against state-of-the-art models (Resnet50 and Resnet101) by stepwise reduction of the number of samples available for training. The authors show that the proposed approach outperforms the state-of-the-art models in the low data regime

GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets

Recent years have seen the advent of molecular simulation datasets that are orders of magnitude larger and more diverse. These new datasets differ substantially in four aspects of complexity: 1. Chemical diversity (number of different elements), 2. system size (number of atoms per sample), 3. dataset size (number of data samples), and 4. domain shift (similarity of the training and test set). Despite these large differences, benchmarks on small and narrow datasets remain the predominant method of demonstrating progress in graph neural networks (GNNs) for molecular simulation, likely due to cheaper training compute requirements. This raises the question -- does GNN progress on small and narrow datasets translate to these more complex datasets? This work investigates this question by first developing the GemNet-OC model based on the large Open Catalyst 2020 (OC20) dataset. GemNet-OC outperforms the previous state-of-the-art on OC20 by 16% while reducing training time by a factor of 10. We then compare the impact of 18 model components and hyperparameter choices on performance in multiple datasets. We find that the resulting model would be drastically different depending on the dataset used for making model choices. To isolate the source of this discrepancy we study six subsets of the OC20 dataset that individually test each of the above-mentioned four dataset aspects. We find that results on the OC-2M subset correlate well with the full OC20 dataset while being substantially cheaper to train on. Our findings challenge the common practice of developing GNNs solely on small datasets, but highlight ways of achieving fast development cycles and generalizable results via moderately-sized, representative datasets such as OC-2M and efficient models such as GemNet-OC. Our code and pretrained model weights are open-sourced

An overview of ensemble and feature learning in few-shot image classification using siamese networks

Siamese Neural Networks (SNNs) constitute one of the most representative approaches for addressing Few-Shot Image Classification. These schemes comprise a set of Convolutional Neural Network (CNN) models whose weights are shared across the network, which results in fewer parameters to train and less tendency to overfit. This fact eventually leads to better convergence capabilities than standard neural models when considering scarce amounts of data. Based on a contrastive principle, the SNN scheme jointly trains these inner CNN models to map the input image data to an embedded representation that may be later exploited for the recognition process. However, in spite of their extensive use in the related literature, the representation capabilities of SNN schemes have neither been thoroughly assessed nor combined with other strategies for boosting their classification performance. Within this context, this work experimentally studies the capabilities of SNN architectures for obtaining a suitable embedded representation in scenarios with a severe data scarcity, assesses the use of train data augmentation for improving the feature learning process, introduces the use of transfer learning techniques for further exploiting the embedded representations obtained by the model, and uses test data augmentation for boosting the performance capabilities of the SNN scheme by mimicking an ensemble learning process. The results obtained with different image corpora report that the combination of the commented techniques achieves classification rates ranging from 69% to 78% with just 5 to 20 prototypes per class whereas the CNN baseline considered is unable to converge. Furthermore, upon the convergence of the baseline model with the sufficient amount of data, still the adequate use of the studied techniques improves the accuracy in figures from 4% to 9%.First author is supported by the “Programa I+D+i de la Generalitat Valenciana” through grant APOSTD/2020/256. This research work was partially funded by the Spanish “Ministerio de Ciencia e Innovación” and the European Union “NextGenerationEU/PRTR” programmes through project DOREMI (TED2021-132103A-I00). Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature

Diversity vs. Recognizability: Human-like generalization in one-shot generative models

Robust generalization to new concepts has long remained a distinctive feature of human intelligence. However, recent progress in deep generative models has now led to neural architectures capable of synthesizing novel instances of unknown visual concepts from a single training example. Yet, a more precise comparison between these models and humans is not possible because existing performance metrics for generative models (i.e., FID, IS, likelihood) are not appropriate for the one-shot generation scenario. Here, we propose a new framework to evaluate one-shot generative models along two axes: sample recognizability vs. diversity (i.e., intra-class variability). Using this framework, we perform a systematic evaluation of representative one-shot generative models on the Omniglot handwritten dataset. We first show that GAN-like and VAE-like models fall on opposite ends of the diversity-recognizability space. Extensive analyses of the effect of key model parameters further revealed that spatial attention and context integration have a linear contribution to the diversity-recognizability trade-off. In contrast, disentanglement transports the model along a parabolic curve that could be used to maximize recognizability. Using the diversity-recognizability framework, we were able to identify models and parameters that closely approximate human data