Rereading the Narrative Paradox for Virtual Reality Theatre

We examined several key issues around audience autonomy in VR theatre. Informed by a literature review and a qualitative user study (grounded theory), we developed a conceptual model that enables a quantifiable evaluation of audience experience in VR theatre. A second user study inspired by the ‘narrative paradox’, investigates the relationship between spatial exploration and narrative comprehension in two VR performances. Our results show that although navigation distracted the participants from following the full story, they were more engaged, attached and had a better overall experience as a result of their freedom to move and interact

Does Synthetic Data Generation of LLMs Help Clinical Text Mining?

Recent advancements in large language models (LLMs) have led to the development of highly potent models like OpenAI's ChatGPT. These models have exhibited exceptional performance in a variety of tasks, such as question answering, essay composition, and code generation. However, their effectiveness in the healthcare sector remains uncertain. In this study, we seek to investigate the potential of ChatGPT to aid in clinical text mining by examining its ability to extract structured information from unstructured healthcare texts, with a focus on biological named entity recognition and relation extraction. However, our preliminary results indicate that employing ChatGPT directly for these tasks resulted in poor performance and raised privacy concerns associated with uploading patients' information to the ChatGPT API. To overcome these limitations, we propose a new training paradigm that involves generating a vast quantity of high-quality synthetic data with labels utilizing ChatGPT and fine-tuning a local model for the downstream task. Our method has resulted in significant improvements in the performance of downstream tasks, improving the F1-score from 23.37% to 63.99% for the named entity recognition task and from 75.86% to 83.59% for the relation extraction task. Furthermore, generating data using ChatGPT can significantly reduce the time and effort required for data collection and labeling, as well as mitigate data privacy concerns. In summary, the proposed framework presents a promising solution to enhance the applicability of LLM models to clinical text mining.Comment: 10 pages, 8 tables, 4 figure

CL-XABSA: Contrastive Learning for Cross-lingual Aspect-based Sentiment Analysis

As an extensive research in the field of Natural language processing (NLP), aspect-based sentiment analysis (ABSA) is the task of predicting the sentiment expressed in a text relative to the corresponding aspect. Unfortunately, most languages lack of sufficient annotation resources, thus more and more recent researchers focus on cross-lingual aspect-based sentiment analysis (XABSA). However, most recent researches only concentrate on cross-lingual data alignment instead of model alignment. To this end, we propose a novel framework, CL-XABSA: Contrastive Learning for Cross-lingual Aspect-Based Sentiment Analysis. Specifically, we design two contrastive strategies, token level contrastive learning of token embeddings (TL-CTE) and sentiment level contrastive learning of token embeddings (SL-CTE), to regularize the semantic space of source and target language to be more uniform. Since our framework can receive datasets in multiple languages during training, our framework can be adapted not only for XABSA task, but also for multilingual aspect-based sentiment analysis (MABSA). To further improve the performance of our model, we perform knowledge distillation technology leveraging data from unlabeled target language. In the distillation XABSA task, we further explore the comparative effectiveness of different data (source dataset, translated dataset, and code-switched dataset). The results demonstrate that the proposed method has a certain improvement in the three tasks of XABSA, distillation XABSA and MABSA. For reproducibility, our code for this paper is available at https://github.com/GKLMIP/CL-XABSA

A multitask deep learning approach for pulmonary embolism detection and identification

Pulmonary embolism (PE) is a blood clot traveling to the lungs and is associated with substantial morbidity and mortality. Therefore, rapid diagnoses and treatments are essential. Chest computed tomographic pulmonary angiogram (CTPA) is the gold standard for PE diagnoses. Deep learning can enhance the radiologists’workflow by identifying PE using CTPA, which helps to prioritize important cases and hasten the diagnoses for at-risk patients. In this study, we propose a two-phase multitask learning method that can recognize the presence of PE and its properties such as the position, whether acute or chronic, and the corresponding right-to-left ventricle diameter (RV/LV) ratio, thereby reducing false-negative diagnoses. Trained on the RSNA-STR Pulmonary Embolism CT Dataset, our model demonstrates promising PE detection performances on the hold-out test set with the window-level AUROC achieving 0.93 and the sensitivity being 0.86 with a specificity of 0.85, which is competitive with the radiologists’sensitivities ranging from 0.67 to 0.87 with specificities of 0.89–0.99. In addition, our model provides interpretability through attention weight heatmaps and gradient-weighted class activation mapping (Grad-CAM). Our proposed deep learning model could predict PE existence and other properties of existing cases, which could be applied to practical assistance for PE diagnosis

MCS-IOV : Real-time I/o virtualization for mixed-criticality systems

In mixed-criticality systems, timely handling of I/O is a key for the system being successfully implemented and functioning appropriately. The criticality levels of functions and sometimes the whole system are often dependent on the state of the I/O. An I/O system for a MCS must provide simultaneously isolation/separation, performance/efficiency and timing-predictability, as well as being able to manage I/O resource in an adaptive manner to facilitate efficient yet safe resource sharing among components of different criticality levels. Existing approaches cannot achieve all of these requirements simultaneously. This paper presents a MCS I/O management framework, termed MCS-IOV. MCS-IOV is based on hardware assisted virtualisation, which provides temporal and spatial isolation and prohibits fault propagation with small extra overhead in performance. MCS-IOV extends a real-time I/O virtualisation system, by supporting the concept of mixed criticalities and customised interfaces for schedulers, which offers good timing-preditability. MCS-IOV supports I/O driven criticality mode switch (the mode switch can be triggered by detection of unexpected I/O behaviors, e.g., a higher I/O utilization than expected) and timely I/O resource reconfiguration up on that. Finally, We evaluated and demonstrate MCS-IOV in different aspects

Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach

Fairness in machine learning has attracted increasing attention in recent years. The fairness methods improving algorithmic fairness for in-distribution data may not perform well under distribution shifts. In this paper, we first theoretically demonstrate the inherent connection between distribution shift, data perturbation, and model weight perturbation. Subsequently, we analyze the sufficient conditions to guarantee fairness (i.e., low demographic parity) for the target dataset, including fairness for the source dataset, and low prediction difference between the source and target datasets for each sensitive attribute group. Motivated by these sufficient conditions, we propose robust fairness regularization (RFR) by considering the worst case within the model weight perturbation ball for each sensitive attribute group. We evaluate the effectiveness of our proposed RFR algorithm on synthetic and real distribution shifts across various datasets. Experimental results demonstrate that RFR achieves better fairness-accuracy trade-off performance compared with several baselines. The source code is available at \url{https://github.com/zhimengj0326/RFR_NeurIPS23}.Comment: NeurIPS 202

ALL IN ONE NETWORK FOR DRIVER ATTENTION MONITORING

Nowadays, driver drowsiness and driver distraction is considered as a major risk for fatal road accidents around the world. As a result, driver monitoring identifying is emerging as an essential function of automotive safety systems. Its basic features include head pose, gaze direction, yawning and eye state analysis. However, existing work has investigated algorithms to detect these tasks separately and was usually conducted under laboratory environments. To address this problem, we propose a multi-task learning CNN framework which simultaneously solve these tasks. The network is implemented by sharing common features and parameters of highly related tasks. Moreover, we propose Dual-Loss Block to decompose the pose estimation task into pose classification and coarse-to-fine regression and Objectcentric Aware Block to reduce orientation estimation errors. Thus, with such novel designs, our model not only achieves SOA results but also reduces the complexity of integrating into automotive safety systems. It runs at 10 fps on vehicle embedded systems which marks a momentous step for this field. More importantly, to facilitate other researchers, we publish our dataset FDUDrivers which contains 20000 images of 100 different drivers and covers various real driving environments. FDUDrivers might be the first comprehensive dataset regarding driver attention monitorin

Retiring Δ\DeltaDP: New Distribution-Level Metrics for Demographic Parity

Demographic parity is the most widely recognized measure of group fairness in machine learning, which ensures equal treatment of different demographic groups. Numerous works aim to achieve demographic parity by pursuing the commonly used metric $\Delta DP$. Unfortunately, in this paper, we reveal that the fairness metric $\Delta DP$ can not precisely measure the violation of demographic parity, because it inherently has the following drawbacks: i) zero-value $\Delta DP$ does not guarantee zero violation of demographic parity, ii) $\Delta DP$ values can vary with different classification thresholds. To this end, we propose two new fairness metrics, Area Between Probability density function Curves (ABPC) and Area Between Cumulative density function Curves (ABCC), to precisely measure the violation of demographic parity at the distribution level. The new fairness metrics directly measure the difference between the distributions of the prediction probability for different demographic groups. Thus our proposed new metrics enjoy: i) zero-value ABCC/ABPC guarantees zero violation of demographic parity; ii) ABCC/ABPC guarantees demographic parity while the classification thresholds are adjusted. We further re-evaluate the existing fair models with our proposed fairness metrics and observe different fairness behaviors of those models under the new metrics. The code is available at https://github.com/ahxt/new_metric_for_demographic_parityComment: Accepted by TMLR. Code available at https://github.com/ahxt/new_metric_for_demographic_parit