ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes

Exploiting synthetic data to learn deep models has attracted increasing attention in recent years. However, the intrinsic domain difference between synthetic and real images usually causes a significant performance drop when applying the learned model to real world scenarios. This is mainly due to two reasons: 1) the model overfits to synthetic images, making the convolutional filters incompetent to extract informative representation for real images; 2) there is a distribution difference between synthetic and real data, which is also known as the domain adaptation problem. To this end, we propose a new reality oriented adaptation approach for urban scene semantic segmentation by learning from synthetic data. First, we propose a target guided distillation approach to learn the real image style, which is achieved by training the segmentation model to imitate a pretrained real style model using real images. Second, we further take advantage of the intrinsic spatial structure presented in urban scene images, and propose a spatial-aware adaptation scheme to effectively align the distribution of two domains. These two modules can be readily integrated with existing state-of-the-art semantic segmentation networks to improve their generalizability when adapting from synthetic to real urban scenes. We evaluate the proposed method on Cityscapes dataset by adapting from GTAV and SYNTHIA datasets, where the results demonstrate the effectiveness of our method.Comment: Add experiments on SYNTHIA, CVPR 2018 camera-ready versio

Facilitating cross-language retrieval and machine translation by multilingual domain ontologies

This paper presents a method for facilitating cross-language retrieval and machine translation in domain specific collections. The method is based on a semi-automatic adaption of a multilingual domain ontology and it is particularly suitable for the eLearning domain. The presented approach has been integrated into a real-world system supporting cross-language retrieval and machine translation of large amounts of learning resources in nine European languages. The system was built in the context of a European Commission Supported project Eurogene and it is now being used as a European reference portal for teaching human genetics

Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays

Real-world application of chest X-ray abnormality classification requires dealing with several challenges: (i) limited training data; (ii) training and evaluation sets that are derived from different domains; and (iii) classes that appear during training may have partial overlap with classes of interest during evaluation. To address these challenges, we present an integrated framework called Generalized Cross-Domain Multi-Label Few-Shot Learning (GenCDML-FSL). The framework supports overlap in classes during training and evaluation, cross-domain transfer, adopts meta-learning to learn using few training samples, and assumes each chest X-ray image is either normal or associated with one or more abnormalities. Furthermore, we propose Generalized Episodic Training (GenET), a training strategy that equips models to operate with multiple challenges observed in the GenCDML-FSL scenario. Comparisons with well-established methods such as transfer learning, hybrid transfer learning, and multi-label meta-learning on multiple datasets show the superiority of our approach.Comment: 17 page

UNIFORM: Automatic Alignment of Open Learning Datasets

Learning Analytics aims at supporting the understanding of learning mechanisms and their effects by means of data-driven strategies. LA approaches commonly face two big challenges: first, due to privacy reasons, most of the analyzed data are not in the public domain. Secondly, the open data collections, which come from diverse learning contexts, are quite heterogeneous. Therefore, the research findings are not easily reproducible and the publicly available datasets are often too small to enable further data analytics. To overcome these issues, there is an increasing need for integrating open learning data into unified models. This paper proposes UNIFORM, an open relational database integrating various learning data sources. It presents also a machine learning supported approach to automatically extending the integrated dataset as soon as new data sources become available. The proposed approach exploits a classifier to predict attribute alignments based on the correlations among the corresponding textual attribute descriptions. The integration phase has reached a promising quality level on most of the analyzed benchmark datasets. Furthermore, the usability of the UNIFORM data model has been demonstrated in a real case study, where the integrated data have been exploited to support learners’ outcome prediction. The F1-score achieved on the integrated data is approximately 30% higher than those obtained on the original data

Experiential Learning in Work-Integrated Learning (WIL) Projects for Metacognition: Integrating Theory with Practice

Work Integrated Learning (WIL) is an educational approach to improve workplace readiness. WIL achieves this by integrating theory with practice. The emphasis is on real experiences and practical problem-solving. Low-code platforms are a suitable teaching tool for the theory-practice integration. Yet, graduates also need metacognition to be workplace-ready. Through metacognition, students learn how to learn by deeply reflecting on their thinking. However, WIL focuses on domain learning, lesser on metacognitive thinking. This study draws on experiential learning theory to examine WIL aspects on their influence on metacognitive thinking. In a survey, we test experiential learning factors (authenticity, active learning, self-relevance, utility) and metacognition when students develop a software app. Results show that authenticity, active learning, and utility influence metacognition; however, self-relevance of the WIL does not. Consequently, IS educators should tailor the WIL to be authentic, useful, for active learning to support metacognition in low-code WIL teaching