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City anomalies such as residential fires or urban floods can cause a lot of damage each year from loss of human lives to negative effects on the productivity of the city. One possible way to deal with these anomalies in a quick and effective manner is to use the information provided by the network of citizens that is likely present at the scene of any serious anomaly. However, this source of information may be of varying quality and there may be too much data for human operators to manually inspect in a speedy manner. This motivates us to propose an architecture to effectively make use of the network of citizens to deal with city anomalies. At the center of our architecture is a neural network that automatically classify incoming image data and use this information to assist anomaly handling efforts. In order for our classifier to work well, we have also collected a dataset of city anomalies specific to Vietnam, consisting of 9963 images. We experiment with several neural network models on our collected dataset. In overall, they all have decent accuracies with the best being MobileNet with 92.3% accuracy.

Writing diary is not only a hobby but also provides a personal lifelog for better analysis and understanding of a user's daily activities and events. However, in a busy society, people may not have enough time to write in diary all their social interaction. This motivates our proposal to develop a ubiquitous system to automatically generate daily text diary using our novel method for image captioning from photos taken periodically from wearable cameras. We propose to incorporate common visual concepts extracted from a photo to enhance the details of the image description. We also propose a wide trail beam search strategy to enhance the naturalness of text caption. Our captioning method improves the results on MSCOCO dataset on four metrics: BLEU, METEOR, ROUGE-L, CIDEr. As compared to the method proposed by Xu et.al and Neuraltalk of Karpathy, our model has better performance on all four metrics. We also develop smart glasses and a prototype smart workplace in which people can have their personal diary generated from photos taken by smart glasses. Furthermore, we also apply a transformer machine translation model in order to translate captions into Vietnamese language. The results are promising and can be used for Vietnamese people.

Recent researches in cognitive science and document recognition have been applied to deal with the problem of categorizing object. Bag-of-Features (BoF) and its extension Spatial Pyramid Matching (SPM) have made a breakthrough in resolving this kind of challenges. Many methods followed this guideline really enhance the recognition accuracy but still have drawbacks in developing a real-world application whose data size is many times bigger. In this paper we propose two kinds of strategy include five criteria to evaluate and select the most appropriate training samples using for building a high performance classifier. We also suggest a method called reinforcement codebook learning to make the codebook training process not only purpose-built to best fits with the most suitable criteria but also much more efficient by reducing significantly its complexity of computation. Experiments on benchmark object dataset demonstrate that our proposed framework outperforms remarkable results and is comparable with the state-of-the-art in spite of using just 20% of 9 · 106 descriptors for training the dictionary. These results give a promise of building a efficient and feasible object categorization system for practical application as so as suggest some ideas to improve the visual feature representation in future.

The wide variation in features and capabilities of mobile devices lead to difficulties in the development of the same application on different platforms. Therefore we propose an online model-driven integrated development environment to provide developers with a platform-independent GUI design for mobile applications. Our proposed system transforms an abstract platform-independent GUI into a platform-dependent GUI on a target platform. The generated project is entirely in the form with which an experienced developer on the platform is already familiar. Furthermore, as the generated application is not a web-based one, it can access naturally native features of a platform. The proposed flexible architecture enable the capability to handle and update different abstract UI and non-UI controls needed to design GUIs for mobile applications. Experimental results with volunteers show that our proposed solution can save up to 25--51% time to create GUIs of an application to three different platforms of Android, iOS and Windows Phone.

Reading traditional newspapers or magazines is a common way to get latest information about events or new products. However these printed materials only provide readers with static information. Readers may want to know more detail information about some product in an article or to watch video clips related to an event mentioned in a news right at the moment when they read that article or news. The authors propose a system with mobile devices that can provide extra information and multimedia for readers by applying augmented reality to traditional magazines. A user can enjoy extra rich multimedia information on a product or news on his/her mobile device just by looking at an article in a traditional magazine through his/her mobile device. The system detects which article in which page of a magazine that is being displayed in a mobile device and provides a reader with related information and multimedia objects. The important feature of our proposed system is using lightweight filter to efficiently filter out candidate covers or articles that do not visually match an image captured by a mobile device. The experiment shows that our proposed system achieves the average accuracy of more than 90% and can process in the real-time manner.