LEARNING FROM INCOMPLETE AND HETEROGENEOUS DATA

Deep convolutional neural networks (DCNNs) have shown impressive performance improvements for object detection and recognition problems. However, a vast majority of DCNN-based recognition methods are designed with two key assumptions in mind, i.e., 1) the assumption that all categories are known a priori and 2) both training and test data are drawn from a similar distribution. However, in many real-world applications, these assumptions do not necessarily hold and limit the generalization capability of a recognition model. Generally, incomplete knowledge of the world is present at training time, and unknown classes can be submitted to an algorithm during testing. If the visual system is trained assuming that all categories are known a priori, it would fail to identify these cases with unknown classes during testing. Ideally, the goal of a visual recognition system would be to reject samples from unknown classes and classify samples from known classes. In this thesis, we consider this constraint and evaluate visual recognition systems under two problem settings, i.e., one-class and multi-class novelty detection. In the one-class setting, the goal is to learn a visual recognition system from a single category and reject any other category samples as unknown during testing. Whereas, in multi-class classification the visual recognition system aims to learn from multiple-categories and reject any other category sample that is not part of the training category set as unknown. With experiments on multiple benchmark datasets we show that the proposed recognition systems are able to perform better compared to existing approaches. Furthermore, we also recognize that in many real world conditions training and testing data distributions are often different. Due to this, the performance of a visual recognition system drops significantly. This is commonly referred to as dataset bias or domain-shift which can be addressed using domain adaptation. In particular, we address unsupervised domain adaptation in which the idea is to utilize an additional set of unlabeled data sampled from a particular domain to help improve the performance in that respective domain. Various experiments on multiple domain adaptation benchmarks show that the proposed strategy is able to generalize better compared to existing methods in the literature

Deep Learning Based Novelty Detection

Given a set of image instances from known classes, the goal of novelty detection is to determine whether an observed image during inference belongs to one of the known classes. In this thesis, deep learning-based approaches to solve novelty detection are studied under four different settings. In the first two settings, availability of out-of- distributional data (OOD) is assumed. With this assumption, novelty detection can be studied for cases where there are multiple known classes and a single known class separately. The thesis further explores this problem in a more constrained setting where only the data from known classes are considered for training. Finally, we study a practical application of novelty detection in mobile Active Authentication (AA) where latency and efficiency are as important as the detection accuracy

Telecommunications Networks

This book guides readers through the basics of rapidly emerging networks to more advanced concepts and future expectations of Telecommunications Networks. It identifies and examines the most pressing research issues in Telecommunications and it contains chapters written by leading researchers, academics and industry professionals. Telecommunications Networks - Current Status and Future Trends covers surveys of recent publications that investigate key areas of interest such as: IMS, eTOM, 3G/4G, optimization problems, modeling, simulation, quality of service, etc. This book, that is suitable for both PhD and master students, is organized into six sections: New Generation Networks, Quality of Services, Sensor Networks, Telecommunications, Traffic Engineering and Routing

Principles of Security and Trust: 7th International Conference, POST 2018, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2018, Thessaloniki, Greece, April 14-20, 2018, Proceedings

authentication; computer science; computer software selection and evaluation; cryptography; data privacy; formal logic; formal methods; formal specification; internet; privacy; program compilers; programming languages; security analysis; security systems; semantics; separation logic; software engineering; specifications; verification; world wide we