Semantic Routing in Peer-to-Peer Systems

Currently search engines like Google, Yahoo and Excite are centralized, which means that all queries that users post are sent to some big servers (or server group) that handle them. In this way it is easy for the systems to relate IP-addresses with the queries posted from them. Clearly privacy is a problem here. Also censoring out certain information which is not 'appropriate' is simple, and shown in recent examples. To give more privacy to the users and make censoring information more difficult, Peer-to-Peer (P2P) systems are a good alternative to the centralized approach. In P2P systems the search functionality can be devided over a large group of autonomous computers (Peers), where each computer only has a very small piece of information instead of everything. Now the problem in such a distributed system is to make the search process efficient in terms of bandwith, storage, time and CPU usage. In this Ph.D. thesis, three approaches are described that try to reach goal of finding the short routes between seeker and providers with high efficiency. These routing algorithms are all applied on 'Semantic-Overlay-Networks' (SONs). In a SON, peers maintain pointers to semantically relevant peers based on content descriptions, which makes them able to choose the relevant peers for queries instead of, for example, choosing random peers. This work tries to show that decentralized search algorithms based on semantic routing are a good alternative to centralized approaches.Harmelen, F.A.H. van [Promotor

Drug discovery FAQs: workflows for answering multidomain drug discovery questions

Modern data-driven drug discovery requires integrated resources to support decision-making and enable new discoveries. The Open PHACTS Discovery Platform (http://dev.openphacts.org) was built to address this requirement by focusing on drug discovery questions that are of high priority to the pharmaceutical industry. Although complex, most of these frequently asked questions (FAQs) revolve around the combination of data concerning compounds, targets, pathways and diseases. Computational drug discovery using workflow tools and the integrated resources of Open PHACTS can deliver answers to most of these questions. Here, we report on a selection of workflows used for solving these use cases and discuss some of the research challenges. The workflows are accessible online from myExperiment (http://www.myexperiment.org) and are available for reuse by the scientific community

AmsterTime: A Visual Place Recognition Benchmark Dataset for Severe Domain Shift

We introduce AmsterTime: a challenging dataset to benchmark visual place recognition (VPR) in presence of a severe domain shift. AmsterTime offers a collection of 2,500 well-curated images matching the same scene from a street view matched to historical archival image data from Amsterdam city. The image pairs capture the same place with different cameras, viewpoints, and appearances. Unlike existing benchmark datasets, AmsterTime is directly crowdsourced in a GIS navigation platform (Mapillary). We evaluate various baselines, including non-learning, supervised and self-supervised methods, pre-trained on different relevant datasets, for both verification and retrieval tasks. Our result credits the best accuracy to the ResNet-101 model pre-trained on the Landmarks dataset for both verification and retrieval tasks by 84% and 24%, respectively. Additionally, a subset of Amsterdam landmarks is collected for feature evaluation in a classification task. Classification labels are further used to extract the visual explanations using Grad-CAM for inspection of the learned similar visuals in a deep metric learning models.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Pattern Recognition and BioinformaticsHistory & Complexit

Sight-seeing in the eyes of deep neural networks

We address the interpretability of convolutional neural networks (CNNs) for predicting a geo-location from an image. In a pilot experiment we classify images of Pittsburgh vs Tokyo and visualize the learned CNN filters. We found that varying the CNN architecture leads to variating in the visualized filters. This calls for further investigation of the effective parameters on the interpretability of CNNs.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Pattern Recognition and BioinformaticsHistory & Complexit