Investigating Ensembles of Single-class Classifiers for Multi-class Classification

Traditional methods of multi-class classification in machine learning involve the use of a monolithic feature extractor and classifier head trained on data from all of the classes at once. These architectures (especially the classifier head) are dependent on the number and types of classes, and are therefore rigid against changes to the class set. For best performance, one must retrain networks with these architectures from scratch, incurring a large cost in training time. As well, these networks can be biased towards classes with a large imbalance in training data compared to other classes. Instead, ensembles of so-called ``single-class'' classifiers can be used for multi-class classification by training an individual network for each class.We show that these ensembles of single-class classifiers are more flexible to changes to the class set than traditional models, and can be quickly retrained to consider small changes to the class set, such as by adding, removing, splitting, or fusing classes. As well, we show that these ensembles are less biased towards classes with large imbalances in their training data than traditional models. We also introduce a new, more powerful single-class classification architecture. These models are trained and tested on a plant disease dataset with high variance in the number of classes and amount of data in each class, as well as on an Alzheimer's dataset with low amounts of data and a large imbalance in data between classes

A Consolidated View of Context for Intelligent Systems

This paper's main objective is to consolidate the knowledge on context in the realm of intelligent systems, systems that are aware of their context and can adapt their behavior accordingly. We provide an overview and analysis of 36 context models that are heterogeneous and scattered throughout multiple fields of research. In our analysis, we identify five shared context categories: social context, location, time, physical context, and user context. In addition, we compare the context models with the context elements considered in the discourse on intelligent systems and find that the models do not properly represent the identified set of 3,741 unique context elements. As a result, we propose a consolidation of the findings from the 36 context models and the 3,741 unique context elements. The analysis reveals that there is a long tail of context categories that are considered only sporadically in context models. However, particularly these context elements in the long tail may be necessary for improving intelligent systems' context awareness

Personenbezogene Daten privatwirtschaftlich nachhaltig nutzen. Regulatorische und technische Zukunftskonzepte

Zunehmende wirtschaftliche Vernetzung und Lobbyismus fordern die Sicherstellung von informierter Zustimmung, Transparenz und Verantwortlichkeit in der privatwirtschaftlichen Verwendung personenbezogener Daten heraus. Unser neues Modell verbindet durch das Zusammenspiel regulatorischer und technischer Datenschutzkonzepte mehr Sicherheit und Selbstbestimmung mit verstärkten Innovationsmöglichkeiten

Personal Information Markets AND Privacy: A New Model to Solve the Controversy

From the early days of the information economy, personal data has been its most valuable asset. Despite data protection laws, companies trade personal information and often intrude on the privacy of individuals. As a result, consumers feel out of control and lose trust in electronic environments. Technologists and regulators are struggling to develop solutions that meet businesses’ demand for more personal information while maintaining privacy. However, no promising proposals seem to be in sight. We propose a 3-tier personal information market model with privacy. In our model, clear roles, rights and obligations for all actors re-establish trust. The ‘relationship space’ enables data subjects and visible business partners to build trusting relationships. The ‘service space’ supports customer relationships with distributed information processing. The ‘rich information space’ enables anonymized information exchange. To transition to this model, we show how existing privacy-enhancing technologies and legal requirements can be integrated

A vision for global privacy bridges: Technical and legal measures for international data markets

From the early days of the information economy, personal data has been its most valuable asset. Despite data protection laws and an acknowledged right to privacy, trading personal information has become a business equated with "trading oil". Most of this business is done without the knowledge and active informed consent of the people. But as data breaches and abuses are made public through the media, consumers react. They become irritated about companies' data handling practices, lose trust, exercise political pressure and start to protect their privacy with the help of technical tools. As a result, companies' Internet business models that are based on personal data are unsettled. An open conflict is arising between business demands for data and a desire for privacy. As of 2015 no true answer is in sight of how to resolve this conflict. Technologists, economists and regulators are struggling to develop technical solutions and policies that meet businesses' demand for more data while still maintaining privacy. Yet, most of the proposed solutions fail to account for market complexity and provide no pathway to technological and legal implementation. They lack a bigger vision for data use and privacy. To break this vicious cycle, we propose and test such a vision of a personal information market with privacy. We accumulate technical and legal measures that have been proposed by technical and legal scholars over the past two decades. And out of this existing knowledge, we compose something new: a four-space market model for personal data

Exciton Energy Transfer in Pairs of Single-Walled Carbon Nanotubes

We studied the exciton energy transfer in pairs of semiconducting nanotubes using high-resolution optical microscopy and spectroscopy on the nanoscale. Photoluminescence from large band gap nanotubes within bundles is observed with spatially varying intensities due to distance-dependent internanotube transfer. The range of efficient energy transfer is found to be limited to a few nanometers because of competing fast nonradiative relaxation responsible for low photoluminescence quantum yield

Purcell effect in wire metamaterials

We study theoretically the enhancement of spontaneous emission in wire metamaterials. We analyze the dependence of the Purcell factor dependence on wire dielectric constant for both electric and magnetic dipole sources, and find an optimal value of the dielectric constant for maximizing the Purcell factor for the electric dipole. We obtain analytical expressions for the Purcell factor and also provide estimates for the Purcell factor in realistic structures operating in both microwave and optical spectral range.Comment: 15 pages, 7 figure