Gunnar Mathiason

Virtual Full Replication by Adaptive Segmentation

We propose virtual full replication by adaptive segmentation (ViFuR-A), and evaluate its ability to maintain scalability in a replicated real-time database. With full replication and eventual consistency, transaction timeliness becomes independent of network delays for all transactions. However, full replication does not scale well, since all updates must be replicated to all nodes, also when data is needed only at a subset of the nodes. With virtual full replication that adapts to actual data needs, resource usage can be bounded and the database can be made scalable. We propose a scheme for adaptive segmentation that detects new data needs and adapts replication. The scheme includes an architecture, a scalable protocol and a replicated directory service that together maintains scalability. We show that adaptive segmentation bounds the required storage at a significantly lower level compared to static segmentation, for a typical workload where the data needs change repeatedly. Adaptation time can be kept constant for the workload when there are sufficient resources. Also, the storage is constant with an increasing amount of nodes and linear with an increasing rate of change to data needs.

Virtual Full Replication for Scalable and Adaptive Real-Time Communication in Wireless Sensor Networks

Sensor networks have limited resources and often support large-scale applications that need scalable propagation of sensor data to users. We propose a white-board style of communication in sensor networks using a distributed real-time database supporting Virtual Full Replication with Adaptive Segmentation. This allows mobile client nodes to access, transparently and efficiently, any sensor data at any node in the network. We present a two-tiered wireless architecture, and an adaptation protocol, for scalable and adaptive white-board communication in large-scale sensor networks. Sensor value readings at nodes of the sensor tier are published at nodes of the database tier as database updates to objects in a distributed real-time database. The search space of client nodes for sensor data is thus limited to the number of database nodes. With this scheme, we can show scalable resource usage and short adaptation times for several hundreds of database nodes and up to 50 moving clients.

A Self-Organizing Ensemble of Deep Neural Networks for the Classification of Data from Complex Processes

We present a new self-organizing algorithm for classification of a data that combines and extends the strengths of several common machine learning algorithms, such as algorithms in self-organizing neural networks, ensemble methods and deep neural networks. The increased expression power is combined with the explanation power of self-organizing networks. Our algorithm outperforms both deep neural networks and ensembles of deep neural networks. For our evaluation case, we use production monitoring data from a complex steel manufacturing process, where data is both high-dimensional and has many nonlinear interdependencies. In addition to the improved prediction score, the algorithm offers a new deep-learning based approach for how computational resources can be focused in data exploration, since the algorithm points out areas of the input space that are more challenging to learn.

Improving the Use of Deep Convolutional Neural Networks for the Prediction of Molecular Properties

We present a flexible deep convolutional neural network method for the analyse of arbitrary sized graph structures representing molecules. The method makes use of RDKit, an open-source cheminformatics software, allowing the incorporation of any global molecular (such as molecular charge) and local (such as atom type) information. We evaluate the method on the Side Effect Resource (SIDER) v4.1 dataset and show that it significantly outperforms another recently proposed method based on deep convolutional neural networks. We also reflect on how different types of information and input data affect the predictive power of our model. This reflection highlights several open problems that should be solved to further improve the use of deep learning within cheminformatics.

A Cyberphysical Learning Approach for Digital Smart Citizenship Competence Development

Smart Cities have emerged as a global concept that argues for the effective exploitation of digital technologies to drive sustainable innovation and well-being for citizens. Despite the large investments being placed on Smart City infrastructure, however, there is still very scarce attention on the new learning approaches that will be needed for cultivating Digital Smart Citizenship competences, namely the competences which will be needed by the citizens and workforce of such cities for exploiting the digital technologies in creative and innovative ways for driving financial and societal sustainability. In this context, this paper introduces cyberphysical learning as an overarching model of cultivating Digital Smart Citizenship competences by exploiting the potential of Internet of Things technologies and social media, in order to create authentic blended and augmented learning experiences.