Statistical Mechanics of On-Line Learning Under Concept Drift

We introduce a modeling framework for the investigation of on-line machine learning processes in non-stationary environments. We exemplify the approach in terms of two specific model situations: In the first, we consider the learning of a classification scheme from clustered data by means of prototype-based Learning Vector Quantization (LVQ). In the second, we study the training of layered neural networks with sigmoidal activations for the purpose of regression. In both cases, the target, i.e., the classification or regression scheme, is considered to change continuously while the system is trained from a stream of labeled data. We extend and apply methods borrowed from statistical physics which have been used frequently for the exact description of training dynamics in stationary environments. Extensions of the approach allow for the computation of typical learning curves in the presence of concept drift in a variety of model situations. First results are presented and discussed for stochastic drift processes in classification and regression problems. They indicate that LVQ is capable of tracking a classification scheme under drift to a non-trivial extent. Furthermore, we show that concept drift can cause the persistence of sub-optimal plateau states in gradient based training of layered neural networks for regression

The Blockchain of Things, Beyond Bitcoin: A Systematic Review

The Internet of Things (IoT) is a name coined to the digital ecosystem of numerous internet connected devices. It brings the physical world closer to the digital one and as a result, allows for new applications and services. The emergence of Blockchain, a distributed ledger technology, presents a possible solution to ensure trust in decentralized systems. Blockchain brings trust, immutability, and verifiability of a distributed ledger in a decentralized network which could be useful to build trust into the IoT. However, the integration of IoT with Blockchain involves a number of challenges. The use of Blockchain within the IoT is a recent and a fast paced topic. Therefore, conducting a systematic literature review is essential to understand what has been proposed on the topic. Recent work has provided a systematic review with a focus on Bitcoin. In this paper, we present a systematic review of more recent work on Blockchain and IoT, with a broader focus on Blockchain platforms beyond Bitcoin. Our work provides an overview of what has been done so far on the use of Blockchain and IoT