Cyber-Physical Systems Taxonomy (CPS Taxonomy)
The world is witnessing an unprecedented growth of cyber-physical systems (CPS), which are foreseen to revolutionize our world {via} creating new services and applications in a variety of sectors such as environmental monitoring, mobile-health systems, intelligent transportation systems and so on. The {information and communication technology }(ICT) sector is experiencing a significant growth in { data} traffic, driven by the widespread usage of smartphones, tablets and video streaming, along with the significant growth of sensors deployments that are anticipated in the near future. {It} is expected to outstandingly increase the growth rate of raw sensed data. In this paper, we present the CPS taxonomy {via} providing a broad overview of data collection, storage, access, processing and analysis. Compared with other survey papers, this is the first panoramic survey on big data for CPS, where our objective is to provide a panoramic summary of different CPS aspects. Furthermore, CPS {require} cybersecurity to protect {them} against malicious attacks and unauthorized intrusion, which {become} a challenge with the enormous amount of data that is continuously being generated in the network. {Thus, we also} provide an overview of the different security solutions proposed for CPS big data storage, access and analytics. We also discuss big data meeting green challenges in the contexts of CPS. …

Hypergraph-based Outlier Test for Categorical Data (HOT)
As a widely used data mining technique, outlier detection is a process which aims to find anomalies while providing good explanations. Most existing detection methods are basically designed for numeric data, however, real-life data such as web pages, business transactions and bioinformatics records always contain categorical data. So it causes difficulty to find reasonable exceptions in the real world applications. In this paper, we introduce a novel outlier mining method based on hypergraph model for categorical data. Since hy- pergraphs precisely capture the distribution characteristics in data subspaces, this method is effective in identifying anomalies in dense subspaces and presents good interpre- tations for the local outlierness. By selecting the most rel- evant subspaces, the problem of ‘curse of dimensionality’ in very large databases can also be ameliorated. Further- more, the connectivity property is used to replace the dis- tance metrics, so that the distance-based computation is not needed anymore, which enhances the robustness for han- dling missing-value data. The fact that connectivity com- putation facilitates the aggregation operations supported by most SQL-compatible database systems, makes the mining process much efficient. Finally, we give experiments and analysis which show that our method can find outliers in categorical data with good performance and quality. …

BELIEF
With the advent of Big Data era, data reduction methods are highly demanded given its ability to simplify huge data, and ease complex learning processes. Concretely, algorithms that are able to filter relevant dimensions from a set of millions are of huge importance. Although effective, these techniques suffer from the ‘scalability’ curse as well. In this work, we propose a distributed feature weighting algorithm, which is able to rank millions of features in parallel using large samples. This method, inspired by the well-known RELIEF algorithm, introduces a novel redundancy elimination measure that provides similar schemes to those based on entropy at a much lower cost. It also allows smooth scale up when more instances are demanded in feature estimations. Empirical tests performed on our method show its estimation ability in manifold huge sets –both in number of features and instances–, as well as its simplified runtime cost (specially, at the redundancy detection step). …

Instance-Level Meta Normalization (ILM~Norm)
This paper presents a normalization mechanism called Instance-Level Meta Normalization (ILM~Norm) to address a learning-to-normalize problem. ILM~Norm learns to predict the normalization parameters via both the feature feed-forward and the gradient back-propagation paths. ILM~Norm provides a meta normalization mechanism and has several good properties. It can be easily plugged into existing instance-level normalization schemes such as Instance Normalization, Layer Normalization, or Group Normalization. ILM~Norm normalizes each instance individually and therefore maintains high performance even when small mini-batch is used. The experimental results show that ILM~Norm well adapts to different network architectures and tasks, and it consistently improves the performance of the original models. The code is available at url{https://…/ILM-Norm. …