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© 2019 IEEE. Multiple multi-dimensional data streams are ubiquitous in the modern world, such as IoT applications, GIS applications and social networks. Detecting anomalies in such data streams in real-time is an important and challenging task. It is able to provide valuable information from data and then assists decision-making. However, exiting approaches for anomaly detection in multi-dimensional data streams have not properly considered the correlations among multiple multi-dimensional streams. Moreover, for multi-dimensional streaming data, online detection speed is often an important concern. In this paper, we propose a fast yet effective anomaly detection approach in multiple multi-dimensional data streams. This is based on a combination of ideas, i.e., stream pre-processing, locality sensitive hashing and dynamic isolation forest. Experiments on real datasets demonstrate that our approach achieves a magnitude increase in its efficiency compared with state-of-the-art approaches while maintaining competitive detection accuracy.

© 2019 IEEE. For classification tasks, such as protein-protein interactions (PPI), support vector machines (SVMs) have been continually utilised as a standard machine learning model. However, most practices in PPIs classifications are limited to common circumstances with small datasets and low feature dimensions, due to the big computation burden of kernel functions and quadratic optimization of SVM. Alternatively, these practical experiences might tend to employ a linear model once the dataset becomes larger, which may have exclusively lost the kernel function's potential. Since there are different defined kernels and various groups of hyperparameter, the time costs in estimating a best set of hyperparameter by traditional grid search are subsequently tremendous for PPI classification. To address this challenge, in this paper, we present a more efficient solution of hyperparameter estimation by gaining acceleration with GPU, which trains SVM efficiently and accurately with kernel functions calculation accelerated on various PPI datasets. The experiments are firstly conducted on PPI classification task, and we have exclusively evaluated the effectiveness on five public classification datasets. Our solution demonstrates a faster and more accurate performance comparing with the state-of-the-art.

Accountability in the workplace is critically important and remains a challenging problem, especially with respect to workplace safety management. In this paper, we introduce a novel notion, the Internet of Responsibilities, for accountability management. Our method sorts through the list of responsibilities with respect to hazardous positions. The positions are interconnected using directed acyclic graphs (DAGs) indicating the hierarchy of responsibilities in the organization. In addition, the system detects and collects responsibilities, and represents risk areas in terms of the positions of the responsibility nodes. Finally, an automatic reminder and assignment system is used to enforce a strict responsibility control without human intervention. Using blockchain technology, we further extend our system with the capability to store, recover and encrypt responsibility data. We show that through the application of the Internet of Responsibility network model driven by Big Data, enterprise and government agencies can attain a highly secured and safe workplace. Therefore, our model offers a combination of interconnected responsibilities, accountability, monitoring, and safety which is crucial for the protection of employees and the success of organizations.

The WorldWideWeb can be considered an infinite source of information for both individuals and organizations. Yet, if the main standard of publication on the Web (HTML) is quite suited to human reading, its poor semantics makes it difficult for computers to process and use embedded data in a smart and automated way. In this paper, we propose to build a bridge between HTML documents and external applications by means of socalled mapping rules. Such rules mainly record a semantic interpretation of recurring types of information in a cluster of similar Web documents and their location in those documents. Relying on these rules, HTML-embedded data can be extracted towards a more computable format. The definition of mapping rules is based on direct user input mainly for the interpretation part, and on automatic computing for the location of data in HTML tree tructures. This approach is supported by a user-friendly tool called Retrozilla. The WorldWideWeb can be considered an infinite source of information for both individuals and organizations. Yet, if the main standard of publication on the Web (HTML) is quite suited to human reading, its poor semantics makes it difficult for computers to process and use embedded data in a smart and automated way. In this paper, we propose to build a bridge between HTML documents and external applications by means of socalled mapping rules. Such rules mainly record a semantic interpretation of recurring types of information in a cluster of similar Web documents and their location in those documents. Relying on these rules, HTML-embedded data can be extracted towards a more computable format. The definition of mapping rules is based on direct user input mainly for the interpretation part, and on automatic computing for the location of data in HTML tree tructures. This approach is supported by a user-friendly tool called Retrozilla.