Every so often, a confluence of novel technologies emerges that radically transforms every aspect of the industry, the global economy, and finally, the way we live. These sharp leaps of human ingenuity are known as industrial revolutions, and we are currently in the midst of the fourth such revolution, coined Industry 4.0 by the World Economic Forum. Building on their guideline set of technologies that encompass Industry 4.0, we present a full set of pillar technologies on which Industry 4.0 project portfolio management rests as well as the foundation technologies that support these pillars. A complete model of an Industry 4.0 factory which relies on these pillar technologies is presented. The full set of pillars encompasses cyberphysical systems and Internet of Things (IoT), artificial intelligence (AI), machine learning (ML) and big data, robots and drones, cloud computing, 5G and 6G networks, 3D printing, virtual and augmented reality, and blockchain technology. These technologies are based on a set of foundation technologies which include advances in computing, nanotechnology, biotechnology, materials, energy, and finally cube satellites. We illustrate the confluence of all these technologies in a single model factory. This new factory model succinctly demonstrates the advancements in manufacturing introduced by these modern technologies, which qualifies this as a seminal industrial revolutionary event in human history.
XML is one of the most important standards for manipulating data on the Internet. However, querying large volumes of XML data represents a bottleneck for several computationally intensive applications. A solution is to pre-process the document in streaming mode with resources approximately proportional to document depth and query selectivity. Limited processing space can then accommodate much larger documents. But the actual savings vary so much as to make them unpredictable. To overcome this limitation of stream-processing we propose a new application of the path tree synopsis data structure. Such a synopsis provides a succinct description of the original document with low computational overhead and high accuracy for processing tasks like selectivity estimation and query answer approximation. In this paper, we formally define the path tree synopsis, informally introduced by [1] and used by [25], and propose a new streaming algorithm to construct it. We also present an online stream-querying system able to estimate the cost for a given query before answering it accurately. The core algorithm is adapted from [9] LQ, we apply it to path tree traversal, cost estimation, query processing and even optimizations.
Malware detection is a challenging and non-trivial task due to ever increase in several attacks and their sophistication level. Detection of such attacks demands the exploration of new approaches to generalize the attack patterns. One such approach is the use of Monte-Carlo simulations to train a reinforcement learning model. In this paper, we propose a self-adaptive Monte-Carlo simulation-based reinforcement model called Heuristic-based Generative Model (HGM), which generalizes the attack patterns in such a way that the new unknown attacks can be detected and flagged in real-time. The results show that HGM can detect a variety of malware with high accuracy.
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