Intelligent techniques for deception detection: a survey and critical study

Machine intelligence methods originated as effective tools for generating learning representations of features directly from the data and have indicated usefulness in the area of deception detection. The success of machine intelligence-based methods covers resolving multiple complex tasks that combine multiple low-level image features with high-level contexts, from feature extraction to classification. The goal of this paper, given this period of rapid evolution, is to provide a detailed overview of the recent developments in the domain of automated deception detection mainly brought about by machine intelligence-based techniques. This study examines about 100 research papers that explores diverse areas of common deception detection through text, speech, and video data analysis. We performed a critical analysis of the existing techniques, tools and available datasets which have been used within the existing works, followed by possible directions for the future developments in this domain

A Fog Computing Approach for Predictive Maintenance

Technological advances in areas such as communications, computer processing, connectivity, data management are gradually introducing the internet of things (IoT) paradigm across companies of different domain. In this context and as systems are making a shift into cyberphysical system of systems, connected devices provide massive data, that are usually streamed to a central node for further processing. In particular and related to the manufacturing domain, Data processing can provide insight in the operational condition of the organization or process monitored. However, there are near real time constraints for such insights to be generated and data-driven decision making to be enabled. In the context of internet of things for smart manufacturing and empowered by the aforementioned, this study discusses a fog computing paradigm for enabling maintenance related predictive analytic in a manufacturing environment through a two step approach: (1) Model training on the cloud, (2) Model execution on the edge. The proposed approach has been applied to a use case coming from the robotic industry