Predictive intelligence to the edge through approximate collaborative context reasoning

We focus on Internet of Things (IoT) environments where a network of sensing and computing devices are responsible to locally process contextual data, reason and collaboratively infer the appearance of a specific phenomenon (event). Pushing processing and knowledge inference to the edge of the IoT network allows the complexity of the event reasoning process to be distributed into many manageable pieces and to be physically located at the source of the contextual information. This enables a huge amount of rich data streams to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized Cloud system. We propose a lightweight, energy-efficient, distributed, adaptive, multiple-context perspective event reasoning model under uncertainty on each IoT device (sensor/actuator). Each device senses and processes context data and infers events based on different local context perspectives: (i) expert knowledge on event representation, (ii) outliers inference, and (iii) deviation from locally predicted context. Such novel approximate reasoning paradigm is achieved through a contextualized, collaborative belief-driven clustering process, where clusters of devices are formed according to their belief on the presence of events. Our distributed and federated intelligence model efficiently identifies any localized abnormality on the contextual data in light of event reasoning through aggregating local degrees of belief, updates, and adjusts its knowledge to contextual data outliers and novelty detection. We provide comprehensive experimental and comparison assessment of our model over real contextual data with other localized and centralized event detection models and show the benefits stemmed from its adoption by achieving up to three orders of magnitude less energy consumption and high quality of inference

eXplainable data processing

Seminario realizado en U & P U Patel Department of Computer Engineering, Chandubhai S. Patel Institute of Technology, Charotar University of Science And Technology (CHARUSAT), Changa-388421, Gujarat, India 2021[EN]Deep Learning y has created many new opportunities, it has unfortunately also become a means for achieving ill-intentioned goals. Fake news, disinformation campaigns, and manipulated images and videos have plagued the internet which has had serious consequences on our society. The myriad of information available online means that it may be difficult to distinguish between true and fake news, leading many users to unknowingly share fake news, contributing to the spread of misinformation. The use of Deep Learning to create fake images and videos has become known as deepfake. This means that there are ever more effective and realistic forms of deception on the internet, making it more difficult for internet users to distinguish reality from fictio

Intelligent data processing

Seminario realizado en U & P U Patel Department of Computer Engineering, Chandubhai S. Patel Institute of Technology, Charotar University of Science And Technology (CHARUSAT), Changa-388421, Gujarat, India 2021[EN]In recent years, disruptive technologies have emerged and have revolutionized our communication capabilities over the internet. One of those technologies is Deep Learning. It fits under the broader branch of Artificial Intelligence known as Machine Learnin