Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770

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Personalised Context Aware Content Relevant Disease Prediction And Diet Recommendation System

Predicting disease is plays an important role in improving the public health. The problem of predicting possible diseases reduce some of the diseases may come in the future. The health recommendation system predicts the disease and recommends the suitable diet and exercises. Context aware recommendation systems produce more relevant recommendations with the help of contextual information. In this paper we have proposed a context aware recommendation system to predict diseases based in the context of the user and recommend a suitable diet and exercises. The experimental results show that the performance of our proposed system is an efficient in predicting the disease and recommending the diet and exercise

Federated Embedded Systems – a review of the literature in related fields

This report is concerned with the vision of smart interconnected objects, a vision that has attracted much attention lately. In this paper, embedded, interconnected, open, and heterogeneous control systems are in focus, formally referred to as Federated Embedded Systems. To place FES into a context, a review of some related research directions is presented. This review includes such concepts as systems of systems, cyber-physical systems, ubiquitous computing, internet of things, and multi-agent systems. Interestingly, the reviewed fields seem to overlap with each other in an increasing number of ways

A Semantic Web approach to ontology-based system: integrating, sharing and analysing IoT health and fitness data

With the rapid development of fitness industry, Internet of Things (IoT) technology is becoming one of the most popular trends for the health and fitness areas. IoT technologies have revolutionised the fitness and the sport industry by giving users the ability to monitor their health status and keep track of their training sessions. More and more sophisticated wearable devices, fitness trackers, smart watches and health mobile applications will appear in the near future. These systems do collect data non-stop from sensors and upload them to the Cloud. However, from a data-centric perspective the landscape of IoT fitness devices and wellness appliances is characterised by a plethora of representation and serialisation formats. The high heterogeneity of IoT data representations and the lack of common accepted standards, keep data isolated within each single system, preventing users and health professionals from having an integrated view of the various information collected. Moreover, in order to fully exploit the potential of the large amounts of data, it is also necessary to enable advanced analytics over it, thus achieving actionable knowledge. Therefore, due the above situation, the aim of this thesis project is to design and implement an ontology based system to (1) allow data interoperability among heterogeneous IoT fitness and wellness devices, (2) facilitate the integration and the sharing of information and (3) enable advanced analytics over the collected data (Cognitive Computing). The novelty of the proposed solution lies in exploiting Semantic Web technologies to formally describe the meaning of the data collected by the IoT devices and define a common communication strategy for information representation and exchange

Enriching companion robots with enhanced reminiscence abilities

In this document I will go on discussing a project conceived by Professor Andrea Giovanni Nuzzolese and Alessandro Russo, both researchers and developers of some of the main aspects of project Mario at CNR Rome. MARIO is a robot, part of a robotics company called KOMPAÏ Robotics that deals with the production and management of Robots who take care of elderly people who suffer from dementia or who still need an aid; more generally speaking, there is talk of weak and lonely people within an organization and / or institutions (nursing homes ...) or in their own homes. There are numerous characteristics of MARIO, which ultimately contribute to all those which are the manufacturing objectives of KOMPAÏ Robotics. My project, or rather my contribution to MARIO, is to look for a specific method which let the robot show a specific set of photos to the user according to the expressions, feelings and emotions, the user will reveal. Example: the robot randomly chooses a marriage photo and the user suddenly start to laugh and to express positive feelings with positive words; the robot will try to understand if it’s a good photo for the user or not, and in the first case will continue to show the same kind of pictures while in the second case, will change completely set of photos to be shown. The pleasure of the subject expressed in relation to a photo must be subject to an index of interest between predefined and specified values that may be to show a certain interest in a picture or the subjects within the image or the situation that surrounds them