Personalized content retrieval in context using ontological knowledge

Personalized content retrieval aims at improving the retrieval process by taking into account the particular interests of individual users. However, not all user preferences are relevant in all situations. It is well known that human preferences are complex, multiple, heterogeneous, changing, even contradictory, and should be understood in context with the user goals and tasks at hand. In this paper, we propose a method to build a dynamic representation of the semantic context of ongoing retrieval tasks, which is used to activate different subsets of user interests at runtime, in a way that out-of-context preferences are discarded. Our approach is based on an ontology-driven representation of the domain of discourse, providing enriched descriptions of the semantics involved in retrieval actions and preferences, and enabling the definition of effective means to relate preferences and context

Access Update, October 2013

Monthly newsletter for the Iowa Department of Public Healt

A deep reinforcement learning based homeostatic system for unmanned position control

Deep Reinforcement Learning (DRL) has been proven to be capable of designing an optimal control theory by minimising the error in dynamic systems. However, in many of the real-world operations, the exact behaviour of the environment is unknown. In such environments, random changes cause the system to reach different states for the same action. Hence, application of DRL for unpredictable environments is difficult as the states of the world cannot be known for non-stationary transition and reward functions. In this paper, a mechanism to encapsulate the randomness of the environment is suggested using a novel bio-inspired homeostatic approach based on a hybrid of Receptor Density Algorithm (an artificial immune system based anomaly detection application) and a Plastic Spiking Neuronal model. DRL is then introduced to run in conjunction with the above hybrid model. The system is tested on a vehicle to autonomously re-position in an unpredictable environment. Our results show that the DRL based process control raised the accuracy of the hybrid model by 32%.N/

Technologie RFID a Blochkchain v dodavatelském řetězci

The paper discusses the possibility of combining RFID and Blockchain technology to more effectively prevent counterfeiting of products or raw materials, and to solve problems related to production, logistics and storage. Linking these technologies can lead to better planning by increasing the transparency and traceability of industrial or logistical processes or such as efficient detection of critical chain sites.Příspěvek se zabývá možností kombinace technologií RFID a Blockchain pro účinnější zabránění padělání výrobků či surovin a řešení problémů spojených s výrobou, logistikou a skladováním. Spojení těchto technologií může vést k lepšímu plánování díky vyšší transparentnosti a sledovatelnosti průmyslových nebo logistických procesů, nebo například k efektivnímu zjišťování kritických míst řetězce