The cloud computing infrastructure has the power to increase the dependability, interoperability, and scalability of emerging cyber-physical systems (CPSs). Integrating intelligent agents and semantic ontologies can help manage the complexity of such systems and enable the development of large-scale CPSs.

Human-in-the-Loop Cyber-Physical-Systems (HiLCPS) is a challenging and very promising class of applications with immense potential of impacting daily lives of many people. HiLCPS systems measure cognitive activity through body and brain sensors, infer the intent through analysis on an embedded system, they then translate the intent into robot control signals influencing the physical environment by robotic actuators, where the effects are then again observed by the human as an input for new decisions -- closing the loop. This article overviews HiLCPS opportunities and design challenges from the view of 4 disciplines: embedded system design, brain-computer interface algorithm design, assistive robotics, and innovative networking. The article outlines a common design scheme unifying these disciplines.

We propose a system for the automatic generation of regular expressions for text-extraction tasks. The user describes the desired task only by means of a set of labeled examples. The generated regexes may be used with common engines such as those that are part of Java, PHP, Perl and so on. Usage of the system does not require any familiarity with regular expressions syntax. We performed an extensive experimental evaluation on 12 different extraction tasks applied to real-world datasets.

We propose an Information Integration and Informatics (III) framework for healthcare applications that leverages the parallel computing capability of a computing cloud based on a large-scale distributed batch processing infrastructure that is built of commodity hardware. Healthcare information integration and informatics presents a potential for building advanced healthcare applications given the massive scale of data which is collected by EHR systems. Traditional EHR systems are based on different EHR standards, different languages and different technology generations. EHRs are mainly designed to store individual-level data on patient-provider interactions. EHRs capture and store information on patient health and provider actions.