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.
In recent years, whole genome sequencing (WGS) evolved from a futuristic-sounding research project to an increasingly affordable technology for determining complete genome sequences of complex organisms, including humans. This prompts a wide range of revolutionary applications, as WGS is a promising means for improving modern healthcare and providing a better understanding of the human genome, in particular its relation to diseases and response to treatments. However, this progress raises worrisome privacy and ethical issues, since, besides uniquely identifying its owner, the genome contains a treasure trove of highly personal and sensitive information. In this article, after summarizing recent advances in genomics, we discuss some important privacy issues associated with human genomic information and identify a number of particularly relevant research challenges.
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 obtained very good results in terms of precision and recall, even in comparison to earlier state-of-the-art proposals. Our results are highly promising toward the achievement of a practical surrogate for the specific skills required for generating regular expressions, and significant as a demonstration of what can be achieved with GP-based approaches on modern IT technology.
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. In this paper we provide a use case of the proposed III framework for development of a healthcare application for epidemiological surveillance. Epidemiological research involves collection, analysis, and interpretation of health data for describing and monitoring a health events. We demonstrate the effectiveness of the proposed III framework for developing advanced healthcare applications that are backed by massive scale healthcare data integrated from heterogeneous and distributed healthcare systems and a scalable cloud infrastructure.