Impacts of Data Synthesis: A Metric for Quantifiable Data Standards and Performances

Clinical data analysis could lead to breakthroughs. However, clinical data contain sensitive information about participants that could be utilized for unethical activities, such as blackmailing, identity theft, mass surveillance, or social engineering. Data anonymization is a standard step during data collection, before sharing, to overcome the risk of disclosure. However, conventional data anonymization techniques are not foolproof and also hinder the opportunity for personalized evaluations. Much research has been done for synthetic data generation using generative adversarial networks and many other machine learning methods; however, these methods are either not free to use or are limited in capacity. This study evaluates the performance of an emerging tool named synthpop, an R package producing synthetic data as an alternative approach for data anonymization. This paper establishes data standards derived from the original data set based on the utilities and quality of information and measures variations in the synthetic data set to evaluate the performance of the data synthesis process. The methods to assess the utility of the synthetic data set can be broadly divided into two approaches: general utility and specific utility. General utility assesses whether synthetic data have overall similarities in the statistical properties and multivariate relationships with the original data set. Simultaneously, the specific utility assesses the similarity of a fitted model’s performance on the synthetic data to its performance on the original data. The quality of information is assessed by comparing variations in entropy bits and mutual information to response variables within the original and synthetic data sets. The study reveals that synthetic data succeeded at all utility tests with a statistically non-significant difference and not only preserved the utilities but also preserved the complexity of the original data set according to the data standard established in this study. Therefore, synthpop fulfills all the necessities and unfolds a wide range of opportunities for the research community, including easy data sharing and information protection

Robotic equipment carrying RN detectors: requirements and capabilities for testing

77 pags., 32 figs., 5 tabs.-- ERNCIP Radiological and Nuclear Threats to Critical Infrastructure Thematic Group . -- This publication is a Technical report by the Joint Research Centre (JRC) . -- JRC128728 . -- EUR 31044 ENThe research leading to these results has received funding from the European Union as part of the European Reference Network for Critical Infrastructure Protection (ERNCIP) projec

Targeted Automatic Integer Overflow Discovery Using Goal-Directed Conditional Branch Enforcement

We present a new technique and system, DIODE, for auto- matically generating inputs that trigger overflows at memory allocation sites. DIODE is designed to identify relevant sanity checks that inputs must satisfy to trigger overflows at target memory allocation sites, then generate inputs that satisfy these sanity checks to successfully trigger the overflow. DIODE works with off-the-shelf, production x86 binaries. Our results show that, for our benchmark set of applications, and for every target memory allocation site exercised by our seed inputs (which the applications process correctly with no overflows), either 1) DIODE is able to generate an input that triggers an overflow at that site or 2) there is no input that would trigger an overflow for the observed target expression at that site.United States. Defense Advanced Research Projects Agency (Grant FA8650-11-C-7192

Adaptive modelling of conditional variance function

Summary. We study a situation where the dependence of conditional variance on explanatory variables varies over time. The possibility and potential advantages of adaptive modelling of conditional variance are recognized. We present approaches for adaptive modelling of the conditional variance function and elaborate two procedures, moving window estimation and online quasi-Newton. The proposed methods were successfully tested in a real industrial data set. Key words: adaptive methods, conditional variance function, variance modelling, time-varying parameter

Incremental learning to personalize human activity recognition models:the importance of human AI collaboration

Abstract This study presents incremental learning based methods to personalize human activity recognition models. Initially, a user-independent model is used in the recognition process. When a new user starts to use the human activity recognition application, personal streaming data can be gathered. Of course, this data does not have labels. However, there are three different ways to obtain this data: non-supervised, semi-supervised, and supervised. The non-supervised approach relies purely on predicted labels, the supervised approach uses only human intelligence to label the data, and the proposed method for semi-supervised learning is a combination of these two: It uses artificial intelligence (AI) in most cases to label the data but in uncertain cases it relies on human intelligence. After labels are obtained, the personalization process continues by using the streaming data and these labels to update the incremental learning based model, which in this case is Learn++. Learn++ is an ensemble method that can use any classifier as a base classifier, and this study compares three base classifiers: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and classification and regression tree (CART). Moreover, three datasets are used in the experiment to show how well the presented method generalizes on different datasets. The results show that personalized models are much more accurate than user-independent models. On average, the recognition rates are: 87.0% using the user-independent model, 89.1% using the non-supervised personalization approach, 94.0% using the semi-supervised personalization approach, and 96.5% using the supervised personalization approach. This means that by relying on predicted labels with high confidence, and asking the user to label only uncertain observations (6.6% of the observations when using LDA, 7.7% when using QDA, and 18.3% using CART), almost as low error rates can be achieved as by using the supervised approach, in which labeling is fully based on human intelligence

Framework for Dependable and Pervasive eHealth Services

Abstract Provision of health care and well-being services at end-user residence, together with its benefits, brings important concerns to be dealt with. This article discusses selected issues in dependable pervasive eHealth services support. Dependable services need to be implemented in a resourceefficient and safe way due to constrained and concurrent, preexisting conditions and radio environment. Security is a must when dealing with personal information, even more critical when regarding health. Once these fundamental requirements are satisfied, and services designed in an effective manner, social significance can be achieved in various scenarios. After having discussed the above viewpoints, the article concludes with the future directions in eHealth IoT including scaling the system down to the nanoscale, to interact more intimately with biological organisms

Robotic inspection of oil and gas plants by hybrid unmanned vehicle and mobile ground support platform

Abstract Safety risks and high costs of human inspection of oil and gas plants drive towards the adoption of robotic inspection. The challenging cluttered inspection environment and the constraints dictated by legislation on potentially explosive atmospheres implying energy-efficient solutions suggest the use of an inspection-tool-equipped hybrid rolling-flying unmanned vehicle and of a mobile ground platform supporting the connected inspection robot. These two design choices together with their development are described in this article