Data Security and Privacy Assurance Considerations in Cloud Computing for Health Insurance Providers

Cloud computing has been massively adopted in healthcare, where it attracts economic, operational, and functional advantages beneficial to insurance providers. However, according to Identity Theft Resource Centre, over twenty-five percent of data breaches in the US targeted healthcare. The HIPAA Journal reported an increase in healthcare data breaches in the US in 2016, exposing over 16 million health records. The growing incidents of cyberattacks in healthcare are compelling insurance providers to implement mitigating controls. Addressing data security and privacy issues before cloud adoption protects from monetary and reputation losses. This article provides an assessment tool for health insurance providers when adopting cloud vendor solutions. The final deliverable is a proposed framework derived from prominent cloud computing and governance sources, such as the Cloud Security Alliance, Cloud Control Matrix (CSA, CCM) v 3.0.1 and COBIT 5 Cloud Assurance.

Information Processing for Generating Recommendations Ahead of Time in an IoT-Based Environment

The evolution of Internet of Things (IoT) has significantly contributed to the development of the sensing enterprise concept and to the use of appropriate information systems for real-time processing of sensor data that are able to provide meaningful insights about potential problems in a proactive way. In the current article, the authors outline a conceptual architecture and describe the system design requirements for deciding and acting ahead of time with the aim to address the Decide and the Act phases of the “Detect-Predict-Decide-Act” proactive principle, which are still underexplored areas. The associated developed information system is capable of being integrated with systems addressing the Detect and the Predict phases in an Event Driven Architecture (EDA).

Class Distribution Curve Based Discretization With Application to Wearable Sensors and Medical Monitoring

Understanding diseases and human activities, and constructing highly accurate classifiers are two important tasks in bio-medicine, healthcare, and wearable sensor technology. Being able to mine high-quality patterns is useful here, as such patterns can help improve understanding and build accurate classifiers. However, most pattern mining algorithms only operate on discrete data; applying them often requires a binning step to discretize continuous attributes. This article presents a new discretization technique, called Class Distribution Curve based Binning (CDC Binning); the main idea is to use a so-called class distribution curve, which measures the class purity in sliding windows over an attribute's range, to construct binning intervals. Experiments show that (1) CDC Binning outperforms existing binning methods in discovering high-quality patterns, especially when the class distribution curve is complicated (e.g. when the two classes are two fairly similar human activities), and (2) it can outperform other binning methods by 10% in classification accuracy when using discovered patterns as features. CDC Binning is particularly useful for applications where the classes/activities to be distinguished are similar to each other. This is especially important in wearable sensor technology where detection of behavioral or activity changes in a person (e.g. fall detection) could indicate a significant medical event.

Manifold Transfer Subspace Learning (MTSL) for Applications in Aided Target Recognition

This article describes how transfer subspace learning has recently gained popularity for its ability to perform cross-dataset and cross-domain object recognition. The ability to leverage existing data without the need for additional data collections is attractive for monitoring and surveillance technology, specifically for aided target recognition applications. Transfer subspace learning enables the incorporation of sparse and dynamically collected data into existing systems that utilize large databases. Manifold learning has also gained popularity for its success at dimensionality reduction. In this contribution, Manifold learning and transfer subspace learning are combined to create a new system capable of achieving high target recognition rates. The manifold learning technique used in this contribution is diffusion maps, a nonlinear dimensionality reduction technique based on a heat diffusion analogy. The transfer subspace learning technique used is Transfer Fisher's Linear Discriminative Analysis. The new system, manifold transfer subspace learning, sequentially integrates manifold learning and transfer subspace learning. In this article, the ability of the new techniques to achieve high target recognition rates for cross-dataset and cross-domain applications is illustrated using a variety of diverse datasets.

A Cyber-Physical Photovoltaic Array Monitoring and Control System

A cyber physical system approach for a utility-scale photovoltaic (PV) array monitoring and control is presented in this article. This system consists of sensors that capture voltage, current, temperature, and irradiance parameters for each solar panel which are then used to detect, predict and control the performance of the array. More specifically the article describes a customized machine-learning method for remote fault detection and a computer vision framework for cloud movement prediction. In addition, a consensus-based distributed approach is proposed for resource optimization, and a secure authentication protocol that can detect intrusions and cyber threats is presented. The proposed system leverages video analysis of skyline imagery that is used along with other measured parameters to reconfigure the solar panel connection topology and optimize power output. Additional benefits of this cyber physical approach are associated with the control of inverter transients. Preliminary results demonstrate improved efficiency and robustness in renewable energy systems using advanced cyber enabled sensory analysis and fusion devices and algorithms.

Integration of Gaussian Processes and Particle Swarm Optimization for Very-Short Term Wind Speed Forecasting in Smart Power

This article describes how the integration of renewable energy in the power grid is a critical issue in order to realize a smart grid infrastructure. To that end, intelligent methods that monitor and currently predict the values of critical variables of renewable energy are essential. With respect to wind power, such variable is the wind speed given that it is of great interest to efficient schedule operation of a wind farm. In this article, a new methodology for predicting wind speed is presented for very short-term prediction horizons. The methodology integrates multiple Gaussian process regressors (GPR) via the adoption of an optimization problem whose solution is given by the particle swarm optimization algorithm. The optimized framework is utilized for the average hourly wind speed prediction for a prediction horizon of six hours ahead. Results demonstrate the ability of the methodology in accurately forecasting the wind speed. Furthermore, obtained forecasts are compared with those taken from single Gaussian process regressors as well from the integration of the same multiple GPR using a genetic algorithm.

Using Physics Inspired Wave Agents in a Virtual Environment

The monitoring and the surveillance of industrial and agricultural sites have become first order tasks mainly for security or the safety reasons. The main issues of this activity is tied to the size of the sites and to their accessibility. Thus, it seems nowadays relevant to tackle with this problem with robots, which can detect potential issues with a low operational cost. To that purpose, in addition to individual patrolling behavior, robots need coordination schemes. The goal of this paper is to explore the possibility of using interference fringes and waves properties in a virtual environment to tackle with the longitudinal distance regulation in the platoon control issue. The proposed model, based on a multi-agent paradigm, is considering each vehicle as an agent wave generator in the virtual environment.

Use of Images of Leaves and Fruits of Apple Trees for Automatic Identification of Symptoms of Diseases and Nutritional Disorders

Rapid diagnosis of symptoms caused by pest attack, diseases and nutritional or physiological disorders in apple orchards is essential to avoid greater losses. This paper aimed to evaluate the efficiency of Convolutional Neural Networks (CNN) to automatically detect and classify symptoms of diseases, nutritional deficiencies and damage caused by herbicides in apple trees from images of their leaves and fruits. A novel data set was developed containing labeled examples consisting of approximately 10,000 images of leaves and apple fruits divided into 12 classes, which were classified by algorithms of machine learning, with emphasis on models of deep learning. The results showed trained CNNs can overcome the performance of experts and other algorithms of machine learning in the classification of symptoms in apple trees from leaves images, with an accuracy of 97.3% and obtain 91.1% accuracy with fruit images. In this way, the use of Convolutional Neural Networks may enable the diagnosis of symptoms in apple trees in a fast, precise and usual way.

Recent Advances in Minimally-Obtrusive Monitoring of People's Health

Monitoring people's health is useful for enhancing the care provided to them by others or self-management of health. This article is a survey of the latest research on monitoring parameters indicating a person's current health or having potential to affect the person's health in future, using various physical sensors. These sensors include accelerometers, gyroscopes, electromyography sensors, fiber optic sensors, textile electrodes, thermistors, infrared sensors, force sensors, and photo diodes. The health parameters monitored include heart rate, respiration rate, weight, body mass index, calories burnt, pressure distribution, diet, blood pressure, blood glucose, oxygen saturation, posture, duration of sleep, quality of sleep, hand movement, body temperature, skin conductance, exposure to ultraviolet light, adherence to medication-intake schedule, gait characteristics, and steps taken. The population monitored includes elderly people, miners, stroke survivors, osteoarthritis patients, people suffering from anorexia nervosa, obese people, people with Parkinson's disease, people having panic attacks, and wheelchair users.

On Automated Generation of Keyboard Layout to Reduce Finger-Travel Distance

The QWERTY keyboard layout can be very inefficient for one-finger typing on virtual keyboards since the letters in many common digrams are placed on opposite sides of the keyboard, resulting in a long finger travel. This paper reports on use of simulated annealing for finding alternate arrangements of the letters of the English alphabet on keyboards with different number of rows, to reduce finger-travel distance for entering text. The use of simulated annealing led to arrangements of the letters on 3 × 10, 4 × 7, and 5 × 6 layouts with a lower weighted sum of finger-travel distances for all digrams (denoted by d) compared to the QWERTY layout (lower by about 40%). The layout with the least value of d among those found in this work is a 5 × 6 layout for which the value of d is only 1.78 key widths compared to 3.31 key widths (the value of d for QWERTY). Alternate automated methods to solve this problem, connections between keyboard layouts and facility layouts, and many new applications of the ideas in this work are discussed.