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Crowdsourcing is the process of obtaining input or enlisting the services of a crowd of people with the aim of finding the best solution to a proposed problem. The method with which the collected input is aggregated affects the outcome of the crowdsourcing process. In this paper, we introduce a modelling framework through which we compare several aggregation methods for crowdsourcing systems. The work reveals a surprising result where confidence-related approaches lack in performance when compared to other approaches such as simple plurality voting or approaches which consider respondent competence. This inadequacy stems from a psychological phenomenon brought forth by David Dunning and Justin Kruger related to people's bias in assessing their own cognitive abilities. [ABSTRACT FROM AUTHOR]

The study introduces and evaluates a unique inter-faculty service learning course as a pedagogical model that enhances students' learning and knowledge of health problems and associated engineering intervention design for populations affected by protracted crises. A protracted crisis is defined as a situation affecting the basic needs of a large population for an extended or unexpected period of time. Background: With an increase in humanitarian protracted crises around the world, due to conflict and natural disasters, we are in need to reinvent how we educate, train, and conduct research in these environments. Engineering and Health Sciences disciplines, individually and collectively, have been working to fill the gaps and address pressing public health issues. However, courses merging those disciplines and focusing on emerging humanitarian challenges have been limited. To meet this need, and to expose the complexity of refugee health and well-being, the Humanitarian Engineering course "Design of Engineering Solutions for Health Challenges in Crisis" was launched in July 2017. Intended outcomes: The contribution is a set of elements that can serve as a foundation for developing an inter-faculty service learning course model through which students of any major acquire skills in design thinking, interdisciplinary approaches, and contextualized innovation to create practical solutions that impact populations in need. Application design: Through a literature review and an iterative course development process, the different components of the course model were developed and assessed to ensure students accomplished learning outcomes and professional skills. Findings: The study recommends a set of five elements that could be used as a foundation for developing an inter-faculty service learning model. Those include modular learning, intermittent session types, participatory active learning, intensive learning, and teamwork. [ABSTRACT FROM AUTHOR]

During an oil spill response, one of the main challenges is the ability to measure, in situ and in real time, the oil slick thickness at different locations throughout the slick. This paper presents an instrument that can respond to this need by employing a capacitive-based sensor designed for oils of different viscosities. There are three main advantages to our proposed instrument over the prior art: first, it can operate in dynamic environments, measuring oil thicknesses under wavy sea conditions and while dragged. Second, our proposed design requires only initial factory calibration; once a baseline capacitance is recorded in the air, the instrument can measure the thickness of oils under varying environmental conditions and for both light and crude oils. Third, using solutions in both software and hardware, our instrument mitigates the effect of oil fouling, an issue long considered problematic in the community of oil thickness measurement. The proposed instrument was tested extensively at the Ohmsett Facility, first in a laboratory, then inside a large outdoor tank, by dragging the sensor at different velocities, through waves of varying amplitude and frequency. Results demonstrated the accuracy, repeatability, and robustness of the proposed instrument using multiple oil types and thicknesses under static and dynamic conditions. [ABSTRACT FROM AUTHOR]

One of the challenges of teleoperation is the recognition of a user’s intended commands, particularly in the manning of highly dynamic systems such as drones. In this paper, we present a solution to this problem by developing a generalized scheme relying on a Convolutional Neural Network (CNN) that is trained to recognize a user’s intended commands, directed through a haptic device. Our proposed method allows the interface to be personalized for each user, by pre-training the CNN differently according to the input data that is specific to the intended end user. Experiments were conducted using two haptic devices and classification results demonstrate that the proposed system outperforms geometric-based approaches by nearly 12%. Furthermore, our system also lends itself to other human-machine interfaces where intention recognition is required. [ABSTRACT FROM AUTHOR]

In this paper we propose a novel system that uses sensory input from both vision and inertial sensors for an improved perception of the robot current status of equilibrium. We use MonoSLAM vision odometry as a basis for the visual perception and a gyro for angular velocity measurements; and we devise a reweighting method within a Kalman filter framework. Moreover, our approach is designed to be robust against visual and measurement noise such as blur, poor lighting conditions, and faulty sensory output. The novelty in this work is a robust humanoid fall avoidance system, which relies on the fusion of sensory input, mainly gyroscope and visual odometry, taking into account changes in the environment. The fusion of the mentioned sensors in addition to the image quality assessment, ensure a more human-like fall avoidance in comparison to currently existing systems. We implement our method on the NAO humanoid, where seven sets of experiments are performed to assess the effectiveness of our approach. The fusion of camera and gyro information not only enables a more human-like behavior, but also provides more humanoid stability and faster recovery, and thus leads to more robust fall avoidance. [ABSTRACT FROM AUTHOR]

Abstract: Support vector machines (SVM) have been showing high accuracy of prediction in many applications. However, as any statistical learning algorithm, SVM's accuracy drops if some of the training points are contaminated by an unknown source of noise. The choice of clean training points is critical to avoid the overfitting problem which occurs generally when the model is excessively complex, which is reflected by a high accuracy over the training set and a low accuracy over the testing set (unseen points). In this paper we present a new multi-level SVM architecture that splits the training set into points that are labeled as ‘easily classifiable’ which do not cause an increase in the model complexity and ‘non-easily classifiable’ which are responsible for increasing the complexity. This method is used to create an SVM architecture that yields on average a higher accuracy than a traditional soft margin SVM trained with the same training set. The architecture is tested on the well known US postal handwritten digit recognition problem, the Wisconsin breast cancer dataset and on the agitation detection dataset. The results show an increase in the overall accuracy for the three datasets. Throughout this paper the word confidence is used to denote the confidence over the decision as commonly used in the literature. [Copyright &y& Elsevier]

Abstract: Forest fire occurrence prediction plays a major role in resource allocation, mitigation and recovery efforts. This paper compares two artificial intelligence based methods, artificial neural networks (ANN) and support vector machines (SVM), utilizing a reduced set of weather parameters. Using a reduced set of parameters results in an efficient and reduced cost prediction system especially for developing countries. In this paper the aim is to predict forest fire occurrence by reducing the number of monitored features, and eliminating the need for weather prediction mechanisms. The reason is to reduce errors due to inaccuracies in weather prediction. The challenge is to choose a limited number of easily measurable features in the aim of reducing the cost of the system and its maintenance. At the same time, the chosen features must have a high correlation with the risk of fire occurrence. A literature review of forest fire prediction methods divided into systems/indices, and artificial intelligence is provided. The two fire danger prediction algorithms utilize relative humidity and cumulative precipitation to output a risk estimate. The assessment of these algorithms, using data from Lebanon, demonstrated their ability to accurately predict the risk of fire occurrence on a scale of four levels. [Copyright &y& Elsevier]

Purpose: Driven by the shortage in qualified nurses and the high percentage of aging populations, the past decade has witnessed a significant growth in the use of robots in nursing, especially in countries like Japan. This article is a scoping review of the different tracks in which robots are used in nursing. Whereas assistive robots are used for physical care, including service and monitoring tasks, social assistive robots focus on the cognitive and emotional well‐being of patients in need of companionship. Methods: A total of six electronic databases were used in the search for journal papers and conference proceedings. The key words used in searching the databases were nursing OR nurses, AND robots OR robotics. Topics covering surgical robotics, nursing education robotics, and clinical procedures were excluded. Findings: A total of 1,758 articles were retrieved, from which 69 articles were included in the final review. The analysis of the chosen papers led to the categorization of robots into two main categories: assistive robots and social assistive robots. Conclusions: After a detailed review of the state of the art in assistive robots and social assistive robots, an insight into the future of robotics in this field is provided. The recommendations include the need to intensify research on human robot interaction, greater focus on monitoring robots, and analysis of the psychological barriers that need to be surmounted to achieve more tolerance and higher acceptance of robots. Clinical Relevance: For researchers and developers to provide suitable technological solutions, a full understanding of robotics in nursing is needed. An overview of the most recent applications and their proper categorization is key to finding areas for contribution. [ABSTRACT FROM AUTHOR]

Site inspection is a notably tedious, time-consuming, and error-prone process when carried out manually by construction inspectors. One portion of site inspection is the generation and comparison of as-built drawings to their as-planned counterparts to ensure conformity. Current as-built evaluation systems rely on manually positioning and transporting scanning stations around the site to collect a cohesive map of the environment and then followed by a manual assessment of the site status using the built drawings. This paper proposes an automated alternative to the generation and assessment of 2D as-built floor maps by relying on robotic simultaneous localisation and mapping (SLAM) to build the 2D as-built maps, followed by automated evaluation of the as-built maps using machine vision. The components of the proposed system have been tested through proof of concept experiments inside 2 different constructed sites. Results indicate average errors below 4 cm (site of 30 m x 15 m) between the disparities identified by our proposed system versus ground truth measured disparities. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, there has been an increasing interest in the monitoring and controlling of pH. It has become an important aspect of many industrial wastewater and water quality treatment processes. At the same time, the demand for smaller electronic devices used for various industrial, commercial, and research applications has greatly increased. In this paper, we propose a fabrication method of ion-sensitive field effect transistor (ISFET) using micro–electro-mechanical systems (MEMS) techniques for pH sensing application. The novelty of this device lies in the detection of target solution with volumes in the sub-microliter range. With proper electrical packaging, our ISFET chip has been able to detect the pH values of 2.5μL solutions. The results reveal a linearity of pH measurement with a corresponding sensitivity of 10.7mV/pH. Furthermore, Nernst equation is employed to analyze the performance, and a promising scheme to improve the sensitivity while measuring tiny solutions is proposed. This achievement has the potential to satisfy the research demands in various areas including chemistry, biology and medicine. [Copyright &y& Elsevier]

Objectives. In heart failure, invasive angiography is often employed to differentiate ischaemic from non-ischaemic cardiomyopathy. We aim to examine the predictive value of echocardiographic strain features alone and in combination with other features to differentiate ischaemic from non-ischaemic cardiomyopathy, using artificial neural network (ANN) and logistic regression modelling. Design. We retrospectively identified 204 consecutive patients with an ejection fraction <50% and a diagnostic angiogram. Patients were categorized as either ischaemic (n = 146) or non-ischaemic cardiomyopathy (n = 58). For each patient, left ventricular strain parameters were obtained. Additionally, regional wall motion abnormality, 13 electrocardiographic (ECG) features and six demographic features were retrieved for analysis. The entire cohort was randomly divided into a derivation and a validation cohort. Using the parameters retrieved, logistic regression and ANN models were developed in the derivation cohort to differentiate ischaemic from non-ischaemic cardiomyopathy, the models were then tested in the validation cohort. Results. A final strain-based ANN model, full feature ANN model and full feature logistic regression model were developed and validated, F1 scores were 0.82, 0.79 and 0.63, respectively. Conclusions. Both ANN models were more accurate at predicting cardiomyopathy type than the logistic regression model. The strain-based ANN model should be validated in other cohorts. This model or similar models could be used to aid the diagnosis of underlying heart failure aetiology in the form of the online calculator (https://cimti.usj.edu.lb/strain/index.html) or built into echocardiogram software. [ABSTRACT FROM AUTHOR]

We propose a new multiplier design that fulfills the need for low-power circuit blocks used in error-tolerant applications on energy-constrained devices. The design trades accuracy for higher speed, lower energy consumption, and lower transistor count. The average relative error of an N -bit multiplier is modeled as a function of N and saturates at a constant (around 17%) as the multiplier width increases. An 8-bit implementation simulated in HSPICE achieved almost 90% energy savings for a random sample of operands as compared to a conventional parallel multiplier. The design is flexible whereby simple variations to the circuit structure lead to a perfectly accurate multiplier. Tests performed on multimedia applications such as JPEG compression showed a promising outcome. [ABSTRACT FROM AUTHOR]

Digital technology is increasingly used in humanitarian action and promises to improve the health and social well-being of populations affected by both acute and protracted crises. We set out to (1) review the current landscape of digital technologies used by humanitarian actors and affected populations, (2) examine their impact on health and well-being of affected populations, and (3) consider the opportunities for and challenges faced by users of these technologies. Through a systematic search of academic databases and reports, we identified 50 digital technologies used by humanitarian actors, and/or populations affected by crises. We organized them according to the stage of the humanitarian cycle that they were used in, and the health outcomes or determinants of health they affected. Digital technologies were found to facilitate communication, coordination, and collection and analysis of data, enabling timely responses in humanitarian contexts. A lack of evaluation of these technologies, a paternalistic approach to their development, and issues of privacy and equity constituted major challenges. We highlight the need to create a space for dialogue between technology designers and populations affected by humanitarian crises. [ABSTRACT FROM AUTHOR]

Marine pollution by oil spills is a devastating environmental hazard, requiring a low-cost efficient system, for continuous and real-time thickness measurement and localization of oil. Knowing that none of the previous detection methods has managed to fully meet these requirements, it is necessary to devise a new technique for guiding and speeding up the clean-up process of oil spills. This paper presents a sensor device that is capable of sensing, processing, and transmitting information about an oil spill (location and thickness). This paper discusses two new methodologies of detection based on the difference in the absorbance spectral signatures and electric conductivity properties of oil and water. This paper also discusses the mechanical design of the device, the hardware implementation of its components, and the integration and evaluation of the whole system. The experimental results confirm the effectiveness of the proposed method under different lighting, salinity, temperature, and sea conditions. [ABSTRACT FROM AUTHOR]