An Analysis of Heterogeneity in Futuristic Unmanned Vehicle Systems

Recent studies have shown that with appropriate operator decision support and with enough automation aboard unmanned vehicles, inverting the multiple operators to single-vehicle control paradigm is possible. These studies, however, have generally focused on homogeneous teams of vehicles, and have not completely addressed either the manifestation of heterogeneity in vehicle teams, or the effects of heterogeneity on operator capacity. An important implication of heterogeneity in unmanned vehicle teams is an increase in the diversity of possible team configurations available for each operator, as well as an increase in the diversity of possible attention allocation schemes that can be utilized by operators. To this end, this paper introduces a resource allocation framework that defines the strategies and processes that lead to alternate team configurations. The framework also highlights the sub-components of operator attention allocation schemes that can impact overall performance when supervising heterogeneous unmanned vehicle teams. A subsequent discrete event simulation model of a single operator supervising multiple heterogeneous vehicles and tasks explores operator performance under different heterogeneous team compositions and varying attention allocation strategies. Results from the discrete event simulation model show that the change in performance when switching from a homogeneous team to a heterogeneous one is highly dependent on the change in operator utilization. Heterogeneous teams that result in lower operator utilization can lead to improved performance under certain operator strategies.Prepared for Charles River Analytic

Audio Decision Support for Supervisory Control of Unmanned Vehicles : Literature Review

Purpose of this literature review: To survey scholarly articles, books and other sources (dissertations, conference proceedings) relevant to the use of the audio supervisory control of unmanned vehicles.Prepared for Charles River Analytic

Information Requirements for MCM and ISR Missions : PUMA Phase II

This document contains display requirements for Littoral Combat Ship (LCS) control station displays to be used by unmanned vehicle units in support of heterogeneous unmanned vehicle missions (such as Special Operations Force (SOF) insertion). The method used for generating the requirements was that of a Hybrid Cognitive Task Analysis (CTA)1 which entails describing a scenario overview of a representative mission, generating event flow diagrams, and depicting decision ladders for the key decisions identified in the event flow diagrams. These steps are then used together to generate an informational requirements summary which includes the situational awareness requirements that are derived from the event flow and display requirements of the decision ladders. This method was developed in Phase I of the PUMA (Plan Understanding for Mixed-initiative control of Autonomous systems) project2. In PUMA I, the mission scenario primarily consisted of Intelligence, Surveillance and Reconnaissance (ISR) tasks. For PUMA II, the scenario has been expanded to include Mine Counter Measures (MCM), Harbor Bottom Image-Mapping (HBI), and Anti- Terrorism / Force Protection (AT/FP) mission types. There is a specific emphasis on the MCM and ISR missions to highlight the informational requirement differences between the two task types. This document incorporates the expanded vehicle and mission type heterogeneities that are present in PUMA II in order to develop a cohesive set of informational requirements necessary for such a complex mission.Prepared for Charles River Analytic

A UAV Mission Hierarchy

In the following sections, each of the primary missions are decomposed into mission planning, management, and replanning segments in order to identify what the primary functions a human operator will need to perform. The goal is to understand what tasks/functions are common across different UAV missions and platforms in order to map the generalizability of any particular research project.Prepared for Charles River Analytic

University Image and its Relationship to Student Satisfaction: Case of the Holy Spirit University of Kaslik, Lebanon

This work focuses on the study of the university’s image with the aim of explaining the components of image and attributes of student satisfaction. Our study investigates the relationships between the different components of the university image and to what extent they may affect the students’ satisfaction. Hypotheses were drawn setting the relationships between the affective, cognitive and overall image in relation with satisfaction. The results of the empirical work carried out on a representative sample of 200 students studying at Holy Spirit University of Kaslik (USEK) demonstrate that the cognitive component of image is an antecedent of the affective component. In turn, both of these components influence the formation of the overall image of a university. However, the affective and overall images statistically and significantly affect the overall satisfaction of students with their university. The research could also be extended to cover the area of the Middle East and study the process of formation of the university image by various public universities

One Work Analysis, Two Domains: A Display Information Requirements Case Study

Work domain analyses can be time consuming, requiring extensive interviews, documentation review, and observations, among other techniques. Given the time and resources required, we examine how to generalize a work domain analysis technique, namely the hybrid Cognitive Task Analysis (hCTA) method across two domains in order to generate a common set of display information requirements. The two domains of interest are field workers troubleshooting low voltage distribution networks and telecommunication problems. Results show that there is a high degree of similarity between the two domains due to their service call nature, particularly in tasking and decision-making. While the primary differences were due to communication protocols and equipment requirements, the basic overall mission goals, functions, phases of operation, decision processes, and situation requirements were very similar. A final design for both domains is proposed based on the joint requirements

CICLAD: A Fast and Memory-efficient Closed Itemset Miner for Streams

Mining association rules from data streams is a challenging task due to the (typically) limited resources available vs. the large size of the result. Frequent closed itemsets (FCI) enable an efficient first step, yet current FCI stream miners are not optimal on resource consumption, e.g. they store a large number of extra itemsets at an additional cost. In a search for a better storage-efficiency trade-off, we designed Ciclad,an intersection-based sliding-window FCI miner. Leveraging in-depth insights into FCI evolution, it combines minimal storage with quick access. Experimental results indicate Ciclad's memory imprint is much lower and its performances globally better than competitor methods.Comment: KDD2

Can surgical simulation be used to train detection and classification of neural networks?

Computer-assisted interventions (CAI) aim to increase the effectiveness, precision and repeatability of procedures to improve surgical outcomes. The presence and motion of surgical tools is a key information input for CAI surgical phase recognition algorithms. Vision-based tool detection and recognition approaches are an attractive solution and can be designed to take advantage of the powerful deep learning paradigm that is rapidly advancing image recognition and classification. The challenge for such algorithms is the availability and quality of labelled data used for training. In this Letter, surgical simulation is used to train tool detection and segmentation based on deep convolutional neural networks and generative adversarial networks. The authors experiment with two network architectures for image segmentation in tool classes commonly encountered during cataract surgery. A commercially-available simulator is used to create a simulated cataract dataset for training models prior to performing transfer learning on real surgical data. To the best of authors' knowledge, this is the first attempt to train deep learning models for surgical instrument detection on simulated data while demonstrating promising results to generalise on real data. Results indicate that simulated data does have some potential for training advanced classification methods for CAI systems

CaDIS: Cataract dataset for surgical RGB-image segmentation

Video feedback provides a wealth of information about surgical procedures and is the main sensory cue for surgeons. Scene understanding is crucial to computer assisted interventions (CAI) and to post-operative analysis of the surgical procedure. A fundamental building block of such capabilities is the identification and localization of surgical instruments and anatomical structures through semantic segmentation. Deep learning has advanced semantic segmentation techniques in the recent years but is inherently reliant on the availability of labelled datasets for model training. This paper introduces a dataset for semantic segmentation of cataract surgery videos complementing the publicly available CATARACTS challenge dataset. In addition, we benchmark the performance of several state-of-the-art deep learning models for semantic segmentation on the presented dataset. The dataset is publicly available at https://cataracts-semantic-segmentation2020.grand-challenge.org/

Mental health trajectories among the general population and higher-risk groups following the COVID-19 pandemic in Switzerland, 2021-2023.

Mental health deteriorated in the early stages of the COVID-19 pandemic, but improved relatively quickly as restrictions were eased, suggesting overall resilience. However, longer-term follow-up of mental health in the general population is scarce. We examined mental health trajectories in 5624 adults (58 % women; aged 18-97 years) from the Specchio-COVID19 cohort, using the Generalized Anxiety Disorder scale-2 and the Patient Health Questionnaire-2, administered each month from February to June 2021, and in Spring 2022 and 2023. Depressive and anxiety symptoms declined during a pandemic wave from February to May 2021 (β = -0.06 [-0.07, -0.06]; -0.06 [-0.07, -0.05]), and remained lower at longer-term follow-up than at the start of the wave. Loneliness also declined over time, with the greatest decline during the pandemic wave (β = -0.25 [-0.26, -0.24]). Many higher-risk groups, including socioeconomically disadvantaged individuals, those with a chronic condition, and those living alone had poorer mental health levels throughout the study period. Women and younger individuals had a faster improvement in mental health during the pandemic wave. Loneliness trajectories were associated with mental health trajectories throughout the study period. We cannot definitively conclude that the observed changes in mental health were due to experiences of the pandemic. While there was a need for additional mental health support during stricter policy responses to COVID-19, overall, mental health improved relatively soon after measures were eased. Nevertheless, the persistence of mental health disparities highlights the need for further efforts from the government and healthcare practitioners to support vulnerable groups beyond the pandemic