User Workload Research Articles

Effect of interface design on cognitive workload in unmanned aerial vehicle control

Unmanned Aerial Vehicle (UAV) control interfaces are critical channels for transferring information between the vehicle and an operator. Research on system performance has focused on enhancing vehicle automation and some work has evaluated cognitive workload for existing UAV interfaces. The potential for usable interface design to reduce cognitive workload during the early design phase has been largely overlooked. This study addresses these gaps by: (1) evaluating the effectiveness of a contemporary UAV interface design tool (the Modified GEDIS-UAV) to moderate user workload; (2) examining the effectiveness of various UAV interface designs for minimizing cognitive workload under different control task pacing; and (3) exploring the use of eye tracking measures, traditionally applied in other domains, as indicators of cognitive workload in UAV operations. We prototyped three different interface designs, classified as “baseline”, “enhanced” and “degraded” interfaces. Cognitive workload in UAV operation was manipulated in terms of levels of vehicle speed (“low” and “high”). Physiological and subjective measures of workload were collected for all combinations of interface design and task demand. Results revealed the “enhanced” interface to yield the lowest operator cognitive workload and supported operator resilience to increased control task demand, as compared to the “baseline” and “degraded” interfaces. In addition, task demand was found to elevate operator cognitive workload, particularly in terms of "mental" and "temporal" demands and operator perceptions of "performance". The study also demonstrated utility of eye-tracking technology for detecting cognitive workload in UAV operations. This research provides practical guidance for UAV control interface design to manage operator workload. The methods employed in the study are applicable to interface evaluation for various types of UAVs and other unmanned systems to enhance human-automation interaction.

An Overview of Approaches and Methods for the Cognitive Workload Estimation in Human–Machine Interaction Scenarios through Wearables Sensors

Background: Human-Machine Interaction (HMI) has been an important field of research in recent years, since machines will continue to be embedded in many human actvities in several contexts, such as industry and healthcare. Monitoring in an ecological mannerthe cognitive workload (CW) of users, who interact with machines, is crucial to assess their level of engagement in activities and the required effort, with the goal of preventing stressful circumstances. This study provides a comprehensive analysis of the assessment of CW using wearable sensors in HMI. Methods: this narrative review explores several techniques and procedures for collecting physiological data through wearable sensors with the possibility to integrate these multiple physiological signals, providing a multimodal monitoring of the individuals’CW. Finally, it focuses on the impact of artificial intelligence methods in the physiological signals data analysis to provide models of the CW to be exploited in HMI. Results: the review provided a comprehensive evaluation of the wearables, physiological signals, and methods of data analysis for CW evaluation in HMI. Conclusion: the literature highlighted the feasibility of employing wearable sensors to collect physiological signals for an ecological CW monitoring in HMI scenarios. However, challenges remain in standardizing these measures across different populations and contexts.

Application of form honeypot based on CSS style in ticket booking system

Since various means of transportation and entertainment activities are restricted by objective conditions, the number of tickets is always limited, so ticket scalpers continue to be a companion product to this day. In the current Internet environment, the web application of the ticketing system faces challenges from ticket-grabbing robots controlled by ticket dealers. The current security procedures have significantly increased the workload and time consumption of effective users. The question now is whether ticket-grabbing bots can be stopped without increasing the workload of legitimate users and being as unnoticed by users as possible. In response to this problem, this article proposes a ticket booking system model integrated with a form-based honeypot, which can achieve the function of deceiving robots without being noticed by ordinary users. This model provides a feasible method for implementing network applications that are both secure and efficient, ensuring that users have a more seamless and protected experience.

The Proposed IT-TALB in Infrastructure as a Service Cloud

Objectives: The purpose of the proposed IT-TALB load balancing algorithm is to dynamically allocate the user's workload to the appropriate virtual machine in an Infrastructure as a Service (IaaS) cloud environment. Methods: This research work includes several key procedures. The user's workloads are distributed to the data center controller (DCC), which in turn uses the ECO-SBP service broker policy to select the efficient data center (DC) for processing the loads. The DCC forwards the load to the selected DC, and the IT-TALB load balancer picks the best Virtual Machine (VM) using CloudAnalyst simulation tool for load allocations according to metrics such as its size, current number of loads, and load size. IT-TALB partitions the available and busy VMs separately and stores them in the TreeMap structure. This algorithm also incorporates the scalability of the given VM when the load size is not compatible with the existing VMs by extending the resources of underutilized VMs. Findings: The research finding demonstrates that the proposed IT-TALB algorithm improves IaaS cloud performance compared to the existing algorithms. It achieves optimum load balancing, reduces the searching time of the VM, avoids the load waiting time, improves throughput, minimizes the response time, and enhances the resource utilization ratio. IT-TALB yields a throughput and resource utilization ratio of 98 to 99 percent. Novelty: The novelty of this research is that the IT-TALB algorithm incorporates the scalability of the underutilized VM and also introduces new metrics such as throughput and resource utilization ratio in the CloudAnalyst simulation tool for assessing the performance of the proposed algorithm. This study provides information for analyzing the proposed IT-TALB strategies with the existing two algorithms such as TLB and TALB in order to show its performance. Keywords: Cloud Computing, Infrastructure as a Service, Load Balancing, Throttled Load Balancing, Virtual Machine

Attribute Based Signature Encryption Scheme Based on Cloud Computing in Medical Social Networks

The emergence of mobile medical social networks has provided great convenience for patients to communicate with each other about their medical conditions, promoting efficient and high-quality communication and exchange among patients. However, at the same time, it has also raised issues of confidentiality and privacy of patient data. In response to this issue, this article proposes a cloud-based attribute-based signature encryption scheme that can effectively protect the privacy of patient data. The patient encrypts their medical information and uploads it to the cloud server. When the data user wants to access the patient’s information, the cloud server helps the data user partially decrypt and verify the integrity of the data, which to some extent reduces the computational workload of the data user. Meanwhile, under the random oracle model, it has been proven that the scheme satisfies the unforgeability under selective message attacks, indistinguishability under selective cipher text attacks, and attribute privacy security. Theoretical analysis and numerical simulation experiments show that this scheme has higher efficiency than existing schemes in the signing and decryption stages.

Efficient criticality oriented service brokering policy in cloud datacenters

Cloud service provider (CSP) offers a huge number of datacenters and virtual servers to the users for processing their workloads in an infrastructure as a service (IaaS) cloud computing environment. Due to the heterogeneous volume of these resources and the immense number of user workloads arriving simultaneously in the cloud, it is necessary to use an effective load distribution technique for scheduling the resources to achieve high performance and high user satisfaction. Service brokering policy and load balancing techniques are the two crucial areas to be focused on while selecting the datacenters and virtual machines, respectively. In this study, we have proposed a dynamic efficient criticality-oriented service brokering policy for load allocations among datacenters by considering task criticality, datacenter proximity, and traffic, the size of the datacenter, its present load and makespan value. The proposed methodology is examined against the current policies in the CloudAnalyst simulation tool and the analysis report confirms that our proposed policy gives priority to processing the urgent loads and chooses the optimum datacenter to diminish the load response time, datacenter processing time, minimizes the cost, achieves optimum resource utilization and workload balancing among resources.

A Software for Optimum Design of Laterally Loaded Bolted Joints

Bolted joints, which are one of the detachable joining methods, are used extensively. The reliability of bolted joints is extremely important for the strength and life of the system. The determination of the number of bolts to be used in a system, the position and other data of the bolt requires very serious design and engineering studies. In this study, the design and optimization of bolted joints subjected to lateral forces are performed. For this purpose, a software has been developed in C# programming language. The software visually guides the user and asks a minimum number of questions to the designer and all other parameters are calculated by the program. The visual design of the program was done in Visual Studio environment. The graphic designs used in the software help the user to enter correct data. When the program is run for bolted joint under lateral loading, the user only enters the number of plates and force as input. Although the program performs its calculations between 1.5-2 factor of safety, it is possible to change it according to the characteristics of the design. The program developed in the light of these inputs calculates the appropriate bolt diameter, number, material and dimensions of the plates for the designed joint according to the optimum design options. The design options determined by the program are also presented to the designer visually. With the developed software, the user workload is minimized by determining the optimum options of bolted joints according to the minimum amount of user input. This resulted in significant savings in the time spent in design and engineering calculations. The user-friendly interface provides an easy and visual use. The results obtained by the program were also solved manually to check the reliability of the program.

A self-stabilizing and auto-provisioning orchestration for microservices in edge-cloud continuum

Modern user-facing services are progressively evolving from large monolithic applications to complex graphs of loosely-coupled microservices. While this shift provides opportunities to offload some microservices of a user-facing service to edge devices that are close to the end users, it also complicates the application deployment and resource provisioning in the edge-cloud continuum, due to complex relationships across microservices and unstable public networks. To reduce resource wastage and improve user experience, this paper presents SMO, a self-managed orchestration system for microservices. SMO leverages a self-stabilizing placement mechanism to optimally deploy microservices through perceiving both interferences and communication overheads. During runtime, it further tailors a multi-agent deep deterministic policy gradient (MADDPG)-based model in combination with attention and prioritized replay, which automatically provisions appropriate resources for each microservice subject to the differentiated tail latency Service Level Objectives (SLOs) of multiple user workloads. Experimental results demonstrate that SMO saves up to 39% of CPU cores and 47% of memory footprint while providing guarantees for heterogeneous tail latency SLOs.

OptiMS: An Accessible Program for Automating Mass Spectrometry Parameter Optimization and Configuration.

Mass spectrometers have an enormous number of user-changeable parameters that drastically affect the observed mass spectrum. Using optimal parameters can significantly improve mass spectrometric data by increasing signal stability and signal-to-noise ratio, which decreases the limit of detection, thus revealing previously unobservable species. However, ascertaining optimal parameters is time-consuming, tedious, and made further challenging by the fact that parameters can act dependently on each other. Consequently, suboptimal parameters are frequently used during characterization, reducing the quality of results. OptiMS, an open-source, cross-platform program, was developed to simplify, accelerate, and more accurately determine optimal mass spectrometer parameters for a given system. It addresses common difficulties associated with existing software such as slow performance, high costs, and limited functionality. OptiMS efficacy was demonstrated through its application to multiple systems, quickly and successfully optimizing instrument parameters unassisted to maximize a user-defined metric, such as the intensity of a particular analyte. Additionally, among other features, OptiMS allows running of a sequence of predefined parameter configurations, reducing the workload of users wishing to obtain mass spectra under multiple sets of conditions.

Synphage: a pipeline for phage genome synteny graphics focused on gene conservation.

Visualization and comparison of genome maps of bacteriophages can be very effective, but none of the tools available on the market allow visualization of gene conservation between multiple sequences at a glance. In addition, most bioinformatic tools running locally are command line only, making them hard to setup, debug, and monitor. To address these motivations, we developed synphage, an easy-to-use and intuitive tool to generate synteny diagrams from GenBank files. This software has a user-friendly interface and uses metadata to monitor the progress and success of the data transformation process. The output plot features colour-coded genes according to their degree of conservation among the group of displayed sequences. The strength of synphage lies also in its modularity and the ability to generate multiple plots with different configurations without having to re-process all the data. In conclusion, synphage reduces the bioinformatic workload of users and allows them to focus on analysis, the most impactful area of their work. The synphage tool is implemented in the Python language and is available from the GitHub repository at https://github.com/vestalisvirginis/synphage. This software is released under an Apache-2.0 licence. A PyPI synphage package is available at https://pypi.org/project/synphage/ and a containerized version is available at https://hub.docker.com/r/vestalisvirginis/synphage. Contributions to the software are welcome whether it is reporting a bug or proposing new features and the contribution guidelines are available at https://github.com/vestalisvirginis/synphage/blob/main/CONTRIBUTING.md.

Towards Semi-Autonomous Colon Screening Using an Electromagnetically Actuated Soft-Tethered Colonoscope Based on Visual Servo Control.

Conventional colonoscopy using a flexible colonoscope remains two major limitations, including patient discomfort and difficult manipulations for surgeons. Robotic colonoscopes have been developed to conduct colonoscopy in a patient-friendly manner. However, most robotic colonoscopes still maintain nonintuitive and difficult manipulations, which limits their clinical applications. In this paper, we demonstrated visual servo-based semi-autonomous manipulations of an electromagnetic actuated soft-tethered (EAST) colonoscope, which aims to lower difficulties of robotic colonoscope manipulations. Kinematic modeling of EAST colonoscope is conducted, with an adaptive visual servo controller established. Template matching method and a lumen and polyp detection model are developed to enable semi-autonomous manipulations, including region-of-interest automatic tracking and autonomous navigation with automatic polyp detection. The EAST colonoscope demonstrates visual servoing with an average convergence time of around 2.5 s and performs disturbance rejection within 3.0 s. Semi-autonomous manipulations were conducted in both a commercialized colonoscopy simulator and an ex-vivo porcine colon to show the efficacy of reducing the user workload compared to manual control. The EAST colonoscope can perform visual servoing and semi-autonomous manipulations with the developed methods in both laboratory and ex-vivo environments. The proposed solutions and techniques improve the autonomy level of robotic colonoscopes and reduce user workloads, which promotes the development and clinical translation of robotic colonoscopy.

Social Cost Analysis of Shared/Buy-in Computing Systems

Shared/buy-in computing systems offer users the option to select between buy-in and shared services. In such systems, idle buy-in resources are made available to other users for sharing. With strategic users, resource purchase and allocation in such systems can be cast as a non-cooperative game, whose corresponding Nash equilibrium does not necessarily result in the optimal social cost. In this study, we first derive the optimal social cost of the game in closed form, by casting it as a convex optimization problem and establishing related properties. Next, we derive a closed-form expression for the social cost at the Nash equilibrium, and show that it can be computed in linear time. We further show that the strategy profiles of users at the optimum and the Nash equilibrium are directly proportional. We measure the inefficiency of the Nash equilibrium through the price of anarchy, and show that it can be quite large in certain cases, e.g., when the operating expense ratio is low or when the distribution of user workloads is relatively homogeneous. To improve the efficiency of the system, we propose and analyze two subsidy policies, which are shown to converge using best-response dynamics.

The globus compute dataset: An open function-as-a-service dataset from the edge to the cloud

We present a unique function-as-a-service (FaaS) dataset capturing the use of the Globus Compute (previously funcX) platform. Globus Compute implements a federated model via which users may deploy endpoints on arbitrary remote computers, from the edge to high performance computing (HPC) cluster, and they may then invoke Python functions on those endpoints via a reliable cloud-hosted service. The dataset covers 31 weeks and includes 2121472 task submissions from 252 users executed on 580 remote computing endpoints. It includes 277386 registered functions. We describe the dataset and various observations, some that are similar to other FaaS datasets, for example, that 74% of tasks run for less than 1 s, and some that are unique to Globus Compute, for example, that endpoints are used in different ways and that the majority of functions are related to scientific computing and machine learning. To the best of our knowledge, this dataset represents the first federated FaaS dataset that includes user workloads, distributed computing endpoints, and analysis of registered function bodies. We expect the dataset to be useful for researching FaaS architectures, workload modeling, container warming, and other distributed computing architectures.

A multi-reservoir extruder for time-resolved serial protein crystallography and compound screening at X-ray free-electron lasers

Serial crystallography at X-ray free-electron lasers (XFELs) permits the determination of radiation-damage free static as well as time-resolved protein structures at room temperature. Efficient sample delivery is a key factor for such experiments. Here, we describe a multi-reservoir, high viscosity extruder as a step towards automation of sample delivery at XFELs. Compared to a standard single extruder, sample exchange time was halved and the workload of users was greatly reduced. In-built temperature control of samples facilitated optimal extrusion and supported sample stability. After commissioning the device with lysozyme crystals, we collected time-resolved data using crystals of a membrane-bound, light-driven sodium pump. Static data were also collected from the soluble protein tubulin that was soaked with a series of small molecule drugs. Using these data, we identify low occupancy (as little as 30%) ligands using a minimal amount of data from a serial crystallography experiment, a result that could be exploited for structure-based drug design.

An investigation into the effectiveness of using acoustic touch to assist people who are blind.

Wearable smart glasses are an emerging technology gaining popularity in the assistive technologies industry. Smart glasses aids typically leverage computer vision and other sensory information to translate the wearer's surrounding into computer-synthesized speech. In this work, we explored the potential of a new technique known as "acoustic touch" to provide a wearable spatial audio solution for assisting people who are blind in finding objects. In contrast to traditional systems, this technique uses smart glasses to sonify objects into distinct sound auditory icons when the object enters the device's field of view. We developed a wearable Foveated Audio Device to study the efficacy and usability of using acoustic touch to search, memorize, and reach items. Our evaluation study involved 14 participants, 7 blind or low-visioned and 7 blindfolded sighted (as a control group) participants. We compared the wearable device to two idealized conditions, a verbal clock face description and a sequential audio presentation through external speakers. We found that the wearable device can effectively aid the recognition and reaching of an object. We also observed that the device does not significantly increase the user's cognitive workload. These promising results suggest that acoustic touch can provide a wearable and effective method of sensory augmentation.

A step towards the final frontier: Lessons learned from acceptance testing of the first HPE/Cray EX 3000 system at ORNL

SummaryIn this article, we summarize the deployment of the Air Force Weather (AFW) HPC11 system at Oak Ridge National Laboratory (ORNL) including the process followed to successfully complete acceptance testing of the system. HPC11 is the first HPE/Cray EX 3000 system that has been successfully released to its user community in a federal facility. HPC11 consists of two identical 800‐node supercomputers, Fawbush and Miller, with access to two independent and identical lustre parallel file systems. HPC11 is equipped with Slingshot 10 interconnect technology and relies on the HPE Performance Cluster Manager software for system configuration. ORNL has a clearly defined acceptance testing process used to ensure that every new system deployed can provide the necessary capabilities to support user workloads. We worked closely with HPE and AFW to develop a set of tests that used the United Kingdom's Meteorological Office's Unified Model and 4‐dimensional variational data assimilation. We also included benchmarks and applications from the Oak Ridge Leadership Computing Facility portfolio to fully exercise the HPE/Cray programming environment and evaluate the functionality and performance of the system. Acceptance testing of HPC11 required parallel execution of each element on Fawbush and Miller. In addition, careful coordination was needed to ensure successful acceptance of the newly deployed lustre file systems alongside the compute resources. In this work, we present test results from specific system components and provide an overview of the issues identified, challenges encountered, and the lessons learned along the way.

Rectifying Skewed Kernel Page Reclamation in Mobile Devices for Improving User-Perceivable Latency

A crucial design factor for users of smart mobile devices is the latency of graphical interface interaction. Switching a background app to foreground is a frequent operation on mobile devices and the latency of this process is highly perceivable to users. Based on an Android smartphone, through analysis of memory reference generated during the app-switching process, we observe that file (virtual) pages and anonymous pages are both heavily involved. However, to our surprise, the amounts of the two types of pages in the main memory are highly imbalanced, and frequent I/O operations on file pages noticeably slows down the app-switching process. In this study, we advocate to improve the app-switching latency by rectifying the skewed kernel page reclaiming. Our approach involves two parts: proactive identification of unused anonymous pages and adaptive balance between file pages and anonymous pages. As mobile apps are found inflating their anonymous pages, we propose identifying unused anonymous pages in sync with the app-switching events. In addition, Android devices replaces the swap device with RAM-based zram, and swapping on zram is much faster than file accessing on flash storage. Without causing thrashing, we propose swapping out as many anonymous pages to zram as possible for caching more file pages. We conduct experiments on a Google Pixel phone with realistic user workloads, and results confirm that our method is adaptive to different memory requirements and greatly improves the app-switching latency by up to 43% compared with the original kernel.

Energy-efficient virtual machine placement in distributed cloud using NSGA-III algorithm

Cloud computing is the most widely adapted computing model to process scientific workloads in remote servers accessed through the internet. In the IaaS cloud, the virtual machine (VM) is the execution unit that processes the user workloads. Virtualization enables the execution of multiple virtual machines (VMs) on a single physical machine (PM). Virtual machine placement (VMP) strategically assigns VMs to suitable physical devices within a data center. From the cloud provider's perspective, the virtual machine must be placed optimally to reduce resource wastage to aid economic revenue and develop green data centres. Cloud providers need an efficient methodology to minimize resource wastage, power consumption, and network transmission delay. This paper uses NSGA-III, a multi-objective evolutionary algorithm, to simultaneously reduce the mentioned objectives to obtain a non-dominated solution. The performance metrics (Overall Nondominated Vector Generation and Spacing) of the proposed NSGA-III algorithm is compared with other multi-objective algorithms, namely VEGA, MOGA, SPEA, and NSGA-II. It is observed that the proposed algorithm performs 7% better that the existing algorithm in terms of ONVG and 12% better results in terms of spacing. ANOVA and DMRT statistical tests are used to cross-validate the results.

Introducing an adaptive model forauto‐scalingcloud computing based on workload classification

SummaryWith the increasing expansion of cloud computing services, one of the main goals of researchers is to solve its major challenges. Cloud service providers must satisfy the service level agreement for customers and prevent resource wastage as much as possible. Without a precise, optimal, and dynamic policy, this is unattainable. The key idea is the ability to acquire resources as you need them and release resources when you no longer need them, named “Cloud Elasticity.” Elasticity is a trade‐off between resource acquisition and release, and if this optimization is done best, the service level agreement will be fully achieved and the cloud provider will have the least waste of resources. The researchers used machine learning techniques to predict user workload and decide to scale up/out the resources. A challenging issue is the different characteristics of the users' workloads. The results show that each prediction algorithm works well on a class of users' workloads not all. Hence, in this study, a new architecture has been suggested to automatically classify the workloads based on their sequential statistical characteristics. First, the sequential statistical characteristics of the users' workload are extracted and then a trained neural network classifies the user's workload. The developed adaptive model chooses the best suitable algorithm among LR, SVM, and ARIMA to predict the workload. The results indicate a 10% improvement in forecast error.

Prioritization Based Task Offloading in UAV-Assisted Edge Networks

Under demanding operational conditions such as traffic surges, coverage issues, and low latency requirements, terrestrial networks may become inadequate to provide the expected service levels to users and applications. Moreover, when natural disasters or physical calamities occur, the existing network infrastructure may collapse, leading to formidable challenges for emergency communications in the area served. In order to provide wireless connectivity as well as facilitate a capacity boost under transient high service load situations, a substitute or auxiliary fast-deployable network is needed. Unmanned Aerial Vehicle (UAV) networks are well suited for such needs thanks to their high mobility and flexibility. In this work, we consider an edge network consisting of UAVs equipped with wireless access points. These software-defined network nodes serve a latency-sensitive workload of mobile users in an edge-to-cloud continuum setting. We investigate prioritization-based task offloading to support prioritized services in this on-demand aerial network. To serve this end, we construct an offloading management optimization model to minimize the overall penalty due to priority-weighted delay against task deadlines. Since the defined assignment problem is NP-hard, we also propose three heuristic algorithms as well as a branch and bound style quasi-optimal task offloading algorithm and investigate how the system performs under different operating conditions by conducting simulation-based experiments. Moreover, we made an open-source contribution to Mininet-WiFi to have independent Wi-Fi mediums, which were compulsory for simultaneous packet transfers on different Wi-Fi mediums.