Task Completion Time Research Articles

User Experience Study on Self-Ticketing Machine in Cinema

Self-ticketing machines have been implemented in cinemas to alleviate long queues during ticket purchases. However, their adoption has highlighted significant usability challenges for many users. This study aims to identify these obstacles and assess user experiences with self-ticketing machines to inform design improvements. This research employed a mixed-method approach involving usability testing and the administration of a User Experience Questionnaire (UEQ) among 125 respondents familiar with self-ticketing machines. Participants were tasked with purchasing tickets, printing them, and accessing transaction history through the machines. The findings revealed that interface complexities significantly impacted task completion times. Moreover, low scores in the stimulation domain of the UEQ indicated a lack of attractiveness and motivation for users. Based on these insights, recommendations include enhancing the interface design to improve intuitiveness, visual appeal, and user-friendliness. Such improvements aim to enhance user satisfaction and streamline the ticketing process, ultimately benefiting both cinemas and patrons.

IMG-32. USER EVALUATION OF AN AI-POWERED WEB APPLICATION TO SUPPORT RADIOGRAPHIC INTERPRETATION OF PATIENTS WITH ADAMANTINOMATOUS CRANIOPHARYNGIOMA

Abstract BACKGROUND In neuro-oncology, accurately interpreting radiographic images, especially for diagnosing central nervous system (CNS) tumors like Adamantinomatous Craniopharyngioma (ACP), presents formidable challenges. Even with advanced imaging technologies such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT), pinpointing features related to ACP invasiveness remains daunting. METHODS We introduced an interactive web application that utilizes a custom AI model to analyze preoperative MRI and CT images of 50 patients with ACP. This application can predict a 12-point radiographic feature profile and rank patients based on similarity, aiming to support clinicians in making more precise diagnoses. We deployed our application on a hospital-grade radiographic reading terminal. Our application’s utility and user-friendliness were evaluated through a user study involving four neurosurgeons and two neuroradiologists, who constructed 12-point radiographic feature profiles for ten patients within a 30-minute timeframe given patient MRI and CT images. RESULTS Our study subjects reported high self-assessed numeracy (SNS scores ranging from 4 to 5.25) despite varied preferences in engaging with numerical data. Subjects could efficiently learn to navigate our software interface, with an average task completion time of approximately 3 minutes and 25 seconds per patient. Introducing AI into the task workflow resulted in a slight, statistically non-significant increase in completion time, indicating that while AI modifies interaction dynamics, it does not significantly hinder efficiency in task performance. We observed distinct agreement patterns between clinicians and AI prediction, utilizing Fleiss’ Kappa for categorization. AI assistance subtly boosted clinicians’ confidence and slightly lessened the perceived difficulty of interpreting radiographic images. However, these benefits were not statistically significant. CONCLUSIONS Our study highlights the promise of AI in improving neuro-oncology diagnostics, showing that clinicians quickly adapted to an AI-enhanced application, which subtly influenced their radiographic interpretation. This finding underscores the potential for AI to enhance decision-making accuracy in challenging cases like ACP.

LIDON: Loopback interconnect datacenter optical network

A loopback interconnect datacenter optical network (LIDON) is proposed for switching in datacenters with commercial devices. The intra- and inter-Pod switching in datacenters is accomplished with the Rack Expansion Module (REM) and the Loopback Interconnect Module (LIM) operating collectively. By developing the new functions of diagnostic ports of a wavelength selective switch, LIDON boasts several features, such as high path diversity and robust tolerance. Moreover, the scale of LIDON can be expanded by the proposed scheme to support the flexible switching volume. The feasibility of dividing intra- and inter-Pod signals was demonstrated by the filtering performance experiments. The simulations of the task completion time (TCT) for different services bore witness to the robust fault tolerance of LIDON.

Efficient VR-AR communication method using virtual replicas in XR remote collaboration

When using Virtual Reality (VR) and Augmented Reality (AR) to support remote collaboration, effective communication between a remote expert in VR and a local worker in AR is important for guiding and following task instructions. This is especially crucial for assembly tasks, which require precise identification of parts and clear directions for their combination. Despite the increasing interest in efficient VR-AR communication methods, previous studies have been limited to complex hardware setups and simplified assembly tasks. In this research, we introduce a communication approach for remote collaboration in complex assembly tasks, utilizing simplified hardware configurations. We conducted a user study (n=30) and compared three interaction interfaces (hand gestures, 3D drawing, and virtual replicas) in task completion time, subjective questionnaires, and preference rank. The results showed that the use of virtual replicas not only enhances task efficiency but also receives strong preference by users. These findings indicate that virtual replicas can provide intuitive instructions to local workers, resulting in a clearer understanding of the expert’s guidance.

Viewpoint-sharing method with reduced motion sickness in object-based VR/AR collaborative virtual environment

We propose a viewpoint-sharing method with reduced motion sickness in an object-based remote collaborative virtual environment (CVE). The method is designed with an assumption of asymmetric, object-based CVE where collaborators use non-homogeneous devices, such as immersive virtual reality head-mounted display (VR HMD) and tablet-based augmented reality (AR), and simultaneously interact with 3D virtual objects. Therefore, collaborators interact with different interfaces such as virtual reality (VR) users relying on controllers for virtual locomotion and object manipulation, while AR users perform physical locomotion and multi-touch input for object manipulation. The proposed viewpoint-sharing method allows both users to observe and manipulate the objects in interest from the shared point of view, enabling participants to interact with the objects without the need for virtual/physical locomotion. While viewpoint-sharing, instead of changing point of view, the proposed method performs seamless object transformation to provide a shared point of view, reducing motion sickness and associated discomfort. From our user experiment, the viewpoint-share condition resulted in a 35.47% faster task completion time than the baseline condition which is without proposed viewpoint-sharing. The advantage of viewpoint-sharing regarding system usability was significant, while task workloads were similar in the baseline and viewpoint-sharing conditions. We expect that the proposed viewpoint-sharing method allows users to quickly, efficiently, and collaboratively communicate in an object-based CVE, and represents a step forward in the development of effective remote, asymmetric CVE.

Effects of Feedback Type on Touchscreen Navigation for Younger and Older Users

Abstract This study investigates how feedback from different modalities affect window navigation tasks performed on a touchscreen by younger and older users. Thirty-two younger and 32 older users moved a window toward one of four or eight target areas with no feedback or visual, tactile or visual–tactile feedback. Visual feedback produced significantly higher accuracy and shorter task completion times than tactile feedback. Generally, visual–tactile feedback did not produce statistically higher accuracy or shorter task completion time than visual feedback. However, visual–tactile feedback prevented both younger and older users from overshooting the windows. Additionally, when users attempted to move the window to one of the eight target areas, the visual–tactile feedback helped younger users perform better. According to theories about human processing, the reason for the subtle advantage of visual–tactile feedback is that the unintuitive tactile force induces a higher cognitive load than straightforward visual information during window navigation. Nevertheless, visual–tactile feedback elicited the best user experience, as measured by questionnaires on emotion and fatigue, gesture usability and preference. Furthermore, more older users prefer visual–tactile feedback than younger users. Therefore, visual–tactile feedback should be considered, and the tactile feedback should be modified to be more intuitive in window navigation tasks. Research Highlights Visual feedback improved task performance more than tactile feedback in window navigation task. Visual–tactile feedback did not improve task performance compared to visual feedback. Visual–tactile feedback prevented users from overshooting the window. More older users than younger users prefer visual–tactile feedback.

Swiss Round Selection Algorithm for Multi-Robot Task Scheduling

Efficient and stable control and task assignment optimization in electronic commerce logistics and warehousing systems involving multiple robots executing multiple tasks is highly challenging. Hence, this paper proposes a Swiss round selection algorithm for multi-robot task allocation to address the challenges mentioned. Firstly, based on the shipping process of electronic commerce logistics and warehousing systems, the tasks are divided into packaging and sorting stages, and a grid model for the electronic commerce warehousing system is established. Secondly, by increasing the probabilities of crossover and mutation in the population and adopting a full crossover and full mutation approach, the search scope of the population is expanded. Then, a Swiss round selection mechanism with burst probability is proposed, which ensures the smooth inheritance of high-quality individuals while improving the diversity of the population. Finally, 12 comparative experiments are designed with different numbers of robots and tasks. The experimental results demonstrate that the Swiss round selection algorithm outperforms the genetic algorithm in terms of maximum task completion time and convergence time to reach the optimal value. Thus, the effectiveness of the Swiss round selection algorithm in solving the multi-robot task allocation problem is verified.

Multi-objective hybrid optimized task scheduling in cloud computing under big data perspective

The new and rising paradigm of cloud computing offers customers various possibilities of task computation based on their desires and choices. Customers receive services from cloud computing systems as a utility. Customers are enthusiastic about low-cost service availability and task completion times that are kept to be minimum. To achieve client fulfilment, the service provider must schedule the jobs to the right resources if the cloud server gets many user requests. The rapid growth in data volume necessitates petabytes processing of data each day. Unstructured, semi-structured, and structured data are all described in terms of their rapid growth and availability. In order to make correct and timely decisions, it must be processed appropriately. In this research, we present BWUJS (Black Widow Updated Jellyfish Search), a multi-objective hybrid optimization-based task scheduling algorithm. This work considers task generation from the Bigdata perspective. The clustering of tasks is performed via the Map Reduce framework with an Improved K-means clustering model. After task clustering, the task priority estimation is performed. Finally, the scheduling is performed via BWJSU based on certain constraints like priority, makespan, completion time, resource utilization, and degree of imbalance.

A crew task allocation and sequencing method considering workload distribution and minimum task completion time

A crew task allocation and sequencing method considering workload distribution and minimum task completion time

Usability Analysis of the Ronnie Baugh (Oggun II) Tractor Using the Single Usability Metric

Agricultural mechanization is one of the policies created by the Philippine government to alleviate systemic problems facing the country. In 2020, a local company was licensed to manufacture the American-designed Ronnie Baugh tractor (RBT), which aims to enable smaller-scale production with low-cost and customizable features. This study assessed the usability of the RBT and evaluated the appropriateness of its design for Filipino tractor operators using the single usability metric (SUM). A total of 21 participants were classified according to their age and experience (A – young and experienced, B – old and experienced, and C – young and inexperienced) and were given driving tasks. Task completion, completion time, error rate, and satisfaction were then measured and averaged, leading to a computed SUM result of 64.69%. This value translates into a high marginal experience under the System Usability Scale (SUS) with a D-grade scale, which implies that the design is still unacceptable and may be further improved to ensure satisfaction among users. Moreover, a singular assessment of the four metrics showed that (1) 100% task completion was attained, (2) task performance and satisfaction did not differ between the experienced and inexperienced participants, and (3) the error rate had an erratic behavior and a contradicting result as compared to empirical evidence. Overall, the usability of the RBT might influence user dissatisfaction and its economic and practical impact on integrations. The existing design of RBT also poses an opportunity for localization, considering that it is more economical to manufacture.

Developing a Cost-Effective Surgical Scheduling System Applying Lean Thinking and Toyota's Methods for Surgery-Related Big Data for Improved Data Use in Hospitals: User-Centered Design Approach.

Surgical scheduling is pivotal in managing daily surgical sequences, impacting patient experience and hospital resources significantly. With operating rooms costing approximately US $36 per minute, efficient scheduling is vital. However, global practices in surgical scheduling vary, largely due to challenges in predicting individual surgeon times for diverse patient conditions. Inspired by the Toyota Production System's efficiency in addressing similar logistical challenges, we applied its principles as detailed in the book "Lean Thinking" by Womack and Jones, which identifies processes that do not meet customer needs as wasteful. This insight is critical in health care, where waste can compromise patient safety and medical quality. This study aims to use lean thinking and Toyota methods to develop a more efficient surgical scheduling system that better aligns with user needs without additional financial burdens. We implemented the 5 principles of the Toyota system: specifying value, identifying the value stream, enabling flow, establishing pull, and pursuing perfection. Value was defined in terms of meeting the customer's needs, which in this context involved developing a responsive and efficient scheduling system. Our approach included 2 subsystems: one handling presurgery patient data and another for intraoperative and postoperative data. We identified inefficiencies in the presurgery data subsystem and responded by creating a comprehensive value stream map of the surgical process. We developed 2 Excel (Microsoft Corporation) macros using Visual Basic for Applications. The first calculated average surgery times from intra- or postoperative historic data, while the second estimated surgery durations and generated concise, visually engaging scheduling reports from presurgery data. We assessed the effectiveness of the new system by comparing task completion times and user satisfaction between the old and new systems. The implementation of the revised scheduling system significantly reduced the overall scheduling time from 301 seconds to 261 seconds (P=.02), with significant time reductions in the revised process from 99 seconds to 62 seconds (P<.001). Despite these improvements, approximately 21% of nurses preferred the older system for its familiarity. The new system protects patient data privacy and streamlines schedule dissemination through a secure LINE group (LY Corp), ensuring seamless flow. The design of the system allows for real-time updates and has been effectively monitoring surgical durations daily for over 3 years. The "pull" principle was demonstrated when an unplanned software issue prompted immediate, user-led troubleshooting, enhancing system reliability. Continuous improvement efforts are ongoing, except for the preoperative patient confirmation step, which requires further enhancement to ensure optimal patient safety. Lean principles and Toyota's methods, combined with computer programming, can revitalize surgical scheduling processes. They offer effective solutions for surgical scheduling challenges and enable the creation of a novel surgical scheduling system without incurring additional costs.

Mitigating Trunk Compensatory Movements in Post-Stroke Survivors through Visual Feedback during Robotic-Assisted Arm Reaching Exercises.

Trunk compensatory movements frequently manifest during robotic-assisted arm reaching exercises for upper limb rehabilitation following a stroke, potentially impeding functional recovery. These aberrant movements are prevalent among stroke survivors and can hinder their progress in rehabilitation, making it crucial to address this issue. This study evaluated the efficacy of visual feedback, facilitated by an RGB-D camera, in reducing trunk compensation. In total, 17 able-bodied individuals and 18 stroke survivors performed reaching tasks under unrestricted trunk conditions and visual feedback conditions. In the visual feedback modalities, the target position was synchronized with trunk movement at ratios where the target moved at the same speed, double, and triple the trunk's motion speed, providing real-time feedback to the participants. Notably, trunk compensatory movements were significantly diminished when the target moved at the same speed and double the trunk's motion speed. Furthermore, these conditions exhibited an increase in the task completion time and perceived exertion among stroke survivors. This outcome suggests that visual feedback effectively heightened the task difficulty, thereby discouraging unnecessary trunk motion. The findings underscore the pivotal role of customized visual feedback in correcting aberrant upper limb movements among stroke survivors, potentially contributing to the advancement of robotic-assisted rehabilitation strategies. These insights advocate for the integration of visual feedback into rehabilitation exercises, highlighting its potential to foster more effective recovery pathways for post-stroke individuals by minimizing undesired compensatory motions.

Towards optimized scheduling and allocation of heterogeneous resource via graph-enhanced EPSO algorithm

Efficient allocation of tasks and resources is crucial for the performance of heterogeneous cloud computing platforms. To achieve harmony between task completion time, device power consumption, and load balance, we propose a Graph neural network-enhanced Elite Particle Swarm Optimization (EPSO) model for collaborative scheduling, namely GraphEPSO. Specifically, we first construct a Directed Acyclic Graph (DAG) to model the complicated tasks, thereby using Graph Neural Network (GNN) to encode the information of task sets and heterogeneous resources. Then, we treat subtasks and independent tasks as basic task units while considering virtual or physical devices as resource units. Based on this, we exploit the performance adaptation principle and conditional probability to derive the solution space for resource allocation. Besides, we employ EPSO to consider multiple optimization objectives, providing fine-grained perception and utilization of task and resource information. It also increases the diversity of particle swarms, allowing GraphEPSO to adaptively search for the global optimal solution with the highest probability. Experimental results demonstrate the superiority of our proposed GraphEPSO compared to several state-of-the-art baseline methods on all evaluation metrics.

Cost-aware quantum-inspired genetic algorithm for workflow scheduling in hybrid clouds

Cloud computing delivers a desirable environment for users to run their different kinds of applications in a cloud. Numerous of these applications (tasks), such as bioinformatics, astronomy, biodiversity, and image analysis, are deadline-sensitive. Such tasks must be properly allocated to virtual machines (VMs) to avoid deadline violations, and they should reduce their execution time and cost. Due to the contradictory environment, minimizing the application task's completion time and execution cost is extremely difficult. Thus, we propose a Cost-aware Quantum-inspired Genetic Algorithm (CQGA) to minimize the execution time and cost by meeting the deadline constraints. CQGA is motivated by quantum computing and genetic algorithm. It combines quantum operators (measure, interference, and rotation) with genetic operators (selection, crossover, and mutation). Quantum operators are used for better population diversity, quick convergence, time-saving, and robustness. Genetic operators help to produce new individuals, have good fitness values for individuals, and play a significant role in preserving the evolution quality of the population. In addition, CQGA used a quantum bit as a probabilistic representation because it has higher population diversity attributes than other representations. The simulation outcome exhibits that the proposed algorithm can obtain outstanding convergence performance and reduced maximum cost than benchmark algorithms.

A multifaceted suite of metrics for comparative myoelectric prosthesis controller research.

Upper limb robotic (myoelectric) prostheses are technologically advanced, but challenging to use. In response, substantial research is being done to develop person-specific prosthesis controllers that can predict a user's intended movements. Most studies that test and compare new controllers rely on simple assessment measures such as task scores (e.g., number of objects moved across a barrier) or duration-based measures (e.g., overall task completion time). These assessment measures, however, fail to capture valuable details about: the quality of device arm movements; whether these movements match users' intentions; the timing of specific wrist and hand control functions; and users' opinions regarding overall device reliability and controller training requirements. In this work, we present a comprehensive and novel suite of myoelectric prosthesis control evaluation metrics that better facilitates analysis of device movement details-spanning measures of task performance, control characteristics, and user experience. As a case example of their use and research viability, we applied these metrics in real-time control experimentation. Here, eight participants without upper limb impairment compared device control offered by a deep learning-based controller (recurrent convolutional neural network-based classification with transfer learning, or RCNN-TL) to that of a commonly used controller (linear discriminant analysis, or LDA). The participants wore a simulated prosthesis and performed complex functional tasks across multiple limb positions. Analysis resulting from our suite of metrics identified 16 instances of a user-facing problem known as the "limb position effect". We determined that RCNN-TL performed the same as or significantly better than LDA in four such problem instances. We also confirmed that transfer learning can minimize user training burden. Overall, this study contributes a multifaceted new suite of control evaluation metrics, along with a guide to their application, for use in research and testing of myoelectric controllers today, and potentially for use in broader rehabilitation technologies of the future.

CrowdBot: An Open-Environment Robot Management System for On-Campus Services

In contemporary campus environments, the provision of timely and efficient services is increasingly challenging due to limitations in accessibility and the complexity and openness of the environment. Existing service robots, while operational, often struggle with adaptability and dynamic task management, leading to inefficiencies. To overcome these limitations, we introduce CrowdBot, a robot management system that enhances service in campus environments. Our system leverages a hierarchical reinforcement learning-based cloud-edge hybrid scheduling framework (REDIS), for efficient online streaming task assignment and dynamic action scheduling. To verify the REDIS framework, we have developed a digital twin simulation platform, which integrates large language models and hot-swapping technology. This facilitates seamless human-robot interaction, efficient task allocation, and cost-effective execution through the reuse of robot equipment. Our comprehensive simulations corroborate the system's remarkable efficacy, demonstrating significant improvements with a 24.46% reduction in task completion times, a 9.37% decrease in travel distances, and up to a 3% savings in power usage. Additionally, the system achieves a 7.95% increase in the number of tasks completed and a 9.49% reduction in response time. Real-world case studies further affirm CrowdBot's capability to adeptly execute tasks and judiciously recycle resources, thereby offering a smart and viable solution for the streamlined management of campus services.

TwI-FTM: Two-way IoT-FoG trust management scheme for task offloading in IoT-FoG networks

Based on trust management, the service establishment between IoT/User devices and fog networks reduces the impact of malicious data and nodes. Hence, building a trusted environment in the IoT and fog layers is very important. There is no assurance that the generated data or task is standard data; it may also have abnormal data to modify its originality. Similarly, the nodes in the fog environment also have malicious nodes during task offloading. For this, the novel Two-way IoT data & Fog Trust management (TwI-FTM) algorithm is proposed to identify the abnormal data with the help of the Trusted Data Measurement (TDM) and Fog Access Trust Evaluation (FATE) method for identifying the malicious fog access nodes in fog network. Identifying these abnormal data and malicious fog access nodes is done by continuously monitoring fog control nodes with the threshold level. Once it is identified, they will automatically be removed from the IoT-FoG network. The obtained trust measurements are analyzed and compared with other existing methods; it is identified that the proposed TwI-FTM outperforms and has a higher trust degree of 0.68 compared with others. It achieved higher detection accuracy of malicious fog access nodes in fog networks at 21 %, 16 %, and 14 % compared with OTM, TTM, and SLA-Trust, respectively. Finally, the task completion time was also reduced, and it was achieved at 4.5 ms, 5 ms, and 13 ms compared with TTM, OTM, and SLA-Trust, respectively.

PreVR: Variable-Distance Previews for Higher-Order Disocclusion in VR.

The paper introduces PreVR, a method for allowing the user of a VR application to preview a virtual environment (VE) around any number of corners. This way the user can gain line of sight to any part of the VE, no matter how distant or how heavily occluded it is. PreVR relies on a multiperspective visualization that implements a higher-order disocclusion effect with piecewise linear rays that bend multiple times as needed to reach the visualization target. PreVR was evaluated in a user study ($\mathrm{N}=88$) that investigates four points on the VR interface design continuum defined by the maximum disocclusion order $\delta$. In a first control condition (CC0), $\delta=0$, corresponds to conventional VR exploration with no preview capability. In a second control condition (CC1), $\delta=1$, corresponds to the prior art approach of giving the user a preview around the first corner. In a first experimental condition (EC3), $\delta=3$, so PreVR provided up to third-order disocclusion. In a second experimental condition (ECN), $\delta$ was not capped, so PreVR could provide a disocclusion effect of any order, as needed to reach any location in the VE. Participants searched for a stationary target, for a dynamic target moving on a random continuous trajectory, and for a transient dynamic target that appeared at random locations in the maze and disappeared 5s later. The study quantified VE exploration efficiency with four metrics: viewpoint translation, view direction rotation, number of teleportations, and task completion time. Results show that the previews afforded by PreVR bring a significant VE exploration efficiency advantage. ECN outperforms EC3, CC1, and CC0 for all metrics and all tasks, and EC3 frequently outperforms CC1 and CC0.

The Impact of Multimedia Feedback in Blended Learning Environments on University Students’ Programming Skills

This study investigates the impact of multimedia feedback on university students’ programming skills within a blended learning environment in the United Kingdom. A quasi-experimental design was employed, involving 120 undergraduate students divided into experimental and control groups. The experimental group received multimedia feedback, including screencast videos, annotated coding examples, and interactive quizzes, while the control group received traditional written feedback. Quantitative data from pre-tests and post-tests showed that the experimental group demonstrated a 33.3% increase in programming skills, compared to a 22.5% increase in the control group. The experimental group also exhibited a 35% reduction in errors and a 20% reduction in task completion time. Qualitative data from student interviews and open-ended survey responses revealed increased engagement, understanding, clarity, motivation, and confidence among students who received multimedia feedback. The study highlights the transformative potential of multimedia feedback in enhancing programming education and suggests practical implications for educators and policymakers. Recommendations for future research include exploring the long-term impact, scalability, and applicability of multimedia feedback across different educational contexts and disciplines.

A novel mixture model for characterizing human aiming performance data

Fitts’ law is often employed as a predictive model for human movement, especially in the field of human-computer interaction. Models with an assumed Gaussian error structure are usually adequate when applied to data collected from controlled studies. However, observational data (often referred to as data gathered ‘in the wild’) typically display noticeable positive skewness relative to a mean trend as users do not routinely try to minimize their task completion time. As such, the exponentially modified Gaussian (EMG) regression model has been applied to aimed movements data. However, it is also of interest to reasonably characterize those regions where a user likely was not trying to minimize their task completion time. In this article, we propose a novel model with a two-component mixture structure—one Gaussian and one exponential—on the errors to identify such a region. An expectation-conditional-maximization (ECM) algorithm is developed for estimation of such a model and some properties of the algorithm are established. The efficacy of the proposed model, as well as its ability to inform model-based clustering, are addressed in this work through extensive simulations and an insightful analysis of a human aiming performance study.