Task Completion Research Articles

Remote diagnostics of the parameters of the technical condition of agricultural machinery

The system of remote diagnostics of agricultural machinery for assessing the parameters of its technical condition is considered. In agriculture, time to complete tasks has a certain time limit, as the timing of fi eld operations is crucial. Recently, intelligent online technologies have attracted a lot of attention in the agricultural sector. It has been established that the most important means of increasing the efficiency of using tractors is the indiscriminate determination of the parameters of the technical condition of the machine by improving methods of its control and monitoring the quality of work on the basis of the introduction of modern online monitoring systems.

Cybernic robot hand-arm that realizes cooperative work as a new hand-arm for people with a single upper-limb dysfunction.

A robot hand-arm that can perform various tasks with the unaffected arm could ease the daily lives of patients with a single upper-limb dysfunction. A smooth interaction between robot and patient is desirable since their other arm functions normally. If the robot can move in response to the user's intentions and cooperate with the unaffected arm, even without detailed operation, it can effectively assist with daily tasks. This study aims to propose and develop a cybernic robot hand-arm with the following features: 1) input of user intention via bioelectrical signals from the paralyzed arm, the unaffected arm's motion, and voice; 2) autonomous control of support movements; 3) a control system that integrates voluntary and autonomous control by combining 1) and 2) to thus allow smooth work support in cooperation with the unaffected arm, reflecting intention as a part of the body; and 4) a learning function to provide work support across various tasks in daily use. We confirmed the feasibility and usefulness of the proposed system through a pilot study involving three patients. The system learned to support new tasks by working with the user through an operating function that does not require the involvement of the unaffected arm. The system divides the support actions into movement phases and learns the phase-shift conditions from the sensor information about the user's intention. After learning, the system autonomously performs learned support actions through voluntary phase shifts based on input about the user's intention via bioelectrical signals, the unaffected arm's motion, and by voice, enabling smooth collaborative movement with the unaffected arm. Experiments with patients demonstrated that the system could learn and provide smooth work support in cooperation with the unaffected arm to successfully complete tasks they find difficult. Additionally, the questionnaire subjectively confirmed that cooperative work according to the user's intention was achieved and that work time was within a feasible range for daily life. Furthermore, it was observed that participants who used bioelectrical signals from their paralyzed arm perceived the system as part of their body. We thus confirmed the feasibility and usefulness of various cooperative task supports using the proposed method.

Adaptive Privacy-Preserving Coded Computing with Hierarchical Task Partitioning.

Coded computing is recognized as a promising solution to address the privacy leakage problem and the straggling effect in distributed computing. This technique leverages coding theory to recover computation tasks using results from a subset of workers. In this paper, we propose the adaptive privacy-preserving coded computing (APCC) strategy, designed to be applicable to various types of computation tasks, including polynomial and non-polynomial functions, and to adaptively provide accurate or approximated results. We prove the optimality of APCC in terms of encoding rate, defined as the ratio between the computation loads of tasks before and after encoding, based on the optimal recovery threshold of Lagrange Coded Computing. We demonstrate that APCC guarantees information-theoretical data privacy preservation. Mitigation of the straggling effect in APCC is achieved through hierarchical task partitioning and task cancellation, which further reduces computation delays by enabling straggling workers to return partial results of assigned tasks, compared to conventional coded computing strategies. The hierarchical task partitioning problems are formulated as mixed-integer nonlinear programming (MINLP) problems with the objective of minimizing task completion delay. We propose a low-complexity maximum value descent (MVD) algorithm to optimally solve these problems. The simulation results show that APCC can reduce the task completion delay by a range of 20.3% to 47.5% when compared to other state-of-the-art benchmarks.

MoMa: An assistive mobile manipulator with a webcam-based gaze control system

Mobile Manipulators (MoMa) is a category of mobile robots designed to assist people with motor disabilities to perform object retrieval tasks using a webcam-based gaze control system. Using off-the-shelf components such as reproducible acrylic and 3D-printed plates, and a webcam for eye tracking, MoMa serves as an inexpensive, open-source, and customizable solution in assistive robotics. The robotic system consists of a mobile base that can move forward and backward, as well as turn in place; and a 2-axis cartesian arm equipped with a claw gripper that opens and closes. The simple movement of the robot also allows for a simple control method and graphical user interface (GUI). The user receives information about what is in front of the robot through a mounted camera, and, by looking at parts of the screen that correspond to controls, has their gaze predicted by a convolutional neural network and sends commands wirelessly. The performance of the entire system has been validated through testing of the gaze prediction model, the integration of the control system, as well as its task completion capabilities. All the design, construction and software files are freely available online under the CC BY 4.0 license at https://doi.org/10.17632/k7yfn6wdv7.2.

Monitoring system for physical activity and fitness based on service robots and biomechanics

Exercise is one of the important ways for people to exercise, with characteristics such as sociality and strong participation. Especially with the improvement of the level of economic development and the improvement of the quality of life of the people, more and more people begin to attach importance to the maintenance of their own health. Physical fitness monitoring, as an effective means, is widely used in daily life, especially among the elderly. However, most of the existing monitoring methods are relatively simple, lacking pertinence, and the data collection process is relatively cumbersome and unstable, which cannot meet current needs. Therefore, it is very necessary to explore a new type of equipment that can more comprehensively and accurately monitor various physiological parameters of the human body to replace existing traditional detection technologies. Service Robots are currently the most promising intelligent hardware products. They mainly provide personalized services centered on users, sensing user behavior and implementing intelligent decision-making based on their characteristics, thereby better meeting the needs of different groups of people. This article focused on the research and development of Service Robots, and designed a comprehensive solution for Service Robots based on theories such as the Internet of Things, cloud computing, big data technology, and artificial intelligence. This article compared existing intelligent monitoring systems with fitness monitoring systems based on Service Robots, and proved that the user experience of fitness monitoring with robot participation has improved by about 4.68%. Its application scenarios were richer and its effects were more significant, enabling it to better complete tasks such as analysis and prediction of physical fitness status, real-time warning, etc., reducing the risk of people suffering from diseases, and enhancing individual protection awareness.

The laughter effect: Enhancing cross-cultural learning and cohesiveness in a virtual environment

This practice report investigates the role of humor in enhancing cross-cultural learning and group cohesion in virtual education settings. Most collaborative international learning training focuses on structuring content, co-teaching, and managing coursework for specific outcomes (Rubin & Guth, 2015). Beyond including time for icebreakers for students to become acclimated with each other, often much of the rest of the experience emphasizes task completion; this serious focus may contribute to discomfort and reduced engagement among diverse students. After witnessing this our first year working together in a collaborative online international learning experience connecting North American students with those from Muslim-majority countries, we explored how modeling and creating opportunities for shared laughter in synchronous group exercises might better promote a relaxed and engaging learning environment. Our findings, derived from qualitative feedback and quantitative surveys, demonstrate that humor enhances group cohesion and cultural exploration. Students reported increased comfort, enjoyment, and willingness to engage and humor-driven activities consistently rated as the most impactful and enjoyable aspects of the course.

Cross-Cultural Insights into Phatic Communication: Indonesian and Australian Student Interactions

Phatic communication, a widespread yet often unnoticed language phenomenon, primarily serves to maintain social interactions rather than convey substantive information. This study investigated the use of phatic expressions among Indonesian and Australian students, examining how cultural backgrounds and social contexts influence their communication styles. Using a Discourse Completion Task (DCT) with both qualitative and quantitative methods, this research revealed significant differences between the two groups. Australian students frequently used phatic speech to initiate conversations, provided comfort, and expressed empathy, emphasizing respect and interpersonal connection. In contrast, Indonesian students tended to use phatic expressions to promote social harmony, engage in casual discourse, and offer interpersonal support, often focusing on group cohesion. Social variables, such as social status and distance, played a critical role in shaping the choice of phatic functions and forms, with both groups adapting their communication strategies based on these factors. The findings highlight the intricate relationship among culture, social dynamics, and language in phatic communication, offering a nuanced understanding that can enhance intercultural interactions, minimize misunderstandings, and improve communication effectiveness in multicultural settings. This research underscores the importance of considering cultural and social contexts when analyzing language use, contributing valuable insights for educators, linguists, and communication professionals aiming to foster more effective and harmonious interactions across diverse cultural landscapes.

Scaling up statistical model checking of cyber-physical systems via algorithm ensemble and parallel simulations over HPC infrastructures

Model-based formal verification of industry-relevant Cyber-Physical Systems (CPSs) is often a computationally prohibitive task. In most cases, the complexity of the models precludes any prospect of symbolic analysis, leaving numerical simulation as the only viable option. Unfortunately, exhaustive simulation of a CPS model over the entire set of plausible operational scenarios is rarely possible in practice, and alternative strategies such as Statistical Model Checking (SMC) must be used instead.In this article, we show that the number of model simulations (samples) required by SMC techniques to converge can be significantly reduced by considering multiple (an ensemble of) Adaptive Stopping Algorithms (SAs) at once, and that the simulations themselves (by far the most expensive step of the entire workload) can be efficiently sped up by exploiting massively parallel platforms.With three industry-scale CPS models, we experimentally show that the use of an ensemble of two state-of-the-art SAs (AA and EBGStop) may require dozens of millions fewer samples when compared to running a single algorithm, with reductions in sample size of up to 78%. Furthermore, we show that our implementation, by massively parallelizing system model simulations on a HPC infrastructure, yields speed-ups for the completion time of the verification tasks which are practically linear with respect to the number of computational nodes, thus achieving an efficiency of virtually 100%, even on very large platforms. This makes it possible to complete tasks of model-based SMC verification for complex CPSs in a matter of hours or days, whereas a naïve sequential execution would require from months to many years.

Development of a full ocean depth hydraulic manipulator with heavy-duty capacity

The underwater manipulator is a versatile tool commonly used for various underwater operations. In this study, we developed a heavy-duty hydraulic manipulator capable of operating at full ocean depth. The overall system design integrates both the mechanical structure and the electric control system. The master arm controls the slave arm by transmitting control signals to the controller and valve box. The structural design of the elbow and wrist joints has been optimized to enhance the manipulator's underwater performance and operational range. Laboratory tests demonstrated the manipulator’s excellent response consistency and tracking capability across a wide range of motion. During sea trials in the Mariana Trench, the manned submersible Fendouzhe successfully deployed the manipulator, showcasing its exceptional ability to complete complex tasks.

Improving Patient Wait Times on the First Day of Radiotherapy Treatment

Long wait times on starting day of radiotherapy (day 1) can cause dissatisfaction among both patients and healthcare providers. Reducing these wait times will decrease stress and decongest hospital facilities especially in current coronavirus disease 2019 times. A multidisciplinary core team was formed during the Stanford-India Collaborative Quality Improvement training to reduce the median wait times on day 1 of treatment from 6 to 4.5 hours (a 25% reduction). Several factors were identified on the fishbone diagram, and key causes were identified using a Pareto chart and action prioritization matrix. The Plan-Do-Study-Act Cycle strategy was undertaken for the identified interventions. The outcome measure was time from arrival at the hospital to entry into a treatment room. Data were obtained from time charts at various stations and electronic records. The secondary measures were visual analog scale (VAS) scores, 80th percentile wait times, and the day-2 delay percentage. The balancing measure was “new errors” due to interventions. The interventions included the completion of all administrative tasks not needing patients' presence on the day before day 1. Baseline data from 198 patients and postintervention data from 160 patients were compared and analyzed. The median wait time at baseline, which was 6 hours, was reduced to 4.2 hours. The VAS score showed 70.4, 67.7, and 71.9% satisfaction for the resident physician, therapists, and patients, respectively. The 80th percentile wait times reduced from 8 to 5.7 hours; and the day 2 starting rate decreased from 22.5 to 2.04%, with no new errors reported. Radiotherapy day 1 wait times can be safely decreased, leading to improved satisfaction among patients and healthcare providers, by utilizing classic quality improvement methods and tools.

Implementation and effectiveness of the cashless banking system

This thesis analyses the implementation and effectiveness of the cashless banking system at Ecobank, focusing on various dimensions such as usability, stakeholder satisfaction, and customer perspectives. A mixed-methods approach was employed, incorporating quantitative and qualitative data from 25 participants to evaluate the system's performance. The analysis reveals that while the cashless banking system is generally well-received, particularly in ease of task completion and overall workflow improvements, there are notable challenges in usability, particularly with interface intuitiveness and performance during peak hours. Stakeholders and customers reported moderate satisfaction, with varying experiences regarding ease of use and reliability. The study concludes that although the cashless banking system has enhanced Ecobank’s service delivery and met broad expectations, further user experience, system performance, and continuous evaluation mechanisms are necessary to optimise its effectiveness and customer satisfaction. Recommendations include focusing on user-centred design, improving scalability for peak demand periods, and establishing robust feedback loops for ongoing system refinement.

Comparing typing methods for uppercase input in virtual reality: Modifier Key vs. alternative approaches

Typing tasks are basic interactions in a virtual environment (VE). The presence of uppercase letters affects the meanings of words and their readability. By typing uppercase letters on a QWERTY keyboard, the layers can be switched using a modifier key. Considering that VE controllers are typically used in a VE, this input method can result in user fatigue and errors. Thus, this study proposed new alternative interactions for the modifier key input and compared their typing performance and user experience. In an experiment, 30 participants were instructed to type 10 sentences using different typing interaction methods (shift, long press, and double-tap) on a virtual keyboard in a VE. The typing speed, error rate, and number of backspace inputs were measured to compare typing performance. Upon the completion of the typing task, the usability, workload, and sickness associated with each typing method were evaluated. The results showed that the double-tap method exhibited significantly higher typing speed, error rate, ease of use, satisfaction, and workload. This result is consistent with those of previous studies demonstrating that selection tasks were more efficient with fewer hand movements. Thus, this study implies that the double-tap method can be considered as a potential typing interaction for the VEs instead of the traditional method using the shift as a modifier key. Therefore, this study is expected to contribute to the design and development of user-friendly interactions.

ERTH scheduler: enhanced red-tailed hawk algorithm for multi-cost optimization in cloud task scheduling

Effective task scheduling has become the key to optimizing resource allocation, reducing operation costs, and enhancing the user experience. The complexity and dynamics of cloud computing environments require task scheduling algorithms that can flexibly respond to multiple computing demands and changing resource states. Therefore, we propose an enhanced Red-tailed Hawk algorithm (named ERTH) based on multiple elite policies and chaotic mapping, while applying this approach in conjunction with the proposed scheduling model to optimize the efficiency of task scheduling in cloud computing environments. We apply the ERTH algorithm to a real cloud computing environment and conduct a comparison with the original RTH and other conventional algorithms. The proposed ERTH algorithm has better convergence speed and stability in most cases of small and large-scale tasks and performs better in minimizing the task completion time and system load cost. Specifically, our experiments show that the ERTH algorithm reduces the total system cost by 34.8% and 36.4% relative to the traditional algorithm for tasks of different sizes. Further, evaluations in the IEEE Congress on Evolutionary Computation (CEC) benchmark test sets show that the ERTH algorithm outperforms the traditional or emerging algorithms in several performance metrics such as mean, standard deviation, etc. The proposal and validation of the ERTH algorithm are of great significance in promoting the application of intelligent optimization algorithms in cloud computing.

Promoting Machine Abilities of Discovering and Utilizing Knowledge in a Unified Zero-shot Learning Paradigm

Knowledge discovery and utilization are two essential cognitive processes that enable humans to understand the world and extract new insights from their surroundings. These processes have motivated machine learning studies, particularly zero-shot learning (ZS), which seeks to identify unseen concepts through the use of side information. Previous ZS studies primarily focused on utilizing existing knowledge to infer unseen events, yet they overlook the crucial process of knowledge discovery and the integrated modeling of these knowledge-aware processes. In this study, we present a comprehensive ZS learning approach that explores and evaluates the machine's abilities of discovering and utilizing knowledge. More specifically, to emulate human-like knowledge discovery and utilization processes, we propose a novel visual-aware ZS knowledge graph completion task for evaluation, incorporating a traditional ZS image classification task. Technically, we develop a unified ZS learning paradigm named Cognitive Learner (CoLa) to foster the two knowledge-aware abilities. Including a knowledge representation learning (KRL) module and a knowledge adaptation (KA) module, CoLa adapts well to the two specified tasks with the corresponding data. Extensive experiments on large-scale datasets demonstrate CoLa models’ outstanding performance over compared methods in the two ZS tasks, illustrating their superior ability of discovering and utilizing knowledge.

Exploring trait differences in neurofeedback learners: a single-session sham-controlled pilot study

AbstractNeurofeedback (NF) is a form of biofeedback that involves individuals monitoring and shifting brain activity towards a desired direction. The objective of this study was to investigate whether there are differences between NF learners and non-learners on psychometric traits such as the NEO PI-R personality factors, self-regulation questionnaire (SRQ) and the need for cognition scale (NFC). A total of 34 participants were randomly allocated to a single-blind randomised sham-controlled protocol 3-arm study of single-session theta NF. Twelve participants were administered a spatially defined frontal-midline theta (FM-theta) group, 11 to a functionally defined medial temporal/parietal lobe (MTL-theta) group and 11 allocated to a yoked sham NF group. The baseline session included screening of participants, trait-based behavioural measures (SRQ, NFC and NEO PI-R) and the completion of cognitive tasks with electroencephalography (EEG) recording to determine individualised peak theta activity for NF training. NF learners and non-learners were evaluated using changes in absolute theta power and the percentage of time spent above threshold using Spearman’s correlation coefficient from a total of 30-minutes of NF exposure. Significant differences in psychometric traits between NF learners and non-learners differed depending on learning metrics. Results indicated that NF learners reported higher SRQ total scores, SRQ decision making, SRQ goal setting subfactors and NEO PI-R conscientiousness, but were significantly lower in NEO PI-R extraversion compared to non-learners. This study demonstrates that learning outcomes vary based on the metrics used and emphasises the importance of selecting appropriate learning metrics and further examination of learning within sham NF training.

The Intersecting Time, Administrative and Financial Burdens of a Cancer Diagnosis.

Cancer and its care create substantial financial, time, and administrative burdens both for patients and their loved ones. While cancer-related financial burdens have been well documented in the past decade, time and administrative burdens of cancer care have received substantially less attention. We define time burdens as the burden patients and caregivers experience due to the time needed to complete cancer-related treatment and tasks that take away from other life responsibilities. Relatedly, we conceptualize administrative burdens as those burdens patients and caregivers experience due to cancer-related, resource-consuming bureaucratic and logistical tasks. Finally, financial hardship can be conceptualized as problems patients experience related to the cost of medical care. These burdens do not exist in isolation; time, administrative, and financial burdens intersect with and compound each other. Currently, we have limited evidence-based measures on the objective (e.g., scheduling time, transportation, wait time) and subjective (e.g., mental, emotional and physical stress) measures of time and administrative burden. We have even more limited evidence of the risk factors for and outcomes from increased time and administrative burdens, and how they differentially impact populations across social and demographic groups. In this commentary, we present a research agenda to map, measure, evaluate, and mitigate the time, administrative, and financial burdens of cancer and its care.

Dynamics of Motor Skill Learning in American and Israeli Toddlers With Varied Language Proficiency.

The aim of the present study was twofold: to determine if deficits in motor skill proficiency and learning were present in 2-year-old children identified with early expressive language delay compared to peers without the delay, and to distinguish how motor skill proficiency and learning behaviors may manifest differently across culturally and linguistically diverse backgrounds. The study involved 54 children (24-36 months of age), 23 of whom were identified as having an expressive language delay. Furthermore, 16 participants were American and English-speaking and 38 were Israeli and Hebrew-speaking. After motor and language skill proficiency was assessed using a variety of measures, each child and participating parent were introduced to a nonsymmetrical-shaped insertion task so that motor learning skills could be observed. This block insertion task was observed for each child at three time points and included a transfer task (same task, new nonsymmetrical shape). Children with early expressive language delay were statistically significantly more likely to exhibit deficits in fine-motor proficiency than peers without language delay, regardless of country of origin or language spoken. Furthermore, participants with language delay demonstrated significantly higher error rates in transfer task completion compared with peers. Finally, participants in the U.S. sample indicated lower fine-motor skills and higher error rates than those in the Israeli sample. Differences in motor skill proficiency were universally associated with language delay status, indicating support for the notion that language acquisition deficits may extend beyond the linguistic system even in young children identified as late talkers.

Exploring the potential of AI for pragmatics instruction

Large language models (LLM) (e.g., ChatGPT) have garnered massive attention due to their potential to serve as interactive partners for a wide range of teaching and learning activities. In instructed second language acquisition, one potential use of LLMs is for pragmatics instruction. The current study sought to assess ChatGPT’s suitability for this task by analyzing its output in response to 144 DCTs (discourse completion tasks), which test a language learner’s ability to create apologies, requests, and refusals over differential levels of social and power distance, and degrees of imposition. Quantitatively, ChatGPT produced significantly shorter apologies than the other two speech acts (p < .001, R2 = .49), utilizing fewer pragmatic strategies (p < .001, R2 = .38). Closer social distance and greater imposition also resulted in longer responses, with higher imposition also eliciting ChatGPT to use more pragmatic strategies. Qualitatively, human raters scored ChatGPT’s apologies significantly lower in terms of their appropriateness, politeness, and language use, despite there being no statistical differences in ratings for requests and refusals. The main pedagogical implications are that, while ChatGPT offers an attractive option for students looking for more language exposure and interaction outside of the classroom, some degree of skepticism is still required due to its quantitative and qualitative inconsistencies.

The influence of second-order metacognitive judgments on cognitive offloading within the monitoring-control relationship

Cognitive offloading refers to using an external medium such as paper to remember information rather than relying solely on memory. Previous research has suggested that individuals’ perceived memory performance may influence their decision to employ cognitive offloading as a learning strategy. However, ubiquitous distortions in metacognitive judgments limit the learning gains. Motivated by this, the current study incorporated second-order metacognitive judgments (SOJs) with distorted first-order metacognitive judgments (FOJs) within a learning context that allowed cognitive offloading. We investigated how individuals decide to employ cognitive offloading based on two levels of self-assessments. Eighty participants from the U.S. were recruited remotely to complete online paired-associate tasks. They learned 48 paired associates on an item-by-item basis, providing a FOJ, an SOJ, and a strategy (offload or not) on each trial. Our findings showed a significant effect of interaction between FOJs and SOJs on the selection of cognitive offloading. Additionally, there were significant variabilities in employing cognitive offloading across individual participants and word pairs. We concluded that SOJs influence the association between FOJs and strategy choices. Furthermore, we attributed the observed interaction to the assessment and regulation of SOJ about FOJ.

Understanding the structure of knowledge graphs with ABSTAT profiles

While there has been a trend in the last decades for publishing large-scale and highly-interconnected Knowledge Graphs (KGs), their users often get overwhelmed by the task of understanding their content as a result of their size and complexity. Data profiling approaches have been proposed to summarize large KGs into concise and meaningful representations, so that they can be better explored, processed, and managed. Profiles based on schema patterns represent each triple in a KG with its schema-level counterpart, thus covering the entire KG with profiles of considerable size. In this paper, we provide empirical evidence that profiles based on schema patterns, if explored with suitable mechanisms, can be useful to help users understand the content of big and complex KGs. ABSTAT provides concise pattern-based profiles and comes with faceted interfaces for profile exploration. Using this tool we present a user study based on query completion tasks. We demonstrate that users who look at ABSTAT profiles formulate their queries better and faster than users browsing the ontology of the KGs. The latter is a pretty strong baseline considering that many KGs do not even come with a specific ontology to be explored by the users. To the best of our knowledge, this is the first attempt to investigate the impact of profiling techniques on tasks related to knowledge graph understanding with a user study.