Task Execution Research Articles

Immersive remote telerobotics: foveated unicasting and remote visualization for intuitive interaction

Abstract Precise and efficient performance in remote robotic teleoperation relies on intuitive interaction. This requires both accurate control actions and complete perception (vision, haptic, and other sensory feedback) of the remote environment. Especially in immersive remote teleoperation, the complete perception of remote environments in 3D allows operators to gain improved situational awareness. Color and Depth (RGB-D) cameras capture remote environments as dense 3D point clouds for real-time visualization. However, providing enough situational awareness needs fast, high-quality data transmission from acquisition to virtual reality rendering. Unfortunately, dense point-cloud data can suffer from network delays and limits, impacting the teleoperator’s situational awareness. Understanding how the human eye works can help mitigate these challenges. This paper introduces a solution by implementing foveation, mimicking the human eye’s focus by smartly sampling and rendering dense point clouds for an intuitive remote teleoperation interface. This provides high resolution in the user’s central field, which gradually reduces toward the edges. However, this systematic visualization approach in the peripheral vision may benefit or risk losing information and burdening the user’s cognitive load. This work investigates these advantages and drawbacks through an experimental study and describes the overall system, with its software, hardware, and communication framework. This will show significant enhancements in both latency and throughput, surpassing 60% and 40% improvements in both aspects when compared with state-of-the-art research works. A user study reveals that the framework has minimal impact on the user’s visual quality of experience while helping to reduce the error rate significantly. Further, a 50% reduction in task execution time highlights the benefits of the proposed framework in immersive remote telerobotics applications.

Navigating the moral maze: a literature review of ethical values in business process management

PurposeBusiness process management (BPM), as a pillar of information systems (IS) research, has become more complex with the advent of new technologies, emphasizing the need for moral and ethical perspectives. To foster moral behavior and responsible action, including ethical values in IT systems and processes can be a solid option. By incorporating a socio-technical perspective, we are able to analyze the various aspects of BPM and organizational processes and the incorporated values. We find an overall acknowledgment of the importance of values and ethics in BPM.Design/methodology/approachThis publication explores ethical values within BPM through a systematic literature review (SLR). The study aims to identify the ethical dimensions inherent in BPM and their practical implications in process management and task execution. The methodological approach adopted is a SLR (Boell and Cecez-Kecmanovic, 2015), adapting the PRISMA guidelines (Page et al., 2021) to identify 82 articles from 21 top IS journals suggested by Lowry et al. (2013).FindingsA descriptive framework is developed to explain the use and application of ethical values within business processes. This framework enables practitioners and researchers to categorize and understand the various ethical considerations involved in BPM. It provides a structured approach highlighting the interrelation between process perspectives and ethical values, demonstrating how different BPM approaches may have varying ethical implications. We compare past and future research in business processes, identifying areas for further investigation and theoretical development. A historical analysis of values and literature also helps contextualize contemporary discussions on ethics in BPM, shedding light on the evolution of ethical considerations within this domain.Originality/valueOur study contributes to a more nuanced understanding of BPM, highlighting the importance of considering ethical values and socio-technical perspectives in designing and implementing business processes. These findings contribute to understanding the values associated with different types of processes and their employment and highlight potential areas for future research. Our study provides ethics-oriented research in IS with novel insights by examining BPM from an ethical value perspective. We contribute to the BPM literature by examining which values are applied in which process types from which perspective. In addition, our research suggestions provide food for thought for both research streams.

Regional response to light illuminance across the human hypothalamus.

Light exerts multiple non-image-forming biological effects on physiology including the stimulation of alertness and cognition. However, the subcortical circuitry underlying the stimulating impact of light is not established in humans. We used 7 Tesla functional magnetic resonance imaging to assess the impact of variations in light illuminance on the regional activity of the hypothalamus while healthy young adults (N=26; 16 women; 24.3±2.9 y) were completing two auditory cognitive tasks. We find that, during both the executive and emotional tasks, higher illuminance triggered an activity increase over the posterior part of the hypothalamus, which includes part of the tuberomamillary nucleus and the posterior part of the lateral hypothalamus. In contrast, increasing illuminance evoked a decrease in activity over the anterior and ventral parts of the hypothalamus, encompassing notably the suprachiasmatic nucleus and another part of the tuberomammillary nucleus. Critically, the performance of the executive task was improved under higher illuminance and was negatively correlated with the activity of the posterior hypothalamus area. These findings reveal the distinct local dynamics of different hypothalamus regions that underlie the impact of light on cognition.

Regional response to light illuminance across the human hypothalamus

Light exerts multiple non-image-forming biological effects on physiology including the stimulation of alertness and cognition. However, the subcortical circuitry underlying the stimulating impact of light is not established in humans. We used 7 Tesla functional magnetic resonance imaging to assess the impact of variations in light illuminance on the regional activity of the hypothalamus while healthy young adults (N=26; 16 women; 24.3±2.9 y) were completing two auditory cognitive tasks. We find that, during both the executive and emotional tasks, higher illuminance triggered an activity increase over the posterior part of the hypothalamus, which includes part of the tuberomamillary nucleus and the posterior part of the lateral hypothalamus. In contrast, increasing illuminance evoked a decrease in activity over the anterior and ventral parts of the hypothalamus, encompassing notably the suprachiasmatic nucleus and another part of the tuberomammillary nucleus. Critically, the performance of the executive task was improved under higher illuminance and was negatively correlated with the activity of the posterior hypothalamus area. These findings reveal the distinct local dynamics of different hypothalamus regions that underlie the impact of light on cognition.

Resources don't grow on trees: A framework for resource-driven adaptation

Software systems execute tasks that depend on different types of resources. However, the variability of resources may interfere with the ability of software systems to execute important tasks. In this paper, we present a proactive and reactive resource-driven adaptation framework that supports different types of resources and is based on the prioritisation of tasks. In our previous work, we defined a task modelling notation called SERIES. This paper builds on that notation and focuses on the adaptation process to improve resource utilisation by substituting tasks with their variants, substituting resources with alternative ones, executing tasks in a different order, or cancelling the execution of low-priority tasks. The framework was evaluated in terms of the percentage of executed critical task requests, average criticality of the executed task requests in comparison to the non-executed ones, overhead, and scalability. The evaluation was executed using two datasets related to a medicine consumption system and a manufacturing system. The evaluation results showed that the proposed framework increased the number of executed critical task requests and the average criticality of the executed task requests under resource variability with marginal overhead.

Towards ML Models’ Recommendations

AbstractArtificial Intelligence encompasses a range of technologies that replicate human-like cognitive abilities through computer systems, enabling the execution of tasks associated with intelligent beings. A prominent way to achieve this is machine learning (ML), which optimizes system performance by employing learning algorithms to create models based on data and its inherent patterns. Today, a multitude of ML models exist having diverse characteristics, including the algorithm type, training dataset, and resultant performance. Such diversity complicates the selection of an appropriate model for a specific use case, answering user demands. This paper presents an approach for ML models retrieval based on the matching between user inputs and ML models criteria, all described in a semantic ML ontology named SML model (Semantic Machine Learning model), which facilitates the process of ML models selection. Our approach is based on similarities measures that we tested and experimented to score the ML models and retrieve the ones matching, at best, user inputs.

Cache-assisted computation offloading for workflow applications in industrial internet of things

The Internet of Things plays an important role in the process of industrial intelligence. It facilitates the connection between devices and the Internet to enable the gathering and analysis of industrial data. However, industrial Internet of Things (IIoT) applications are highly susceptible to latency, have strict energy consumption limitations, and impose specific demands on the resources of IIoT devices. Fortunately, edge computing can migrate tasks to the edge server or cloud platform to deal with the above shortcomings. Nevertheless, tasks generated by IIoT devices in industrial production have a higher rate of repetition, and repetitive execution of the same task diminishes the operational efficiency of the edge system. In this regard, we can cache high-value tasks through task caching to reduce redundant execution. The aforementioned idea can be described as a joint optimization problem of computation offloading and task caching with resource constraints in IIoT. To address this challenge, we establish an edge-empowered service model and propose a cache-assisted computation offloading algorithm for IIoT applications. Extensive experiments demonstrate that the proposed algorithm outperforms some critical methods in terms of energy consumption and latency.

Robot learning on the job: Human-in-the-loop autonomy and learning during deployment

With the rapid growth of computing powers and recent advances in deep learning, we have witnessed impressive demonstrations of novel robot capabilities in research settings. Nonetheless, these learning systems exhibit brittle generalization and require excessive training data for practical tasks. To harness the capabilities of state-of-the-art robot learning models while embracing their imperfections, we present Sirius, a principled framework for humans and robots to collaborate through a division of work. In this framework, partially autonomous robots are tasked with handling a major portion of decision-making where they work reliably; meanwhile, human operators monitor the process and intervene in challenging situations. Such a human–robot team ensures safe deployments in complex tasks. Further, we introduce a new learning algorithm to improve the policy’s performance on the data collected from the task executions. The core idea is re-weighing training samples with approximated human trust and optimizing the policies with weighted behavioral cloning. We evaluate Sirius in simulation and on real hardware, showing that Sirius consistently outperforms baselines over a collection of contact-rich manipulation tasks, achieving an 8% boost in simulation and 27% on real hardware than the state-of-the-art methods in policy success rate, with twice faster convergence and 85% memory size reduction. Videos and more details are available at https://ut-austin-rpl.github.io/sirius/ .

Induced Discomfort Promotes Executive Function While Walking With(out) an Artificial Neuromuscular Impairment.

Neuromotor disorders can degrade one's ability to locomote and attend to salient stimuli in the environment. Many disorders are physiologically complex, making it difficult to tease apart interactions between motor adaptation and executive function processes. We address this challenge by giving participants a controlled artificial impairment, using electrical stimulation to produce an uncomfortable disruption in normal muscular coordination during locomotion. While adapting to this gait perturbation, participants performed an executive function task containing neutral and affectively charged stimuli. The artificial impairment was counterbalanced against control and sham (discomfort-only stimulation) walking conditions. Our twofold hypothesis that discomfort would selectively tax hot, emotionally-charged executive function and motor adaptation would challenge cold, logical executive function was not supported. However, we found that the discomfort experienced with both stimulation conditions improved participants' ability to inhibit distracting information, enhancing this aspect of executive function, and the effect did not depend on whether the task was affectively charged. Moderate discomfort during physical activity may have improved inhibitory control by increasing arousal, a known factor mediating executive function. These results show that using a sensorimotor perturbation that acts internally and bridges multiple physiological domains, including discomfort, can reveal effects not seen with purely environmental manipulations. The broader implications are that when high cognitive performance is needed during physical activity, it may be beneficial to, quite literally, operate outside one's comfort zone.

A tri-chromosome-based evolutionary algorithm for energy-efficient workflow scheduling in clouds

Cloud computing is increasingly attracting workflow applications, where workflows need to satisfy execution deadlines and energy consumption is to be minimized. So far, numerous studies have adopted evolutionary algorithms to optimize the energy consumption of workflow execution. Dynamic voltage and frequency scaling (DVFS) has been widely employed to save energy on computing devices running workflow tasks. However, most existing evolutionary algorithms focus on evolving task execution order or mapping from tasks to resources, while neglecting the evolution of task runtime to leverage the dynamic voltage and frequency scaling (DVFS) technology for further energy saving. To compensate for that deficiency, this paper designs a tri-chromosome-based evolutionary algorithm, namely TCEA, to evolve three types of decision vectors (i.e., task order, task and resource mapping, and task runtime) simultaneously using three problem-specific mechanisms. Firstly, we construct a search space by using the tasks’ minimum and optimal runtime, and propose a solution representation mechanism to simplify the decision vector for task runtime between 0 and 1. Secondly, we design a deadline constraint handling mechanism to distribute those durations exceeding the deadline to each task based on their extension of the minimum runtime. Thirdly, we exploit the workflow structure to cluster decision variables without direct constraints into the same group. During each iteration, only the order of tasks within a group evolves to avoid precedence constraints, thus performing searches within the feasible space. At last, we conduct comparison experiments on five types of real-world workflows with 30 to 1000 tasks. The energy consumed by TCEA is much less than those consumed by the state-of-the-art workflow scheduling algorithms, demonstrating the superior performance of TCEA in energy saving.

DETERMINING THE ROLE OF SPATIAL ABILITY IN PERFORMING LUNAR PHASE CHANGE TASK USING BRAIN ACTIVITY ANALYSIS

Many students have difficulty understanding the concept of lunar phase changes (LPCs) due to spatial ability problems such as perspective-taking (PT) and mental rotation (MR). Therefore, this study aimed to compare brain activity during PT and MR tasks while performing the LPC task to determine the involvement of PT and MR. This study measured brain waves using EEG in 20 participants while solving the 3 tasks. First, the power values in the theta band of all cortical areas showed a significant difference between MR and LPC tasks. Second, in the strategy execution section with a statistically significant difference, the occipital lobe and limbic system were mainly active during the PT task, whereas the frontal lobe was mainly active during the LPC task. Third, for strategy execution, during MR and LPC tasks, the frontal lobe, temporal lobe, and limbic system were all activated to significantly different degrees. Therefore, both PT and MR, particularly PT, are required to solve the LPC task. Moreover, for students who have difficulty learning LPC, it is necessary to recognize the need for spatial ability, such as PT ability, and establish an appropriate teaching strategy. Keywords: brain activation, electroencephalography, lunar phase change, mental rotation, perspective-taking

VonEdgeSim: A Framework for Simulating IoT Application in Volunteer Edge Computing

Recently, various emerging technologies have been introduced to host IoT applications. Edge computing, utilizing volunteer devices, could be a feasible solution due to the significant and underutilized resources at the edge. However, cloud providers are still reluctant to offer it as an edge infrastructure service because of the unpredictable nature of volunteer resources. Volunteer edge computing introduces challenges such as reliability, trust, and availability. Testing this infrastructure is prohibitively expensive and not feasible in real-world scenarios. This emerging technology will not be fully realized until dedicated research and development efforts have substantiated its potential for running reliable services. Therefore, this paper proposes VonEdgeSim, a simulation of volunteer edge computing. To the best of our knowledge, it is the first and only simulation capable of mimicking volunteer behavior at the edge. Researchers and developers can utilize this simulation to test and develop resource management models. We conduct experiments with various IoT applications, including Augmented Reality, Infotainment, and Health Monitoring. Our results show that incorporating volunteer devices at the edge can significantly enhance system performance by reducing total task delay, and improving task execution time. This emphasizes the potential of volunteers to provide reliable services in an edge computing environment. The simulation code is publicly available for further development and testing.

Sequential Task Allocation of More Scalable Artificial Dragonfly Swarms Considering Dubins Trajectory

With the rapid advancement of UAV technology and the increasing complexity of tasks, multi-UAV systems face growing challenges in task execution. Traditional task allocation algorithms often perform poorly when dealing with issues such as local optima, slow convergence speed, and low convergence accuracy, making it difficult to meet the demands for efficiency and practicality in real-world applications. To address these problems, this paper focuses on collaborative task allocation technology for multi-UAV. It proposes a collaborative task allocation strategy for multi-UAV in a multi-target environment, which comprehensively considers various complex constraints in practical application scenarios. The strategy utilizes Dubins curves for trajectory planning and constructs a multi-UAV collaborative task allocation model, with targets including the shortest total distance index, the minimum time index, and the trajectory coordination index. Each UAV is set as an artificial dragonfly by modifying the traditional dragonfly algorithm, incorporating differential evolution algorithms and their crossover, mutation, and selection operations to bring UAV swarms closer to the characteristics of biological dragonflies. The modifications can enhance the global scalability of artificial dragonfly swarms (ADSs), including wider search capacity, wider speed range, and more diverse search accuracy. Meanwhile, potential solutions with global convergence properties are stored to better support real-time adjustments to task allocation. The simulation results show that the proposed strategy can generate a conflict-free task execution scheme and plan the trajectory, which has advantages in changing the data scale of the UAV and the target and improves the reliability of the system to a certain extent.

Leader-follower control and APF for Multi-USV coordination and obstacle avoidance

To overcome the limitations of a single USV in task execution, this paper investigates the issues of cooperative motion and obstacle avoidance for USV swarm. The leader-follower control approach is chosen as the strategy for coordinating the formation of multiple USVs. Kinematic and kinetic controllers are designed using sliding mode control, with a saturation function replacing the sign function to avoid chattering. The validity of the kinematic and kinetic controllers is demonstrated using the Lyapunov stability theorem. To tackle the problem of goal reachability when obstacles are nearby in the artificial potential field method for obstacle avoidance, the repulsive potential field function is modified by including the distance factor between the USV and the target point. To address the problem of local minimum, the simulated annealing algorithm is incorporated into the traditional artificial potential field method. Multi-condition simulation experiments are conducted using MATLAB. The experimental results demonstrate that multiple USVs can form a formation in a short time and maintain the formation while navigating, exhibiting high robustness. Furthermore, they are able to effectively navigate through complex marine environments, successfully avoiding obstacles and ultimately reaching their target destination.

Efficient Data Collection Strategy for Modeling the Dynamics of Floating Robotic Vehicles Using Neural Networks

As the hydrodynamic model of a surface vehicle is highly nonlinear and of high dimension, an extensive set of experiments is required to estimate the parameters of the model. Typically, the dynamic behavior of a surface vehicle is investigated through experimental tests such as planar motion mechanisms or free running tests in specialized test facilities. However, the cost of such tests is very high, and it is challenging to design effective and efficient test conditions, especially when the model is represented by numerous parameters such as neural networks. In this paper, we propose an efficient data collection strategy for modeling the dynamics of a floating robotic vehicle using neural networks. For generalized modeling, it is important to collect diverse data by exploring all the reachable state space of the vehicle. To achieve this, a data collection policy is built based on sampling-based planning toward reducing the uncertainty of the neural network-based model. The proposed method allows for collecting more comprehensive data than other commonly used random-based data collection policies. We show experimentally that the proposed method enables more accurate modeling of both fully actuated and under-actuated floating robotic vehicles, as demonstrated by the effective execution of various tasks.

Towards invertible 2D crystal structure representation for efficient downstream task execution

Abstract In the study of MoS2 lattice defects, we explore the use of Siamese Neural Networks (SNN) to create invariant embeddings, which respect the crystalline symmetry of the lattice. By training our model with contrastive learning, we successfully differentiate configurations with varying defects, achieving perfect accuracy in recognizing equivalent placements. Our method showcases the capability to predict physical properties like formation energy per site and the bandgap with strong performance across both low and high-defect density scenarios, outperforming traditional methods when enhanced with polynomial features. Despite its effectiveness, the model presents limitations at high defect densities, indicating a need for further refinement. Our approach lays the groundwork for reverse-engineering processes. Thus, we open pathways for generative models that can navigate from specified property ranges to optimal defect configurations, fostering an efficient solution-space exploration for bespoke material synthesis.

Two-Dimensional MoS2-Based Anisotropic Synaptic Transistor for Neuromorphic Computing by Localized Electron Beam Irradiation.

Neuromorphic computing, a promising solution to the von Neumann bottleneck, is paving the way for the development of next-generation computing and sensing systems. Axon-multisynapse systems enable the execution of sophisticated tasks, making them not only desirable but essential for future applications in this field. Anisotropic materials, which have different properties in different directions, are being used to create artificial synapses that can mimic the functions of biological axon-multisynapse systems. However, the restricted variety and unadjustable conductive ratio limit their applications. Here, it is shown that anisotropic artificial synapses can be achieved on isotropic materials with externally localized doping via electron beam irradiation (EBI) and purposefully induced trap sites. By employing the synapses along different directions, artificial neural networks (ANNs) are constructed to accomplish variable neuromorphic tasks with optimized performance. The localized doping method expands the axon-multisynapse device family, illustrating that this approach has tremendous potentials in next-generation computing and sensing systems.

Observing nociceptive detection thresholds and evoked potentials in diabetic patients with and without painful neuropathy

Aim: Diabetic polyneuropathy is the most described complication in patients with diabetes mellitus. A significant percentage of these patients experience disabling neuropathic pain (painful diabetic polyneuropathy). Small nerve fibers are primarily responsible for peripheral nociception, but objectively assessing its function is challenging. The primary objective of this study was to explore the task execution and outcomes of intra-epidermal electrical stimulation technique that combines nociceptive detection thresholds (NDT) and evoked potentials (EPs) in patients with diabetes. We compared the results of diabetic patients, both with and without painful diabetic polyneuropathy, with those of healthy controls to explore potential clinically relevant information. Methods: The NDT-EP method was applied to 38 patients with diabetes (18 with and 20 without chronic painful neuropathy) and 38 age- and sex-matched healthy controls. Individual mean NDTs, psychometric slopes, EP amplitudes, and the effect of the stimuli on EP amplitudes were analyzed and compared between groups using linear regression. Results: The findings revealed significantly lower detection rates, higher NDTs, and lower psychometric slopes in patients with painful diabetic polyneuropathy than in healthy controls. Both patient groups significantly exhibited lower mean EP amplitudes than healthy controls, which were not linked to pulse amplitudes but influenced by stimulus detection. Conclusions: This study showed altered NDT-EP outcomes in patients with painful diabetic polyneuropathy. Whereas the task execution, NDTs, and psychometric slopes may provide valuable insights into small fiber dysfunction, pulse amplitudes seemed not differently encoded in neurophysiological responses to intra-epidermal electrical stimulation near the detection threshold compared to controls. Future studies should investigate whether the altered NDT-EP outcomes could quantify small fiber dysfunction in patients with diabetes mellitus. We recommend further exploration of NDT-EP measures in other patient groups with nociceptive dysfunction.

Continuing Safety Education and Workplace Efficacy of Student Workers

This study aims to evaluate students' awareness of safety education and its impact on work effectiveness. By analyzing background variables such as students' gender, age, grade, work experience, student status, and college affiliation, this study explored the relationship between safety education and work effectiveness. The results showed that students' awareness of safety education directly affects their task execution and situational performance on campus and in the workplace. In addition, the study showed that strengthening the popularization and effective cooperation of safety education on campus can help improve students' work effectiveness and campus safety management. This study recommends further improving the quality of safety education by strengthening training, promoting safety knowledge, and improving safety facilities, and strengthening the cultivation of safety awareness during students' internships and employment.

Study of the varietal behavior of 07 durum wheat varieties (Triticum durum Desf.) in the Oued Righ Valley (ITDAS Station)

Cereal production in the Oued Righ Valley is limited by several factors, including high temperatures and the salinity of irrigation water. These constraints cause significant variations in crop behavior and yield. This study aims to evaluate the performance of seven durum wheat varieties at the El Arfiane demonstration and seed production farm (ITDAS) in the Oued Righ Valley in the south East of Algeria. The evaluation focuses on morphological parameters and yield components To identify the variety or varieties that demonstrate favorable performance and yield, thus encouraging local farmers to cultivate them. The results of this study indicate significant differences in morphological parameters such as plant height, spike length, number of spikes per square meter, and number of grains per spike. However, other measured parameters, such as the number of tillers per plant, thousand-grain weight TGW, grain yield, and harvest index, The methodology employed in this study follows a completely randomized block design with three replications. The experimental design includes a set of experimental units arranged in blocks, with treatments randomly assigned within each block, hence the term "completely randomized block." Each treatment appears only once in each block. The blocks are typically placed contiguously to facilitate observations and task execution. This design is widely used in agronomy as it simplifies task execution and the comparison of treatments. Despite an acceptable thousand-grain weight this year, ranging from 37.06g for variety T4 to 27.6g for variety T1, the yields were low. The highest biological yield was 33.11 quintals per hectare for variety T5, while the lowest yield was 11.39 (T2) quintals per hectare. Several factors, including pigeon attacks and harvest losses, contributed to the reduced real yield.