Task Execution Research Articles

Software patterns and data structures for the runtime coordination of robots, with a focus on real-time execution performance.

This paper introduces software patterns (registration, acquire-release, and cache awareness) and data structures (Petri net, finite state machine, and protocol flag array) to support the coordinated execution of software activities (also called "components" or "agents"). Moreover, it presents and tests an implementation for Petri nets that supports real-time execution in shared memory for deployment inside one individual robot and separates event firing and handling, enabling distributed deployment between multiple robots. Experimental validation of the introduced patterns and data structures is performed within the context of activities for task execution, control and perception, and decision making for an application on coordinated navigation.

Multi-task oriented team formation in online collaborative learning

Rational and personalized team formation can greatly enhance the efficiency of online collaborative learning. However, in the process of forming learning teams and completing learning tasks, online synchronous communication among team members is often overlooked, and the abilities of team members may change during task execution, which may have an effect on the completion of team tasks. To address these problems, this paper comprehensively considers the impact of learners’ online time and skill levels on team formation. We quantify the potential development space of learners’ skills and design optimization objectives accordingly based on the Zone of Proximal Development theory. Additionally, we propose a Near-Informer deep learning model to predict learners’ future online states, aiming to measure communication efficiency. Then, we propose a multi-task oriented multi-objective optimization team formation problem, and transform it into a single-objective one by using weighted aggregation of all objectives. After that, we present a Three-dimensional Monte Carlo Tree Search algorithm (TDMCTS). The experiments are conducted based on two real datasets. Compared with the baselines, our Near-informer model achieved the highest number of optimal predictions, with 22 out of 32. Besides, TDMCTS algorithm achieved more comprehensive team formation results, with average skill satisfaction degrees of 99.8% and 98.9% on two task sets, respectively, while satisfying the task requirements.

Feature/vector entity retrieval and disambiguation techniques to create a supervised and unsupervised semantic table interpretation approach

Recently, there has been an increasing interest in extracting and annotating tables on the Web. This activity allows the transformation of textual data into machine-readable formats to enable the execution of various artificial intelligence tasks, e.g., semantic search and dataset extension. Semantic Table Interpretation (STI) is the process of annotating elements in a table. The paper explores Semantic Table Interpretation, addressing the challenges of Entity Retrieval and Entity Disambiguation in the context of Knowledge Graphs (KGs). It introduces LamAPI, an Information Retrieval system with string/type-based filtering and s-elBat, an Entity Disambiguation technique that combines heuristic and ML-based approaches. By applying the acquired know-how in the field and extracting algorithms, techniques and components from our previous STI approaches and the state of the art, we have created a new platform capable of annotating any tabular data, ensuring a high level of quality.

Clustering-based hyper-heuristic algorithm for multi-region coverage path planning of heterogeneous UAVs

In the context of multi-heterogeneous UAV coverage path planning, an effective solution method has been proposed. Firstly, regions are set up as fully connected graphs which are cut into multiple subgraphs by spectral clustering method to assign tasks to multi-heterogeneous UAVs. Additionally, an RL-based hyper-heuristic algorithm is proposed. Heuristic space is parameterized by GNN which is trained with the reward provided by the optimization goal to automate design and enhance the heuristic metrics, avoiding the inefficiency and suboptimality of expert design and manual parameter tuning. Compared with existing methods, the proposed algorithm has a better performance in task completion time, execution time and deviation rate, which shows its potential application in the coverage path planning problem of multi-heterogeneous UAVs.

Changes in electrophysiological aperiodic activity during cognitive control in Parkinson’s disease

Abstract Cognitive symptoms in Parkinson’s disease are common and can significantly affect patients’ quality of life. Therefore, there is an urgent clinical need to identify a signature derived from behavioural and/or neuroimaging indicators that could predict which patients are at increased risk for early and rapid cognitive decline. Recently, converging evidence identified that aperiodic activity of the EEG reflects meaningful physiological information associated with age, development, cognitive and perceptual states or pathologies. In this study, we aimed to investigate aperiodic activity in Parkinson’s disease during cognitive control and characterize its possible association with behaviour. Here, we recorded high-density EEG in 30 healthy controls and 30 Parkinson’s disease patients during a Simon task. We analysed task-related behavioural data in the context of the activation–suppression model and extracted aperiodic parameters (offset, exponent) at both scalp and source levels. Our results showed lower behavioural performances in cognitive control as well as higher offsets in patients in the parieto-occipital areas, suggesting increased excitability in Parkinson’s disease. A small congruence effect on aperiodic parameters in pre- and post-central brain areas was also found, possibly associated with task execution. Significant differences in aperiodic parameters between the resting-state, pre- and post-stimulus phases were seen across the whole brain, which confirmed that the observed changes in aperiodic activity are linked to task execution. No correlation was found between aperiodic activity and behaviour or clinical features. Our findings provide evidence that EEG aperiodic activity in Parkinson’s disease is characterized by greater offsets, and that aperiodic parameters differ depending on arousal state. However, our results do not support the hypothesis that the behaviour-related differences observed in Parkinson’s disease are related to aperiodic changes. Overall, this study highlights the importance of considering aperiodic activity contributions in brain disorders and further investigating the relationship between aperiodic activity and behaviour.

Low-latency intelligent service combination caching strategy with density peak clustering algorithm in vehicle edge computing

In the dynamic field of Vehicle Edge Computing (VEC), the demand for intelligent vehicular systems to process vast amounts of data is escalating, driven by advancements in autonomous driving and real-time navigation technologies. Optimizing service latency and minimizing transmission costs are crucial for enhancing the performance of vehicular networks. Traditional service caching strategies, which largely rely on the popularity of individual services, often fail to account for the intricate interdependencies between services. The oversight can result in redundant data transfers and inefficient use of storage resources. In response, our paper introduces a novel approach to service combination caching within a heterogeneous computational framework comprising vehicles, edge servers, and the cloud. Our strategy focuses on minimizing user wait times and data transmission costs during task execution, while adhering to the caching budget constraints of service providers. Key contributions include the development of an Improved Density Peak Clustering (IDPC) algorithm to facilitate cooperative clustering among edge servers and the design of a Service Combination Caching Strategy (SCCS). The SCCS approach reduces caching costs by categorizing servers, forming efficient clusters, and strategically allocating storage. Simulation results demonstrate that the method outperforms existing strategies by significantly decreasing task execution delays and transmission costs, thereby greatly enhancing the quality of service in vehicular applications.

An Analysis the Energy Consumption of Dynamic Task Offloading and Task Execution in MCC

Cloud computing is a group of distributed computing resources that may run any application. MCC is the result of merging the cloud environment with mobile devices. computational offloading is carried out on the cloud in order to save the processing capacity of handheld devices. There will be a rise in computing as the number of sensors used increases, necessitating greater data analysis. Another problem with mobile environments is the power drain on batteries. One solution is to move the work to a remote location where it can be more efficiently executed. In this off-site setting, you'll find powerful cloud servers with plenty of processing capacity. One way that dense mobile apps were able to move their work to the cloud is through computational offload. why it's important to provide a proper explanation of the time constraint in wireless & distant environments, and how reaction time affects offloading for mobile devices & remote servers.

AI-based R&D for frozen and thawed meat: Research progress and future prospects.

Frozen and thawed meat plays an important role in stabilizing the meat supply chain and extending the shelf life of meat. However, traditional methods of research and development (R&D) struggle to meet rising demands for quality, nutritional value, innovation, safety, production efficiency, and sustainability. Frozen and thawed meat faces specific challenges, including quality degradation during thawing. Artificial intelligence (AI) has emerged as a promising solution to tackle these challenges in R&D of frozen and thawed meat. AI's capabilities in perception, judgment, and execution demonstrate significant potential in problem-solving and task execution. This review outlines the architecture of applying AI technology to the R&D of frozen and thawed meat, aiming to make AI better implement and deliver solutions. In comparison to traditional R&D methods, the current research progress and promising application prospects of AI in this field are comprehensively summarized, focusing on its role in addressing key challenges such as rapid optimization of thawing process. AI has already demonstrated success in areas such as product development, production optimization, risk management, and quality control for frozen and thawed meat. In the future, AI-based R&D for frozen and thawed meat will also play an important role in promoting personalization, intelligent production, and sustainable development. However, challenges remain, including the need for high-quality data, complex implementation, volatile processes, and environmental considerations. To realize the full potential of AI that can be integrated into R&D of frozen and thawed meat, further research is needed to develop more robust and reliable AI solutions, such as general AI, explainable AI, and green AI.

Centralized multi-robot logistic system: An approach using the island model genetic algorithm as task scheduler

The practicality allied with technological and logistical advances led customers increasingly to join e-commerce. This phenomenon was even more expanded during the COVID-19 pandemic. Because of the growing demand for logistic services, warehouses must adapt and seek faster and more efficient processes. A means to achieve that is to use mobile robots in transportation-related tasks. However, their effectiveness is only achieved if the system has an efficient work distributor. This research developed a centralized coordination architecture using the island model genetic algorithm for multi-robot logistic task allocation. The coordination receives, allocates, and monitors tasks performed by robots with different payload capacities and average speeds. The architecture was developed upon the robotic operating system. Thus, any robot with a robotic operating system-based internal system can work under the coordination of this architecture. On the results, the task scheduler, developed in previous work, had the evaluation complemented. The scheduler based on the island model genetic algorithm allocates more tasks than the standard genetic algorithm when using the same heuristic. Regarding coordination architecture, the system successfully managed a group of robots in a simulated environment. It also detected and notified failures during task execution. Therefore, this system provides a complete task scheduling, allocation, and monitoring solution.

Improved unmanned aerial vehicle control for efficient obstacle detection and data protection

<p>The article centers on the research objectives and tasks associated with developing a swarm control system for unmanned aerial vehicles (UAVs) utilizing artificial intelligence (AI). A comprehensive literature review was undertaken to assess the effectiveness of the "swarm" method in UAV management and identify key challenges in this domain. Swarm algorithms were implemented in the MATLAB/Simulink environment for modeling and simulation purposes. The study successfully instantiated and simulated a UAV swarm control system adhering to fundamental principles and laws. Each UAV operates autonomously, following target-swarm principles inspired by the collective behavior of bees and ants. The collective movement and behavior of the swarm are controlled by an AI-based program. The system demonstrated effective obstacle detection and avoidance through computer simulations. Results obtained highlight key features contributing to success, including decentralized autonomy, collective intelligence, UAV coordination, scalability, and flexibility. The deployment of a local radio communication system in UAV swarm control and remote object monitoring is also discussed. The research findings hold practical significance as they enable the effective execution of complex tasks and have potential applications in various fields.</p>

Specifics of applying operational and combat measures and tools in counterintelligence operations under the conditions of the ongoing Russian military aggression

The Security Service of Ukraine (SSU) has faced the challange to sophisticate actual and fundamentally new approaches, methods and means of countering threats to national security under conditions of the ongoing full-scale invasion of Russian troops and occupation the territory of Ukraine. The imposition of martial law in Ukraine has considerably expanded the range of practical tasks performed by the bodies and departments of the Security Service of Ukraine. The conditions of the intelligence activities, specific features of counterintelligence activities during the war have been forming new approaches of counterintelligence operations. These factors set and manage new operational and combat tasks for the Security Service of Ukraine. Moreover, SSU is an authorized body in the sphere of counterintelligence activities, that performs tasks of preventing, detecting and analyzing threats to the national security of Ukraine and make challenges to the vital interests of the state. The accomplishment of thesre tasks is plausible on the basis of efficient and prompt identification and threats prevention to the national security of Ukraine, identifying the sources of threats, origin and consequences in order to successfully select methods and means to counter them. The application peculiarities of the operational and combat methods and means by the counterintelligence units of the Security Service of Ukraine during the execution of operational and combat missions in the conditions of the ongoing Russian military aggression against Ukraine are considered in the study. It has been determined that under martial law the counterintelligence units have developed a set of measures to counteract the Russian special services activities as a result of the special tasks that are under provisions of the Security Service of Ukraine. This was accomplished due to the efficiency and enemy goals identification, thoroughly selected methods and forms to prevent intelligence and subversive activities, using appropriate means to overcome the enemy. Mainly, the Security Service of Ukraine adjusts to modern realities, changes, supplements and improves its methods of activity. The Security Service of Ukraine develops a far-sighted strategy, amends its skills with new tactical techniques, creates and uses cutting-edge operational and combat tools and methods, which are transformed and sophisticated in the conditions of war. All these measures prevent threats to national security posed by the enemy. These methods are successfully applied by the counterintelligence units of the Security Service of Ukraine during the execution of operational and combat tasks, and require further theoretical justification.

Early childhood executive control modulates negative affectivity's role in increasing adolescent body mass index trajectories.

Executive control and temperament have been associated with pediatric obesity. However, interactions between these constructs in relation to future weight outcomes have not been investigated. This longitudinal study examined early childhood executive control, early temperament (negative affectivity and surgency), and their interactions as predictors of adolescent BMI trajectories. At age 5.25, children (N = 229) completed executive control tasks, and parents completed the Child Behavior Questionnaire to assess temperament. BMI was calculated annually between ages 14-17. Greater early negative affectivity predicted more positive BMI growth. Although early childhood executive control was not associated with BMI growth, greater negative affectivity predicted greater BMI escalation at average and below average executive control abilities. For children without robust executive control abilities early in development, negative affectivity may be a risk factor for accelerated adolescent BMI growth. Targeted assessment of early risk factors may be useful for childhood obesity prevention efforts.

Linking persistence and executive functions with later academic achievement

Executive functions (EF) and task persistence are key factors in academic development. However, EF and persistence have rarely been examined together, and it remains unclear whether these two constructs are independently related to intellectual development. The present study addressed this gap by examining whether EF and persistence in kindergarten predict math and reading achievement in second grade. We assessed 88 children (51% female; mean age = 73.4 months) on EF and persistence tasks at T1 and obtained teacher ratings of their academic competence at T2 (mean age = 94.6 months). Regression analyses showed that both EF and persistence predicted math achievement, but only EF predicted reading achievement. To sum up, our findings suggest that persistence may be particularly relevant for math, reflecting the ability to endure and sustain. These are skills that are typically not captured by EF measures. Consequently, early interventions should target EF and persistence to support children’s mathematical potential.

Don’t try to make me laugh, let me do: persuading employees to action

ABSTRACT As Computer-Mediated-Communication (CMC) gains prevalence in managerial practices, this study investigates the effect of managerial humor on employees’ perceptions of ability, motivation, and tendency to act. To date, the effect of humor on communication effectiveness remains unclear. Utilizing an experimental design with 923 participants, we compare humorous and non-humorous microcopy requests in CMC through a diff-in-diff regression analysis. Results show that non-humorous microcopy fosters higher motivation, action propensity, and perceived task execution ability compared to humorous microcopy. Perceived ability interacts with the other variables, yielding diverse contextual outcomes. This study reveals both the negative and positive effects of humor in CMC, providing insights for managers. The findings emphasize the importance of considering situational context and employees’ perceived task execution ability when choosing a persuasive approach in CMC requests, offering practical guidance for managers aiming to enhance communication with employees.

Prototype development of a multi-user augmented reality framework for collaborative medical visualization in shared environments

Abstract This study developed and evaluated a prototype for augmented reality (AR) interaction among multiple users sharing a three-dimensional hologram within a shared physical space. A systems requirements analysis was conducted to determine necessary interactions, followed by an iterative implementation plan with testing at each step. The application for the HoloLens 2 (HL2) headset was developed using Unity for 3D design, C# for programming, and Photon for multiplayer capabilities. The prototype was evaluated in a user study utilizing custom and NASA TLX questionnaires, comparing participants' self-reported task success with recorded data to assess the accuracy of selfassessments. The prototype effectively facilitated multi-user interactions within a shared environment, achieving acceptable synchronization for two users. Most participants successfully completed tasks, with TLX scores indicating low cognitive and mechanical load during AR task execution. This study demonstrates AR's viability for displaying and manipulating complex three-dimensional structures within a collaborative medical context among multiple individuals in the same physical space.

Efficiently bounding deadline miss probabilities of Markov chain real-time tasks

In real-time systems analysis, probabilistic models, particularly Markov chains, have proven effective for tasks with dependent executions. This paper improves upon an approach utilizing Gaussian emission distributions within a Markov task execution model that analyzes bounds on deadline miss probabilities for tasks in a reservation-based server. Our method distinctly addresses the issue of runtime complexity, prevalent in existing methods, by employing a state merging technique. This not only maintains computational efficiency but also retains the accuracy of the deadline-miss probability estimations to a significant degree. The efficacy of this approach is demonstrated through the timing behavior analysis of a Kalman filter controlling a Furuta pendulum, comparing the derived deadline miss probability bounds against various benchmarks, including real-time Linux server metrics. Our results confirm that the proposed method effectively upper-bounds the actual deadline miss probabilities, showcasing a significant improvement in computational efficiency without significantly sacrificing accuracy.

Vector Storage Based Long-term Memory Research on LLM

Abstract Current large language model (LLM) intelligences face the challenges of high inference cost and low decision quality when dealing with complex tasks, and are especially deficient in maintaining context coherence during long tasks. This research presents an innovative vector storage long-term memory mechanism model (VIMBank) to enhance the long-term context retention ability and task execution efficiency of LLM intelligences by storing and retrieving historical interaction data through a vector database. VIMBank utilizes a dynamic memory updating strategy and the Ebbinghaus forgetting curve theory to efficiently manage the memory of intelligences and reinforce critical information, forgetting unimportant data, and optimizing storage and reasoning costs. The experimental results show that VIMBank significantly improves the decision quality and efficiency of LLM intelligences in multi-tasking scenarios and reduces the computational cost. Compared with different agents, the success rate of task decision is increased by 10% to 20%, and the reasoning cost is reduced by about 23%, which provides an important theoretical basis and practical support for the future development of intelligences with long term memory and adaptive learning ability.

Task-Oriented Robot Cognitive Manipulation Planning Using Affordance Segmentation and Logic Reasoning.

The purpose of task-oriented robot cognitive manipulation planning is to enable robots to select appropriate actions to manipulate appropriate parts of an object according to different tasks, so as to complete the human-like task execution. This ability is crucial for robots to understand how to manipulate and grasp objects under given tasks. This article proposes a task-oriented robot cognitive manipulation planning method using affordance segmentation and logic reasoning, which can provide robots with semantic reasoning skills about the most appropriate parts of the object to be manipulated and oriented by tasks. Object affordance can be obtained by constructing a convolutional neural network based on the attention mechanism. In view of the diversity of service tasks and objects in service environments, object/task ontologies are constructed to realize the management of objects and tasks, and the object-task affordances are established through causal probability logic. On this basis, the Dempster-Shafer theory is used to design a robot cognitive manipulation planning framework, which can reason manipulation regions' configuration for the intended task. The experimental results demonstrate that our proposed method can effectively improve the cognitive manipulation ability of robots and make robots preform various tasks more intelligently.

The Influence of the Assembly Line Configuration and Reliability Parameter Symmetry on the Key Performance Indicators

In the context of the demand for mass customization of products, a trade-off between highly efficient automated systems and flexible manual operators is sought. The linear arrangement of workstations made it possible to divide the process into many simple operations, which increases production efficiency, but also results in an increase in the number of workstations and a significant extension of the line. A human operator is usually treated as a quasi-mechanical object, and a human error is considered, similarly, as a failure of a technical component. However, human behavior is more complex and difficult to predict. A mathematical model of a new production organization is presented, including dividing the traditional production line into shorter sections or replacing the serial assembly line with a U-line with cells. Moreover, the reliability of operator and technical means are distinguished. Work-in-progress inventories are located between line sections to improve system stability. The stability of the assembly line is examined based on the system configuration and probabilistic estimates of human failure. The influence of the symmetry of reliability parameters of people on key performance indicators (KPI (headcount), KPI (surface) and KPI (Overall Equipment Effectiveness) is examined. KPI (solution robustness) and KPI (quality robustness) are also presented in order to evaluate the impact of a disruption on the assembly line performance. New rules for assigning tasks to stations are proposed, taking into account the risk of disruptions in the execution of tasks. For comparison of assembly problems, heuristic methods with newly developed criteria are used. The results show the impact of symmetry/asymmetry on assembly line performance and an asymmetric distribution of manual assembly times that is significantly skewed to the right due to human errors. On the assembly line, the effects of these errors are cumulative and lead to longer assembly times and lower KPIs.

Exercise and Acute Cognitive Enhancement.

Substantial research has explored the effects of a single session of exercise on cognitive performance. Meta-analytic reviews provide summary statistics relative to these effects when the cognitive task is performed while exercising and when performed after exercise. When performed concomitantly the effects of exercise are typically small or negative with the exception of moderate effects for speed of cognitive performance during moderate intensity exercise. When cognitive performance is assessed following the cessation of exercise, the effects are typically positive. When considering the effects following exercise, most studies have focused on executive function and episodic memory as the cognitive outcomes of interest with meta-analyses supporting small-to-moderate benefits. When potential moderators related to exercise parameters or participants characteristics are examined, results suggest that 20 minutes of moderate to vigorous intensity exercise consistently benefits performance with larger effects evident for executive function tasks with higher cognitive demands, for long-term memory tasks when exercise is performed prior to information encoding, and when considering individuals with lower initial cognitive performance levels. These observed benefits are likely due to the impacts acute exercise can have on physiological arousal, brain function, and brain neurotrophic factors. Advancements in our understanding of the potential of a single session of exercise to benefit cognitive performance are important for exercise prescription and to inform chronic exercise programs designed to benefit cognition.