Task Processing Research Articles

A Multi-Strategy Siberian Tiger Optimization Algorithm for Task Scheduling in Remote Sensing Data Batch Processing

With advancements in integrated space–air–ground global observation capabilities, the volume of remote sensing data is experiencing exponential growth. Traditional computing models can no longer meet the task processing demands brought about by the vast amounts of remote sensing data. As an important means of processing remote sensing data, distributed cluster computing’s task scheduling directly impacts the completion time and the efficiency of computing resource utilization. To enhance task processing efficiency and optimize the allocation of computing resources, this study proposes a Multi-Strategy Improved Siberian Tiger Optimization (MSSTO) algorithm based on the original Siberian Tiger Optimization (STO) algorithm. The MSSTO algorithm integrates the Tent chaotic map, the Lévy flight strategy, Cauchy mutation, and a learning strategy, showing significant advantages in convergence speed and global optimal solution search compared to the STO algorithm. By combining stochastic key encoding schemes and uniform allocation encoding schemes, taking the task scheduling of aerosol optical depth retrieval as a case study, the research results show that the MSSTO algorithm significantly shortens the completion time (21% shorter compared to the original STO algorithm and an average of 15% shorter compared to nine advanced algorithms, such as a particle swarm algorithm and a gray wolf algorithm). It demonstrates superior solution accuracy and convergence speed over various competing algorithms, achieving the optimal execution sequence and machine allocation scheme for task scheduling.

The aging of emotional words processing in implicit and explicit emotion task: an ERP study

Abstract This study examined how cognitive aging affects emotional word processing using event-related potential technique. Young and older adults completed both implicit lexical decision and explicit emotion categorization tasks involving positive, negative, and neutral words. Behaviorally, older adults displayed a negative emotion effect in the implicit task, which was absent in young adults. While both age groups exhibited both positive and negative emotion effects in the explicit task, older adults demonstrated a greater positivity bias compared to young adults. Event-related potential technique data revealed that young adults exhibited an early negative emotion effect on the P2 and a late emotion effect on the late positivity potentials in the implicit task. In contrast, older adults exhibited an early negativity bias effect on the P2, as well as both negative and positive emotion effects on the N400, and positive emotion effects on the late positivity potentials. In the explicit task, young adults showed both early and late negative emotion effects on the P2 and late positivity potentials, while older adults showed both negative and positive emotion effects on the late positivity potentials. The results suggest distinct processing mechanisms for emotion words in young and older adults, involving both bottom–up and top–down mechanisms, which support the socioemotional selectivity theory.

Optimizing the Agricultural Internet of Things (IoT) with Edge Computing and Low-Altitude Platform Stations

Using low-altitude platform stations (LAPSs) in the agricultural Internet of Things (IoT) enables the efficient and precise monitoring of vast and hard-to-reach areas, thereby enhancing crop management. By integrating edge computing servers into LAPSs, data can be processed directly at the edge in real time, significantly reducing latency and dependency on remote cloud servers. Motivated by these advancements, this paper explores the application of LAPSs and edge computing in the agricultural IoT. First, we introduce an LAPS-aided edge computing architecture for the agricultural IoT, in which each task is segmented into several interdependent subtasks for processing. Next, we formulate a total task processing delay minimization problem, taking into account constraints related to task dependency and priority, as well as equipment energy consumption. Then, by treating the task dependencies as directed acyclic graphs, a heuristic task processing algorithm with priority selection is developed to solve the formulated problem. Finally, the numerical results show that the proposed edge computing scheme outperforms state-of-the-art works and the local computing scheme in terms of the total task processing delay.

Transcranial Direct-current Stimulation of the Dorsolateral Prefrontal Cortex Modulates Voluntary Task-order Coordination in Dual-task Situations.

Dual tasks (DTs) require additional control processes that temporally coordinate the processing of the two component tasks. Previous studies employing imaging as well as noninvasive stimulation techniques have demonstrated that the dorsolateral prefrontal cortex (dlPFC) is causally involved in these task-order coordination processes. However, in these studies, participants were instructed to match their processing order to an externally provided and mandatory order criterion during DT processing. Hence, it is still unknown whether the dlPFC is also recruited for rather voluntary order control processes, which are required in situations that allow for intentional and internally generated order choices. To address this issue, in two experiments, we applied anodal (Experiment 1) and cathodal (Experiment 2) transcranial direct-current stimulation during a random-order DT in which participants could freely decide about their order of task processing. In our results, we found facilitatory and inhibitory effects on voluntary task-order coordination because of anodal and cathodal transcranial direct-current stimulation, respectively. This was indicated by shorter RTs when participants intentionally switched the task order relative to the preceding trial during anodal as well as a reduced tendency to switch the task order relative to the preceding trial during cathodal stimulation compared with the sham stimulation. Overall, these findings indicate that the dlPFC is also causally involved in voluntary task-order coordination processes. In particular, we argue that the dlPFC is recruited for intentionally updating and implementing task-order information that is necessary for scheduling the processing of two temporally overlapping tasks.

Integrated convolutional kernel based on two-dimensional photonic crystals.

Optical neural networks (ONNs) exhibit significant potential for accelerating artificial intelligence task processing due to their low latency, high bandwidth, and parallel processing capabilities. Photonic crystals (PhCs) are extensively utilized in integrated optoelectronics because of their unique photonic bandgap properties and precise control of light waves. In this study, we propose an optical reconfigurable convolutional kernel based on PhCs. This kernel can perform convolutional operations on weights by constructing a PhC weight bank. The convolutional kernel demonstrates exceptional performance within the developed optical convolutional neural network framework, successfully realizing various image edge processing tasks. It achieves blind recognition accuracies of 97.81% for the MNIST dataset and 80.31% for the Fashion-MNIST dataset. This study not only demonstrates the feasibility of constructing optical neural networks based on PhCs but to our knowledge, also offers new avenues for the future development of optical computing.

Enhancing 5G Vehicular Edge Computing Efficiency with the Hungarian Algorithm for Optimal Task Offloading

The rapid advancements in vehicular technologies have enabled modern autonomous vehicles (AVs) to perform complex tasks, such as augmented reality, real-time video surveillance, and automated parking. However, these applications require significant computational resources, which AVs often lack. To address this limitation, Vehicular Edge Computing (VEC) has emerged as a promising solution, allowing AVs to offload computational tasks to nearby vehicles and edge servers. This offloading process, however, is complicated by factors such as high vehicle mobility and intermittent connectivity. In this paper, we propose the Hungarian Algorithm for Task Offloading (HATO), a novel approach designed to optimize the distribution of computational tasks in 5G-enabled VEC systems. HATO leverages 5G’s low-latency, high-bandwidth communication to efficiently allocate tasks across edge servers and nearby vehicles, utilizing the Hungarian algorithm for optimal task assignment. By designating an edge server to gather contextual information from surrounding nodes and compute the best offloading scheme, HATO reduces computational burdens on AVs and minimizes task failures. Through extensive simulations in both urban and highway scenarios, HATO achieved a significant performance improvement, reducing execution time by up to 75.4% compared to existing methods under full 5G coverage in high-density environments. Additionally, HATO demonstrated zero energy constraint violations and achieved the highest task processing reliability, with an offloading success rate of 87.75% in high-density urban areas. These results highlight the potential of HATO to enhance the efficiency and scalability of VEC systems for autonomous vehicles.

Expertise-dependent visuocognitive performance of chess players in mating tasks: evidence from eye movements during task processing

Expertise-dependent visuocognitive performance of chess players in mating tasks: evidence from eye movements during task processing

Neural changes in sign language vocabulary learning: Tracking lexical integration with ERP measures

The present study aimed to investigate the neural changes related to the early stages of sign language vocabulary learning. Hearing non-signers were exposed to Catalan Sign Language (LSC) signs in three laboratory learning sessions over the course of a week. Participants completed two priming tasks designed to examine learning-related neural changes by means of N400 responses. In a semantic decision task, participants evaluated whether written Catalan word pairs were semantically related or not. The experimental manipulation included prime-target phonological overlap (or not) of the corresponding LSC sign translations. In a LSC primed lexical decision task, participants saw pairs of signs and had to determine if the targets were real LSC signs or not. The experimental design included pairs of signs that were semantically related or unrelated. The results of the LSC lexical decision task showed N400 lexicality and semantic priming effects in the third session. Also in the third session, N400 effects related to the activation of LSC phonology were observed during word processing in the semantic decision task. Overall, our findings suggest rapid neural changes occurring during the initial stages of intensive sign language vocabulary training. The results are discussed in relation to the temporality of lexicality and semantic effects, as well as their potential relation to linguistic features of sign languages.

Dual-timescale resource management for multi-type caching placement and multi-user computation offloading in Internet of Vehicle

In Internet of Vehicle (IoV), edge computing can effectively reduce task processing delays and meet the real-time needs of connected-vehicle applications. However, since the requirements for caching and computing resources vary across heterogeneous vehicle requests, a new challenge is posed on the resource management in the three-tier cloud–edge–end architecture, particularly when multi users offload tasks in the same time. Our work comprehensively considers various scenarios involving the deployment of multiple caching types from multi-users and the distinct time scales of offloading and updating, then builds a joint optimization caching placement, computation offloading and computational resource allocation model, aiming to minimize overall latency. Meanwhile, to better solving the model, we propose the Multi-node Collaborative Caching, Offloading, and Resource Allocation Algorithm (MCCO-RAA). MCCO-RAA utilizes dual time scales to optimize the problem: employing a Bellman optimization idea-based multi-node collaborative greedy caching placement strategy at large time scales, and a computational offloading and resource allocation strategy based on a two-tier iterative Deep Deterministic Policy Gradient (DDPG) and cooperative game at small time scales. Experimental results demonstrate that our proposed scheme achieves a 28% reduction in overall system latency compared to the baseline scheme, with smoother latency variations under different parameters.

Photonic reservoir computing for parallel task processing based on a feedback-free spin-polarized VCSEL

Time delayed reservoir computing (RC) is a novel artificial neural network that is easy to implement in hardware due to its extremely simple structure. Because of its time-division multiplexed information processing, laser-based photonic time-delayed RCs usually realize parallel processing with polarization/wavelength multiplexing. However, the performance of two different tasks is difficult to regulate separately and simultaneously in the time delayed RC system, especially for the chip-scale configuration. Here, we propose a feedback-free RC system based on a spin-polarized vertical-cavity surface-emitting semiconductor laser (VCSEL), which simplifies the whole system structure and can process time series prediction and waveform recognition tasks in parallel, with employing the input and output coding to provide the effect from past states. By separately setting the number of past states introduced by the coding for the two tasks, the performance of the two tasks can be adjusted respectively. Furthermore, by appropriately tuning the pump polarization ellipticity which is the unique feature for the spin-polarized VCSEL, the computational ability of the proposed RC can be focused on one of the two parallel tasks. Therefore, the proposed RC system is capable of dealing with different tasks with high performance, and also expected to provide a viable solution for integrated neuromorphic computing systems due to its compact, feedback-free structure.

A Comparative Study of Transformer-based Models for Hate-Speech Detection in English-Kiswahili Code-Switched Social Media Text

The transformer architecture, first introduced in 2017 by researchers at Google, has revolutionized natural language processing in various tasks, including text classification. This architecture formed the basis of future models such as those used in hate speech detection in code-switched text. In this research, we conduct a comparative study of transformer-based models for hate speech detection in English-Kiswahili code-switched text. First, the models were compared as feature extractors using a traditional classifier and then as end-to-end classifiers. The three multilingual transformer-based models compared include mBERT, mDistilBERT and XLM-RoBERTa, using SVM as the traditional classifier for the extracted features. The HateSpeech_Kenya dataset, sourced from Kaggle, was utilized in this study. As a feature extractor, mBERT’s hidden states trained the highest-performing SVM with an accuracy of 0.5461 and a macro f1 score of 0.40. Among the three models evaluated, XLM-RoBERTa achieved the highest accuracy of 0.6069 and a macro f1 score of 0.49 on a balanced dataset. In contrast, mBERT achieved the highest accuracy of 0.7820 and a macro f1 score of 0.53 on an imbalanced dataset. The comparative study establishes that using transformer-based models as end-to-end classifiers generally performs better than using them as feature extractors with traditional classifiers. This is because directly training the models allows them to learn more task-specific features. Furthermore, the varying performance across balanced and imbalanced datasets highlights the need for careful model selection based on the dataset characteristics and specific task requirements.

Investigation of students' use of online information in higher education using eye tracking

To successfully learn using freely available (and non-curated) Internet resources, university students need to search for, critically evaluate and select online information, and verify sources (defined as Critical Online Reasoning, COR). Recent research indicates substantial deficits in COR skills among higher education students. To support students in learning how to critically use online information for their learning, it is necessary to better understand the strategies and practices that might elicit less critically-reflective judgments about online information and thus account for such deficits. To this end, using eye tracking data, we investigate how the COR behaviors of students who critically-reflectively evaluate the credibility of online information (‘high performers’) differ from those of students who do not critically-reflectively evaluate it (‘low performers’): 19 students were divided into high and low performers according to their performance in the newly developed Critical Online Reasoning Assessment (CORA). The fixation and dwell times of both groups during CORA task processing were compared regarding time spent on the different processing steps and eye movements on the visited web pages. The results show noticeable differences between the two groups, indicating that low performers indeed approached the task rather heuristically than systematically, and that COR skills require targeted and effective training in higher education.

The benefit of choice on task performance: Reduced difficulty effects in free-choice versus forced-choice tasks.

We investigated how self-determined (free) versus imposed (forced) choices influence task performance. To this end, we examined how changes in perceptual and central decision-processing difficulties affect task performance in an environment where free-choice and forced-choice tasks were intermixed. In Experiments 1 (N = 43) and 2 (N = 42), perceptual processing difficulty was varied by altering colored dot proportions (easy vs. hard color discrimination task). In Experiment 3 (N = 58), decision-processing difficulty was adjusted by changing the rotation degree of letters (easy vs. hard letter rotation task). Across all experiments, both free-choice and forced-choice performance were more impaired with harder stimuli, but this effect was generally less pronounced in freely chosen tasks. Specifically, this was evident from significant interactions between processing mode (free vs. forced) and difficulty (easy vs. hard) in the mean reaction times (RTs) for the tasks with the difficulty manipulation. Thus, processing in free-choice tasks is generally less affected by environmental changes (i.e., variation in information difficulties). We discuss how the benefit of self-determined choices over imposed choices can be explained by motivational and performance-optimization accounts, while also considering the finding that participants adjusted their task choices toward tasks with easier stimuli (i.e., significant main effect of task difficulty on choosing the task with the difficulty manipulation). Specifically, we discuss how having control over task choices might lead to more stable information processing and allow people to choose more difficult tasks when this increased difficulty has a relatively small impact on their performance.

Dynamic scheduling of workshop resource in cloud manufacturing environment

In the cloud manufacturing environment, workshop resource scheduling serves as a pivotal component, characterized by increased dynamics and complexities. Nevertheless, existing dynamic scheduling methods are often limited to solving specific dynamic events. Thus, considering the actual workshop resource scheduling in a cloud manufacturing environment, this article examines the methods to address unexpected events including randomly arriving tasks, resource breakdown, as well as resource maintenance. Besides, a dynamic scheduling method based on the Game Theory, considering workshop capacity in cloud manufacturing, was developed. In the first place, the priority of workshop tasks was evaluated by Game Theory, and the optimal task processing sequence in the workshop was determined to maximize benefits. Secondly, to verify the dynamic regulation performance of the method, it was combined with the particle swarm optimization (PSO) algorithm considering multi-objective factors to obtain an ameliorated PSO algorithm addressing the challenge of resource optimization scheduling in a genuinely dynamic workshop environment. Finally, this method was tested through a case study, and the results demonstrate that it can achieve superior dynamic and static performance compared to alternative algorithms.

A lightweight network-based sign language robot with facial mirroring and speech system

Human–robot interaction is an essential capability for humanoid robots to enter the physical world and become companions in people’s lives, learning, and work. While the majority of current research focuses on the voice-based interactions of robots, yet over 60% of communication occurs through nonverbal behaviors, such as facial expressions and hand gestures. Endowing robots with the ability to communicate through nonverbal behavior not only enhances the interactive experience with robots but also provides a potential communication tool for individuals with hearing or speech impairments. Here, we develop a humanoid robot capable of adjusting facial movements by driving servos, and design a novel framework for the robot to integrate sign language recognition and facial landmark detection algorithms. This framework facilitates the robot recognize sign language and translate it into spoken language, while also imitating the facial expressions of the signers. To achieve this, we also propose a lightweight deep learning network called RealTimeSignNet for real-time sign language recognition. Leveraging lightweight 3D convolution modules and time-dependent constraints, this model adapts to various time scales, ensuring efficient processing of sign language recognition tasks. Experimental results demonstrate the outstanding performance of the RealTimeSignNet model on mainstream sign language datasets, achieving an accuracy of 88.1% on the large continuous sign language dataset (continuous SLR), 98.2% on the isolated sign language dataset (SLR 500), and 91.50% on the English sign language dataset (WLAS). The overall assessment demonstrates that our humanoid robot is capable of recognizing sign language and translating it into spoken language, while imitating the facial emotions, providing a comprehensive solution to the communication challenges faced by individuals with hearing and speech impairments.

System Dynamics Modeling For Co2 Emission Reduction Through Paper Replacement By Electronic Processes In A Public Institution

Background: The study addresses the importance of reducing CO2 emissions to mitigate climate change, focusing on replacing paper use with electronic processes in a public institution. Paper usage significantly contributes to CO2 emissions, while electronic management can reduce these emissions. The study is a proposal that encouraged the electronic processing of administrative tasks to ensure speed. Decree 8.539/2015 made the electronic processing of documents in federal public administration mandatory. The Judiciary branch was a pioneer in implementing electronic processes in Brazilian public institutions. For this article, the Systems Dynamics (SD) methodology was used, and a simulation model was developed to assess the environmental impact of process automation at the Federal University of Santa Maria (UFSM). Key results highlighted the reduction of CO2 emissions and cost savings. The implementation of digital processes resulted in a significant reduction in CO2 emissions, and the adoption of electronic processes generated financial savings. Therefore, System Dynamics modeling is a useful tool for institutions to plan scenarios and promote environmental sustainability. The transition to electronic processes in public institutions can bring significant environmental and economic benefits.

A multiple-input fluid queue model for performance evaluation of fog server in an intelligent vehicular network

Nowadays, there is a growing trend towards fog computing which is a relatively new concept that has been introduced as an extension of cloud computing. It is considered as a promising paradigm for intelligent vehicular networks due to its ability to reduce delays and enhance network efficiency. The utilization of fog computing plays a crucial role in tackling the distinct obstacles encountered in vehicular computing, including issues like real-time data processing, and limitations on bandwidth. By relocating computational resources closer to the network’s edge, it enhances the effectiveness, dependability, and safety of vehicular applications while simultaneously boosting privacy and security measures. In addition, fog offloading has played a pivotal role in fog computing and edge computing structures. Its objective is to efficaciously allocate the data and assign the processing of tasks among edge devices, fog nodes, and cloud resources. Efficient fog offloading strategies are adaptable, scalable, and are able to handle faults, making them indispensable for optimizing these architectures. Although efficiency and quality of service are crucial objectives in a fog computing based intelligent vehicular network environment, performance remains a significant concern that cannot be overlooked. In light of this, to evaluate the efficacy of the probabilistic offloading approach on a fog server, a thorough performance evaluation is necessary. This research proposes a fluid queue approach that accounts for the constant flow of data packets while evaluating a fog server’s performance. For a fog computing-based intelligent vehicular network (FCIVN) with numerous heterogeneous smart vehicles (SVs), we construct a multiple-input fluid queue to model the tasks handed over to the fog server in order to evaluate its performance. In an FCIVN, the arrivals are drawn from various sources at the fog server. Accordingly, in the proposed model the fluid queue is modulated by more than one independent and distinct finite-state birth–death processes (BDPs) which control the variable inflow, and another BDP which controls the variable outflow. We present the details concerning an intermediate fog server’s buffer occupancy distribution within an FCIVN. Further, we assess the performance measures in terms of the offered load, expected buffer-level, average throughput and average latency of the tasks in an FCIVN. Finally, quantitative illustrations are presented to demonstrate the appropriateness of the fluid queue model developed in this study. The results are found to be in consistence with the expected behaviour of these performance indicators.

A two-stage strategy for brain-inspired unsupervised learning in spiking neural networks

Spiking Neural Networks (SNNs) are emerging as a promising model in the field of neuromorphic computing, offering possibilities for efficient bio-inspired computing. Unsupervised learning in SNNs plays a crucial role in exploring neural network dynamics and achieving efficient artificial intelligence systems. However, current unsupervised learning in SNNs mostly relies on single-stage learning strategies, which limits the possibility of combining diverse learning mechanisms and network structures, resulting in networks that lack flexibility and adaptability for low-power task processing. Inspired by the multi-stage and multi-strategy learning in the human brain, this paper proposes a two-stage learning strategy from coarse to fine to enhance the efficiency of unsupervised learning in SNNs. To quickly extract structural features from spike data in the first stage, we propose the Spiking-SOM algorithm, which effectively learns patterns in spike data and achieves clustering effects. Additionally, we integrate Rate encoding and Temporal encoding through a visual attention mechanism, where this hybrid encoding helps meet the need for rapid information processing in the first stage, while also aiding in detailed feature representation in the second stage. Compared to the traditional single-stage learning framework, this work maintains high accuracy in unsupervised classification while significantly reducing computational costs and speeding up training. This exploration offers a new direction for future learning frameworks in SNNs, helping to further improve the energy efficiency of the network.

Caribbean libraries 2.0: the robotic revolution in Caribbean academic library spaces

Purpose This paper is the genesis for robots and robotic technology and their introduction to the Caribbean Academic library community. This paper aims to explore the specific areas that this technology can improve as well as their adaptability and dynamic yet multifaceted nature it possesses. Design/methodology/approach A thorough assessment of literature was done of all developed libraries that are employing the services of robots and robotic technology in their daily operations. Additionally, a meticulous analysis was done of all Caribbean Libraries that have explored, are currently exploring or actively explored the implementation of robots and robotic technology for effective use in their libraries. Findings Seamless functionality as well as the reduction of mundane repetitive tasks by library staff is at the fore. Efficacy and heightened levels of accuracy are also found to be a great factor for implementation as well as speed of retrieval and offsite storage are further benefits to the implementation of robots and robotic technology. Research limitations/implications This research primarily assessed material on robotics and robotic technology that offers unprecedented efficacy and accuracy in the processing of information and tasks assigned as well as smooth location and retrieval of library material resulting in reduction in wait time for all library users. Originality/value To the best of the author’s knowledge, this paper is the first of its kind and is intended to trigger a “light bulb” in the minds of decision-makers and managers of Library spaces as to the potential robots and robotic technology has on fostering greater levels of efficacy in certain key areas of libraries and help improve user services while adding to the theoretical body of knowledge available in the field on this fast rising area.

A survey on pragmatic processing techniques

Pragmatics, situated in the domains of linguistics and computational linguistics, explores the influence of context on language interpretation, extending beyond the literal meaning of expressions. It constitutes a fundamental element for natural language understanding in machine intelligence. With the advancement of large language models, the research focus in natural language processing has predominantly shifted toward high-level task processing, inadvertently downplaying the importance of foundational pragmatic processing tasks. Nevertheless, pragmatics serves as a crucial medium for unraveling human language cognition. The exploration of pragmatic processing stands as a pivotal facet in realizing linguistic intelligence. This survey encompasses important pragmatic processing techniques for subjective and emotive tasks, such as personality recognition, sarcasm detection, metaphor understanding, aspect extraction, and sentiment polarity detection. It spans theoretical research, the forefront of pragmatic processing techniques, and downstream applications, aiming to highlight the significance of these low-level tasks in advancing natural language understanding and linguistic intelligence.