Task Processing Research Articles

The More, the Better? Exploring the Effects of Modal and Codal Redundancy on Learning and Cognitive Load: An Experimental Study

This study explores how receiving identical information from different sources affects learning and cognitive load, focusing on two types of redundancy: modal redundancy, where redundant information comes from two visual sources (images and written text), and codal redundancy, where redundant information comes from two sources of different modalities which utilize the same symbol system (spoken and written text). Using a 2 × 2 between-subjects design involving modal (yes/no) and codal (yes/no) redundancy, 158 participants completed twenty learning tasks, consisting of ten construction and ten recall tasks. Additionally, they rated their cognitive load by indicating their perceived task difficulty and mental load. Overall, results indicate positive main effects of modal redundancy and negative effects of codal redundancy on learning and cognitive load. Furthermore, significant interaction effects suggest that modal redundancy may counterbalance the negative effects of codal redundancy, implying a compensatory mechanism in cognitive processing for construction tasks. These results highlight the importance of considering both modal and codal redundancy and their interaction in instructional design.

Unlocking Potential: Assessing Motivation, Learning Strategies, and Cognitive Load in Flipped Learning during Online Teaching in Higher Education

Flipped learning has become the focus in many educational contexts especially with the integration of technological tools in learning space for the last decade. In flipped learning, learners perform surface learning at home, whereas deep learning such as problem-solving, critical thinking exercises etc. is covered in the classroom which potentially increases motivation and satisfaction of learners. Cognitive load, the assumed load of the working memory caused by the processing of tasks, has also attracted attention in online learning settings which pose different processing demands. This study took place in the extraordinary settings of education during the Covid-19 pandemic which required online education for all education levels. A fourth-year course of an ELT program was delivered through a flipped learning approach during online education. Learners’ motivation, use of learning strategies, satisfaction and cognitive load levels were calculated at the end of the intervention and their perceptions regarding the experience were investigated through an open-ended questionnaire. The results showed that their motivation and satisfaction levels were quite high as well as their strategy uses whereas cognitive load levels were comparatively low. Inferential statistics showed that relationships exist between and among the motivation, learning strategies and cognitive load at various levels. Motivation emerged to be a significant predictor of course satisfaction whereas time spent for the flipped lectures predicted course achievement. Learners’ perceptions of flipped learning process were generally positive while they also articulated some drawbacks of it like instructional tools-related and individual learner-related problems.

Perceptual target discriminability modulates the Simon effect beyond the fading of distractor-based activation: Insights from delta plots and diffusion model analyses.

The visual Simon task is widely employed to explore the underlying mechanisms of sensorimotor processing in the presence of task-relevant (targets) and task-irrelevant (distracting) location information. Critically, the Simon effect is considered as an indicator of action-related interference resulting from distractor-based activation, which fades out over time. In this study, we tested whether attenuated Simon effects with slower task processing may be fully explained by the fading of distractor-based response activation. To that end, we selectively manipulated perceptual target discriminability by varying the ratio of differently colored dots within (Experiment 1) and between blocks (Experiment 2). According to pure fading activation accounts, the negative-going delta plots of the two discriminability conditions should overlap across the entire reaction time (RT) distribution. In contrast to this prediction, the negative-going DPs for the two discriminability conditions did not overlap in either experiment. Instead, the Simon effect was either consistently smaller (Experiment 1) or larger (Experiment 2) across the entire RT distribution in the easy condition compared to the hard condition. This result pattern indicates that perceptual target discriminability affected conflict resolution beyond the mere fading of distractor-based activation. Exploratory model-based analyses suggest a stronger processing of relevant perceptual information with more discriminable targets, which may counteract the influence of distracting location information. However, as the exact effects of discriminability on conflict processing seem to depend on variation mode (trialwise vs. blockwise), the importance of global strategic effects is also highlighted. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Adaptive task migration strategy with delay risk control and reinforcement learning for emergency monitoring

The timely and reliable handling of post-disaster emergency monitoring tasks is crucial for effective rescue operations. UAV-assisted edge computing plays a pivotal role in the rapid deployment of such systems. However, challenges persist due to communication and computation resource bottlenecks when dealing with delay-sensitive monitoring tasks. In dynamic post-disaster environments, effective task scheduling and resource allocation decisions directly impact the system’s ability to process tasks. Therefore, this paper proposes an adaptive task migration decision-making system for emergency monitoring tasks in UAV-assisted edge computing. Firstly, We decomposed the optimization objectives based on the task processing workflow, then devised a stepwise delay risk control and resource recovery mechanism based on early discarding. Secondly, by integrating multi-agent reinforcement learning (MARL), optimal strategies for task offloading, UAV queue scheduling, and communication resource allocation are learned to enhance the decision system’s environmental awareness and maximize the successful completion of emergency monitoring tasks. Simulation experiments demonstrate that the algorithm significantly improves the success rate of migration tasks and data processing capacity, thereby validating its convergence and effectiveness.

Queuing‐based energy‐efficient processing algorithm for smart transportation through V2V communication

SummaryApplications of intelligent systems installed in vehicles require substantial computational processing for various tasks. These intensive computations result in high energy consumption and power demands within vehicles. Computational offloading based on Vehicle‐to‐Vehicle (V2V) communication in vehicular fog computing (VFC) has been proposed as a promising solution to enhance energy efficiency in transportation applications. In this paper, the primary objective is addressing this concern by identifying the optimal nearby vehicle that minimizes energy consumption for the offloading and execution of computational tasks. Therefore, a decision‐making and intelligent task offloading mechanism based on queueing theory is proposed. By modeling the problem environment based on queueing theory and modeling the behavior of distributed tasks with discrete‐time Markov chain, the proposed solution can predict the future behavior of vehicles in selecting the most energy‐efficient processing node. Therefore, this paper investigates three energy decision parameters based on queueing theory extracted from the Markov model to enhance the performance of the proposed algorithm. Experimental results demonstrate that the computational energy parameter achieves the most significant improvement. The proposed algorithm outperforms previous methods, improving energy‐efficient system performance by 6.25% and 2.67%, and reducing delivery failure rate by 6.52% and 2.72%. It also decreases overall transportation system processing energy consumption by 0.05% for 100–500 vehicle arrival rates, resulting in an average total processing energy consumption of 0.48%.

The effect of task processing management on energy consumption at the edge of Internet of things network with using reinforcement learning method

In this paper, the task processing approach at the edge of the Internet of Things (IoT) network in improving energy consumption was investigated. First, the subject of maximizing the use of energy is defined as the main problem and then based on the state of assignment of task processing from end devices at the edge of the network to smart gateways and local servers at the edge using possible low bandwidth and low battery consumption, the problem is divided into two subject which are obtained independently. After modeling by recombining these two sub-problems, the subject of maximizing the utility gain and battery life of the end devices is solved despite the limitations of processing time and energy resources.Considering that the environment of the problem and how the terminal devices are connected with the edge devices are unknown, a repetitive reinforcement learning algorithm has been used to create an optimal solution to maximize the operational benefit of the energy consumed in edge processing. The results achieved from the simulation show the increase in network load and processing overhead with the increase in the number of active devices. The suggested method, while increasing the edge processing speed, decreases the delay and maximizes the performance gain of the IOT edge system compared to the central cloud system. It should be noted that when the number of active end devices dramatically increases, the energy consumption and bandwidth at the network edge improves, and energy consumption at the network edge decreases lonely by 12.5%.

Energy-efficiency optimization for heterogeneous computing-assisted NOMA-MEC edge AI tasks

Edge artificial intelligence (AI) is an emerging paradigm that leverages edge computing to pave the last-mile delivery of AI. To satisfy the increasing demand for high-performance computing and low latency of edge service, heterogeneous computing accelerators, especially Neural Processor Units (NPUs), are widely deployed on edge nodes. However, the edge AI heterogeneous computing system encounters the challenge of energy constraints. In this paper, we investigate the energy efficiency (EE) optimization problem in heterogeneous computing-assisted non-orthogonal multiple access mobile edge computing (NOMA-MEC) network model. Edge devices are configured with CPU-NPU heterogeneous computing processors to improve the AI task processing, and NOMA is applied to edge task offloading to enable massive connectivity. The system-centric energy efficiency maximization problem is studied. We formulate a system-centric energy efficiency maximization problem. To solve this problem, we decouple the original problem into two subproblems, i.e., the optimization problems of heterogeneous computing workload splitting and task offloading. A heuristic algorithm and binary search algorithm are proposed to optimize NOMA user pairing and task offloading delay, respectively. Furthermore, we propose a multi-objective alternative iterative algorithm to optimize system energy efficiency. Numerical results show that the proposed scheme can significantly improve the energy efficiency of heterogeneous computing-assisted NOMA-MEC networks compared to the existing baselines.

Towards a Flexible Semantic Guided Model for Single Image Enhancement and Restoration.

Low-light image enhancement (LLIE) investigates how to improve the brightness of an image captured in illumination-insufficient environments. The majority of existing methods enhance low-light images in a global and uniform manner, without taking into account the semantic information of different regions. Consequently, a network may easily deviate from the original color of local regions. To address this issue, we propose a semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model. We concentrate on incorporating semantic knowledge from three key aspects: a semantic-aware embedding module that adaptively integrates semantic priors in feature representation space, a semantic-guided color histogram loss that preserves color consistency of various instances, and a semantic-guided adversarial loss that produces more natural textures by semantic priors. Our SKF is appealing in acting as a general framework in the LLIE task. We further present a refined framework SKF++ with two new techniques: (a) Extra convolutional branch for intra-class illumination and color recovery through extracting local information and (b) Equalization-based histogram transformation for contrast enhancement and high dynamic range adjustment. Extensive experiments on various benchmarks of LLIE task and other image processing tasks show that models equipped with the SKF/SKF++ significantly outperform the baselines and our SKF/SKF++ generalizes to different models and scenes well. Besides, the potential benefits of our method in face detection and semantic segmentation in low-light conditions are discussed. The code and pre-trained models have been publicly available at https://github.com/langmanbusi/Semantic-Aware-Low-Light-Image-Enhancement.

Retraction Note: Database task processing optimization based on performance evaluation and machine learning algorithm

Retraction Note: Database task processing optimization based on performance evaluation and machine learning algorithm

Stimulus-related oscillatory brain activity discriminates hoarding disorder from OCD and healthy controls

Hoarding disorder (HD) and obsessive-compulsive disorder (OCD) are highly comorbid and genetically related, but their similarities and differences at the neural level are not well characterized. The present study examined the time-frequency information contained in stimulus-related EEG data as participants worked on a visual flanker task. Three groups were included: participants diagnosed with HD (N = 33), OCD (N = 26), and healthy controls (N = 35). Permutation-controlled mass-univariate analyses found no differences between groups in terms of the magnitude of the oscillatory responses. Differences between groups were found selectively for phase-based measures (phase-locking across trials and across sensors) in time ranges well after those consistent with initial visuocortical processes, in the alpha (10 Hz) as well as theta and beta frequency bands, centered around 6 Hz and 15 Hz, respectively. Specifically, HD showed attenuated phase locking in theta and alpha compared to OCD and HC, while OCD showed heightened inter-site phase locking in alpha/beta. Including age as a covariate attenuated, but did not eliminate, the group differences. These findings point to signatures of cortical dynamics and cortical communication task processing that are unique to HD, and which are specifically present during higher-order visual cognition such as stimulus-response mapping, response selection, and action monitoring.

LSMOF-AD: Three-Stage Optimization Approach with Adaptive Differential for Large-Scale Container Scheduling

Container technology has gained a widespread application in cloud computing environments due to its low resource overhead and high flexibility. However, as the number of containers grows, it becomes increasingly challenging to achieve the rapid and coordinated optimization of multiple objectives for container scheduling, while maintaining system stability and security. This paper aims to overcome these challenges and provides the optimal allocation for a large number of containers. First, a large-scale multi-objective container scheduling optimization model is constructed, which involves the task completion time, resource cost, and load balancing. Second, a novel optimization algorithm called LSMOF-AD (large-scale multi-objective optimization framework with muti-stage and adaptive differential strategies) is proposed to effectively handle large-scale container scheduling problems. The experimental results show that the proposed algorithm has a better performance in multiple benchmark problems compared to other advanced algorithms and can effectively reduce the task processing delay, while achieving a high resource utilization and load balancing compared to other scheduling strategies.

Integrated Quality of Service for Offline and Online Services in Edge Networks via Task Offloading and Service Caching.

Edge servers frequently manage their own offline digital twin (DT) services, in addition to caching online digital twin services. However, current research often overlooks the impact of offline caching services on memory and computation resources, which can hinder the efficiency of online service task processing on edge servers. In this study, we concentrated on service caching and task offloading within a collaborative edge computing system by emphasizing the integrated quality of service (QoS) for both online and offline edge services. We considered the resource usage of both online and offline services, along with incoming online requests. To maximize the overall QoS utility, we established an optimization objective that rewards the throughput of online services while penalizing offline services that miss their soft deadlines. We formulated this as a utility maximization problem, which was proven to be NP-hard. To tackle this complexity, we reframed the optimization problem as a Markov decision process (MDP) and introduced a joint optimization algorithm for service caching and task offloading by leveraging the deep Q-network (DQN). Comprehensive experiments revealed that our algorithm enhanced the utility by at least 14.01% compared with the baseline algorithms.

A many-to-many matching with externalities solution for parallel task offloading in IoT networks

The efficient and timely execution of tasks is a fundamental challenge in the realm of future Internet of Things (IoT) networks. To address this challenge, fog devices are often deployed close to end devices to facilitate task processing on behalf of IoT nodes. One strategy for improving task computational delay is to employ parallel task offloading, in which tasks are subdivided into subtasks and sent to different fog devices for execution in parallel. However, allocating computational resources to fog nodes and mapping these resources to IoT subtasks is a key challenge in this area. This work models the parallel task offloading problem as a graph-matching problem and utilizes a many-to-many matching technique to achieve a stable mapping of IoT subtasks to fog node resources. Unfortunately, the proposed solution is subject to the problem of externalities due to the dynamic preference profiling of fog nodes. To address this issue, we employ an iterative algorithm to resolve any blocking pairs that may arise. Our results demonstrate that the proposed technique reduces the task latency by 29% as compared to other matching-based techniques available in the literature.

Efficient Workflow Scheduling in Edge Cloud-Enabled Space-Air-Ground-Integrated Information Systems

To address the challenges posed by the dynamism, high latency, and resource scarcity in integrated air-space-ground hybrid edge cloud environments on task completion times and node load, we designed a task scheduling system for scenarios involving the transmission and processing of interdependent tasks. This system integrates a graph neural network with attention mechanism and deep reinforcement learning. Specifically, we employ a graph encoder to extract features from DAG tasks and resources. Task scheduling solutions for dynamic environments are then generated using attention mechanism-equipped graph decoder, which are subsequently optimized based on performance metrics through the use of an Advantage Actor-Critic algorithm. Experimental results indicate that this algorithm performs well in terms of completion time and node load balance across tasks with different workflow structures, demonstrating its adaptability to highly dynamic edge cloud environments.

Efficient Task Offloading for Mobile Edge Computing in Vehicular Networks

In vechcular networks, a promising approach to enhance vehicle task processing capabilities involves using a combination of roadside base stations or vehicles, there are two challenges when integrating the two offloading modeth: 1) the high mobility of vehicles can easily lead to connectivity interruptions between nodes, which in turn affects the processing of the tasks that are being offloaded; and 2) vehicles on the road are not completely trustworthy, and vehicle tasks that contain private information may suffer from result errors or privacy leakage and other problems. This paper investigates the computing offloading problem for minimizing task completion delay in vehicular networks. Specifically, we design a trust model for mobile in-vehicle networks and construct a migration decision problem to minimize the overall delay of task execution for all vehicle users. The simulation results show that the scheme proposed in this paper can effectively reduce the execution delay of the task compared to the baseline scheme.

Event Vision-based Corner Detection with Count-normalized Multi-Layer Perceptron and Throughput Indicator

The advancement of event cameras has sparked a revolution in imaging technology, presenting exciting opportunities for vision-based measurement tasks. Event cameras operate on an innovative asynchronous imaging principle, which offers several advantages over traditional cameras, including ultra-high dynamic range and exceptional temporal resolution. As a result, event cameras excel in challenging environments characterized by motion blur, overexposure, or underexposure, outperforming conventional frame-based cameras. However, the asynchronous signal streams generated by event cameras pose compatibility challenges with existing vision-based measurement algorithms. This paper focuses specifically on corner detection, a critical vision-based measurement task, tailored for event cameras. To address this challenge, we propose novel corner detectors that leverage advanced optimization techniques, including enhanced time surface representations, multi-layer perceptron classifiers, and an innovative throughput mechanism. Through rigorous experimentation, our method consistently shows lower projection errors compared to state-of-the-art methods across all datasets while also maintaining longer tracking times in low-textured scenarios. Specifically, our CMLP and CMLP-T methods achieve an average valid tracking rate of 83.38% and 84.65%, respectively, on the DAVIS240C dataset collection, surpassing all existing methods. We validate the effectiveness of our proposed corner detectors by demonstrating their enhanced performance compared to state-of-the-art methods. Furthermore, our work contributes to the application of machine learning in event signal processing for vision-based measurement tasks, providing insights into optimizing models for the unique characteristics of event cameras.

Task Offloading and Execution in Edge-Cloud Collaborative Network Using Genetic Algorithm

Mobile Edge Computing (MEC) system is now one of the most emerging sectors in wireless communication. It provides computation capability near the edge users and reduces the dependency on the Internet Cloud (IC). The edge users offload the task to the MEC server due to the lack of computing resources for executing resource-hungry applications. MEC servers can serve faster as they are quite close to the edge users but have limited computation resources compared to IC. On the other hand, IC has abundant computation resources but offloading to IC will add extra latency to complete tasks execution. In this scenario, an MEC has to decide intelligently which tasks should be executed by itself and which should be sent to IC. In this paper, we addressed this issue of task assignment and execution either on MEC or on IC and formulated an optimization problem to reduce the task processing latency. The problem is Integer Linear and NP-Complete, thus having higher time complexity. In this regard, we proposed a Genetic algorithm-based Meta-Heuristic method to solve the task completion problem considering the delay constraint of the user. Simulation results of the proposed method indicated improvement in terms of successful task execution, task turnaround time, and better Quality of experience (QoE) compared to the state-of-the-art works. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 9(1), 2022 P 42-51

Non‐orthogonal multiple access‐based task processing and energy optimization in vehicular edge computing networks

SummaryVehicular edge computing (VEC) is envisioned as a promising approach to process explosive vehicle tasks, where vehicles can choose to upload tasks to nearby edge nodes for processing. However, since the communication between vehicles and edge nodes is via wireless network, which means the channel condition is complex. Moreover, in reality, the arrival time of each vehicle task is stochastic, so efficient communication methods should be designed for VEC. As one of the key communication technologies in 5G, non‐orthogonal multiple access (NOMA) can effectively increase the number of simultaneous transmission tasks and enhance transmission performance. In this article, we design a NOMA‐based task allocation scheme to improve the VEC system. We first establish the mathematical model and divide the allocation of tasks into two processes: the transmission process and the computation process. In the transmission process, we adopt the NOMA technique to upload the tasks in batches. In the computation process, we use a high response‐ratio strategy to determine the computation order. Then we define the optimization objective as maximizing task completion rate and minimizing task energy consumption, which is an integer nonlinear problem with lots of integer variables and cannot be solved directly. Through further analysis, we design a heuristics algorithm which we name as the AECO (average energy consumption optimization) algorithm. By using the AECO, we obtain the optimal allocation strategy by constantly adjusting the optimal variables. Simulation results demonstrate that our algorithm has a significant number of advantages.

Network mechanisms of ongoing brain activity’s influence on conscious visual perception

Sensory inputs enter a constantly active brain, whose state is always changing from one moment to the next. Currently, little is known about how ongoing, spontaneous brain activity participates in online task processing. We employed 7 Tesla fMRI and a threshold-level visual perception task to probe the effects of prestimulus ongoing brain activity on perceptual decision-making and conscious recognition. Prestimulus activity originating from distributed brain regions, including visual cortices and regions of the default-mode and cingulo-opercular networks, exerted a diverse set of effects on the sensitivity and criterion of conscious recognition, and categorization performance. We further elucidate the mechanisms underlying these behavioral effects, revealing how prestimulus activity modulates multiple aspects of stimulus processing in highly specific and network-dependent manners. These findings reveal heretofore unknown network mechanisms underlying ongoing brain activity’s influence on conscious perception, and may hold implications for understanding the precise roles of spontaneous activity in other brain functions.

Location Privacy Protection in Edge Computing: Co-Design of Differential Privacy and Offloading Mode

Edge computing has emerged as an innovative paradigm that decentralizes computation to the network’s periphery, empowering edge servers to manage user-initiated complex tasks. This strategy alleviates the computational load on end-user devices and increases task processing efficiency. Nonetheless, the task offloading process can introduce a critical vulnerability, as adversaries may infer a user’s location through an analysis of their offloading mode, thereby threatening the user’s location privacy. To counteract this vulnerability, this study introduces differential privacy as a protective mechanism to obscure the user’s offloading mode, thereby safeguarding their location information. This research specifically addresses the issue of location privacy leakage stemming from the correlation between a user’s location and their task offloading ratio. The proposed strategy is based on differential privacy. It aims to increase the efficiency of offloading services and the benefits of task offloading. At the same time, it ensures privacy protection. An innovative optimization technique for task offloading that maintains location privacy is presented. Utilizing this technique, users can make informed offloading decisions, dynamically adjusting the level of obfuscation in response to the state of the wireless channel and their privacy requirements. This study substantiates the feasibility and effectiveness of the proposed mechanism through rigorous theoretical analysis and extensive empirical testing. The numerical results demonstrate that the proposed strategy can achieve a balance between offloading privacy and processing overhead.