Cognitive Computation and Systems最新文献

An investigation on the effect of mental activity in quality perception is presented using simultaneous measurement of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), in a subject-independent approach. Building a subject-independent model is a harder problem due to noise and high EEG variability between individuals, correlated components analysis (CorrCA) have been proposed to extract significant correlated components for a single subject that experiences multiple identical trials; this is done by identifying spatio-temporal patterns of activity that are well preserved across trials. The aim is to build a model based on neurophysiological data to assess text-to-speech quality. In order to build a subject independent model, we extended the use of CorrCA such that it can be applied to the subject independent model. The authors used two preprocessing steps, namely the subject dependent and the stimulus dependent preprocessing. The second preprocessing used the denoising source separation (DSS) to remove noise/artefact that are subject specific. The discrete convolution is used for data fusion and the support vector machine for regression. With the proposed model, the fusion of EEG and fNIRS performs better than single modality. Using our defined regression accuracy metrics, the authors obtained accuracy of 81.346% for overall impression, 83.28% for valence and 89.714% for arousal. The model compete the baseline that is subject dependent.

Magnetic Resonance Imaging (MRI) based Autism Spectrum Disorder (ASD) detection approaches face various challenges due to variations in brain connectivity patterns, limited sample sizes, and heterogeneity of available data. These challenges make it hard to find consistent imaging markers. To address these issues, researchers have focused on advanced analysis methods, such as multi-modal imaging techniques and graph-based approaches to gain a comprehensive understanding of ASD neurobiology. However, existing graph-based approaches for ASD detection have primarily focused on pairwise similarities between individuals, neglecting individual characteristics and features. A novel framework to detect ASD using a Multi-Scale Enhanced Graph Convolutional Network (MSE-GCN). The framework combines the functional connectivity of resting-state functional MRI (rs-fMRI) with non-imaging phenotype data from Autism Brain Imaging Data Exchange-I (ABIDE-I). The framework uses MSE-GCN to represent individuals as node in a population graph. Each node corresponds to an individual and connects to feature vectors from imaging data. Edge weights between nodes are assigned to integrate phenotypic information. Then, the multiple parallel GCN layers are designed using random walk embedding. The output of these GCN layers is then combined in the fully connected layer to detect ASD effectively. The performance of the framework is evaluated using the ABIDE-I dataset. In addition, Recursive Feature Elimination and Multilayer Perceptron are utilised for feature selection. The outcome of this approach shows more than 10% advancement in accuracy, achieving an accuracy of 83% by incorporating phenotypic data in conjunction with MRI data within a GCN.

The pace of technological advancement is accelerating, and one of the latest developments is the emergence of Augmented Reality (AR) and Mixed Reality (MR) glasses as an extension of smartphones. The key to success lies in innovative research and technology that can reach a wide audience. To ensure a positive user experience, AR/MR glasses must offer interfaces that are easy to use, memorable, and leave a lasting impression. While Nielsen's heuristics are widely accepted as the standard for usability, it is clear that non-traditional applications require a rethinking of these heuristics to best suit their unique needs. A fresh usability heuristic for augmented and MR applications is designed by combining and modifying the existing models, such as Nielsen's 10 heuristics, Technology Acceptance Model, and Software Usability Measurement Inventory. The resulting framework incorporates 21 main heuristics and 60 sub heuristics. The 21 main heuristics are further grouped into the Norman's cognitive theory model based on the three levels of processing. The industry experts evaluated and validated the usability framework and established a higher level of effectiveness in identifying more usability problems compared with Nielsen's heuristics.

The ability to effectively classify human emotion states is critically important for human-computer or human-robot interactions. However, emotion classification with physiological signals is still a challenging problem due to the diversity of emotion expression and the characteristic differences in different modal signals. A novel learning-based network architecture is presented that can exploit four-modal physiological signals, electrocardiogram, electrodermal activity, electromyography, and blood volume pulse, and make a classification of emotion states. It features two kinds of attention modules, feature-level, and semantic-level, which drive the network to focus on the information-rich features by mimicking the human attention mechanism. The feature-level attention module encodes the rich information of each physiological signal. While the semantic-level attention module captures the semantic dependencies among modals. The performance of the designed network is evaluated with the open-source Wearable Stress and Affect Detection dataset. The developed emotion classification system achieves an accuracy of 83.88%. Results demonstrated that the proposed network could effectively process four-modal physiological signals and achieve high accuracy of emotion classification.

Although Dynamic Movement Primitives (DMP) is an effective tool for robotic arm trajectory generalisation, the application of DMP in the 3C (Computer, Communication, Consumer Electronics) industry still faces challenges such as low precision and high-time consumption. To address this problem, we propose a novel Cauchy DMP framework. The main improvements and advantages of Cauchy DMP, compared to the original DMP, are (1) since the Cauchy distribution has a simpler model and wider shape, using the Cauchy distribution instead of the Gaussian distribution in the original DMP reduces the complexity of the algorithm and saves time. (2) Singular Value Decomposition (SVD) can effectively model the error. To reduce the interference of the rounding and human error on the trajectory, SVD can be used to obtain the weight of each basis function. The proposed Cauchy DMP framework combines the above two points and is validated on a real UR5 robotic arm. The results show that Cauchy DMP retains the learnability of the original DMP and has the advantages of short time consumption and low error rate.

In this study, to address the issues of sluggish convergence and poor learning efficiency at the initial stages of training, the authors improve and optimise the Deep Deterministic Policy Gradient (DDPG) algorithm. First, inspired by the Artificial Potential Field method, the selection strategy of DDPG has been improved to accelerate the convergence speed during the early stages of training and reduce the time it takes for the mobile robot to reach the target point. Then, optimising the neural network structure of the DDPG algorithm based on the Long Short-Term Memory accelerates the algorithm's convergence speed in complex dynamic scenes. Static and dynamic scene simulation experiments of mobile robots are carried out in ROS. Test findings demonstrate that the Artificial Potential Field method-Long Short Term Memory Deep Deterministic Policy Gradient (APF-LSTM DDPG) algorithm converges significantly faster in complex dynamic scenes. The success rate is improved by 7.3% and 3.6% in contrast to the DDPG and LSTM-DDPG algorithms. Finally, the usefulness of the method provided in this study is similarly demonstrated in real situations using real mobile robot platforms, laying the foundation for the path planning of mobile robots in complex changing conditions.

The online recommendation system has benefited the traditional restaurant business economically. However, finding the best restaurant during rush time and visiting new places is tough. This objective is addressed through a restaurant recommendation approach, which impacts the human decision-making method. With the help of collaborative filtering, some user-based recommendation systems were designed to generate the best recommendation based on user choices. Thus, a user preferences-based method is presented using A Lite Bidirectional Encoder Representations from Transformers and Simple Recurrent Unit to suggest restaurants based on user preferences. Here, a publicly available dataset from Kaggle called Kzomato is used with 9552 samples and 21 features. And the system obtained an F1-score, precision, and recall of 86%, which will save time and provide the best recommendation based on user preferences easily.

An improved method for recognising clouds on the ground map is proposed incorporating the DeepLabV3+ model to solve three issues. Firstly, an image preprocessing module is developed to enrich image quality, particularly at night, as clouds can appear to be too blurry to distinguish. Secondly, the CBAM attention mechanism is applied to safeguard texture and boundary information, ensuring the depiction of cirrus edges is not lost. Finally, the feature extraction network can be optimised to enhance the model and decrease its computational complexity. In comparison to the original model, the accuracy of the proposed method increased by 10.89%, resulting in an overall accuracy of 94.18%. Furthermore, the MIoU has improved from 66.02% to 79.31%. The number of parameters was reduced by 51.45% to 13.4 M. Of the various cloud types, the improvement in cirrus is particularly striking, with the MIoU increasing from 1.78% to 56.01%.

IT services can be requested via cloud computing, a model based on services as well as the Internet. It includes all computer systems resources from hardware components to software platforms and software applications in a distributed environment. Scheduling is a key step in processing tasks using remote resources. Serious issues have been brought forward, including the ineffective use of resources and task execution failure. The concurrent provision of fault tolerance and resource optimisation is achallenging task. In the context of cloud computing, this research offers a brand-new job scheduling and fault-tolerant system. Tasks submitted by users are taken as an input for the proposed method. Several virtual machines (VM) are initially arranged for scheduling work and execution process. Initially, Horse Herd Optimisation is employed here to allocate the job based on key factors such as deadline and user budget. Once the jobs are assigned to each VM, then each job's deadline is confirmed and transferred to VM which has sufficient capacity. Here, the Reptile Search Optimisation technique is applied to identify the VM error. Any VM that does not have enough capacity is the one that has a problem. When a fault is found, a fault-tolerant process is instantly started. A replication-based fault-tolerant mechanism is used in this manuscript. The proposed approach is tested with several metrics which attains better performance like 80 s response time, turnaround time of 32 s, 17% resource utilisation and a success rate of 92%. Thus the designed model is the best choice for fault-tolerant aware task scheduling.