Cognitive Computation and Systems最新文献

Traumatic brain injury (TBI) can affect normal brain function and may be caused by a vehicle accident, falling, and so on. The purpose of this survey is to provide clear knowledge of TBI, the causes of TBI, the impacts of TBI, and the role of family members and friends in recovery. TBI affects the daily life of the patients, both physically and mentally. After TBI, the patients may experience many emotional and behavioural changes because of a lack of certain brain functions. These changes affect their personal and social relationships. On the other hand, these changes depend on the severity of the TBI (i.e. mild, moderate, or severe), which is measured using the Glasgow coma score. Generally, three processes are used for emotion recognition: preprocessing, feature extraction, and emotion recognition. Preprocessing is performed for landmark detection and pose normalisation, which improves the performance of emotion detection. Feature extraction and emotion recognition are performed by various deep learning techniques, such as convolution neural networks and long short-term memory. These techniques recognise the behavioural and emotional changes (depression, anxiety, anger, personality changes etc.) of TBI patients using facial expressions. Family members and friends play an important role in TBI patients' recovery, the extent of which is based on the severity of the TBI. The care of family members and friends leads to quick recovery and rehabilitation of patients from TBI. Finally, testing is performed using Computed Tomography images, Magnetic Resonance Imaging images, Electroencephalography signals, and patient demographics, which together show that the deep learning methods achieve better performance in terms of accuracy, precision, recall, and F-measure in recognising emotional and behavioural changes after TBI. The authors conclude with a summary of the future of emotional and behavioural change prediction methods for TBI patients.

Accurate detection of pedestrians and vehicles on the road is an important content in autonomous driving technology. In this article, a method to optimise the object detection network using the channel attention mechanism is proposed. In general, small object detection problems and difficult sample detection problems in object detection tasks can be solved by using feature pyramids. Different from building a feature pyramid, the authors did not make extensive changes to the network, but used the channel attention mechanism to dynamically adjust the output of a layer during the feature extraction process, allowing each neuron to adjust its receptive field size adaptively according to multiple scales of the input information, so that the network pays attention to the extraction of important features, especially the features of small objects and difficult samples. In order to evaluate the performance of the proposed method, experiments were conducted on standard benchmark data sets. It has been observed that the proposed method is superior to the original object detection network in terms of the detection accuracy of pedestrians and vehicles, especially the detection of small objects.

In recent years, human hand-based robotic hands or dexterous hands have gained attention due to their enormous capabilities of handling soft materials compared to traditional grippers. Back in the earlier days, the development of a hand model close to that of a human was an impossible task but with the advancements made in technology, dexterous hands with three, four or five-fingered robotic hands have been developed to mimic human hand nature. However, human-like manipulation of dexterous hands to this date remains a challenge. Thus, this review focuses on (a) the history and motivation behind the development of dexterous hands, (b) a brief overview of the available multi-fingered hands, and (c) learning-based methods such as traditional and data-driven learning methods for manipulating dexterous hands. Additionally, it discusses the challenges faced in terms of the manipulation of multi-fingered or dexterous hands.

The classifier efficiency of the brain-computer interface systems is significantly impacted by the non-stationarity of electroencephalogram (EEG) signals. We propose an adaptive variant of the linear discriminant analysis (LDA) classifier as a solution to this problem. This classifier constantly adjusts its parameters to account for the most recent EEG data. In this study, the authors will update the mean values as well as the covariance matrix of each class pair. Visually evoked cortical potential datasets are used to check how well the proposed classifier performs. The authors prove that the proposed adaptive LDA performs much better than both static multiclass LDA and adaptive PMean LDA.

Alzheimer's disease is a type of progressive neurological disorder which is irreversible and the patient suffers from severe memory loss. This disease is the seventh largest cause of death across the globe. As yet there is no cure for this disease, the only way to control it is its early diagnosis. Deep Learning techniques are mostly preferred in classification tasks because of their high accuracy over a large dataset. The main focus of this paper is on fine-tuning and evaluating the Deep Convolutional Networks for Alzheimer's disease classification. An empirical analysis of various deep learning-based neural network models has been done. The architectures evaluation includes InceptionV3, ResNet with 50 layers and 101 layers and DenseNet with 169 layers. The dataset has been taken from Kaggle which is publicly available and comprises of four classes which represents the various stages of Alzheimer's disease. In our experiment, the accuracy of DenseNet consistently improved with the increase in the number of epochs resulting in a 99.94% testing accuracy score better than the rest of the architectures. Although the results obtained are satisfactory, but for future research, we can apply transfer learning on other deep models like Inception V4, AlexNet etc., to increase accuracy and decrease computational time. Also, in future we can work on other datasets like ADNI or OASIS and use Positron emitted tomography, diffusion tensor imaging neuroimages and their combinations for better result.

If understanding sentiments is already a difficult task in human-human communication, this becomes extremely challenging when a human-computer interaction happens, as for instance in chatbot conversations. In this work, a machine learning neural network-based Speech Emotion Recognition system is presented to perform emotion detection in a chatbot virtual assistant whose task was to perform contact tracing during the COVID-19 pandemic. The system was tested on a novel dataset of audio samples, provided by the company Blu Pantheon, which developed virtual agents capable of autonomously performing contacts tracing for individuals positive to COVID-19. The dataset provided was unlabelled for the emotions associated to the conversations. Therefore, the work was structured using a sort of transfer learning strategy. First, the model is trained using the labelled and publicly available Italian-language dataset EMOVO Corpus. The accuracy achieved in testing phase reached 92%. To the best of their knowledge, thiswork represents the first example in the context of chatbot speech emotion recognition for contact tracing, shedding lights towards the importance of the use of such techniques in virtual assistants and chatbot conversational contexts for psychological human status assessment. The code of this work was publicly released at: https://github.com/fp1acm8/SER.

The authors wish to bring to the readers' attention the following error in the article by Zijin Li and Stephen McAdams, “Guest editorial: Music perception and cognition in music technology” [1].

The co-author Stephen McAdams' name should be removed from the article.