The Tokyo Virtual Living Lab is an experimental space based on 3D Internet technology that lets researchers conduct controlled driving and travel studies, including those involving multiple users in the same shared space. This shared-use feature is crucial for analyzing interactive driving behaviors in future smart cities. The lab's novelty is two-fold: it outputs a semantically enriched graphical navigation network using free map data as input, and it includes a navigation segment agent that coordinates a multiagent traffic simulator. This simulator, which is based on the navigation network, supports the integration of user-controlled vehicles. The lab's approach can significantly reduce the effort of preparing realistic driving behavior studies. To demonstrate this, the authors built a 3D model of a part of Tokyo to perform experiments with human drivers in two conditions: normal traffic and ubiquitous eco-traffic.

Background: As a physiological signal, EEG data cannot be subjectively changed or hidden. Compared with other physiological signals, EEG signals are directly related to human cortical activities with excellent temporal resolution. After the rapid development of machine learning and artificial intelligence, the analysis and calculation of EEGs has made great progress, leading to a significant boost in performances for content understanding and pattern recognition of brain activities across the areas of both neural science and computer vision. While such an enormous advance has attracted wide range of interests among relevant research communities, EEG-based classification of brain activities evoked by images still demands efforts for further improvement with respect to its accuracy, generalization, and interpretation, yet some characters of human brains have been relatively unexplored.Methods: We propose a region-level stacked bi-directional deep learning framework for EEG-based image classification. Inspired by the hemispheric lateralization of human brains, we propose to extract additional information at regional level to strengthen and emphasize the differences between two hemispheres. The stacked bi-directional long short-term memories are used to capture the dynamic correlations hidden from both the past and the future to the current state in EEG sequences.Results: Extensive experiments are carried out and our results demonstrate the effectiveness of our proposed framework. Compared with the existing state-of-the-arts, our framework achieves outstanding performances in EEG-based classification of brain activities evoked by images. In addition, we find that the signals of Gamma band are not only useful for achieving good performances for EEG-based image classification, but also play a significant role in capturing relationships between the neural activations and the specific emotional states.Conclusions: Our proposed framework provides an improved solution for the problem that, given an image used to stimulate brain activities, we should be able to identify which class the stimuli image comes from by analyzing the EEG signals. The region-level information is extracted to preserve and emphasize the hemispheric lateralization for neural functions or cognitive processes of human brains. Further, stacked bi-directional LSTMs are used to capture the dynamic correlations hidden in EEG data. Extensive experiments on standard EEG-based image classification dataset validate that our framework outperforms the existing state-of-the-arts under various contexts and experimental setups.

Over the past decade, “content-based” multimedia systems have realized success. By comparison, brain imaging and classification systems demand more efforts for improvement with respect to accuracy, generalization, and interpretation. The relationship between electroencephalogram (EEG) signals and corresponding multimedia content needs to be further explored. In this paper, we integrate implicit and explicit learning modalities into a context-supported deep learning framework. We propose an improved solution for the task of brain imaging classification via EEG signals. In our proposed framework, we introduce a consistency test by exploiting the context of brain images and establishing a mapping between visual-level features and cognitive-level features inferred based on EEG signals. In this way, a multimodal approach can be developed to deliver an improved solution for brain imaging and its classification based on explicit learning modalities and research from the image processing community. In addition, a number of fusion techniques are investigated in this work to optimize individual classification results. Extensive experiments have been carried out, and their results demonstrate the effectiveness of our proposed framework. In comparison with the existing state-of-the-art approaches, our proposed framework achieves superior performance in terms of not only the standard visual object classification criteria, but also the exploitation of transfer learning. For the convenience of research dissemination, we make the source code publicly available for downloading at GitHub (https://github.com/aneeg/dual-modal-learning).

Essentially, the current concept of multimedia is limited to presenting what people see in their eyes. What people think inside brains, however, remains a rich source of multimedia, such as imaginations of paradise and memories of good old days etc. In this paper, we propose a dual conditioned and lateralization supported GAN (DCLS-GAN) framework to learn and visualize the brain thoughts evoked by stimulating images and hence enable multimedia to reflect not only what people see but also what people think. To reveal such a new world of multimedia inside human brains, we coin such an attempt as "brain-media". By examining the relevance between the visualized image and the stimulation image, we are able to measure the efficiency of our proposed deep framework regarding the quality of such visualization and also the feasibility of exploring the concept of "brain-media". To ensure that such extracted multimedia elements remain meaningful, we introduce a dually conditioned learning technique in the proposed deep framework, where one condition is analyzing EEGs through deep learning to extract a class-dependent and more compact brain feature space utilizing the distinctive characteristics of hemispheric lateralization and brain stimulation, and the other is to extract expressive visual features assisting our automated analysis of brain activities as well as their visualizations aided by artificial intelligence. To support the proposed GAN framework, we create a combined-conditional space by merging the brain feature space with the visual feature space provoked by the stimuli. Extensive experiments are carried out and the results show that our proposed deep framework significantly outperforms the representative existing state-of-the-arts under several settings, especially in terms of both visualization and classification of brain responses to the evoked images. For the convenience of research dissemination, we make the source code openly accessible for downloading at GitHub.

With the rapid growth of video data, video summarization is a promising approach to shorten a lengthy video into a compact version. Although supervised summarization approaches have achieved state-of-the-art performance, they require frame-level annotated labels. Such an annotation process is time-consuming and tedious. In this article, we propose a novel deep summarization framework named Deep Semantic and Attentive Network for Video Summarization (DSAVS) that can select the most semantically representative summary by minimizing the distance between video representation and text representation without any frame-level labels. Another challenge associated with video summarization tasks mainly originates from the difficulty of considering temporal information over a long time. Long Short-Term Memory (LSTM) performs well for temporal dependencies modeling but does not work well with long video clips. Therefore, we introduce a self-attention mechanism into our summarization framework to capture the long-range temporal dependencies among the frames. Extensive experiments on two popular benchmark datasets, i.e., SumMe and TVSum, show that our proposed framework outperforms other state-of-the-art unsupervised approaches and even most supervised methods.