The application of deep architectures inspired by the fields of artificial intelligence and computer vision has made a significant impact on the task of crack detection. As the number of studies being published in this field is growing fast, it is important to categorize the studies at deeper levels. In this paper, a comprehensive literature review of deep learning-based crack detection studies and the contributions they have made to the field is presented. The studies are categorised according to the computer vision aspect and at deeper levels to facilitate exploring the studies that utilised similar approaches to address the crack detection problem. Moreover, the authors perform a comparison between the studies which use the same publicly available data sets, in order to find the most promising crack detection approaches. Critical future directions for research are proposed, based on these reviewed studies as well as on trends and developments in areas similar to the crack detection area.

With recent advancements in graph neural networks (GNN), GNN-based recommender systems (gRS) have achieved remarkable success in the past few years. Despite this success, existing research reveals that gRSs are still vulnerable to poison attacks , in which the attackers inject fake data to manipulate recommendation results as they desire. This might be due to the fact that existing poison attacks (and countermeasures) are either model-agnostic or specifically designed for traditional recommender algorithms (e.g., neighborhood-based, matrix-factorization-based, or deep-learning-based RSs) that are not gRS. As gRSs are widely adopted in the industry, the problem of how to design poison attacks for gRSs has become a need for robust user experience. Herein, we focus on the use of poison attacks to manipulate item promotion in gRSs. Compared to standard GNNs, attacking gRSs is more challenging due to the heterogeneity of network structure and the entanglement between users and items. To overcome such challenges, we propose GSPAttack —a generative surrogate-based poison attack framework for gRSs. GSPAttack tailors a learning process to surrogate a recommendation model as well as generate fake users and user-item interactions while preserving the data correlation between users and items for recommendation accuracy. Although maintaining high accuracy for other items rather than the target item seems counterintuitive, it is equally crucial to the success of a poison attack. Extensive evaluations on four real-world datasets revealed that GSPAttack outperforms all baselines with competent recommendation performance and is resistant to various countermeasures.

This paper presents the components and overall architecture of the ubiquitous robot (Ubibot) system developed to demonstrate ubiquitous robotics, a new paradigm for integrated services. The system has been developed on the basis of the definition of the ubiquitous robot as that of encompassing the software robot Sobot, embedded robot Embot and the mobile robot Mobot. This tripartite partition, which independently manifests intelligence, perception and action, enables the abstraction of intelligence through the standardization of sensory data and motor or action commands. The Ubibot system itself is introduced along with its component subsystems of Embots, the position Embot, vision Embot and sound Embot, the Mobots of Mybot and HSR, the Sobot, Rity, a virtual pet modeled as an artificial creature, and finally the middleware which seamlessly enables interconnection between other components. Three kinds of experiments are devised to demonstrate the fundamental features, of calm sensing, context awareness and seamless service transcending the spatial limitations in the abilities of earlier generation personal robots. The experiments demonstrate the proof of concept of this powerful new paradigm which shows great promise.

Physical activity (PA) can influence heart rate(HR). But the relationship between HR and PA is hard to describe. In our previous works, HR prediction models based on PA were designed. However, the prediction time length and accuracy are usually hard to compromise. In this study, a new HR prediction method is proposed. The predicted HR is used as the input in the next prediction step. Only HR at the initial time step and PA signals are needed in a long prediction time length. Evolutionary neural network is used as the mathematic basic of the predictor to ensure the prediction accuracy. The results show the predicted HR can trace the actual HR well.