The accessibility and potential of deep learning techniques have increased considerably over the past years. Image segmentation is one of the many fields which have seen novel implementations being developed to solve problems in the domain. U-Net is an example of a popular deep learning model designed specifically for biomedical image segmentation, initially proposed for cell segmentation. We propose a variation of the U-Net++ model, which is itself an adaptation of U-Net, and evaluate its brain tumor segmentation capabilities. The proposed approach obtained Dice Coefficient scores of 0.7192, 0.8712, and 0.7817 for the Enhancing Tumor, Whole Tumor and Tumor Core classes of the BraTS 2019 challenge Validation Dataset. The proposed approach differs from the standard U-Net++ model in a number of ways, including the loss function, number of convolutional blocks, and method of employing deep supervision. Data augmentation and post-processing techniques were also implemented and observed to substantially improve the model predictions. Thus, this article presents a novel adaptation of the U-Net++ architecture, which is both lightweight, and performs comparably with peer-reviewed work evaluated on the same data.

Concrete has been one of the most essential building materials for decades, valued for its durability, cost efficiency, and wide availability of required components. Over time, the number of concrete bridges has been drastically increasing, highlighting the need for timely structural health monitoring (SHM) to ensure their safety and long-term durability. Therefore, a narrative review was conducted to examine the use of Artificial Intelligence (AI)-integrated techniques in the SHM of concrete bridges for more effective monitoring. Moreover, this review also examined significant damage observed in various types of concrete bridges, with particular emphasis on concrete cracking, detection methods, and identification accuracy. Evidence points to the fact that the conventional SHM of concrete bridges relies on manual inspections that are time-consuming, error-prone, and require frequent checks, while AI-driven SHM methods have emerged as promising alternatives, especially through Machine Learning- and Deep Learning-based solutions. In addition, it was noticeable that integrating multimodal AI approaches improved the accuracy and reliability of concrete bridge assessments. Furthermore, this review is essential as it also addresses critical gaps in SHM approaches and suggests developing more accurate detection techniques, providing enhanced spatial resolution for monitoring concrete bridges.

Hyperspectral data classification is one of the fundamental problems in remote sensing. Several algorithms based on supervised machine learning have been proposed to address it. The performance, however, of the proposed algorithms is inherently dependent on the amount and quality of annotated data. Due to recent advances in hyperspectral imaging and autonomous (unmanned) aerial vehicles collecting new hyperspectral data is an easy task. Annotating those data, however, is a tedious, time-consuming and costly task requiring the in-situ presence of human experts. One way to loosen the requirement of a large number of annotated data is the shift to semi-supervised learning combined with highly sample-efficient tensor-based neural networks. This study provides a comprehensive experimental analysis of the performance of a variety of graph-based semi-supervised learning techniques combined with tensor-based neural network embeddings for the problem of hyperspectral data classification. Experimental results suggest that the combination of tensor-based neural network embeddings with graph-based semi-supervised learning can significantly improve the classification results minimizing human annotation effort.

Many modern computer vision systems include several modules that perform different processing operations packaged as a single pipeline architecture. This generally introduces a challenge in the evaluation process since most datasets provide evaluation data for just one of the operations. In this paper, we present an RGB-D dataset that was designed from first principles to cater for applications that involve salient object detection, segmentation, inpainting and blending techniques. This addresses a gap in the evaluation of image inpainting and blending applications that generally rely on subjective evaluation due to the lack of availability of comparative data. A set of experiments were carried out to demonstrate how the COTS dataset can be used to evaluate these different applications. This dataset includes a variety of scenes, where each scene is captured multiple times, each time adding a new object to the previous scene. This allows for a comparative analysis at pixel level in image inpainting and blending applications. Moreover, all objects were manually labeled in order to offer the possibility of salient object detection even in scenes that contain multiple objects. An online test with 1267 participants was also carried out, and this dataset also includes the click coordinates of users' selection for every image, introducing a user interaction dimension in the same RGB-D dataset. This dataset was also validated using state of the art techniques, obtaining an $F_\beta $ of 0.957 in salient object detection and a mean (Intersection over Union) IoU of 0.942 in Segmentation. Results demonstrate that the COTS dataset introduces novel possibilities for the evaluation of computer vision applications.

The success of social networking sites has led people to require the use of multiple accounts on different platforms which effectively increases the risks in managing them. Following and finding information about friends and family has become an issue too. Guided by these observations and by careful research of existing adaptive web technologies, the authors’ team worked on the development of SNAP - an adaptive social network integrator which aimed to amalgamate various social networks (Facebook, Twitter, and Flickr) in one adaptive environment, which unobtrusively sorts the users’ feed according to his/her preference. To achieve data transfer and authorisation, SNAP uses the newest version of the OAuth protocol. Adaptivity was achieved through statistical filtering. The initial field tests show that the system works, however there is definitely room for improvement in terms of Social Network Integration, and testers generally expressed an interest in the idea of using an adaptive social integrator such as SNAP. On top of this, the authors will be suggesting a number of improvements which will change the way society uses social networks forever.