Abstract

Abstract
Bin picking based on deep learning techniques is a promising approach that can solve several analytical methods problems. These systems can provide accurate solutions to bin picking in cluttered environments, where the scenario is always changing. This article proposes a robust and accurate system for segmenting bin picking objects, employing an easy configuration procedure to adjust the framework according to a specific object. The framework is implemented in Robot Operating System (ROS) and is divided into a detection and segmentation system. The detection system employs Mask R-CNN instance neural network to identify several objects from two dimensions (2D) grayscale images. The segmentation system relies on the point cloud library (PCL), manipulating 3D point cloud data according to the detection results to select particular points of the original point cloud, generating a partial point cloud result. Furthermore, to complete the bin picking system a pose estimation approach based on matching algorithms is employed, such as Iterative Closest Point (ICP). The system was evaluated for two types of objects, knee tube, and triangular wall support, in cluttered environments. It displayed an average precision of 79% for both models, an average recall of 92%, and an average IOU of 89%. As exhibited throughout the article, this system demonstrates high accuracy in cluttered environments with several occlusions for different types of objects.

Abstract
With the introduction of Industry 4.0 technological concepts, suppliers and manufacturers envision new or improved products and services, cost reductions, and productivity gains. In this context, data exchanges between companies in the same or different activity sectors are necessary, while assuring data security and sovereignty. Thus, it is crucial to select and implement adequate standards which enable the interconnection requirements between companies and also feature security by design. The International Data Spaces (IDS) is a current standard that provides data sharing through data spaces mainly composed of homogeneous rules, certified data providers/consumers, and reliability between partners. Implementing IDS in sectors such as textile and clothing is expected to open new opportunities and challenges. This paper proposes a prototype for the IDS Security Components in the Textile and Clothing Industry context. This prototype assures data sovereignty and enables the interactions required by all participants in this supply chain industry using secure communications. The adoption of IDS as a base model in this activity sector fosters productive collaboration, lowers entry barriers for business partnerships, and enables an innovation environment. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
Nowadays, Virtual Reality (VR) and Augmented Reality (AR) systems are not exclusively associated with the gaming industry. Their potential is also useful for other business areas such as healthcare, automotive, and educational domains. Companies need to accompany technological advances and enhance their business processes and thus, the adoption of VR or AR technologies could be advantageous in reducing resource usage or improving the overall efficiency of processes. However, before implementing these technologies, companies must be aware of potential cyberattacks and security risks to which these systems are subject. This study presents a survey of attacks related to VR and AR scenarios and their risk assessment when considering healthcare, automation, education, and gaming industries. The main goal is to make companies aware of the possible cyberattacks that can affect the devices and their impact on their business domain. © 2023 IEEE.

Abstract
Cloud computing environments provide users with Internet-based services and one of their main challenges is security issues. Hence, using Intrusion Detection Systems (IDSs) as a defensive strategy in such environments is essential. Multiple parameters are used to evaluate the IDSs, the most important aspect of which is the feature selection method used for classifying the malicious and legitimate activities. We have organized this research to determine an effective feature selection method to increase the accuracy of the classifiers in detecting intrusion. A Hybrid Ant-Bee Colony Optimization (HABCO) method is proposed to convert the feature selection problem into an optimization problem. We examined the accuracy of HABCO with BHSVM, IDSML, DLIDS, HCRNNIDS, SVMTHIDS, ANNIDS, and GAPSAIDS. It is shown that HABCO has a higher accuracy compared with the mentioned methods.

Abstract
The most malignant tumors of the central nervous system are adult-type diffuse gliomas. Historically, glioma subtype classification has been based on morphological features. However, since 2016, WHO recognizes that molecular evaluation is critical for subtyping. Among molecular markers, the mutation status of IDH1 and the codeletion of 1p/19q are crucial for the precise diagnosis of these malignancies. In pathology laboratories, however, manual screening for those markers is time-consuming and susceptible to error. To overcome these limitations, we propose a novel multimodal biomarker classification method that integrates image features derived from brain magnetic resonance imaging and histopathological exams. The proposed model consists of two branches, the first branch takes as input a multi-scale Hematoxylin and Eosin whole slide image, and the second branch uses the pre-segmented region of interest from the magnetic resonance imaging. Both branches are based on convolutional neural networks. After passing the exams by the two embedding branches, the output feature vectors are concatenated, and a multi-layer perceptron is used to classify the glioma biomarkers as a multi-class problem. In this work, several fusion strategies were studied, including a cascade model with mid-fusion; a mid-fusion model, a late fusion model, and a mid-context fusion model. The models were tested using a publicly available data set from The Cancer Genome Atlas. Our cross-validated classification models achieved an area under the curve of 0.874, 0.863, and 0.815 for the proposed multimodal, magnetic resonance imaging, and Hematoxylin and Eosin stain slide images respectively, indicating our multimodal model outperforms its unimodal counterparts and the state-of-the-art glioma biomarker classification methods.