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Automatic segmentation of brain tumor from medical images is a challenging task because of tumor's uneven and irregular shapes. In this paper, the authors propose an attention-based nested segmentation network, named DAU-Net. In total, two types of attention mechanisms are introduced to make the U-Net network focus on the key feature regions. The proposed network has a deep supervised encoder–decoder architecture and a redesigned dense skip connection. DAU-Net introduces an attention mechanism between convolutional blocks so that the features extracted at different levels can be merged with a task-related selection.

In the coding layer, the authors designed a channel attention module. It marks the importance of each feature graph in the segmentation task. In the decoding layer, the authors designed a spatial attention module. It marks the importance of different regional features. And by fusing features at different scales in the same coding layer, the network can fully extract the detailed information of the original image and learn more tumor boundary information.

To verify the effectiveness of the DAU-Net, experiments were carried out on the BRATS 2018 brain tumor magnetic resonance imaging (MRI) database. The segmentation results show that the proposed method has a high accuracy, with a Dice similarity coefficient (DSC) of 89% in the complete tumor, which is an improvement of 8.04 and 4.02%, compared with fully convolutional network (FCN) and U-Net, respectively.

The experimental results show that the proposed method has good performance in the segmentation of brain tumors. The proposed method has potential clinical applicability.

Automatic defect detection is a fundamental and vital topic in the research field of industrial intelligence. In this work, the authors develop a more flexible deep learning method for the industrial defect detection.

The authors propose a unified framework for detecting defects in industrial products or planar surfaces based on an end-to-end learning strategy. A lightweight deep learning architecture for blade defect detection is specifically demonstrated. In addition, a blade defect data set is collected with the dual-arm image collection system.

Numerous experiments are conducted on the collected data set, and experimental results demonstrate that the proposed system can achieve satisfactory performance over other methods. Furthermore, the data equalization operation helps for a better defect detection result.

An end-to-end learning framework is established for defect detection. Although the adopted fully convolutional network has been extensively used for semantic segmentation in images, to the best knowledge of the authors, it has not been used for industrial defect detection. To remedy the difficulties of blade defect detection which has been analyzed above, the authors develop a new network architecture which integrates the residue learning to perform the efficient defect detection. A dual-arm data collection platform is constructed and extensive experimental validation are conducted.

This paper aims to use fully convolutional network (FCN) to predict pixel-wise antipodal grasp affordances for unknown objects and improve the grasp detection performance through multi-scale feature fusion.

A modified FCN network is used as the backbone to extract pixel-wise features from the input image, which are further fused with multi-scale context information gathered by a three-level pyramid pooling module to make more robust predictions. Based on the proposed unify feature embedding framework, two head networks are designed to implement different grasp rotation prediction strategies (regression and classification), and their performances are evaluated and compared with a defined point metric. The regression network is further extended to predict the grasp rectangles for comparisons with previous methods and real-world robotic grasping of unknown objects.

The ablation study of the pyramid pooling module shows that the multi-scale information fusion significantly improves the model performance. The regression approach outperforms the classification approach based on same feature embedding framework on two data sets. The regression network achieves a state-of-the-art accuracy (up to 98.9%) and speed (4 ms per image) and high success rate (97% for household objects, 94.4% for adversarial objects and 95.3% for objects in clutter) in the unknown object grasping experiment.

A novel pixel-wise grasp affordance prediction network based on multi-scale feature fusion is proposed to improve the grasp detection performance. Two prediction approaches are formulated and compared based on the proposed framework. The proposed method achieves excellent performances on three benchmark data sets and real-world robotic grasping experiment.

This work presents a system based on an ensemble of Convolutional Neural Networks (CNNs) and descriptors for bioimage classification that has been validated on different datasets of color images. The proposed system represents a very simple yet effective way of boosting the performance of trained CNNs by composing multiple CNNs into an ensemble and combining scores by sum rule. Several types of ensembles are considered, with different CNN topologies along with different learning parameter sets. The proposed system not only exhibits strong discriminative power but also generalizes well over multiple datasets thanks to the combination of multiple descriptors based on different feature types, both learned and handcrafted. Separate classifiers are trained for each descriptor, and the entire set of classifiers is combined by sum rule. Results show that the proposed system obtains state-of-the-art performance across four different bioimage and medical datasets. The MATLAB code of the descriptors will be available at https://github.com/LorisNanni.

The purpose of this paper is to focus on the design of a dual-branch balance saliency model based on fully convolutional network (FCN) for automatic fabric defect detection, and improve quality control in textile manufacturing.

This paper proposed a dual-branch balance saliency model based on discriminative feature for fabric defect detection. A saliency branch is firstly designed to address the problems of scale variation and contextual information integration, which is realized through the cooperation of a multi-scale discriminative feature extraction module (MDFEM) and a bidirectional stage-wise integration module (BSIM). These modules are respectively adopted to extract multi-scale discriminative context information and enrich the contextual information of features at each stage. In addition, another branch is proposed to balance the network, in which a bootstrap refinement module (BRM) is trained to guide the restoration of feature details.

To evaluate the performance of the proposed network, we conduct extensive experiments, and the experimental results demonstrate that the proposed method outperforms state-of-the-art (SOTA) approaches on seven evaluation metrics. We also conduct adequate ablation analyses that provide a full understanding of the design principles of the proposed method.

The dual-branch balance saliency model was proposed and applied into the fabric defect detection. The qualitative and quantitative experimental results show the effectiveness of the detection method. Therefore, the proposed method can be used for accurate fabric defect detection and even surface defect detection of other industrial products.

This study aims to empirically explore several factors that encourage muzakki (zakat payers) to pay their zakat through institutions by elaborating on their extrinsic and intrinsic motivations as the composite factors regarding the attitude and intention improvement of muzakki. This study specifically studies zakat payment via digital means and categorizes the muzakki groups into two (urban and suburban) to be considered in the results.

Overall, this study gathers the data from 298 muzakki using a partial least squares technique the multigroup analysis to compare the analysis.

This study found that different sociodemographic aspects will result in varied performances of motivation in using technology between the two groups. Furthermore, positive preference aspects, such as muzakki’s attitude, can be a catalyst in improving their motivation to pay zakat through institutions.

The findings of this study can be used as a foundation to improve the technology-based services that will be more accessible and reachable. Provision of technical follow-ups regarding the utilization of technology, including community-based digital platform socializations, availability of online customer service that will respond to muzakki’s needs and synergy between stakeholders, are the primary obligations that a zakat institution must fulfill.

As far as the researchers are concerned, the studies focusing on the motivational factors and attitude of muzakki as an intervention in paying zakat via institutions are limited in numbers, especially studies on digital payment. In this study, however, classifying the groups into two will help gain a deeper understanding of this topic.

The purpose of this paper is to use a risk management perspective to identify the risks of employment discrimination law liability for multinational employers.

Data from 101 US Federal Court cases that involved multinational employers operating both inside and outside of the USA were content coded and then used to identify factors that predict the frequency that foreign employers operating inside the USA – and US employers operating outside the USA – were subject to lawsuits under US employment discrimination laws.

This study found that employment lawsuits based on sex discrimination against females was the most significant risk exposure. Employers whose home country was from a Western culture were at comparatively greater risk for charges of both age and religious discrimination. Employers whose home country was from an Asian culture were at comparatively greater risk for charges of both race and national origin discrimination.

This study demonstrates the viability and usefulness of a risk management framework for examination of issues related to law and management.

This study enables the identification of risk factors that multinational employers can use to strategically target their loss prevention efforts in order to more effectively and efficiently avoid or reduce potential liability for employment discrimination.

The risk factors identified in this study can help employers to take efforts to reduce employment discrimination in their multinational operations, thereby reducing the frequency and likelihood that such discrimination may occur.

This is the first study to use a risk management framework to empirically identify employment law risk exposures for multinational employers.

The Russian Energy Service Company (ESCO) market emerges rapidly due to the new energy efficiency legislation that has been implemented since 2009. However, a clear identification of the Russian ESCOs, comparable to those operating on the basis of Energy Performance Contracting (EPC) in the Western markets, remains rather difficult. Hence, aside from the independent ESCOs identified, further energy service‐providing companies (ESPCs) are within the scope of this survey. This paper aims to address these issues.

Building on comprehensive qualitative research of the international and Russian academic and non‐academic literature on the ESCO concept and an expert interview, an explorative, questionnaire‐based survey among 161 Russian energy companies and organizations was conducted. A total of 28 usable responses were returned, corresponding to a response rate of 17 per cent. Non‐parametric exact tests are used for the statistical analysis.

The authors' findings show that only nine of the surveyed ESCOs have acquired energy performance‐based projects. In line with the new energy efficiency legislation, such projects are strongly supported in the state sector but much less so in the commercial sector. Most of the projects are financed either through ESCOs' own funds, direct loans to customers, or by the customers themselves. Russian banks, however, rarely provide direct loans for energy performance‐based projects of ESCOs, but rather prefer to offer financial leasing contracts. The contractual form “guaranteed savings”, which is generally more applicable in mature ESCO markets, is gaining in importance, while “shared savings” is barely used.

This paper delivers, to the best of the authors' knowledge, the first systematic empirical investigation of the Russian ESCO industry, taking into account experiences from the international ESCO markets.