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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.

Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

Existing clothing parsing methods make little use of dataset-level information. This paper aims to propose a novel clothing parsing method which utilizes higher-level outfit combinatorial consistency knowledge from the whole clothing dataset to improve the accuracy of segmenting clothing images.

In this paper, the authors propose an Outfit Memory Net (OMNet) that augments original feature by aggregating dataset-level prior clothing combination information. Specifically, the authors design an Outfit Matrix (OM) to represent clothing combination information of single image and an Outfit Memory Module (OMM) to store the clothing combination information of all images in the training set, i.e. dataset-level clothing combination information. In addition, the authors propose a Multi-scale Aggregation Module (MAM) to aggregate the clothing combination information in a multi-scale manner to solve the problem of large variance in the scale of objects in the clothing images.

Experiments on Colorful Fashion Parsing Dataset (CFPD) dataset show that the authors' method achieves 93.15% pixel accuracy (PA) and 51.24% mean of class-wise intersection over union (mIoU), which are satisfactory parsing results compared with existing methods such as PSPNet, DANet and DeepLabV3. Moreover, through comparing the segmentation accuracy of different methods for each category, MAM could effectively improve the segmentation of small objects.

With the rise of various online shopping platforms and the continuous development of deep learning technology, emerging applications such as clothing recommendation, matching, classification and virtual try-on system have emerged in the clothing field. Clothing parsing is the key technology to realize these applications. Therefore, improving the accuracy of clothing parsing is necessary.

Existing Simultaneous Localization and Mapping (SLAM) algorithms have been relatively well developed. However, when in complex dynamic environments, the movement of the dynamic points on the dynamic objects in the image in the mapping can have an impact on the observation of the system, and thus there will be biases and errors in the position estimation and the creation of map points. The aim of this paper is to achieve more accurate accuracy in SLAM algorithms compared to traditional methods through semantic approaches.

In this paper, the semantic segmentation of dynamic objects is realized based on U-Net semantic segmentation network, followed by motion consistency detection through motion detection method to determine whether the segmented objects are moving in the current scene or not, and combined with the motion compensation method to eliminate dynamic points and compensate for the current local image, so as to make the system robust.

Experiments comparing the effect of detecting dynamic points and removing outliers are conducted on a dynamic data set of Technische Universität München, and the results show that the absolute trajectory accuracy of this paper's method is significantly improved compared with ORB-SLAM3 and DS-SLAM.

In this paper, in the semantic segmentation network part, the segmentation mask is combined with the method of dynamic point detection, elimination and compensation, which reduces the influence of dynamic objects, thus effectively improving the accuracy of localization in dynamic environments.

High-resolution remote sensing images possess a wealth of semantic information. However, these images often contain objects of different sizes and distributions, which make the semantic segmentation task challenging. In this paper, a bidirectional feature fusion network (BFFNet) is designed to address this challenge, which aims at increasing the accurate recognition of surface objects in order to effectively classify special features.

There are two main crucial elements in BFFNet. Firstly, the mean-weighted module (MWM) is used to obtain the key features in the main network. Secondly, the proposed polarization enhanced branch network performs feature extraction simultaneously with the main network to obtain different feature information. The authors then fuse these two features in both directions while applying a cross-entropy loss function to monitor the network training process. Finally, BFFNet is validated on two publicly available datasets, Potsdam and Vaihingen.

In this paper, a quantitative analysis method is used to illustrate that the proposed network achieves superior performance of 2–6%, respectively, compared to other mainstream segmentation networks from experimental results on two datasets. Complete ablation experiments are also conducted to demonstrate the effectiveness of the elements in the network. In summary, BFFNet has proven to be effective in achieving accurate identification of small objects and in reducing the effect of shadows on the segmentation process.

The originality of the paper is the proposal of a BFFNet based on multi-scale and multi-attention strategies to improve the ability to accurately segment high-resolution and complex remote sensing images, especially for small objects and shadow-obscured objects.

This paper aims to analyse the key Faro notions of “heritage community” and “democratic participation” as defined in the Faro Convention, and how they challenge core notions of authority and expertise in the discipline and professional practice of cultural heritage.

This paper examines notions of “heritage community” and “democratic participation” as they are framed in the Faro Convention, and it briefly introduces two cases (Finland and Marseille) to explore their application. It then focusses on the implications of these two notions for heritage administration (expertise) in terms of citizen agency, co-creation of knowledge and forms of decision-making processes.

The Faro Convention favours an innovative approach to social, politic and economic problems using cultural heritage. To accomplish this, it empowers citizens as actors in developing heritage-based approaches. This model transforms heritage into a means for achieving socioeconomic goals and attributes to the public the ability to undertake heritage initiatives, leaving the administration and expert bodies as mediators in this process. To bring about this shift, Faro institutes the notion of “heritage communities” and fosters participative governance. However, how heritage communities practise participation may follow different paths and result in different experiences due to local and national political circumstances.

The Faro Convention opens up a window by framing cultural heritage within the realm of social and democratic instrumentality, above and beyond the heritage per se. But it also poses some questions regarding the rationale of heritage management (authority in governability), at least as understood traditionally under official heritage management discourses.

Template matching is one of the most suitable choices for full six degrees of freedom pose estimation in many practical industrial applications. However, the increasing number of templates while dealing with a wide range of viewpoint changes results in a long runtime, which may not meet the real-time requirements. This paper aims to improve matching efficiency while maintaining sample resolution and matching accuracy.

A multi-pyramid-based hierarchical template matching strategy is proposed. Three pyramids are established at the sphere subdivision, radius and in-plane rotation levels during the offline template render stage. Then, a hierarchical template matching is performed from the highest to the lowest level in each pyramid, narrowing the global search space and expanding the local search space. The initial search parameters at the top level can be determined by the preprocessing of the YOLOv3 object detection network to further improve real-time performance.

Experimental results show that this matching strategy takes only 100 ms under 100k templates without loss of accuracy, promising for real industrial applications. The authors further validated the approach by applying it to a real robot grasping task.

The matching framework in this paper improves the template matching efficiency by two orders of magnitude and is validated using a common template definition and viewpoint sampling methods. In addition, it can be easily adapted to other template definitions and viewpoint sampling methods.

Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.

To enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.

The study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.

With an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.

The proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.

This research aimed to measure the influence of resource dependency (trading and/or shareholder's dependencies) squad age structure by building archetypes to identify strategic dominant schemes.

Based on the Ligue 1 football clubs from the 2009/2010 season to the 2018/2019 data, the authors use the k-means classification to build archetypes of resource dependency and squad structure variables. The influence of resource dependency on squad structure is then analysed through a table of contingency.

Firstly, the authors identify archetypes of resource dependency with some clubs that are dependent on the transfer market and others that do not count on sales to balance their account. Secondly, they provide different archetypes of squad structure choices. The contingency between those archetypes allows to identify three main strategic schemes (avoidance, shaping and adaptation).

The research tests an original relationship between resource dependency of clubs and their human resource strategy to respond to it. This paper can help to provide detailed profiles for big clubs looking for affiliate clubs to know which clubs have efficient academy or player development capacities.