Reviewerly/exHarmony huggingface dataset:
Application of Blockchain in Mining 4.0

The mining sector is one of the important sectors of the economy and is a major contributor to the growth of an economy. Many industries like iron, steel, petroleum, natural gas, fertilisers, cement, glass, ceramics, etc. depend on the mining sector for their basic raw materials. Thus, mining can be considered as a catalyst for other industries growth. Due to increased rivalry, environmental and governmental legislation, and regulations, the operating environment of mines today is more complicated than ever before. This is because many businesses rely on the mining sector for their primary raw materials. Rising energy costs, access to energy, increase in demands, change in customer demands, reduced ore grades, social and geopolitical risks, strict labour rules, cost control, etc. are some major challenges of the mining industry. To take the competitive advantage in the global mining industry, industries are now taking more risks and facing uncertainty to meet their demand and it has become increasingly apparent that technology will play a growing role in the coming future. The fourth industrial revolution or Industry 4.0 is about automation and digitisation of the manufacturing environment and revolutionising the way companies manufacture, optimise the operation, and distribute their products. To extract the full potential of Industry 4.0 movement, there are many technologies like artificial intelligence and machine-to-machine (M2M) communication, internet of things (IoT), Blockchain, data analytics, etc. The Blockchain is an emerging technology that plays a crucial role to bring transparency, security, and trust in all transactions among different entities of the mining industry like production, transportation, and distribution. Blockchain records information in such a way that makes it impossible to alter or hack. Blockchain is a digitally distributed and decentralised ledger of transactions that is immutable in nature. This chapter examines the use of blockchain in Mining 4.0. The study highlights the challenges, opportunities, and requirements of blockchain technology to handle the risk and mining.
Statistical inference with exchangeability and martingales

In this paper, we start by reviewing exchangeability and its relevance to the Bayesian approach. We highlight the predictive nature of Bayesian models and the symmetry assumptions implied by beliefs of an underlying exchangeable sequence of observations. By taking a closer look at the Bayesian bootstrap, the parametric bootstrap of Efron and a version of Bayesian thinking about inference uncovered by Doob based on martingales, we introduce a parametric Bayesian bootstrap. Martingales play a fundamental role. Illustrations are presented as is the relevant theory. This article is part of the theme issue ‘Bayesian inference: challenges, perspectives, and prospects’.
A Review on Dimensionality Reduction for Machine Learning

In recent years growing volumes of data have made the task of applying various machine learning algorithms a challenge in a great number of cases. This challenge is posed in two main ways: training time and processing load. Normally, problems in these two categories may be attributed to irrelevant, redundant, or noisy features. So as to avoid this type of feature most pre-processing pipelines include a step dedicated so selecting the most relevant features or combining existing ones into a single better representation. These techniques are denominated dimensionality reduction techniques. In this work, we aim to present a short look at the current state of the art in this area.
Generalized zero-shot domain adaptation via coupled conditional variational autoencoders

Domain adaptation aims to exploit useful information from the source domain where annotated training data are easier to obtain to address a learning problem in the target domain where only limited or even no annotated data are available. In classification problems, domain adaptation has been studied under the assumption all classes are available in the target domain regardless of the annotations. However, a common situation where only a subset of classes in the target domain are available has not attracted much attention. In this paper, we formulate this particular domain adaptation problem within a generalized zero-shot learning framework by treating the labelled source-domain samples as semantic representations for zero-shot learning. For this novel problem, neither conventional domain adaptation approaches nor zero-shot learning algorithms directly apply. To solve this problem, we present a novel Coupled Conditional Variational Autoencoder (CCVAE) which can generate synthetic target-domain image features for unseen classes from real images in the source domain. Extensive experiments have been conducted on three domain adaptation datasets including a bespoke X-ray security checkpoint dataset to simulate a real-world application in aviation security. The results demonstrate the effectiveness of our proposed approach both against established benchmarks and in terms of real-world applicability.
The Impact of Marketing Mix Elements on Tourist's Satisfaction towards Five Stars Hotel Services in Dubai during COVID-19

Purpose: Tourist satisfaction measures the prosperity of a business and tourism sector. If there are many satisfied tourists, the tourism sector will profit, but if there are many less satisfied tourists, the tourism sector will lose money. Therefore, it is necessary to know the factors that affect tourist satisfaction. This study aims to develop and implement a method for five-star hotels to identify attributes that will increase tourists' satisfaction during the pandemic.&#x0D; Method: The study used 100 tourists in Dubai as respondents who were selected randomly at five-star hotels. The results showed no impact of all marketing mix elements on tourist satisfaction except the physical evidence of the COVID-19 pandemic.&#x0D; Outcomes: The results of this study indicated that the COVID-19 pandemic influenced five-star hotel services in Dubai and that the marketing mix elements used by the hotels are an important factor in tourist satisfaction. In conclusion, Dubai must understand the importance of a marketing mix to fulfil tourist satisfaction and improve the service that makes it more significant in the tourism industry.&#x0D; Originality: Numerous modifying variables impacting the management process have been examined in the literature on complaint management. It is the first study investigating the five-star hotel in Dubai to check the operative services on how to work on tourist satisfaction and determine the impact of marketing mix elements on tourism during the pandemic.
MFA-Net: Multiple Feature Association Network for medical image segmentation

Medical image segmentation plays a crucial role in computer-aided diagnosis. However, due to the large variability of medical images, accurate segmentation is a highly challenging task. In this paper, we present a novel medical image segmentation network named the Multiple Feature Association Network (MFA-Net), which is based on deep learning techniques. The MFA-Net utilizes an encoder–decoder architecture with skip connections as its backbone network, and a parallelly dilated convolutions arrangement (PDCA) module is integrated between the encoder and the decoder to capture more representative deep features. Furthermore, a multi-scale feature restructuring module (MFRM) is introduced to restructure and fuse the deep features of the encoder. To enhance global attention perception, the proposed global attention stacking (GAS) modules are cascaded on the decoder. The proposed MFA-Net leverages novel global attention mechanisms to improve the segmentation performance at different feature scales. We evaluated our MFA-Net on four segmentation tasks, including lesions in intestinal polyp, liver tumor, prostate cancer, and skin lesion. Our experimental results and ablation study demonstrate that the proposed MFA-Net outperforms state-of-the-art methods in terms of global positioning and local edge recognition.
E-Voting System Using Blockchain Technology

In every nation, democratic elections are a momentous and weighty occurrence, and the voting system that is now in place requires the use of ballots or electronic voting machines (EVM). Transparency, poor turnout, vote manipulation, distrust of electoral organizations, fabrication of unique IDs (voting party IDs), and delays in posting results are some of the issues that arise as a result of these procedures. The matter of safety is of the utmost importance. When considering the installation of a computerized voting system, voter confidentiality has always been one of the most important concerns. There is no question regarding the system's capability to secure itself in contrast to prospective assaults and safeguard data in the face of such big choices. Utilization of blockchain technology is one approach that might be taken to resolve security concerns. The blockchain technology has an endless number of different uses that might be implemented. The technology known as blockchain is a distributed ledger that makes it possible for peer-to-peer networks all over the world to handle digital assets. In this context, distributed ledger technology represents an intriguing development. A grouping of all transactions is referred to as a block. Immutability, decentralisation, security, transparency, and anonymity are some of the outstanding properties offered by blockchain technology. The combination of blockchain technology with smart contracts has shown promise as a viable option for the development of trustworthy and open-source electronic voting systems. In this article, we demonstrate how to use blockchain technology with the help of a wallet and the Solidity programming language to build an electronic voting application. The programme was designed as a smart contract for the Ethereum network. In order to avoid having the same person vote twice, the user's wallet will only hold a certain number of tokens (gas), which will be depleted each time the user casts a vote. This article talks about the pros and cons of using blockchain technology. It also shows a practical solution in the form of a web app for voting and analyses its limits.
Don’t Look Up: The Cost of Attention to Stimulus Phrases in Mobile Text Entry Evaluations

Transcription tasks have been long used as the de-facto evaluation method in mobile text entry research. Evaluations use memorable phrase sets, in order to prevent participants from devoting more attention to the stimulus phrase than the bare minimum. We present evidence from an eye-tracking study, demonstrating that the attention devoted to the stimulus phrase is much higher than might be expected. In fact, attention to the stimulus phrase takes up almost 50% of participant attention spent outside the keyboard area, and overall 25% of participant attention throughout any single transcription task. We explore a modification to the transcription task aimed at reducing this level of visual attention, without finding any statistically significant differences. These findings raise important questions on the continued use of the transcription task as the mainstream evaluation method for mobile text entry research.
Adaptive Expert Models for Federated Learning

Federated Learning (FL) is a promising framework for distributed learning when data is private and sensitive. However, the state-of-the-art solutions in this framework are not optimal when data is heterogeneous and non-IID. We propose a practical and robust approach to personalization in FL that adjusts to heterogeneous and non-IID data by balancing exploration and exploitation of several global models. To achieve our aim of personalization, we use a Mixture of Experts (MoE) that learns to group clients that are similar to each other, while using the global models more efficiently. We show that our approach achieves an accuracy up to 29.78% better than the state-of-the-art and up to 4.38% better compared to a local model in a pathological non-IID setting, even though we tune our approach in the IID setting.
Ultrasound Image Segmentation of Breast Tumors Based on Swin-transformerv2

Breast cancer is one of the most common cancers with a high mortality rate. Early detection is essential to reduce the risk of mortality and morbidity for breast cancer. The diagnosis of breast cancer relies heavily on the segmentation accuracy of the breast tumor region. Convolutional neural networks, a form of deep learning, have now been applied extensively to breast tumor segmentation, but they still cannot focus on global information and accurately locate features. In this paper, the swin-transformerv2-UNet(S2UNet) method for segmenting breast ultrasound images based on swin-transformerv2 is proposed. S2UNet adopts the structure of upsampling and downsampling as a whole. First, the breast ultrasound image is divided into patches, and these patches are input into the downsampling structure for feature extraction. Then, the skip connection structure is introduced to fuse the features extracted from swin-transfermerv2 in the downsampling structure with the features of the corresponding modules in the upsampling structure. Next, these features are labeled and spliced through the upsampling structure to obtain the final breast tumor image. The S2UNet method was evaluated on the breast ultrasound images dataset (BUSI) with five-fold cross-validation. The results demonstrate that compared to UNet and other advanced segmentation models (Attention-UNet, TransUNet, Swin-UNet), the S2UNet with fewer parameters shows better segmentation performance with dice similarity coefficient (DSC) of 73.26% and intersection over union (IoU) of 58.30%.
Contrast Optimization for Size Invariant Visual Cryptography Scheme

Visual cryptography scheme (VCS) serves as an effective tool in image security. Size-invariant VCS (SI-VCS) can solve the pixel expansion problem in traditional VCS. On the other hand, it is anticipated that the contrast of the recovered image in SI-VCS should be as high as possible. The investigation of contrast optimization for SI-VCS is carried out in this article. We develop an approach to optimize the contrast by stacking <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> ≤ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> ≤ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> ) shadows in ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k, n</i> )-SI-VCS. Generally, a contrast-maximizing problem is linked with a ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k, n</i> )-SI-VCS, where the contrast by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> shadows is considered as an objective function. An ideal contrast by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> shadows can be produced by addressing this problem using linear programming. However, there exist ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> – <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> +1) different contrasts in a ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k, n</i> ) scheme. An optimization-based design is further introduced to provide multiple optimal contrasts. These ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> – <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> + 1) different contrasts are regarded as objective functions and it is transformed into a multi-contrast-maximizing problem. The ideal point method and lexicographic method are adopted to address this problem. Additionally, if the Boolean XOR operation is used for secret recovery, a technique is also provided to offer multiple maximum contrasts. The effectiveness of the proposed schemes is verified by extensive experiments. Comparisons illustrate significant advancement on contrast is provided.
A Scalable Byzantine Fault Tolerance Algorithm Based on a Tree Topology Network

The consortium chain is the main form of application of blockchain technology in the actual industry, and its consensus mechanism mostly adopts the practical Byzantine fault tolerance (PBFT) algorithm. The traditional PBFT algorithm is only suitable for small-scale local area networks, but in large-scale wide-area network environments, its scalability bottleneck has a serious impact on the performance of the system. Therefore, in this paper, a scalable Byzantine fault tolerance algorithm based on a tree topology network is proposed (STBFT), which can take different steps to reach consensus according to the abnormal situation of the system. First, the STBFT algorithm divides the consensus nodes into different layers and groups based on the tree topology network structure, which transforms from global consensus to local consensus and drastically reduces communication consumption. Then, the division method of the group is based on a verifiable random function (VRF), with the purpose of preventing targeted attacks and colluding Byzantine nodes from affecting the normal consensus of the system. Finally, a feedback mechanism is proposed for the first time to reduce the influence of Byzantine failure on hierarchical network systems. The simulation results show that the proposed algorithm reduces the communication complexity and improves the fault tolerance of the system, and the scalability of the tree topology network structure can be better applied in large-scale scenarios such as IoT and health care.
Five Things We Need to Learn About Incel Extremism: Issues, Challenges and Avenues for Fresh Research

In recent years research on the involuntarily celibate or “Incel” community has contributed a small but significant stock of knowledge about toxic varieties of extreme misogyny being shared on lightly regulated online communication boards. Simultaneously, we have witnessed the potential for Incel ideas to find expression in disturbing outbursts of extremist violence. This paper uses existing research to consider what we know and what we need to find out about this milieu if we are to further our knowledge and understanding. Ultimately, we highlight five key questions that need to be investigated more thoroughly and call for a wider range of methodological approaches in this endeavor. In particular, we argue that there is a need to build stronger connections with members of the Incel community in order to pursue a better-crafted body of research. While this is challenging, it is necessary if we are to gain a sense of how much support there is in Incel communities for violence, the role of pornography within the community, the key dimensions of Femcel ideology, the connections between Incel ideology and extreme right-wing thinking, and the measures that might aid deradicalization once an individual has adopted ideas related to extremist misogyny. Ultimately, we argue that while certain pieces of research are leading the way in contributing fresh thinking and innovative methodological approaches, there is much work to be done to develop our understanding in these areas of enquiry.
Comparative Analysis on Aspect-based Sentiment using BERT

Aspect-based Sentiment Analysis (ABSA) is a complex model within the domain of Sentiment Analysis (SA) tasks which deals with classifying the sentiments related to particular aspects (or targets) in the given text. ABSA task has gained popularity due to its various sub-tasks related to the aspect-based sentiment analysis task. This work provides a comparative study of various approaches used to solve the ABSA task using the BERT technique. The selected approaches include a fine-tuned BERT model, adversarial training using BERT (Bidirectional Encoder Representations from Transformers) and the incorporation of disentangled attention in BERT or the DeBERTa for the ABSA task. One of the challenges faced during implementation of the ABSA task is that it requires an in-depth understanding about the language. Experiment results indicate that the approach, which uses the fine-tuned BERT model yields the best mean F1 score of 85.65 and the best mean accuracy score of 85.98 is yielded by the DeBERTa model.
Adversarial learning-based method for recognition of bionic and highly contextual underwater targets

Underwater bionic robots are very similar to real underwater creatures in appearance and features. This makes it difficult for autonomous underwater vehicles (AUVs) to distinguish their appearance features. Moreover, the existing underwater target detection methods have large model sizes and many parameters. This leads to difficulty in practical application of such UAV methods. To solve this, we propose an underwater camouflaged target recognition algorithm based on an adversarial graph structure. First, the salient features and environmental features of underwater images are fully explored by a custom salient feature extraction network and a related environmental feature extraction network. Second, the camouflaged target graph structure and the natural target graph structure of the fusion environment feature are constructed to obtain the dependency between the target and environment features. Finally, an adaptive frame regression camouflaged target detection network is constructed based on adversarial learning to recognize camouflaged targets and natural targets. In contrast to the existing target recognition model in the simulation of the four datasets of concealed target detection, quantitative underwater image dataset, underwater image enhancement benchmark dataset, and underwater image dataset, the proposed model gets higher average recognition accuracy of 0.820, which is an improvement of 3.4%, and the number of parameters is only 1.2 m. Proved by the experiments, the proposed target recognition algorithm is more effective and superior to the existing algorithms.