Reviewerly/exHarmony huggingface dataset:
Histopathological auxiliary system for brain tumour (HAS-Bt) based on weakly supervised learning using a WHO CNS5-style pipeline

Classification and grading of central nervous system (CNS) tumours play a critical role in the clinic. When WHO CNS5 simplifies the histopathology diagnosis and places greater emphasis on molecular pathology, artificial intelligence (AI) has been widely used to meet the increased need for an automatic histopathology scheme that could liberate pathologists from laborious work. This study was to explore the diagnosis scope and practicality of AI. A one-stop Histopathology Auxiliary System for Brain tumours (HAS-Bt) is introduced based on a pipeline-structured multiple instance learning (pMIL) framework developed with 1,385,163 patches from 1038 hematoxylin and eosin (H&E) slides. The system provides a streamlined service including slide scanning, whole-slide image (WSI) analysis and information management. A logical algorithm is used when molecular profiles are available. The pMIL achieved an accuracy of 0.94 in a 9-type classification task on an independent dataset composed of 268 H&E slides. Three auxiliary functions are developed and a built-in decision tree with multiple molecular markers is used to automatically formed integrated diagnosis. The processing efficiency was 443.0 s per slide. HAS-Bt shows outstanding performance and provides a novel aid for the integrated neuropathological diagnostic workflow of brain tumours using CNS 5 pipeline.
Formal verification of fraud-resilience in a crowdsourcing consensus protocol

Crowdsourcing has emerged as a promising computing paradigm that utilizes human intelligence to achieve complex tasks, but it encounters several security and trust issues. Blockchain is a potential technology that can resolve most of these issues, however, it is difficult to find an appropriate consensus protocol applicable to crowdsourcing systems. Therefore, this work presents a Trust and Transactions Chain (TTC) consensus protocol built upon blockchain technology. It selects a trusted leader and validators considering a trust model which depends on deposit ratio, block generation and validation rate, and waiting rate. The TTC protocol addresses the main challenge of ensuring correctness related to critical systems of crowdsourcing which has extreme significance as their failure can result in disastrous consequences. This work is primarily focused on fraud-resilience avoiding double-spending attack. It also deals with sybil and eclipse attacks. Model checking is exploited because it is effective and automatic to conduct formal verification. The TTC protocol is formally modeled utilizing Communicating Sequential Programs, and the fraud-resilience property is specified using Linear Temporal Logic. The verification of the model is done using Process Analysis Toolkit that takes the formal model and specified properties as input to inspect the properties’ satisfaction or violation. The results of the formal verification are analyzed with respect to the verification time and the number of visited states.
Anti-money laundering measures and financial sector development: Empirical evidence from Africa

The study’s main objective was to evaluate the link between anti-money laundering (AML) regulations and financial sector development (FSD) in Africa and to test the nonlinearities in the AML regulations-FSD nexus. Panel data of 51 African countries from the World Bank’s indicators, the IMF, and the Basel Institute on Governance over the period 2012 to 2019 were used. The study employs the two-step system GMM and the dynamic panel threshold regression in estimating the model. The Hansen test and Arellano—Bond test for AR (2) were conducted to check for the robustness of the model specification. The study employed STATA 15 in analysing the study. The analysis shows that anti-money laundering regulation positively influences African financial sector development. However, the study found a significant positive coefficient for AML below the threshold value at a 1% significant level and a significant negative coefficient for AML above the threshold. This indicates that AML laws favour financial sector development below the threshold, but this link disappears for regimes with high AML requirements in Africa. This suggests that excessive AML structures might discourage financial sector development in Africa due to the cost associated with AML. Financial institutions in Africa should invest in technology solutions to support financial crime compliance efforts in combating criminal crimes involving digital payments, cryptocurrency, third parties and trafficking of proceeds and other crime-related activities such as drug trafficking, corruption, terrorism, arms dealing, confiscation of their illegal funds and bribery.
Mapping user behaviors to identify professional accounts in Ethereum using semi-supervised learning

Ethereum is one of the largest blockchain platforms currently that has become a digital business environment. This platform allows for decentralized transactions between anonymous users. Thus, the development of methods to identify users' behaviors and keep them anonymous can potentially leverage business on this platform. In this work, we aim to combine different categories of machine learning approaches, namely, unsupervised and semi-supervised, to map the behaviors of users' owned accounts and identify users with professional activities in Ethereum. In addition, we provide here data to the community and analyze different machine learning techniques to characterize the users of Ethereum. These are challenging tasks due to the small fraction of publicly labeled data referring to users' accounts that provide services on this platform, such as exchange, payment, and entertainment, among most casual behavior users. Initially, we use unsupervised learning techniques to cluster the unlabeled users' accounts and to identify a set of them with casual behavior. As an outcome, a dataset containing labeled (casual or professional) and unlabeled instances is obtained. Semi-supervised learning methods are then applied (i) to generate models that classify accounts' behaviors into casual or professional ones and (ii) to discover accounts with professional behaviors among the unlabeled ones. Computational experiments were conducted, and the results obtained by the proposed procedure are compared to those achieved by supervised learning techniques from the literature. The proposal outperformed those from the literature and reached values higher than 95% for the accuracy, precision, recall, Fβ-scores, MCC, and AUC-ROC.
Blockchain-Fog Computing Integration Applications: A Systematic Review

Abstract The Fog computing concept has been introduced to aid in the data processing of Internet of things applications using Cloud computing. Due to the profitable benefits of this combination, several papers have lately been published proposing the deployment of Blockchain alongside Fog computing in a variety of fields. A comprehensive evaluation and synthesis of the literature on Blockchain-Fog computing integration applications that have emerged in recent years is required. Although there have been several articles on the integration of Blockchain with Fog computing, the applications connected with this combination are still fragmented and require further exploration. Hence, in this paper, the applications of Blockchain-Fog computing integration are identified using a systematic literature review technique and tailored search criteria generated from the study objectives. This article found and evaluated 144 relevant papers. The findings of this article can be used as a resource for future Fog computing research and designs.
Deep Hybrid Neural Network With Attention Mechanism for Video Hash Retrieval Method

With the advent of 5G technology, a large amount of video data will be generated and uploaded to the Internet every day, which makes it an urgent problem to effectively retrieve similar video content from massive video databases.However, most of the existing video retrieval methods usually ignore the time information contained in the video, which causes the problem that the temporal information feature is not obvious when extracting video features.Therefore, this paper proposes a deep hybrid neural network with attention mechanism for video hash retrieval method.The method first feeds the extracted video key frames into the residual network embedded with the attention mechanism and the bidirectional gated recurrent unit network to extract the spatio-temporal information features of the video, so as to improve the representation ability of the features, and then integrates the hash learning technology into the network framework to generate the binary hash code of the video to shorten the video retrieval time.By designing objective function to capture the information of the global data structure using the similarity relationship between codes, the class label information can be effectively used to improve the retrieval performance.Compared with the existing methods, the experimental results show that the method has a certain improvement in the retrieval accuracy on the commonly used video datasets UCF-101 and JHMDB, which verififies the feasibility of the method.
Adaptive client and communication optimizations in Federated Learning

Federated Learning (FL) is a very effective distributed machine learning framework that enables a large number of devices to jointly train models without sharing raw data. However, the process of iterative learning and data communication in FL can be time-consuming, depending heavily on the number of clients participating in training and the number of local iterations between two consecutive global aggregations (communication period). In this paper, we analyze the runtime of the FL framework and propose a joint optimization problem, which considers both the number of local clients involved in global training and the communication period to minimize the error-runtime convergence of FedAvg. By theoretical analysis, we obtain the optimal solutions of this optimization problem in closed forms under the assumption of different probability distributions of the client's local computation time. In addition, we design an adaptive control algorithm that can dynamically select the number of clients and communication period during FL training to speed up the convergence of the model. Experimental results validate our theoretical analysis and show that the proposed algorithm has outstanding performance compared with other related FL control algorithms.
TCCL-Net: Transformer-Convolution Collaborative Learning Network for Omnidirectional Image Super-Resolution

As virtual reality and metaverse become more and more popular, the Omnidirectional Image (OI) has attracted extreme attention due to its immersive display characteristics. However, users only watch a portion of the content in a specific viewport extracted from a panoramic view, which will lead to a problem of resolution mismatch that requires High-Resolution (HR) for clear near-eye displays in viewports. Hence, it is necessary to exploit a Super Resolution (SR) solution for reconstructing Low-Resolution (LR) OIs. Different from 2D SR methods, the variation of pixel distributions along latitudes is a critical factor in designing an Omnidirectional Image Super-Resolution (OISR) scheme. In this paper, we put forward a novel end-to-end network with a Transformer and Convolution Collaborative Learning Network (TCCL-Net) for OISR. Firstly, Swin Transformer blocks and residual convolution blocks are employed to extract long-range and short-range dependencies, thereby digging into more rich and heterogeneous features from these two branches. Secondly, to better fuse these two features, cross-guided enhanced attention mechanisms are designed for bidirectional information enhancement onto both channel and spatial features. Thirdly, to alleviate nonuniformly pixel distributions across latitudes, we add an absolute positional encoding into Swin Transformer to represent patch weights at different positions and propose a tile-based panoramic reconstruction module to super-resolve various bands with different pixel sampling characteristics across latitudes. Experimental results on two available benchmark datasets demonstrate the superiority of the proposed approach over the state-of-the-art method in achieving OISR task.
Neighborhood-based credibility anchor learning for universal domain adaptation

Universal domain adaptation (UniDA) aims to transfer knowledge from the source domain to the target domain in the presence of distribution shift and class mismatch. Most existing works design threshold-relied methods to reject target private classes by carefully-proposed uncertainty scoring functions which are very sensitive to thresholds. To overcome this problem, a few threshold-free methods are proposed but ignore the neighborhood structure information of the target domain, leading to poor performance. In this paper, we propose Neighborhood-based Credibility Anchor Learning (NCAL), a new threshold-free framework that fully mines the neighborhood structure information to explore better target representations. NCAL contains three key components: a class anchor learning module to learn target class distribution, a credibility-weighted conditional adversarial module to learn class-invariant features of common classes, and an open-set neighborhood clustering module to learn well-clustered features. Extensive experiments demonstrate that our method outperforms the state-of-the-art.
A Digital Twin Mixed-reality System for Testing Future Advanced Air Mobility Concepts: A Prototype

The UK Future Flight Vision and Roadmap defines how aviation in the UK is envisioned to develop by 2030. As part of the Future Flight demonstration segment, project HADO (High-intensity Autonomous Drone Operations) will develop, test, and deploy fully automated Unmanned Aircraft System (UAS) operations at London Heathrow airport. The resource-demanding nature of real-world tests, however, suggests that developing and improving the reliability and efficiency of virtual environment-based testing methods is indispensable for the evolution of such operations. Nonetheless, developing a high-fidelity and real-time virtual environment that enables the safe, scalable, and sustainable development, verification, and validation of UAS operations remains a daunting task. Notably, the need to integrate physical and virtual elements with a high degree of correlation presents a significant challenge. Consequently, as part of the synthetic test environment work package within the HADO project, this paper proposes a Digital Twin (DT) system to enable mixed-reality tests in the context of autonomous UAS operations. This connects a physical world to its digital counterpart made up of five distinct layers and several digital elements to support enhanced mixed-reality functionality. The paper highlights how the static layers of the synthetic test environment are built, and presents a DT prototype that supports mixed-reality test capabilities. In particular, the ability to inject virtual obstacles into physical test environments is demonstrated, highlighting how the sharp boundaries between virtual environments and reality can be blurred for safe, flexible, efficient, and effective testing of UAS operations.
The impact of Internet of Things (IoT) and Logistics Activities on Digital Operations

The Internet of Things (IoT) paradigm enables numerous resource-constrained, integrated devices, things, and people to communicate with one another over the internet protocol for a continuous data exchange. According to this perspective, logistics is seen as a vital player ready to collect trustworthy and secure real-time data by utilizing the extensive interconnectivity. Additionally, the useful data that may be retrieved and processed from IoT data and utilized to create intelligent services and applications that improve both corporate digital operations and the efficiency of logistical activities. This research is aimed to empirically explore the impact of internet of things and logistics activities on digital operations of an organization. In order to proceed this research a quantitative technique was applied using descriptive and analytical design. 199 respondent’s data were used to assess that has been collected from security providing companies located in Dubai UAE. The collected data were assessed using SmartPLS 4.0 by employing reliability, discriminant validity and hypothesis test that declared a positive significant relationship between IoT and logistics activities with digital operations.
Impact of Blockchain Technology and Fintech on Sustainable Performance

The financial industries have grown concerned over the last ten years about the rapid development of financial technology, or Fintech, and the conversation surrounding sustainable performance. Achieving cutting-edge performance through ongoing product and process development is encouraged by global competition, and blockchain technology knowledge is the most compelling tool for long-term sustainability and business growth. The main goal of this research was to examine the potential application of blockchain technology and fintech in the insurance industry, which may need to manage client data and transactions with a secure and reliable blockchain record in order to maintain sustainable performance. A descriptive research was incorporated using simple convenient sampling technique. Data recorded for 196 employees working in insurances companies based in Dubai UAE. data gathered through online questionnaire and evaluated through SmartPLS software by applying hypothesis testing using PLS-SEM Algorithm, Reliability and validity test for model validation. As a result, a positive significant relationship of the proposed construct further expands the implementation of blockchain technology will result in significant efficiency gains, cost savings, transparency, quicker payouts, and a reduction in fraud while allowing data to be shared in real-time across several parties in a trustworthy and verifiable manner.
Digital Transformation and its Impact on Operational Efficiency and Competitive Advantage in Islamic Banks

This study aimed to identify the impact of digital transformation on Islamic banks operating in Jordan by using the descriptive analytical method. Primary data were obtained through a questionnaire and distributed to the study sample, which consisted of 68 employees of 4 Islamic banks operating in Jordan. The structural equation model was used in the process of testing the study hypotheses. We obtained a number of results, the most important of which is the presence of a statistically significant effect at the significance level (α ≤ 0.05) of digital transformation in Islamic banks operating in Jordan in relation to operational efficiency and competitive advantage. We recommend that Islamic banks need to develop a strategy for digital transformation aimed at innovation and competition, without prejudice to the controls of Islamic finance. There is a need to focus on managing the risks associated with digital transformation, as well as treating the risks associated with modern technologies, especially those that threaten Islamic banks and financial stability.
A Comprehensive Review on Big Data Challenges

Increased hardware capability has allowed many businesses to store massive amounts of data in the previous few years. Streams of data may go on forever, so they are used in so many different applications. Analyzing and querying these streams is challenging for a stream of dynamic information. The term "big data" refers to a collection of methods and tools that must be combined in novel ways to unearth the vast amounts of previously unseen information buried within vast, diverse, and intricate databases. There are several ways to obtain, organize and analyze data in the big data world. In order to deal with streaming data, efficient data mining procedures must be in place. In this article, we examine a variety of Big Data-related concerns and challenges. This paper introduces the features, and challenges of managing big data.
The Impact of Digital marketing and Digital Payment on Financial Performance

In the digital age, digital marketing affected people's lives on a professional and societal level. The financial sector, in particular, makes substantial use of information technology that has emerged in the digital age to make it simpler for the general public to access financial products and services. The community can benefit from opportunities thanks to information technology improvements, especially for entrepreneurs. This research aims to examine the impact of digital marketing, digital payments on financial performance. This research uses quantitative methodologies, and structural equation modeling-based data analysis techniques are used. with the aid of SmartPLS 4.0 software. the process of choosing a sample using convenient random sampling techniques. 191 employees working in financial companies based in the Dubai UAE who responded to online questionnaires had their responses analyzed. According to data analysis findings, digital payments, and digital marketing concluded with a positive and significant impact on financial performance.
Identity Threats in the Metaverse and Future Research Opportunities

The metaverse is the next-generation internet, which can be described as three-dimensional virtual environments that reflect different aspects of the physical world. Users can engage in various experiences while representing themselves as customizable digital avatars. Virtual Reality (VR), Augmented Reality (AR), Digital Twin, Artificial Intelligence (AI), and Blockchain are key technologies that power immersion, interoperability, automation, and 3D elements of the metaverse. Recently, the metaverse has gained significant attention from the tech industry, especially with Facebook rebranding to Meta and announcing its vision for the future metaverse. The metaverse has many applications, including gaming, entertainment, shopping, education, healthcare, and work. Despite the revolutionary potential of the metaverse, several cybersecurity and privacy issues are inevitable and need to be addressed. Specifically, this paper focuses on highlighting identity-related risks for metaverse users. First, an overview of the metaverse elements, technologies, and characteristics is presented, along with a proposed definition of what a user’s digital identity represents in the metaverse. Accordingly, potential identity risks and cybersecurity threats are discussed. Finally, AI and biometric authentication techniques are presented as possible solutions and future research opportunities.
Distributed Particle Filter Based on Particle Exchanges

Multi-agent networks are known for their scalability, robustness, flexibility, and are typically tasked with a variety of tasks such as target tracking, surveillance, traffic control, and environmental monitoring.Distributed Particle Filters (DPF) are often employed when the for non-linear parameter estimation with non-Gaussian noise.In this paper, we propose a novel distributed particle filter whose transmitted quantities are particles.The fusion process of particles is implemented in a distributed and iterative fashion.To reduce the communication overhead, we adopt the Gaussian process-enhanced resampling algorithm, which reduces the size of local particle set, while still ensures acceptable filtering performance.To determine the local particle set after the communication, we propose two solutions.Our first algorithm (GP-DPF) adopts a "scoring mechanism", allowing local agents score the received particles and using the scores as the selection criterion.Our second proposed solution (FA-DPF) is a meta-heuristic approach, which uses the well known firefly algorithm as a selection method for particle-based distributed particle filtering.Our simulations demonstrate the superiority of our proposed algorithms under the condition of limited communication and computational resources against other state-of-the-art distributed particle filters.