Reviewerly/exHarmony huggingface dataset:
Ontology Development for Creating Identical Software Environments to Improve Learning Outcomes in Higher Education Institutions

Students engage in remote learning within a diverse computer environment. While virtual machines can address the challenges posed by heterogeneity, there remain unresolved issues, particularly related to the complexity of software management. An imperative is to discover an automated solution that facilitates the creation of consistent software environments for educational purposes. This paper introduces ontology engineering principles as a means to tackle the complexities associated with software management. A suitable ontology is developed using OWL syntax, integrating knowledge pertaining to the required software within a specific academic domain. The practical applicability of this knowledge is enabled through the implementation of dedicated SPARQL queries within a Python program. The effectiveness of the automated solution in achieving identical software environments is verified through testing, conducted in both controlled laboratory settings and by students themselves, thus simulating authentic teaching scenarios. The solution not only adheres to the principles of reusability but can also be adapted or integrated into existing ontologies. Furthermore, it presents an opportunity to create automated and self-adjusting virtual machines, offering significant potential for educational and other domains.
Entrepreneurial Risk and Digital Financial Inclusion: A <scp>Cross‐Country</scp> Analysis

The inception of financial technologies (Fintech) has enabled expanded use of financial services. Fintech contributes to financial inclusion and by doing so provides impacts on income inequality, poverty and economic growth. A prospective route of influence exerted by Fintech is to assist economic agents in taking entrepreneurial risks. The purpose of this study was to investigate the impact of digital financial inclusion, as enhanced by FinTech, on entrepreneurial risk. An index for measuring levels of digital financial inclusion was constructed based on variation patterns in several indicators of digital activities. A cross‐country model was proposed that relates entrepreneurial risk to digital financial inclusion as well as to a variety of other economic and social factors. Estimation results based on a panel of countries show that digital financial inclusion (0.265), institutional quality (0.169), per capita GDP (0.0456) and education (0.0475) have positive and significant effects on entrepreneurial risk. In contrast, time required to start a business (−0.136) and effective tax rates on capital (−0.494) provide negative and significant effects. In addition, a significant quadratic relationship between entrepreneurial risk and remittances (employee compensation and personal transfers) was found.
A Dynamic Scalable Auto-Scaling Model as a Load Balancer in the Cloud Computing Environment

INTRODUCTION: Cloud services are becoming increasingly important as advanced technology changes. In these kinds of cases, the volume of work on the corresponding server in public real-time data virtualized environment can vary based on the user’s needs. Cloud computing is the most recent technology that provides on-demand access to computer resources without the user’s direct interference. Consequently, cloud-based businesses must be scalable to succeed.OBJECTIVES: The purpose of this research work is to describe a new virtual cluster architecture that allows cloud applications to scale dynamically within the virtualization of cloud computing scale Using auto-scaling, resources can be dynamically adjusted to meet multiple demands. METHODS: An auto-scaling algorithm based on the current implementation sessions will be initiated for automated provisioning and balancing of virtualized resources. The suggested methodology also considers the cost of energy.RESULTS: The proposed research work has shown that the suggested technique can handle sudden load demands while maintaining higher resource usage and lowering energy costs efficiently.CONCLUSION: Auto-scaling features are available in measures in order groups, allowing you to automatically add or remove instances from a managed instance group based on changes in load. This research work provides an analysis of auto-scaling mechanisms in cloud services that can be used to find the most efficient and optimal solution in practice and to manage cloud services efficiently.
MeT: mesh transformer with an edge

Transformers have been widely applied in various vision tasks processing different data, such as images, videos and point clouds. However, the use of transformers in 3D mesh analysis remains largely unexplored. To address this gap, we propose a mesh transformer (MeT) that utilizes local self-attention on edges. MeT is based on a transformer layer that uses vector attention for edges, which is a kind of attention operator that supports adaptive modulation to both feature vectors and individual feature channels. Based on the transformer block, we build a lightweight mesh transformer network that consists of encoder and decoder. MeT provides general backbones for subsequent 3D mesh analysis tasks. To evaluate the effectiveness of our network MeT, we conduct experiments on two classic mesh analysis tasks: shape classification and shape segmentation. MeT achieves the state-of-the-art performance on multiple datasets for two tasks. We also conduct ablation studies to show the effectiveness of key designs in our network.
Domain adaptation for complex shadow removal with shadow transformer network

Shadows observed in practical scenes have complex shapes, and removing them is a very challenging task in computer vision. In particular, previous studies on complex shadow removal have limitations in that they can be learned only on paired datasets. Taking the issues into consideration, we present a new domain-adaptive shadow removal framework. The proposed approach includes domain adaptation, detection, and removal stages. The shadow-preserving domain translator in the first stage compensates for the lack of real data through domain transformation of the synthetic data. In the second stage, efficient shadow detection is performed through the domain adaptive mean teacher network. Last, a novel attention network removes complex shadows using detected shadows as a query, effectively removing complex shadows. The feasibility and effectiveness of the proposed framework are validated through the newly collected Grand Theft Auto-Road Shadow dataset. The proposed method outperforms existing methods for quantitative and qualitative metrics related to shadow detection and removal.
Joint filter combination-based central difference feature extraction and attention-enhanced Dense-Res2Block network for short-utterance speaker recognition

Speech authentication in smart services typically involves short utterances. However, due to the short duration of these utterances (e.g. less than 3 s) and the limited enrolment and/or test data available, it is difficult to learn enough information to accurately distinguish the person. As a result, speaker recognition from short utterances is very challenging. In this paper, in the acoustic end. we propose a novel Bark-scaled Gaussian and linear filter bank cepstral coefficients (BGLCC) and multi-dimensional central difference (MDCD) acoustic features extraction method. Also, to enhance the discriminative embedding, in the network end, a novel attention-enhanced Dense-Res2Block network is proposed. First, the rich low-frequency information is extracted based on the high distribution density of the Bark-scaled Gaussian filter bank in the low-frequency domain, and more high-frequency information is extracted based on the linear filter bank uniformly distributed in the high-frequency domain. This means that the combined filters can produce more discriminative and richer acoustic features from short-duration audio signals. In addition, the multi-dimensional central difference method captures better speaker dynamic features in the relative BGLCC domain to improve the performance of short utterance speaker recognition. Finally, an attention-enhanced Dense-Res2Block architecture can obtain a variety of feature expressions of different scale combinations to enhance features. Extensive analysis of a variety of datasets, which speech samples of different types, diverse lengths, etc., demonstrate the superiority of the proposed feature extraction method and model over existing acoustic feature extraction methods and speaker recognition models, including those based on MFCCs, LPCCs, and fusion features, and X-vector-PLDA, BLSTM-ResNet, ResNet34-SP, ECAPA-TDNN respectively. The experimental results show that the proposed method achieves the best performance compared to the existing approach.
Both Homophily and Heterophily Matter: Bi-Path Aware Graph Neural Network for Ethereum Account Classification

In recent years, the cryptocurrency market has been booming with an ever-increasing market capitalization. However, due to the anonymity of blockchain technology, this market has become a hotbed of financial crimes. As the largest blockchain platform supporting smart contracts, financial crimes including scams and hacking frequently happen on Ethereum and have caused serious losses. Therefore, it is necessary to classify Ethereum accounts in order to better identify those involved in illegal transactions and analyze the behavior patterns of different classes of accounts. In this paper, we construct an Ethereum transaction network based on transaction records and find that this network is with heterophily. However, most of the current work on account classification ignores the role of this heterophily information. We first figure out that the heterophily information of the neighborhood may also be beneficial for the final predictions. Based on this, we propose a new graph neural network (GNN) model, named BPA-GNN, which incorporates both homophilic and heterophilic information into the neighborhood aggregations. Specifically, BPA-GNN consists of three main modules including bi-path neighbor sampling, separated neighborhood aggregation, and attention-based node representation learning. Comprehensive experiments on a real Ethereum transaction dataset demonstrate the state-of-the-art performance of BPA-GNN, showing that the model can effectively extract and utilize neighborhood information to improve the distinguishability of node representations. As an effective solution for Ethereum account de-anonymization, BPA-GNN can help identify illegal activities and promote the healthy development of the Ethereum ecosystem.
Blockchain-Based Decentralized Learning for Security in Digital Twins

This work aims to analyze malicious communication behaviors that pose a threat to the security of digital twins (DTs) and safeguard user privacy. A unified and integrated multidimensional DTs Network (DTN) architecture is constructed. On this basis, the propagation process model of malware in the network is built to analyze the malicious propagation behavior that threatens network security. This model ensures the protection of mobile distributed machine learning system security. Blockchain technology is a distributed data protection mechanism with broad prospects. It is characterized by decentralization, transparency, and anonymity, which can help ensure secure network data sharing and privacy protection. Based on this, this work designs a secure distributed data sharing (DDS) architecture based on blockchain to improve the security and reliability of data protection with the support of the Internet of Things (IoT). Then, digital resource allocation based on semi-distributed learning is examined to propose a broad learning federated continuous learning (BL-FCL) algorithm combining blockchain and DTs. This algorithm significantly speeds up the model training process. Broad learning technology supports incremental learning. In this way, each client does not need to retrain when learning the newly generated data. In the experimental part, the prediction accuracy of BL-FCL on the mixed national institute of standards and technology data set is similar to that of the FedAvg-50 and FedAvg-80 schemes. As the number of devices increases from 1 to 6, the detection probability exhibits a rapid decrease. However, as the number of devices further increases from 6 to 10, the detection probability gradually decreases at a slower rate until it reaches 0. Comparatively, the prediction accuracy of the BL-FCL outperforms the federated averaging algorithm-based scheme by 20%–60%. The BL-FCL reported here can deal with the problem of inaccurate training while ensuring the privacy and security of users. This work is of great significance for ensuring the security of the DTN and promoting the development of the digital economy. The results can provide references for applying blockchain and distributed learning in the DT field.
The Current State and Challenges of Fairness in Federated Learning

The proliferation of artificial intelligence systems and their reliance on massive datasets have led to a renewed demand on privacy of data.Both the large data processing need and its associated data privacy demand have led to the development of techniques such as Federated Learning, a distributed machine learning technique with privacy preservation built-in.Within Federated Learning, as with other machine learning based techniques, the concern and challenges of ensuring that the decisions being made are fair and equitable to all users is paramount.This paper presents an up-to-date review of the motivations, concepts, characteristics, challenges, and techniques/methods related to fairness in Federated Learning from the literature.It also highlights open challenges and future research directions in evaluating and enforcing fairness in Federated Learning systems.
A Discrete Hough Transform-Based Hierarchical Statistics Detection Method for Linearly Distributed Group Targets in SAR Images

Detection of group targets in synthetic aperture radar (SAR) images is of great importance in civil and military applications. However, most of the existing methods focus on the detection of single targets, while less attention is paid to group targets. This work investigates the detection of linearly distributed group targets in SAR. Though there are several methods that can be used to detect the linearly distributed group targets, their performances are limited, as they do not consider layout errors of targets and use simple detection statistics. To overcome these problems, a discrete Hough transform (DHT)-based hierarchical statistics detection method is proposed for linearly distributed group targets in this work. Instead of detecting the lines in the transformed parameter space, the targets along a line are collected via DHT, which allows the employment of more sophisticated detection statistics. Then, the geometry consistency statistics (GCSs) and noncoherent accumulation statistics (NASs), which can utilize the SAR characteristics and the spatial distribution of targets, are proposed. These two statistics in combination with the number statistics (NSs) act as high-performance hierarchical statistics. Finally, a line-refining stage, which consists of line expanding, line pruning, and parameter re-estimation, is applied to further improve the detection result. The effectiveness of the proposed method is validated by simulated and measured SAR images.
Attention-Aware Three-Branch Network for Salient Object Detection in Remote Sensing Images

Although remarkable advances have been achieved on salient object detection (SOD) for natural scene images (NSIs), SOD for optical remote sensing images (RSIs) still remains a big challenge due to the unique imaging conditions and various scene patterns. To enable effective SOD for RSIs, this letter proposes a novel end-to-end network, called attention-aware three-branch network (AATBNet). First, an attention feature encoding branch is constructed for learning more discriminative features. Then, a hierarchical feature decoding branch, equipped with three streams, i.e., a decoding stream, a dilated reverse attention stream, and a fusion dense up-sampling convolution stream, is proposed to effectively and robustly compute saliency maps and salient edge maps. Third, a two losses computation branch is designed to further boost SOD performance. Comprehensive evaluations on two well-known RSIs benchmarks, as well as comparisons with 20 state-of-the-art technologies validate the superiority of our AATBNet. The code of our method is publicly available at: https://github.com/WangXin81/AATBNet.
DAO Tokens: The Role for the Web 3.0 Industry and Pricing Factors

The paper discusses the factors influencing the pricing of cryptocurrency tokens for decentralized autonomous organization (DAO) projects. The formation and development of DAOs are among the promising applications of blockchain technology. DAO Project participants who own governance tokens can influence important decisions regarding project development through a voting function. The authors demonstrate the prospects for using DAO tokens as a tool for developing the Web 3.0 industry and conduct econometric modeling of the factors influencing the pricing of DAO tokens on panel data of the top 10 DAO projects in the industry. The analysis results show that the trading dynamics of DAO tokens depend more on the internal factors of development of this sector of the blockchain industry and only partially on the price quotes of traditional financial assets. However, the modeling results during the period of growth of the blockchain industry and DAO segment in 2021 differ somewhat from the modeling results for the period of blockchain industry stagnation in 2022. In 2022, DAO projects show greater dependence on traditional financial markets, demonstrating a more mature state of the DAO segment in the blockchain industry.
A3T: accuracy aware adversarial training

Abstract Adversarial training has been empirically shown to be more prone to overfitting than standard training. The exact underlying reasons are still not fully understood. In this paper, we identify one cause of overfitting related to current practices of generating adversarial examples from misclassified samples. We show that, following current practice, adversarial examples from misclassified samples results in harder-to-classify samples than the original ones. This leads to a complex adjustment of the decision boundary during training and hence overfitting. To mitigate this issue, we propose A3T, an accuracy aware AT method that generate adversarial example differently for misclassified and correctly classified samples. We show that our approach achieves better generalization while maintaining comparable robustness to state-of-the-art AT methods on a wide range of computer vision, natural language processing, and tabular tasks.
An Empirical Study on the Application of Blockchain Technology in E-Agriculture

At present, the application of blockchain is still at the level of private chain, lacking of systematicness and comprehensiveness, neglecting the participation process of multi-agent stakeholders, and the overall application system is not clear. This paper aims at applying the blockchain technology to the field of e-agriculture, building the hierarchical theoretical framework of e-agriculture, and revealing the impact of blockchain on e-agriculture. Through dividing the hierarchy, this paper finally structures an innovative service UIS application system for e-agriculture using blockchain technology. The main conclusions are as follows: (1) the first step is to solve social problems; (2) economic tasks are influenced by social problems and focus on smart contracts; (3) sustainable improvement of environmental problems requires a balance between economic and social dimensions; (4) application system is constructed at the three levels of unity, information, and supply chain.
Fooling Examples: Another Intriguing Property of Neural Networks

Neural networks have been proven to be vulnerable to adversarial examples; these are examples that can be recognized by both humans and neural networks, although neural networks give incorrect predictions. As an intriguing property of neural networks, adversarial examples pose a serious threat to the secure application of neural networks. In this article, we present another intriguing property of neural networks: the fact that well-trained models believe some examples to be recognizable objects (often with high confidence), while humans cannot recognize such examples. We refer to these as "fooling examples". Specifically, we take inspiration from the construction of adversarial examples and develop an iterative method for generating fooling examples. The experimental results show that fooling examples can not only be easily generated, with a success rate of nearly 100% in the white-box scenario, but also exhibit strong transferability across different models in the black-box scenario. Tests on the Google Cloud Vision API show that fooling examples can also be recognized by real-world computer vision systems. Our findings reveal a new cognitive deficit of neural networks, and we hope that these potential security threats will be addressed in future neural network applications.
Incorporating automatic speech recognition methods into the transcription of police-suspect interviews: factors affecting automatic performance

Introduction In England and Wales, transcripts of police-suspect interviews are often admitted as evidence in courts of law. Orthographic transcription is a time-consuming process and is usually carried out by untrained transcribers, resulting in records that contain summaries of large sections of the interview and paraphrased speech. The omission or inaccurate representation of important speech content could have serious consequences in a court of law. It is therefore clear that investigation into better solutions for police-interview transcription is required. This paper explores the possibility of incorporating automatic speech recognition (ASR) methods into the transcription process, with the goal of producing verbatim transcripts without sacrificing police time and money. We consider the potential viability of automatic transcripts as a “first” draft that would be manually corrected by police transcribers. The study additionally investigates the effects of audio quality, regional accent, and the ASR system used, as well as the types and magnitude of errors produced and their implications in the context of police-suspect interview transcripts. Methods Speech data was extracted from two forensically-relevant corpora, with speakers of two accents of British English: Standard Southern British English and West Yorkshire English (a non-standard regional variety). Both a high quality and degraded version of each file was transcribed using three commercially available ASR systems: Amazon, Google, and Rev. Results System performance varied depending on the ASR system and the audio quality, and while regional accent was not found to significantly predict word error rate, the distribution of errors varied substantially across the accents, with more potentially damaging errors produced for speakers of West Yorkshire English. Discussion The low word error rates and easily identifiable errors produced by Amazon suggest that the incorporation of ASR into the transcription of police-suspect interviews could be viable, though more work is required to investigate the effects of other contextual factors, such as multiple speakers and different types of background noise.