Reviewerly/exHarmony huggingface dataset:
Business Email Compromise (BEC) Attacks: Threats, Vulnerabilities and Countermeasures—A Perspective on the Greek Landscape

Business Email Compromise (BEC) attacks have emerged as serious threats to organizations in recent years, exploiting social engineering and malware to dupe victims into divulging confidential information and executing fraudulent transactions. This paper provides a comprehensive review of BEC attacks, including their principles, techniques, and impacts on enterprises. In light of the rising tide of BEC attacks globally and their significant financial impact on business, it is crucial to understand their modus operandi and adopt proactive measures to protect sensitive information and prevent financial losses. This study offers valuable recommendations and insights for organizations seeking to enhance their cybersecurity posture and mitigate the risks associated with BEC attacks. Moreover, we analyze the Greek landscape of cyberattacks, focusing on the existing regulatory framework and the measures taken to prevent and respond to cybercrime in accordance with the NIS Directives of the EU. By examining the Greek landscape, we gain insights into the effectiveness of countermeasures in this region, as well as the challenges and opportunities for improving cybersecurity practices.
Robust Visual Recognition in Poor Visibility Conditions: A Prior Knowledge-Guided Adversarial Learning Approach

Deep learning has achieved remarkable success in numerous computer vision tasks. However, recent research reveals that deep neural networks are vulnerable to natural perturbations from poor visibility conditions, limiting their practical applications. While several studies have focused on enhancing model robustness in poor visibility conditions through techniques such as image restoration, data augmentation, and unsupervised domain adaptation, these efforts are predominantly confined to specific scenarios and fail to address multiple poor visibility scenarios encountered in real-world settings. Furthermore, the valuable prior knowledge inherent in poor visibility images is seldom utilized to aid in resolving high-level computer vision tasks. In light of these challenges, we propose a novel deep learning paradigm designed to bolster the robustness of object recognition across diverse poor visibility scenes. By observing the prior information in diverse poor visibility scenes, we integrate a feature matching module based on this prior knowledge into our proposed learning paradigm, aiming to facilitate deep models in learning more robust generic features at shallow levels. Moreover, to further enhance the robustness of deep features, we employ an adversarial learning strategy based on mutual information. This strategy combines the feature matching module to extract task-specific representations from low visibility scenes in a more robust manner, thereby enhancing the robustness of object recognition. We evaluate our approach on self-constructed datasets containing diverse poor visibility scenes, including visual blur, fog, rain, snow, and low illuminance. Extensive experiments demonstrate that our proposed method yields significant improvements over existing solutions across various poor visibility conditions.
Dialogue Explanations for Rule-Based AI Systems

The need for AI systems to explain themselves is increasingly recognised as a priority, particularly in domains where incorrect decisions can result in harm and, in the worst cases, death. Explainable Artificial Intelligence (XAI) tries to produce human-understandable explanations for AI decisions. However, most XAI systems prioritize factors such as technical complexities and research-oriented goals over end-user needs, risking information overload. This research attempts to bridge a gap in current understanding and provide insights for assisting users in comprehending the rule-based system's reasoning through dialogue. The hypothesis is that employing dialogue as a mechanism can be effective in constructing explanations. A dialogue framework for rule-based AI systems is presented, allowing the system to explain its decisions by engaging in "Why?" and "Why not?" questions and answers. We establish formal properties of this framework and present a small user study with encouraging results that compares dialogue-based explanations with proof trees produced by the AI System.
Decentralized Machine Learning Governance: Overview, Opportunities, and Challenges

Researchers have started to recognize the necessity for a well-defined ML governance framework based on the principle of decentralization and comprehensively defining its scope of research and practice due to the growth of machine learning (ML) research and applications in the real world and the success of blockchain-based technology. In this paper, we study decentralized ML governance, which includes ML value chain management, decentralized identity for the ML community, decentralized ownership and rights management of ML assets, community-based decision-making for the ML process, decentralized ML finance, and risk management.
Bottom-Up and Top-Down Workflows for Hypercube- And Clustering-Based Knowledge Extractors

Machine learning opaque models, currently exploited to carry out a wide variety of supervised and unsupervised learning tasks, are able to achieve impressive predictive performances. However, they act as black boxes (BBs) from the human standpoint, so they cannot be entirely trusted in critical applications unless there exists a method to extract symbolic and human-readable knowledge out of them. In this paper we analyse a recurrent design adopted by symbolic knowledge extractors for BB predictors—that is, the creation of rules associated with hypercubic input space regions. We argue that this kind of partitioning may lead to suboptimum solutions when the data set at hand is sparse, high-dimensional, or does not satisfy symmetric constraints. We then propose two different knowledge-extraction workflows involving clustering approaches, highlighting the possibility to outperform existing knowledge-extraction techniques in terms of predictive performance on data sets of any kind.
Single Object Tracking in Satellite Videos Based on Feature Enhancement and Multi-Level Matching Strategy

Despite significant advancements in remote sensing object tracking (RSOT) in recent years, achieving accurate and continuous tracking of tiny-sized targets remains a challenging task due to similar object interference and other related issues. In this paper, from the perspective of feature enhancement and a better feature matching strategy, we present a tracker SiamTM specifically designed for RSOT, which is mainly based on a new target information enhancement (TIE) module and a multi-level matching strategy. First, we propose a TIE module to address the challenge of tiny object sizes in satellite videos. The proposed TIE module goes along two spatial directions to capture orientation and position-aware information, respectively, while capturing inter-channel information at the global 2D image level. The TIE module enables the network to extract discriminative features of the targets more effectively from satellite images. Furthermore, we introduce a multi-level matching (MM) module that is better suited for satellite video targets. The MM module firstly embeds the target feature map after ROI Align into each position of the search region feature map to obtain a preliminary response map. Subsequently, the preliminary response map and the template region feature map are subjected to the Depth-wise Cross Correlation operation to get a more refined response map. Through this coarse-to-fine approach, the tracker obtains a response map with a more accurate position, which lays a good foundation for the prediction operation of the subsequent sub-networks. We conducted extensive experiments on two large satellite video single-object tracking datasets: SatSOT and SV248S. Without bells and whistles, the proposed tracker SiamTM achieved competitive results on both datasets while running at real-time speed.
Romance baiting, cryptorom and ‘pig butchering’: an evolutionary step in romance fraud

Romance fraud uses the guise of a genuine relationship to deceive a victim into transferring often large amounts of money to an offender/s. Romance fraud has been in the top three categories of financial loss for Australian victims over the past decade, and this is a trend mirrored internationally. However, in recent years there has been a convergence of romance fraud and investment fraud approaches. Terms such as romance baiting, cryptorom and ‘pig butchering' have all emerged to describe how offenders are evolving in their attempts to defraud victims through offering fake cryptocurrency investment opportunities, using a relationship as the mechanism. This article analyses this shift in romance fraud offending and its embracing of investment scheme opportunities. It highlights the underlying reasons for the success of this amalgamated approach and further demonstrates how it potentially distorts the reporting of fraud as well as prevention messaging targeting these incidents.
Path Planning of Rail-Mounted Logistics Robots Based on the Improved Dijkstra Algorithm

With the upgrading of manufacturing production lines and innovations in information technology, logistics robot technology applied in factories is maturing. Rail-mounted logistics robots are suitable for precise material distribution in large production workshops with fixed routes and over long distances. However, designing an efficient path-planning algorithm is the key to realizing high efficiency in multi-robot system operations with rail logistics. Therefore, this paper proposes an improved Dijkstra algorithm that introduces real-time node occupancy and a time window conflict judgment model for global path planning and conflict coordination in multi-robot systems. More specifically, the introduction of real-time node occupancy can determine the shortest feasible routes for each task, and the introduction of the time window conflict judgment model can avoid the route conflict problem in the execution of multiple tasks, planning the shortest route without conflict. For the robot UBW positioning module, a Chan algorithm based on TDOA is proposed to realize the accurate positioning of rail-mounted logistics robots during their operation. Compared with the traditional Dijkstra algorithm, the results show that the algorithm proposed herein can plan a conflict-free and better path and dynamically adjust the on-orbit conflict in real time to avoid track congestion and efficiently complete multiple distribution tasks.
Anomaly Detection for Agile Satellite Attitude Control System Using Hybrid Deep-Learning Technique

Agile low-Earth-orbit (LEO) observation satellites need a robust attitude control and determination system. It is a critical satellite subsystem, which stabilizes the satellite to different desired orientations during its mission using different actuators. The detection of satellite misorientation is a highly challenging problem because it requires continuous monitoring of data from hundreds of satellite sensors to guarantee healthy operability. In this paper, the authors propose a data-driven deep-learning framework for detecting satellite misorientation by analyzing attitude control subsystem telemetry data. The proposed approach combines a hybrid predictive deep-learning model that consists of long short-term memory and convolutional neural networks in two parallel paths to predict telemetry data and a robust isolation forest classifier for anomaly detection purposes that can classify output residuals as normal or anomalous. The hybrid model was optimized by the particle swarm optimization algorithm to ensure faster fitness function convergence with optimal model hyperparameters. The suggested data-driven model was validated using real telemetry datasets, including real anomalous case studies. The experimental results proved the suggested approach’s superiority for identifying satellite misorientation as well as helping satellite operators monitor the system’s health and deduce the causes of anomalies to aid in decision-making.
SAR image matching based on rotation-invariant description

Abstract The utilization of scale invariant feature transform algorithm in synthetic-aperture radar images (SAR–SIFT) to match image features may lead to principal orientation assignments of descriptors being affected by speckle noise, thereby diminishing accuracy. In this study, we propose using the Fourier histogram of oriented ratio gradient (Fourier HORG) descriptor for robust matching of SAR images. This method is based on the SAR–SIFT algorithm framework. During feature description, the rotation-invariant Fourier HORG descriptor is established by performing Fourier analysis on the ratio gradient in the polar coordinate system, whereby the principal orientation assignment process is avoided and the robustness of SAR image registration improved. A matching experiment was conducted involving four sets of SAR image pairs, and the results demonstrated that our method exhibited higher accuracy and robustness compared to image matching based on the Fourier histogram of oriented gradient (Fourier HOG) descriptor and the SAR–SIFT algorithm, thus confirming the effectiveness of our proposed method.
Utilizing <scp>OpenAI</scp>'s <scp>GPT</scp>‐4 for written feedback

TESOL JournalEarly View e759 MEDIA REVIEW Utilizing OpenAI's GPT-4 for written feedback Makenna Carlson, Makenna Carlson Brigham Young University–HawaiiSearch for more papers by this authorAustin Pack, Corresponding Author Austin Pack [email protected] orcid.org/0000-0002-3861-5620 Brigham Young University–HawaiiSearch for more papers by this authorJuan Escalante, Juan Escalante Brigham Young University–HawaiiSearch for more papers by this author Makenna Carlson, Makenna Carlson Brigham Young University–HawaiiSearch for more papers by this authorAustin Pack, Corresponding Author Austin Pack [email protected] orcid.org/0000-0002-3861-5620 Brigham Young University–HawaiiSearch for more papers by this authorJuan Escalante, Juan Escalante Brigham Young University–HawaiiSearch for more papers by this author First published: 04 September 2023 https://doi.org/10.1002/tesj.759Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat REFERENCES Alexander, K., Savvidou, C., & Alexander, C. (2023). Who wrote this essay? Detecting AI-generated writing in second language education in higher education. Teaching English With Technology, 23(2), 25–43. https://doi.org/10.56297/BUKA4060/XHLD5365 Hartshorn, K. J., Evans, N. W., Merrill, P. F., Sudweeks, R. R., Strong-Krause, D., & Anderson, N. J. (2010). Effects of dynamic written corrective feedback on ESL writing accuracy. TESOL Quarterly, 44, 84–109. https://doi.org/10.5054/tq.2010.213781 Hubbard, P. (2021). An invitation to CALL: Foundations of computer-assisted language learning. Toowoomba, Australia: Asia-Pacific Association for Computer-Assisted Language Learning (APACALL). Ingley, S. J., & Pack, A. (2023). Leveraging AI tools to develop the writer rather than the writing. Trends in Ecology & Evolution, 38(9), 785–787. https://doi.org/10.1016/j.tree.2023.05.007 Roselli, D., Matthews, J., & Talagala, N. (2019). Managing bias in AI. Companion Proceedings of The 2019 World Wide Web Conference. https://doi.org/10.1145/3308560.3317590 Early ViewOnline Version of Record before inclusion in an issuee759 ReferencesRelatedInformation
Blockchain-Based Traceability for Agricultural Products: A Systematic Literature Review

In recent years, with the frequent crises of trust in the food industry, food safety issues have become an issue of particular concern to consumers. The traditional agricultural food logistics and transportation model can no longer guarantee the traceability of food safety information, and it is particularly important to establish an effective traceability system. Therefore, a traceability system is applied as a tool to assist in the assurance of food safety and quality, as well as to achieve consumer confidence. Blockchain is a new decentralized infrastructure and distributed computing paradigm that has gradually emerged. Blockchain has the characteristics of decentralization, immutable information and trustworthiness. When blockchain is applied to the traceability system of agricultural products, the quality of agricultural products will be well guaranteed. This article aims to provide a comprehensive review of the recent research efforts on traceability in agricultural products based on blockchain technology. First, the method of content analysis used in this article to sort out the papers is introduced. Second, the background concepts of blockchain and the key technologies are presented. Thirdly, the traceability applications based on blockchain technology for agricultural products are described in detail. Finally, we expand on the current trends and provide new perspectives pertaining to this exciting application of this field.
Efficient and accurate compound scaling for convolutional neural networks

Designing efficient and accurate network architectures to support various workloads, from servers to edge devices, is a fundamental problem as the use of Convolutional Neural Networks (ConvNets) becomes increasingly widespread. One simple yet effective method is to scale ConvNets by systematically adjusting the dimensions of the baseline network, including width, depth, and resolution, enabling it to adapt to diverse workloads by varying its computational complexity and representation ability. However, current state-of-the-art (SOTA) scaling methods for neural network architectures overlook the inter-dimensional relationships within the network and the impact of scaling on inference speed, resulting in suboptimal trade-offs between accuracy and inference speed. To overcome those limitations, we propose a scaling method for ConvNets that utilizes dimension relationship and runtime proxy constraints to improve accuracy and inference speed. Specifically, our research notes that higher input resolutions in convolutional layers lead to redundant filters (convolutional width) due to increased similarity between information in different positions, suggesting a potential benefit in reducing filters while increasing input resolution. Based on this observation, the relationship between the width and resolution is empirically quantified in our work, enabling models with higher parametric efficiency to be prioritized through our scaling strategy. Furthermore, we introduce a novel runtime prediction model that focuses on fine-grained layer tasks with different computational properties for more accurate identification of efficient network configurations. Comprehensive experiments show that our method outperforms prior works in creating a set of models with a trade-off between accuracy and inference speed on the ImageNet datasets for various ConvNets.
Educators’ Perspectives on Usability of the Moodle LMS: A Case of the National Institute of Transport, Tanzania

The purpose of this study was to assess the usability of the Moodle LMS at the National Institute of Transport in Tanzania. The study employed a descriptive research design that involved the use of a questionnaires and interview to collect data from 35 educators who were using the Moodle LMS. Quantitative data was analyzed using the SPSS while qualitative data involved content analysis. The study identified effectiveness, satisfaction, memorability, learnability and errors factors to be used to assess usability issues. The study revealed that educators encountered usability problems related to navigation, content, layout, interaction, feedback, help and support. The study recommends that the NIT should focus on providing a user-friendly interface, clear content, consistent layout, enhanced interaction and timely feedback to improve the usability of the Moodle LMS. By addressing these usability problems, NIT will enhance the effectiveness of teaching and learning activities. Moreover, the study recommends regular training to teachers on how to use the Moodle LMS.
E-Business Security and Control

E-business has revolutionized how companies conduct business, bringing numerous benefits such as cost savings, increased efficiency, and global reach. However, with the advantages come challenges, particularly in security and control. This chapter will discuss the various threats faced by e-businesses, as well as strategies for ensuring their security and control. First, an overview of e-business security and control will be provided, examining the different security threats faced by organizations conducting business online. Next, this chapter will delve into the specific threats to e-business security and control, such as hacking, phishing, malware, and denial of service attacks. It will also explore the consequences of these threats, including financial losses, reputational damage, and legal liabilities. To combat these threats, various strategies that e-businesses can adopt to ensure their security and control will be discussed. These strategies include implementing strong passwords, using encryption and firewalls, conducting regular security audits, and providing employee training on security best practices. Additionally, this chapter will examine the role of government regulations and industry standards in promoting e-business security and control. Finally, the legal and ethical considerations in e-business security and control will be addressed. E-businesses need to comply with various laws and regulations, including data privacy and protection laws, intellectual property laws, and consumer protection laws. Ethical considerations also come into play, such as balancing the need for security with customers’ privacy rights.
Cyber Vaccine for Deepfake Immunity

Deepfakes pose an evolving cybersecurity threat that calls for the development of automated countermeasures. While considerable forensic research has been devoted to the detection and localisation of deepfakes, solutions for ‘fake to real’ reversal are yet to be developed. In this study, we introduce the concept of cyber vaccination for conferring immunity to deepfakes. In other words, we aim to impart self-healing ability to the face media so that the original content is possible to be recovered after manipulated by AI-based deepfake technology. Analogous to biological vaccination which uses injected antigens to induce immunity prior to infection by an actual pathogen, cyber vaccination simulates deepfakes and performs adversarial training to build a defensive immune system. Aiming at building up attack-agnostic immunity with limited computational resources, we propose simulating various deepfakes with one single overpowering attack: face masking. The proposed immune system consists of a vaccinator for inducing immunity and a neutraliser for recovering facial content. Experimental evaluations demonstrate effective immunity to face replacement and various types of corruptions.