Reviewerly/exHarmony huggingface dataset:
Exploring the Dark Side of AI: Advanced Phishing Attack Design and Deployment Using ChatGPT

This paper explores the possibility of using Chat-GPT to develop advanced phishing attacks and automate their large-scale deployment. We make ChatGPT generate the following parts of a phishing attack: i) cloning a targeted website, ii) integrating code for stealing credentials, iii) obfuscating code, iv) automating website deployment on a hosting provider, v) registering a phishing domain name, and vi) integrating the website with a reverse proxy. The initial assessment of the automatically generated phishing kits highlights their rapid generation and deployment process as well as the close resemblance of the resulting pages to the target website. More broadly, we demonstrate that recent advances in AI underscore the potential risks of its misuse in phishing attacks, which can lead to their increased prevalence and severity. This highlights the necessity for enhanced countermeasures within AI systems.
Personalized E-Learning Recommender System Based on Autoencoders

Through the Internet, learners can access available information on e-learning platforms to facilitate their studies or to acquire new skills. However, finding the right information for their specific needs among the numerous available choices is a tedious task due to information overload. Recommender systems are a good solution to personalize e-learning by proposing useful and relevant information adapted to each learner using a set of techniques and algorithms. Collaborative filtering (CF) is one of the techniques widely used in such systems. However, the high dimensions and sparsity of the data are major problems. Since the concept of deep learning has grown in popularity, various studies have emerged to improve this form of filtering. In this work, we used an autoencoder, which is a powerful model in data dimension reduction, feature extraction and data reconstruction, to learn and predict student preferences in an e-learning recommendation system based on collaborative filtering. Experimental results obtained using the database created by Kulkarni et al. show that this model is more accurate and outperforms models based on K-nearest neighbor (KNN), singular value decomposition (SVD), singular value decomposition plus plus (SVD++) and non-negative matrix factorization (NMF) in terms of the root-mean-square error (RMSE) and mean absolute error (MAE).
Analyzing Teachers’ Perception of the Development of Lifelong Learning as Personal, Social and Learning to Learn Competence in University Students

In recent decades, European education systems and policies have prioritized the need for lifelong learning for all citizens, both within and outside the education system. This research is an observational study using a cross-sectional quantitative design involving 446 participants from Spain, Mexico and Chile. The objective of this study is as follows: To understand how active methodologies impact the development and mastery of Personal, Social and Learning to Learn competence (PSLL) throughout the life of university students. The INNOVAPRENDE questionnaire, previously validated by 17 university experts and comprised of 43 items, was implemented. It is considered essential for students to develop a series of competences, in addition to acquiring knowledge, since lifelong learning is a necessity. It has been shown that less experienced teachers (vs. more experienced teachers), men (vs. women), and teachers with PSLL training during their university studies (vs. teachers with training during their work experience or without experience) have a more negative perception of their students’ overall development of PSLL. In conclusion, it should be emphasized that lifelong learning as PSLL is an educational paradigm open to any educational stage of teachers’ and learners’ lives.
SKATEBOARD: Semantic Knowledge Advanced Tool for Extraction, Browsing, Organisation, Annotation, Retrieval, and Discovery

This paper introduces Semantic Knowledge Advanced Tool for Extraction Browsing Organisation Annotation Retrieval and Discovery (SKATEBOARD), a tool designed to facilitate knowledge exploration through the application of semantic technologies. The demand for advanced solutions that streamline Knowledge Extraction, management, and visualisation, characterised by abundant information, has grown substantially in the current era. Graph-based representations have emerged as a robust approach for uncovering intricate data relationships, complementing the capabilities offered by AI models. Acknowledging the transparency and user control challenges faced by AI-driven solutions, SKATEBOARD offers a comprehensive framework encompassing Knowledge Extraction, ontology development, management, and interactive exploration. By adhering to Linked Data principles and adopting graph-based exploration, SKATEBOARD provides users with a clear view of data relationships and dependencies. Furthermore, it integrates recommendation systems and reasoning capabilities to augment the knowledge discovery process, thus introducing a serendipity effect generated by the SKATEBOARD interface exploration. This paper elucidates SKATEBOARD’s functionalities while emphasising its user-centric design. After reviewing related research, we provide an overview of the SKATEBOARD pipeline, demonstrating its capacity to bridge RDF and LPG representations. Subsequent sections delve into Knowledge Extraction and exploration, culminating in the evaluation of the tool. SKATEBOARD empowers users to make informed decisions and uncover valuable insights within their data domains, with the added dimension of serendipitous discoveries facilitated by its interface exploration capabilities.
Image Steganography Using LSB and Hybrid Encryption Algorithms

In today’s era of widespread web technology and cloud computing, ensuring data security has become a crucial concern across various industries. Instances of data breaches and vulnerabilities in cloud storage have emphasized the need for robust data protection and communication protocols, particularly in sectors like social media, military, and research. This research proposes a Multi-Level Steganography (MLS) algorithm that employs two encryption algorithms, AES and Blow-Fish, to secure the cover image and embed encryption keys as key images within the stego image. The proposed MLS algorithm incorporates a robust pixel randomization function to enhance the security of the encrypted data. Experimental results demonstrate that the proposed algorithm effectively protects data with high Peak Signal-to-Noise Ratio (PSNR) and low Mean Square Error (MSE) values, ensuring superior image quality, reliable encryption, and decryption of secret messages. The utilization of hybrid encryption with AES and BlowFish algorithms further strengthens the algorithm’s security by augmenting the complexity of the encryption process.
Unveiling and veiling an entangled light-matter quantum state from the vacuum

The ground state of an atom interacting with the electromagnetic field in the ultrastrong coupling regime is composed of virtual photons entangled with the atom. We propose a method to promote to real the entire photonic state, while preserving the entanglement with the atom. The process can be reversed, and the entangled state can be restored in the vacuum. We consider a four-level atom, with two of these levels ultrastrongly coupled to a cavity mode. The process is obtained by making use of either an ideal ultrafast pulse or a more realistic multitone $\ensuremath{\pi}$ pulse that drives only the atom. An experimental realization of this proposal will not only enable the investigation of the exotic phenomena of emission of particles from the vacuum, but will also prove that quantum superposition states can be extracted from the vacuum. Moreover, it will allow one to inspect the ground state in the ultrastrong coupling regime, and to generate on-demand entangled states for quantum information processing.
CamoNet: A Target Camouflage Network for Remote Sensing Images Based on Adversarial Attack

Object detection algorithms based on convolutional neural networks (CNNs) have achieved remarkable success in remote sensing images (RSIs), such as aircraft and ship detection, which play a vital role in military and civilian fields. However, CNNs are fragile and can be easily fooled. There have been a series of studies on adversarial attacks for image classification in RSIs. However, the existing gradient attack algorithms designed for classification cannot achieve excellent performance when directly applied to object detection, which is an essential task in RSI understanding. Although we can find some works on adversarial attacks for object detection, they are weak in concealment and easily detected by the naked eye. To handle these problems, we propose a target camouflage network for object detection in RSIs, called CamoNet, to deceive CNN-based detectors by adding imperceptible perturbation to the image. In addition, we propose a detection space initialization strategy to maximize the diversity in the detector’s outputs among the generated samples. It can enhance the performance of the gradient attack algorithms in the object detection task. Moreover, a key pixel distillation module is employed, which can further reduce the modified pixels without weakening the concealment effect. Compared with several of the most advanced adversarial attacks, the proposed attack has advantages in terms of both peak signal-to-noise ratio (PSNR) and attack success rate. The transferability of the proposed target camouflage network is evaluated on three dominant detection algorithms (RetinaNet, Faster R-CNN, and RTMDet) with two commonly used remote sensing datasets (i.e., DOTA and DIOR).
Electronic Proves as One of the Modern Independent Means of Evidence

Objective: the purpose of this study is a determination of the procedural status of electronic documents as electronic evidence and identification of gaps in legislation regarding their legal regulation.&#x0D; &#x0D; Theoretical framework: theoretical materials were based on international scientific publications, reports, and scientific papers. And also for a more complete and objective presentation of the problem being studied, also practical materials from criminal cases were used.&#x0D; &#x0D; Method: is a dialectical method of understanding general patterns and particular manifestations of the essence of phenomena of objective reality. The comparative legal method made it possible to qualitatively study foreign legislation from the point of view of the legal regulation of electronic evidence and their application in practice. The method of mathematical analysis and logic made it possible to analyze everything that could relate to electronic evidence, identifying their characteristics.&#x0D; &#x0D; Results and conclusion: electronic evidence is information in electronic digital form, suitable for communication, interpretation or processing. In the electronic digital environment, the main interacting objects are various kinds of information objects (data sets, files, programs, etc.) with a complex hierarchical structure. Data in electronic digital form are intended to transmit information through media, information systems and telecommunication networks, and in the event of a crime being committed against them, their protection is of primary interest, and the nature and content of this data fades into the background.&#x0D; &#x0D; Originality/value: the value of the study lies in the fact that, based on a comprehensive analysis, electronic evidence has a unique structure and ensuring their safety is one of the criteria for their use, as well as the possibility of introducing a copying function will expand the process of processing evidence. The concept of “computer information” is the author’s definition, which will allow us to consolidate electronic evidence as an independent type.
Development of Hit The Mouse Games Using Javascript

The idea of this game is that this game includes arcade games that are played traditionally which can be found in night markets or shopping centers such as Plazas. Arcade games are games that aren't focused on a story and can be entertaining when you're bored. This is done to analyze the needs that will be used to design and make a mole game, starting with determining the theme, game title, and programming language used to develop the game. The tools used include hardware and software. the application can run according to what has been designed by the author. The researcher tested the application by playing several times with different IP addresses as a test material for the application, whether the test was successful or not. Based on the results of making a game application for hitting moles, there are several conclusions that researchers can give, namely: a. This game can involve the player's concentration and become cognitive therapy, because the rats come out one by one from the ground to be hit. b. This game can only be played by one person. c. The making of the game Mole Hit has worked well according to the design that was made and compiled at the beginning of the game design, using the Javascript programming language.
Cyber threat assessment and management for securing healthcare ecosystems using natural language processing

Abstract The healthcare sectors have constantly faced significant challenge due to the rapid rise of cyber threats. These threats can pose any potential risk within the system context and disrupt the critical healthcare service delivery. It is therefore necessary for the healthcare organisations to understand and tackle the threats to ensure overall security and resilience. However, threats are continuously evolved and there is large amount of unstructured security-related textual information is available. This makes the threat assessment and management task very challenging. There are a number of existing works that consider Machine Learning models for detection and prediction of cyber attack but they lack of focus on the Natural Language Processing (NLP) to extract the threat information from unstructured security-related text. To this end, this work proposes a novel method to assess and manage threats by adopting natural language processing. The proposed method has been tailored for the healthcare ecosystem and allows to identify and assess the possible threats within healthcare information infrastructure so that appropriate control and mitigation actions can be taken into consideration to tackle the threat. In detail, NLP techniques are used to extract the useful threat information related to specific assets of the healthcare ecosystems from the largely available security-related information on Internet (e.g. cyber security news), to evaluate the level of the identified threats and to select the required mitigation actions. We have performed experiments on real healthcare ecosystems in Fraunhofer Institute for Biomedical Engineering, considering in particular three different healthcare scenarios, namely implantable medical devices, wearables, and biobank, with the purpose of demonstrating the feasibility of our approach, which is able to provide a realistic manner to identify and assess the threats, evaluate the threat level and suggest the required mitigation actions.
The Theory of Immersive Collaborative Learning (TICOL)

Abstract The need to virtually collaborate across distributed locations has drastically increased. Developments such as the COVID-19 pandemic and new IT platforms like the metaverse have spurred a host of new immersive social applications that are accessed through head-mounted displays. This is expected to stimulate a surge in research on extended reality–supported collaborative learning (XRCL) which refers to distributed collaboration situations where immersive technology such as head-mounted displays are used as a medium for collaborative learning. The primary aim of this article is to critically examine the potential pedagogical benefits and limitations of using XRCL with the objective of developing a theoretical framework that describes the fundamental factors that make immersive collaborative learning unique: the theory of immersive collaborative learning (TICOL). In TICOL, we propose that technological features, social affordances, and pedagogical techniques can foster four psychological factors that we define as fundamentally different in XRCL compared to collaboration that occurs through traditional systems (e.g., laptops): social presence, physical presence, body ownership, and agency. These are central factors that we hypothesize can transform the processes and contexts of collaboration through their influence on the quality of cognitive and socio-emotional social interaction, the social space, and ultimately learning outcomes. Since XRCL research is in its infancy, we hope that TICOL can provide a theoretical basis for developing the field by motivating researchers to empirically challenge and build on our hypotheses and ultimately develop a deeper understanding of if and how immersive media influences collaborative learning.
Stratified Sampling-Based Deep Learning Approach to Increase Prediction Accuracy of Unbalanced Dataset

Due to the imbalanced nature of datasets, classifying unbalanced data classes and drawing accurate predictions is still a challenging task. Sampling procedures, along with machine learning and deep learning algorithms, are a boon for solving this kind of challenging task. This study’s objective is to use sampling-based machine learning and deep learning approaches to automate the recognition of rotting trees from a forest dataset. Method/Approach: The proposed approach successfully predicted the dead tree in the forest. Seven of the twenty-one features are computed using the wrapper approach. This research work presents a novel method for determining the state of decay of the tree. The process of classifying the tree’s state of decay is connected to the issue of unequal class distribution. When classes to be predicted are uneven, this frequently hides poor performance in minority classes. Using stratified sampling procedures, the required samples for precise categorization are prepared. Stratified sampling approaches are employed to generate the necessary samples for accurate prediction, and the precise samples with computed features are input into a deep learning neural network. Finding: The multi-layer feed-forward classifier produces the greatest results in terms of classification accuracy (91%). Novelty/Improvement: Correct samples are necessary for correct classification in machine learning approaches. In the present study, stratified samples were considered while deciding which samples to use as deep neural network input. It suggests that the proposed algorithm could accurately determine whether the tree has decayed or not.
Cryptocurrency market microstructure: a systematic literature review

Abstract This study contributes to the unconsolidated cryptocurrency literature, with a systematic literature review focused on cryptocurrency market microstructure. We searched Web of Science database and focused only on journals listed on 2021 ABS list. Our final sample comprises 138 research papers. We employed a quantitative and an integrative analysis, and revealed complex network associations, and a detailed research trending analysis. Our study provides a robust and systematic contribution to cryptocurrency literature by making use of a powerful and accurate methodology—the bibliographic coupling, also by only considering ABS academic journals, using a wider keyword scope, and not enforcing any restrictions regarding areas of knowledge, thus enhancing the contribution of extant literature by allowing the insights of more high-quality peripheral studies on the subject. The conclusions of this study are of extreme importance for researchers, investors, regulators, and the academic community in general. Our study provides high structured networking and clear information for research outlets and literature strands, for future studies on cryptocurrency investment, it also presents valuable insights to better understand the cryptocurrency market microstructure and deliver helpful information for regulators to effectively regulate cryptocurrencies.
Securing the Network: A Red and Blue Cybersecurity Competition Case Study

In today’s dynamic and evolving digital landscape, safeguarding network infrastructure against cyber threats has become a paramount concern for organizations worldwide. This paper presents a novel and practical approach to enhancing cybersecurity readiness. The competition, designed as a simulated cyber battleground, involves a Red Team emulating attackers and a Blue Team defending against their orchestrated assaults. Over two days, multiple teams engage in strategic maneuvers to breach and fortify digital defenses. The core objective of this study is to assess the efficacy of the Red and Blue cybersecurity competition in fostering real-world incident response capabilities and honing the skills of cybersecurity practitioners. This paper delves into the competition’s structural framework, including the intricate network architecture and the roles of the participating teams. This study gauges the competition’s impact on enhancing teamwork and incident response strategies by analyzing participant performance data and outcomes. The findings underscore the significance of immersive training experiences in cultivating proactive cybersecurity mindsets. Participants not only showcase heightened proficiency in countering cyber threats but also develop a profound understanding of attacker methodologies. Furthermore, the competition fosters an environment of continuous learning and knowledge exchange, propelling participants toward heightened cyber resilience.
An overview of Eulerian video motion magnification methods

The concept of video motion magnification has become increasingly relevant due to its ability to detect small and invisible motions that can be of great value in a variety of applications. A variety of approaches have been developed to magnify these motions and variations. While both Eulerian and Lagrangian processing methods are widely used for motion magnification, Eulerian approaches are more commonly employed due to their lower computational cost. This paper provides an overview of the powerful Eulerian motion magnification techniques. We begin with a brief introduction to technical concepts associated with Eulerian motion techniques such as pyramids and filters in image processing. Additionally, we provide a comparison between the Lagrangian and Eulerian perspectives, followed by a comprehensive overview of the various Eulerian motion magnification (EVM) techniques available. Finally, we present implementation results and a comparative analysis of some of the Eulerian motion techniques.
A privacy-preserving robo-advisory system with the Black-Litterman portfolio model: A new framework and insights into investor behavior

Recent financial sector changes, including strict privacy regulations, challenge robo-advisory companies with cybersecurity and data privacy. This study proposes a new framework integrating Homomorphic Encryption into the Black-Litterman portfolio model to safeguard robo-advisory investment strategies. The framework effectively balances privacy and accuracy while maintaining an acceptable level of privacy optimization error. Novel evaluation methods are also proposed to assess the trade-off between losses from privacy optimization and strategy leakage, from an economic viewpoint based on Expected Utility and Prospect Theory. It provides valuable insights into human behavior concerning privacy protection in portfolio management.