Reviewerly/exHarmony huggingface dataset:
A Novel Method for Arabic Text Detection with Interactive Visualization

Text detection, recognition, and visualization from natural scenes are considered significant research topics in the field of information and communication technologies. Written text serves as a crucial source of information that humans rely on in their daily lives. However, detecting text poses several challenges, including variations in writing style, color, size, orientation, and complex backgrounds. In this study, we present a comprehensive application named ATDRV, which aims to address these challenges. For text detection and recognition, we propose a real-time multi-oriented text deep fully convolutional network system trained end-to-end. Additionally, we implement a visualization module based on Augmented Reality to display the results more clearly to users. The output of our application is a large and clear 3D object that enables easy reading of the text. Our approach improves the user experience and facilitates the reading of Arabic text regardless of color, orientation, writing style, or complex backgrounds.
Exploring the Privacy Concerns in Permissionless Blockchain Networks and Potential Solutions

In recent years, permissionless blockchains have gained significant attention for their ability to secure and provide transparency in transactions. The development of blockchain technology has shifted from cryptocurrency to decentralized finance, benefiting millions of unbanked individuals, and serving as the foundation of Web3, which aims to provide the next generation of the internet with data ownership for users. The rise of NFTs has also helped artists and creative workers to protect their intellectual property and reap the benefits of their work. However, privacy risks associated with permissionless blockchains have become a major concern for individuals and institutions. The role of blockchain in the transition from Web2 to Web3 is crucial, as it is rapidly evolving. As more individuals, institutions, and organizations adopt this technology, it becomes increasingly important to closely monitor the new risks associated with permissionless blockchains and provide updated solutions to mitigate them. This paper endeavors to examine the privacy risks inherent in permissionless blockchains, including Remote Procedure Call (RPC) issues, Ethereum Name Service (ENS), miner extractable value (MEV) bots, on-chain data analysis, data breaches, transaction linking, transaction metadata, and others. The existing solutions to these privacy risks, such as zero-knowledge proofs, ring signatures, Hyperledger Fabric, and stealth addresses, shall be analyzed. Finally, suggestions for the future improvement of privacy solutions in the permissionless blockchain space shall be put forward.
SoK: Model Inversion Attack Landscape: Taxonomy, Challenges, and Future Roadmap

A crucial module of the widely applied machine learning (ML) model is the model training phase, which involves large-scale training data, often including sensitive private data. ML models trained on these sensitive data suffer from significant privacy concerns since ML models can intentionally or unintendedly leak information about training data. Adversaries can exploit this information to perform privacy attacks, including model extraction, membership inference, and model inversion. While a model extraction attack steals and replicates a trained model functionality, and membership inference infers the data sample's inclusiveness to the training set, a model inversion attack has the goal of inferring the training data sample's sensitive attribute value or reconstructing the training sample (i.e., image/audio/text). Distinct and inconsistent characteristics of model inversion attack make this attack even more challenging and consequential, opening up model inversion attack as a more prominent and increasingly expanding research paradigm. Thereby, to flourish research in this relatively underexplored model inversion domain, we conduct the first-ever systematic literature review of the model inversion attack landscape. We characterize model inversion attacks and provide a comprehensive taxonomy based on different dimensions. We illustrate foundational perspectives emphasizing methodologies and key principles of the existing attacks and defense techniques. Finally, we discuss challenges and open issues in the existing model inversion attacks, focusing on the roadmap for future research directions.
PHOENI2X – A European Cyber Resilience Framework With Artificial-Intelligence-Assisted Orchestration, Automation &amp; Response Capabilities for Business Continuity and Recovery, Incident Response, and Information Exchange

As digital technologies become more pervasive in society and the economy, cyber-security incidents become more frequent, but also more impactful. Based on the NIS & NIS2 Directives, EU Member States and their Operators of Essential Services (OES) must establish a minimum baseline set of capabil- ities while providing cross-border coordination and cooperation. But this is only a small step towards European cyber resilience. In this landscape, preparedness, shared situational awareness, and coordinated incident response are essential for effective crisis management and cyber-security resilience. This paper presents PHOENI2X which, motivated by the above, aims to design, develop, and deliver a Cyber Resilience Framework (CRF) providing Artificial Intelligence (AI) - assisted orchestration, automation & response capabilities for business continuity and recovery, incident response, and information exchange, tailored to the needs of OES and of the EU Member State (MS) National Authorities entrusted with cyber-security.
General-Purpose Secure Conflict-free Replicated Data Types

Conflict-free Replicated Data Types (CRDTs) are a very popular class of distributed data structures that strike a compromise between strong and eventual consistency. Ensuring the protection of data stored within a CRDT, however, cannot be done trivially using standard encryption techniques, as secure CRDT protocols would require replica-side computation. This paper proposes an approach to lift general-purpose implementations of CRDTs to secure variants using secure multiparty computation (MPC). Each replica within the system is realized by a group of MPC parties that compute its functionality. Our results include: i) an extension of current formal models used for reasoning over the security of CRDT solutions to the MPC setting; ii) a MPC language and type system to enable the construction of secure versions of CRDTs and; iii) a proof of security that relates the security of CRDT constructions designed under said semantics to the underlying MPC library. We provide an open-source system implementation with an extensive evaluation, which compares different designs with their baseline throughput and latency.
Fake Product Identification using Blockchain

The increase in fake goods is severely affecting the industrial sector and consumers. According to the survey, incidences involving fake products have increased in recent years, which have had a negative impact on sales, profits, and brand recognition. Far from reducing the ability of a company to make money, this counterfeiting also affects the consumer's ability to trust their goods in open market. Without proper tracking and security measures, consumers gradually stop trusting brands to keep their consumers safe from theft. This is happening because the manufacturing and distribution processes are hidden to consumers and this information is easily manipulated or falsified by others. So, users must have a method to determine if a product is genuine or not. This study proposes a blockchain based anti-counterfeiting system for product traceability throughout the supply chain. By using public or permissionless blockchain all the information throughout the supply chain will be recorded in the blockchain network in the form of blocks that are immutable, transparent, secure, tamper-proof, and trusted. This proposed method uses QR code for making the system further secure.
Cream Skimming the Underground: Identifying Relevant Information Points from Online Forums

This paper proposes a machine learning-based approach for detecting the exploitation of vulnerabilities in the wild by monitoring underground hacking forums. The increasing volume of posts discussing exploitation in the wild calls for an automatic approach to process threads and posts that will eventually trigger alarms depending on their content. To illustrate the proposed system, we use the CrimeBB dataset, which contains data scraped from multiple underground forums, and develop a supervised machine learning model that can filter threads citing CVEs and label them as Proof-of-Concept, Weaponization, or Exploitation. Leveraging random forests, we indicate that accuracy, precision and recall above 0.99 are attainable for the classification task. Additionally, we provide insights into the difference in nature between weaponization and exploitation, e.g., interpreting the output of a decision tree, and analyze the profits and other aspects related to the hacking communities. Overall, our work sheds insight into the exploitation of vulnerabilities in the wild and can be used to provide additional ground truth to models such as EPSS and Expected Exploitability.
Malicious Relay Detection for Tor Network Using Hybrid Multi-Scale CNN-LSTM with Attention

With the widespread use of the Tor network, attackers who control malicious relays pose a serious threat to user privacy. Therefore, identifying malicious relays is crucial for ensuring the security of the Tor network. We propose a malicious relay detection model called hybrid multi-scale CNN-LSTM with Attention model (MSC-L-A) for the Tor network. The MSC layer uses one-dimensional convolutional neural networks with different convolution kernels to capture complex multi-scale local features and fuse them. The LSTM layer leverages memory cells and gate mechanisms to control the transmission of sequence information and extract temporal correlation information. The attention mechanism automatically learns feature importance and strengthens the weights of parameters that have a substantial impact on the results. Finally, the Sigmoid function is used to classify the data. Experimental results demonstrate that our proposed model achieves higher prediction accuracy and more accurate classification of Tor relays compared to other baseline models.
A law of data separation in deep learning

While deep learning has enabled significant advances in many areas of science, its black-box nature hinders architecture design for future artificial intelligence applications and interpretation for high-stakes decision-makings. We addressed this issue by studying the fundamental question of how deep neural networks process data in the intermediate layers. Our finding is a simple and quantitative law that governs how deep neural networks separate data according to class membership throughout all layers for classification. This law shows that each layer improves data separation at a constant geometric rate, and its emergence is observed in a collection of network architectures and datasets during training. This law offers practical guidelines for designing architectures, improving model robustness and out-of-sample performance, as well as interpreting the predictions.
Parameter-Efficient Transfer Learning for Remote Sensing Image–Text Retrieval

Vision-and-language pre-training (VLP) models have experienced a surge in popularity recently. By fine-tuning them on specific datasets, significant performance improvements have been observed in various tasks. However, full fine-tuning of VLP models not only consumes a significant amount of computational resources but also has a significant environmental impact. Moreover, as remote sensing (RS) data is constantly being updated, full fine-tuning may not be practical for real-world applications. To address this issue, in this work, we investigate the parameter-efficient transfer learning (PETL) method to effectively and efficiently transfer visual-language knowledge from the natural domain to the RS domain on the image-text retrieval task. To this end, we make the following contributions. 1) We construct a novel and sophisticated PETL framework for the RS image-text retrieval (RSITR) task, which includes the pretrained CLIP model, a multimodal remote sensing adapter, and a hybrid multi-modal contrastive (HMMC) learning objective; 2) To deal with the problem of high intra-modal similarity in RS data, we design a simple yet effective HMMC loss; 3) We provide comprehensive empirical studies for PETL-based RS image-text retrieval. Our results demonstrate that the proposed method is promising and of great potential for practical applications. 4) We benchmark extensive state-of-the-art PETL methods on the RSITR task. Our proposed model only contains 0.16M training parameters, which can achieve a parameter reduction of 98.9% compared to full fine-tuning, resulting in substantial savings in training costs. Our retrieval performance exceeds traditional methods by 7-13% and achieves comparable or better performance than full fine-tuning. This work can provide new ideas and useful insights for RS vision-language tasks.
Federated and distributed learning applications for electronic health records and structured medical data: a scoping review

Federated learning (FL) has gained popularity in clinical research in recent years to facilitate privacy-preserving collaboration. Structured data, one of the most prevalent forms of clinical data, has experienced significant growth in volume concurrently, notably with the widespread adoption of electronic health records in clinical practice. This review examines FL applications on structured medical data, identifies contemporary limitations, and discusses potential innovations.We searched 5 databases, SCOPUS, MEDLINE, Web of Science, Embase, and CINAHL, to identify articles that applied FL to structured medical data and reported results following the PRISMA guidelines. Each selected publication was evaluated from 3 primary perspectives, including data quality, modeling strategies, and FL frameworks.Out of the 1193 papers screened, 34 met the inclusion criteria, with each article consisting of one or more studies that used FL to handle structured clinical/medical data. Of these, 24 utilized data acquired from electronic health records, with clinical predictions and association studies being the most common clinical research tasks that FL was applied to. Only one article exclusively explored the vertical FL setting, while the remaining 33 explored the horizontal FL setting, with only 14 discussing comparisons between single-site (local) and FL (global) analysis.The existing FL applications on structured medical data lack sufficient evaluations of clinically meaningful benefits, particularly when compared to single-site analyses. Therefore, it is crucial for future FL applications to prioritize clinical motivations and develop designs and methodologies that can effectively support and aid clinical practice and research.
Implementation of Blockchain Technology for Safe Supply Chain Transactions

Supply Chain Management (SCM) is an activity to transfer the products from one point to another point via various stakeholders. Some of the issues with the traditional SCM systems are security, transparency and centralized approach. The BC has emerged to address all these issues with the SCM and allows easy management of data and storage and also does authorization and verification in the SCM. Various security mechanisms were used in BC for secure transactions in SCM to ensure data security and protection from corruption. This research study discusses about the role of Blockchain(BC) in SCM and the mechanisms and methods to secure transactions using BC.
An Empirical Analysis of CNN for American Sign Language Recognition

American Sign Language recognition is a crucial technology that aims to improve accessibility and communication for the deaf and hard-of-hearing community. This research aims to convert ASL gestures into text or speech by analyzing and interpreting them using gesture recognition in sign language. This study compares different deep learning algorithms for sign language identification to overcome communication difficulties that deaf or hard-of-hearing people face. Three models—VGG16, ResNet, and AlexNet—were created and trained using a dataset of hand motions. The findings show that all three models have excellent accuracy, with AlexNet performing the best at 99.87%, ResNet coming in second at 98.9%, and VGG16 third at 98.2%.
Blockchain Technology in Talent Retention and Capability Development in HRM

Technology has the potential to revolutionize both the fundamental and advanced tasks of Human Resource Management [HRM]. Blockchain (BC) has enabled advanced technological innovations in Human Resource Management (HRM) operations and workflows. Background authorization, employment history authentication, and advanced services such as training and talent management are examples of these functions. Human resource management processes include monitoring the workers in order to make work more dynamic. Despite the hype, this study observed that however blockchain is successfully introduced across various sectors and industries, it is not highly significant in the HR domain throughout its application. The research study focuses on developing human intellectual capital, monitoring the benefits for creating structured organization, sustainability, and reliability through business continuity, for which the training and development levels will differ in blockchain applications, resulting in data analytics and the solution obtained through the introduction of technological assistance and systems. The effects of incorporating a new technology, particularly Blockchain, offer various advantages in individuals, teams, departments, and organizations with real-time insights and advancements. The disadvantage is that data privacy is difficult, especially as India lacks a privacy law. There are also challenges in terms of performance and scalability, interoperability, integrity and security, and laws. This study proposes a concept model for defining training and achieving expected outcomes. The implications are far-reaching for the Human resources personnel and organizational effectiveness.
Human Fall Detection using Gaussian Mixture Model and Fall Motion Mixture Model

Deaths from accidental falls are the second leading cause of mortality from nonfatal injuries. Over the past two decades, there has been an explosion in research into the possibility of using technology to detect human falls on its own. This is an intriguing puzzle, and there are many different ways to solve it. Many various approaches have been presented during the past few years. These resources offer methods for identifying a wide variety of human motions, including walking, running, leaping, jogging, falling, and many others. Since falling is a common and potentially hazardous event for persons of all ages, but has a higher adverse effect on the senior population, prevention and detection of falls is particularly important among all these activities. Many existing fall detection systems cannot reliably reduce false positives due to the computational complexity of the algorithm they utilise. Instead of using wearable sensors, video cameras are the best option for fall detection. This article presents a visual method for recognising activities and detecting falls. The proposed method identifies falls only through the use of video-camera pictures, with no need for additional ambient sensors. Making a GMM (Gaussian Mixture Model) first. Using a fall motion vector, the URFall dataset's fall events are easily detected with minimal training time using a method called Fall Motion Mixture Model (FMMM), which is an implicit extension of GMM.
Machine Learning Techniques to Detect Cyber-Bullying

Cyber-bullying is a growing concern in today's society, especially in countries like India where the language used for online communication is often a mix of Hindi and English, commonly known as ‘Hinglish’. In this study, a machine learning-based approach is proposed for detecting cyber-bullying in Hinglish text. This study utilizes several natural language processing (NLP) techniques and a variety of machine learning algorithms to analyze the linguistic features of Hinglish text and identify instances of cyber-bullying. Proposed experimental results on a dataset of Hinglish tweets show that the proposed approach achieves a high accuracy rate in identifying cyber-bullying instances in Hinglish text.
Developing a Non-Fungible Token-Based Trade Marketplace Platform Using Web 3.0

This research study provides an overview of third-generation blockchain in comparison to first- and second-generation blockchain like Bitcoin and Ethereum, to obtain data on the third-generation Elrond blockchain's actual transaction cost and speed, and to assess the platform's financial viability in light of those costs. The research consists of a blockchain theory and hybrid application that adheres to Web 3.0 principles and is a Non-Fungible Token (NFT) marketplace platform on Elrond. The underpinning work includes project planning for the entire team, the development of the platform's front-end, back-end, smart contracts, and DevOps components, as well as the development of financial models for service to create a commercially viable platform. The app will be integrated with the back-end service, which uses the database as a persistence layer to provide the front end with the UI requirements it requires.
Multi-Label Toxicity Detection: An Analysis

Everybody has the freedom and flexibility to express their opinions and ideas through social networking and online discussion platforms. However, individuals are dealing with circumstances where the vast majority takes these networks casually and misuses them as a way to abuse and threaten others, which can result in cyberattacks, cyberbullying, nightmares, and, in severe cases, suicidal attempts. Such statements must be manually identified and classified, which is a time-taking, exhausting, and unreliable process. This research examines thetoxicity of remark and also investigate the sort of toxicity, categorize the comments into various labels if they are toxic. This research work will also compare various existing machine learning algorithms on the dataset named Toxic Comment Classification Challenge imported from Kaggle that contains 15000 comments. Machine Learning, Deep Learning and Natural Language Processing lend a helping hand in reducing the poisonous environment that exists on numerous discussion forums.
Investigation of the Internet of Things (IoT) Security and Privacy Issues

Connecting diverse devices and facilitating unhindered connection and data exchange, the IoT has dramatically altered the ways in which humans engage with their surroundings. However, there are major privacy and safety risks that come along with this connectedness. With the goal of determining the most common vulnerabilities and their effects on IoT systems, this study explores these concerns. Assessment of risks, algorithms for data encryption, and security measures are all part of the study technique. Weak authentication, unsecured firmware, unencrypted communication, and a lack of security upgrades are all highlighted as significant via this research of vulnerability frequency. By encouraging manufacturers to priorities security measures, pushing the adoption of standards and laws, and improving user awareness and education, these results provide a basis for driving practical improvements in the IoT security environment. The findings of this study inform academics, industry experts, policymakers, and end-users of IoT systems, while also providing a foundation for future studies on this topic. A more secure and trustworthy IoT ecosystem may be built with the use of sophisticated threat modelling, security frameworks, privacy-preserving technology, security automation, and ethical considerations.