Information

23 pages, 4792 KiB

Open AccessArticle

BACH: A Tool for Analyzing Blockchain Transactions Using Address Clustering Heuristics

by Michele Caringella, Francesco Violante, Francesco De Lucci, Stefano Galantucci and Matteo Costantini

Information 2024, 15(10), 589; https://doi.org/10.3390/info15100589 - 26 Sep 2024

Abstract

Cryptocurrencies have now become an emerging blockchain-based payment technology; among them, bitcoin is the best known and most widely used. Users on these networks are pseudo-anonymous, meaning that while all transactions from an address are transparent and searchable by anyone, the users’ true [...] Read more.

Cryptocurrencies have now become an emerging blockchain-based payment technology; among them, bitcoin is the best known and most widely used. Users on these networks are pseudo-anonymous, meaning that while all transactions from an address are transparent and searchable by anyone, the users’ true identities are not directly revealed; to preserve their privacy, users often use many different addresses. In recent years, some studies have been conducted regarding analyzing clusters of bitcoin addresses that, according to certain heuristics, belong to the same entity. This capability provides law enforcement with valuable information for investigating illegal activities involving cryptocurrencies. Clustering methods that rely on a single heuristic often fail to accurately and comprehensively cluster multiple addresses. This paper proposes Bitcoin Address Clustering based on multiple Heuristics (BACH): a tool that uses three different clustering heuristics to identify clusters of bitcoin addresses, which are displayed through a three-dimensional graph. The results lead to several analyses, including a comparative evaluation of WalletExplorer, which is a similar address clustering tool. BACH introduces the innovative feature of visualizing the internal structure of clusters in a graphical format. The study also shows how the combined use of different heuristics provides better results and more complete clusters than those obtained from their individual use. Full article

15 pages, 4030 KiB

Open AccessArticle

A Training-Free Latent Diffusion Style Transfer Method

by Zhengtao Xiang, Xing Wan, Libo Xu, Xin Yu and Yuhan Mao

Information 2024, 15(10), 588; https://doi.org/10.3390/info15100588 - 26 Sep 2024

Abstract

Diffusion models have attracted considerable scholarly interest for their outstanding performance in generative tasks. However, current style transfer techniques based on diffusion models still rely on fine-tuning during the inference phase to optimize the generated results. This approach is not merely laborious and [...] Read more.

Diffusion models have attracted considerable scholarly interest for their outstanding performance in generative tasks. However, current style transfer techniques based on diffusion models still rely on fine-tuning during the inference phase to optimize the generated results. This approach is not merely laborious and resource-demanding but also fails to fully harness the creative potential of expansive diffusion models. To overcome this limitation, this paper introduces an innovative solution that utilizes a pretrained diffusion model, thereby obviating the necessity for additional training steps. The scheme proposes a Feature Normalization Mapping Module with Cross-Attention Mechanism (INN-FMM) based on the dual-path diffusion model. This module employs soft attention to extract style features and integrate them with content features. Additionally, a parameter-free Similarity Attention Mechanism (SimAM) is employed within the image feature space to facilitate the transfer of style image textures and colors, while simultaneously minimizing the loss of structural content information. The fusion of these dual attention mechanisms enables us to achieve style transfer in texture and color without sacrificing content integrity. The experimental results indicate that our approach exceeds existing methods in several evaluation metrics. Full article

(This article belongs to the Special Issue Feature Papers in Artificial Intelligence 2024)

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14 pages, 405 KiB

Open AccessArticle

Understanding Online Purchases with Explainable Machine Learning

by João A. Bastos and Maria Inês Bernardes

Information 2024, 15(10), 587; https://doi.org/10.3390/info15100587 - 26 Sep 2024

Abstract

Customer profiling in e-commerce is a powerful tool that enables organizations to create personalized offers through direct marketing. One crucial objective of customer profiling is to predict whether a website visitor will make a purchase, thereby generating revenue. Machine learning models are the [...] Read more.

Customer profiling in e-commerce is a powerful tool that enables organizations to create personalized offers through direct marketing. One crucial objective of customer profiling is to predict whether a website visitor will make a purchase, thereby generating revenue. Machine learning models are the most accurate means to achieve this objective. However, the opaque nature of these models may deter companies from adopting them. Instead, they may prefer simpler models that allow for a clear understanding of the customer attributes that contribute to a purchase. In this study, we show that companies need not compromise on prediction accuracy to understand their online customers. By leveraging website data from a multinational communications service provider, we establish that the most pertinent customer attributes can be readily extracted from a black box model. Specifically, we show that the features that measure customer activity within the e-commerce platform are the most reliable predictors of conversions. Moreover, we uncover significant nonlinear relationships between customer features and the likelihood of conversion. Full article

(This article belongs to the Special Issue Application of Machine Learning in Data Science and Computational Intelligence)

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17 pages, 313 KiB

Open AccessArticle

On the Extended Adjacency Eigenvalues of a Graph

by Alaa Altassan, Hilal A. Ganie and Yilun Shang

Information 2024, 15(10), 586; https://doi.org/10.3390/info15100586 - 26 Sep 2024

Viewed by 92

Abstract

Let

H

be a graph of order n with m edges. Let

d_{i} = d (v_{i})

be the degree of the vertex

v_{i}

. The extended adjacency matrix

A_{e x} (H)

of

H

is an [...] Read more.

Let

H

be a graph of order n with m edges. Let

d_{i} = d (v_{i})

be the degree of the vertex

v_{i}

. The extended adjacency matrix

A_{e x} (H)

of

H

is an

n \times n

matrix defined as

A_{e x} (H) = (b_{i j})

, where

b_{i j} = \frac{1}{2} (\frac{d_{i}}{d_{j}} + \frac{d_{j}}{d_{i}})

, whenever

v_{i}

and

v_{j}

are adjacent and equal to zero otherwise. The largest eigenvalue of

A_{e x} (H)

is called the extended adjacency spectral radius of

H

and the sum of the absolute values of its eigenvalues is called the extended adjacency energy of

H

. In this paper, we obtain some sharp upper and lower bounds for the extended adjacency spectral radius in terms of different graph parameters and characterize the extremal graphs attaining these bounds. We also obtain some new bounds for the extended adjacency energy of a graph and characterize the extremal graphs attaining these bounds. In both cases, we show our bounds are better than some already known bounds in the literature. Full article

(This article belongs to the Special Issue Feature Papers in Information in 2024–2025)

16 pages, 886 KiB

Open AccessArticle

Exploring the Potential of Neural Machine Translation for Cross-Language Clinical Natural Language Processing (NLP) Resource Generation through Annotation Projection

by Jan Rodríguez-Miret, Eulàlia Farré-Maduell, Salvador Lima-López, Laura Vigil, Vicent Briva-Iglesias and Martin Krallinger

Information 2024, 15(10), 585; https://doi.org/10.3390/info15100585 - 25 Sep 2024

Viewed by 173

Abstract

Recent advancements in neural machine translation (NMT) offer promising potential for generating cross-language clinical natural language processing (NLP) resources. There is a pressing need to be able to foster the development of clinical NLP tools that extract key clinical entities in a comparable [...] Read more.

Recent advancements in neural machine translation (NMT) offer promising potential for generating cross-language clinical natural language processing (NLP) resources. There is a pressing need to be able to foster the development of clinical NLP tools that extract key clinical entities in a comparable way for a multitude of medical application scenarios that are hindered by lack of multilingual annotated data. This study explores the efficacy of using NMT and annotation projection techniques with expert-in-the-loop validation to develop named entity recognition (NER) systems for an under-resourced target language (Catalan) by leveraging Spanish clinical corpora annotated by domain experts. We employed a state-of-the-art NMT system to translate three clinical case corpora. The translated annotations were then projected onto the target language texts and subsequently validated and corrected by clinical domain experts. The efficacy of the resulting NER systems was evaluated against manually annotated test sets in the target language. Our findings indicate that this approach not only facilitates the generation of high-quality training data for the target language (Catalan) but also demonstrates the potential to extend this methodology to other languages, thereby enhancing multilingual clinical NLP resource development. The generated corpora and components are publicly accessible, potentially providing a valuable resource for further research and application in multilingual clinical settings. Full article

(This article belongs to the Special Issue Machine Translation for Conquering Language Barriers)

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17 pages, 8908 KiB

Open AccessArticle

Detection of Random Body Movements Using Clustering-Based Methods in Bioradar Systems

by André Rouco, Filipe Silva, Beatriz Soares, Daniel Albuquerque, Carolina Gouveia, Susana Brás and Pedro Pinho

Information 2024, 15(10), 584; https://doi.org/10.3390/info15100584 - 25 Sep 2024

Viewed by 188

Abstract

Bioradar systems, in general, refer to radar systems used for the detection of vital signs. These systems hold significant importance across various sectors, particularly in healthcare and surveillance, due to their capacity to provide contactless solutions for monitoring physiological functions. In these applications, [...] Read more.

Bioradar systems, in general, refer to radar systems used for the detection of vital signs. These systems hold significant importance across various sectors, particularly in healthcare and surveillance, due to their capacity to provide contactless solutions for monitoring physiological functions. In these applications, the primary challenge lies in the presence of random body movements (BMs), which can significantly hinder the accurate detection of vital signs. To compensate the affected signal in a timely manner, portions of BM must be correctly identified. To address this challenge, this work proposes a solution based on the Density-Based Spatial Clustering of Applications with Noise (DBScan) algorithm to detect the occurrence of BM in radar signals. The main idea of this algorithm is to cluster the radar samples, aiming to differentiate between segments in which the subject is stable and segments in which the subject is moving. Using a dataset involving eight subjects, the proposed method successfully detects three types of body movements: chest movement, body rotation, and arm movement. The achieved results are promising, with F1 scores of 0.83, 0.73, and 0.8, respectively, for the detection of each specific movement type. Full article

(This article belongs to the Special Issue Signal Processing in Radio Systems)

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20 pages, 3271 KiB

Open AccessArticle

Smart Collaborative Intrusion Detection System for Securing Vehicular Networks Using Ensemble Machine Learning Model

by Mostafa Mahmoud El-Gayar, Faheed A. F. Alrslani and Shaker El-Sappagh

Information 2024, 15(10), 583; https://doi.org/10.3390/info15100583 - 24 Sep 2024

Viewed by 277

Abstract

The advent of the Fourth Industrial Revolution has positioned the Internet of Things as a pivotal force in intelligent vehicles. With the source of vehicle-to-everything (V2X), Internet of Things (IoT) networks, and inter-vehicle communication, intelligent connected vehicles are at the forefront of this [...] Read more.

The advent of the Fourth Industrial Revolution has positioned the Internet of Things as a pivotal force in intelligent vehicles. With the source of vehicle-to-everything (V2X), Internet of Things (IoT) networks, and inter-vehicle communication, intelligent connected vehicles are at the forefront of this transformation, leading to complex vehicular networks that are crucial yet susceptible to cyber threats. The complexity and openness of these networks expose them to a plethora of cyber-attacks, from passive eavesdropping to active disruptions like Denial of Service and Sybil attacks. These not only compromise the safety and efficiency of vehicular networks but also pose a significant risk to the stability and resilience of the Internet of Vehicles. Addressing these vulnerabilities, this paper proposes a Dynamic Forest-Structured Ensemble Network (DFSENet) specifically tailored for the Internet of Vehicles (IoV). By leveraging data-balancing techniques and dimensionality reduction, the DFSENet model is designed to detect a wide range of cyber threats effectively. The proposed model demonstrates high efficacy, with an accuracy of 99.2% on the CICIDS dataset and 98% on the car-hacking dataset. The precision, recall, and f-measure metrics stand at 95.6%, 98.8%, and 96.9%, respectively, establishing the DFSENet model as a robust solution for securing the IoV against cyber-attacks. Full article

(This article belongs to the Special Issue Intrusion Detection Systems in IoT Networks)

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