Global Information Exchange Research Articles

Cybersecurity on social media platforms: The use of deep learning methodologies for detecting anomalous user behavior

The exponential growth of social media platforms has brought unprecedented opportunities for global communication, networking, and information exchange. However, this expansion has also given rise to significant challenges, particularly in identifying and mitigating anomalous or malicious user behaviors such as spamming, cyberbullying, and misinformation dissemination. Traditional anomaly detection methods, which often rely on rule-based systems or basic statistical models, have proven inadequate in addressing the complex, dynamic, and evolving nature of user behavior on these platforms. In response, this paper explores the application of deep learning methodologies—specifically Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), Autoencoders, and Generative Adversarial Networks (GANs)—for detecting anomalous behavior in social media environments. We present a novel deep learning architecture that integrates these models to enhance the detection accuracy of behavioral anomalies. By leveraging large-scale datasets and advanced feature extraction techniques, our approach demonstrates significant improvements over traditional methods, with higher precision, recall, and overall detection rates. The proposed model's ability to adapt to new and emerging patterns of behavior underscores its potential for real-world application in monitoring social media platforms. This research contributes to the growing body of literature on deep learning for cybersecurity and digital trust, offering a robust solution for maintaining the integrity of online social spaces. Our findings suggest that the implementation of these advanced methodologies can provide a more secure and reliable social media environment, benefiting both users and platform providers alike.

ОБ ИТОГАХ МЕЖДУНАРОДНОГО ФОРУМА «KAZAN DIGITAL WEEK–2024» И ПРОШЕДШЕЙ В ЕГО РАМКАХ НАУЧНО-ПРАКТИЧЕСКОЙ СЕКЦИИ «МЕДИЦИНА И ИСКУССТВО»

This article is dedicated to the results and most important events of the International Forum «Kazan Digital Week-2024»— a grand event designed to promote the global exchange of scientific and technical information and the consolidation of scientific and technological assets, which was held on September 9–11, 2024, in the capital of the Republic of Tatarstan, Kazan.

Technological Advancements and Their Impact on Organisational Information Security

This paper explores the crucial role of strategic management and integration of information security within organisations to safeguard against diverse threats in a rapidly evolving digital environment. It posits that effective management of information security significantly enhances organisational resilience and protects strategic assets amidst global information exchange and technological advancements. The research employs a systematic literature review method, analysing existing definitions and perspectives on information security, supplemented by an analytical examination of potential vulnerabilities within corporate security systems and a case study of the IBM model in order to provide a comprehensive approach to information security in practice. The study shows that technological progress and the development of comprehensive security frameworks by governmental and non-governmental organisations directly improve the effectiveness of information security measures. Furthermore, it emphasises information as a strategic asset that requires a holistic security approach incorporating organisational and legal measures. The findings advocate for a multidimensional approach to information security, highlighting the necessity of continuous adaptation to effectively combat evolving threats. This comprehensive analysis contributes to the scholarly discourse on information security management, offering insights that can inform future research and practical applications in enhancing organisational resilience.

Blockchain-Based LSTM Approach for Secure Data Transmission in IoT-Enabled Healthcare System

Blockchain technology can help with a number of issues facing the healthcare sector. Its potential as trust brokers includes the provision of incentive machines and the facilitation of innovative healthcare solutions. Additionally, its potential as trust brokers includes the facilitation of novel business models that offer fresh perspectives on the interactions between patients and providers, among other diverse healthcare stakeholders. Blockchain, for instance, makes incentive structures easier to implement and promotes decentralised trust in patient-centered healthcare models and global health information exchange (HIE). On the other hand, blockchain is characterised as a decentralised network or service that can help remove obstacles in the healthcare sector. The healthcare sector has benefited greatly from the effective resolution of problems relating to adoption and technological obstacles, interoperability and integration with current systems, cost unpredictability, scale, and regulatory compliance through the use of Blockchain technology. This work proposed discusses fault tolerance techniques in relation to sharing health-related data.

Performance Analysis of an E-commerce Website Using Distributed Servers (Case Study: Ecommerce Bumdes Sarining Kukuh Winangun)

Digital transformation has significantly altered our interactions by leveraging the internet for fast global information exchange and collaboration. This shift impacts businesses, individuals, organizations, and governments by increasing the demand for fast, reliable web services. To meet these demands, many organizations are turning to distributed server infrastructures, which help handle large storage needs and avoid performance issues. This research focuses on creating a distributed web server system that uses Round-Robin load balancing to evenly distribute traffic and enhance performance. The Round-Robin algorithm’s simplicity and effectiveness in balancing loads, combined with the advantages of virtualization, such as cost efficiency, improved performance, and better resource management are central to this approach. Virtualization also offers improved scalability, accuracy, and security, further enhancing overall system efficiency and effectiveness in data centers. This research evaluates and contrasts the performance of websites utilizing distributed servers against those using single servers. The findings indicate that websites with distributed servers significantly outperform those with single servers. Specifically, distributed servers offer response times that are 5.8 times faster, achieve 2.2 times more successful responses, and transfer 2.1 times more data than single servers. Additionally, single servers experience a much higher rate of timeouts, with 14.2 times more occurrences compared to distributed servers.

Fault diagnosis method for rotating machinery based on SEDenseNet and Gramian Angular Field

The fault diagnosis in rotating machinery is crucial for ensuring the safe and dependable operation of intricate mechanical systems. Addressing the limitations inherent in traditional deep learning approaches concerning extended time sequence encoding and subpar generalization capability is paramount. The study utilizes the Gramian Angular Field (GAF) and Squeeze and Excitation (SE) attention mechanisms to alleviate these constraints. GAF enhances feature extraction by emphasizing the angular relationships among adjacent signal points to uncover latent fault characteristics. Simultaneously, through the integration of SE with DenseNet architecture, the network facilitates global information exchange and improves multi-scale fusion, thereby enhancing the precise identification of fault type and location within the signal. Experiments conducted on two datasets achieved accuracies of 100% and 99.85%, respectively, outperforming other methods and models, thereby validating the effectiveness of this study.

Perlindungan Data Pribadi Dalam Kejahatan Transnasional Lintas-Negara

Protection of personal data in the context of transnational crime across borders is becoming increasingly important in the current digital era. Rapidly developing information and communication technologies have brought great benefits in global information exchange, but also brought serious risks to the security of individual data. This research aims to investigate the influence of developments in information and communication technology on personal data protection in facing the threat of transnational crime, explore the challenges faced, and identify strategies and the influence of international cooperation in increasing the effectiveness of personal data protection. The research method used is literature study, with analysis of relevant information from various sources.

Introduction to Data Protection Frameworks: A Review

In the digital era, where data breaches are prevalent and harmful, "An Introduction to Data Protection Frameworks: A Review" provides a critical analysis of mechanisms aimed at safeguarding personal data. The review commences by discussing data protection, underscoring its significance as a basic right and its pivotal role in the digital realm. It then explores the historical backdrop, tracing the progression of data protection laws from their origins to the extensive frameworks in place today. The review meticulously details the fundamental principles of data protection—fairness, accuracy, minimization, and accountability—which form the bedrock of efficient data management. Additionally, it highlights the rights of data subjects, such as access, rectification, and erasure, empowering individuals amidst the rise of data-driven practices. It further delves into the obligations of data controllers and processors, stressing the importance of adherence and integrity in data management. The paper also conducted a comparative examination of global frameworks such as the GDPR, which is critical for international data regulation. Lastly, the review explores the obstacles and future prospects of data protection, recognizing the challenges posed by technological progress and the global exchange of information.

Inversion of soil carbon, nitrogen, and phosphorus in the Yellow River Wetland of Shaanxi Province using field in situ hyperspectroscopy

Soil nitrogen and phosphorus are directly related to soil quality and vegetation growth and are, therefore, a common research topic in studies on global climate change, material cycling, and information exchange in terrestrial ecosystems. However, collecting soil hyperspectral data under in situ conditions and predicting soil properties, which can effectively save time, manpower, material resources, and financial costs, have been generally undervalued. Recent optimization techniques have, however, addressed several of the limitations previously restricting this technique. In this study, hyperspectral data were taken from surface soils under different vegetation types in the wetlands of the Shaanxi Yellow River Wetland Provincial Nature Reserve. Through in situ original and first-order differential transformation spectral data, three prediction models for soil carbon, nitrogen, and phosphorus contents were established: partial least squares (PLSR), random forest (RF), and Gaussian process regression (GPR). The R2 and RMSR of the constructed models were then compared to select the optimal model for evaluating soil content. The soil organic carbon, total nitrogen, and total phosphorus content models established based on the first-order differential had a higher accuracy when modeling and during model validation than those of other models. Moreover, the PLSR model based on the original spectrum and the Gaussian process regression model had a superior inversion performance. These results provide solid theoretical and technical support for developing the optimal model for the quantitative inversion of wetland surface soil carbon, nitrogen, and phosphorus based on in situ hyperspectral technology.

TransPose: 6D object pose estimation with geometry-aware Transformer

Efficient and accurate estimation of objects’ pose is essential in numerous practical applications. Due to the depth data contains abundant geometric information, some existing methods devote to extract features from 3D point cloud. However, these depth-based methods focus on extracting the point cloud local features and consider less about the global information. How to extract and utilize the local and global geometry features in depth information is crucial to achieve accurate predictions. To this end, we propose TransPose, a novel 6D pose framework that exploits Transformer Encoder with geometry-aware module to develop better learning of point cloud feature representations. To better extract local geometry features, we finely design the graph convolution network-based feature extractor that first uniformly sample point cloud and extract point pair features of point cloud. To further improve robustness to occlusion, we adopt Transformer to perform the propagation of global information, making each local feature obtains global information. Moreover, we introduce geometry-aware module in Transformer Encoder, which to form an effective constrain for point cloud feature learning and makes the global information exchange more tightly coupled with point cloud tasks. Extensive experiments indicate the effectiveness of TransPose, our pose estimation pipeline achieves competitive results on three benchmark datasets.

Diagnosis of skin lesion using shift-invariant network and an improved grey wolf optimizer

The global incidence of skin cancer has been rising, resulting in increased mortality and morbidity if left untreated. Accurate diagnosis of skin malignancies is crucial for early intervention through excision. While various innovative medical imaging techniques, such as dermoscopy, have improved the way we examine skin cancers, the progress in medical imaging for identifying skin lesions has not kept pace. Skin lesions exhibit diverse visual features, including variations in size, shape, boundaries, and artifacts, necessitating an efficient image-processing approach to assist dermatologists in decision-making. In this research, we propose an automated skin lesion classifier called GreyNet, which utilizes optimized convolutional neural networks (CNNs) or shift-invariant networks (SIN). GreyNet comprises three components: (i) a trained fully deep CNN for semantic segmentation, relating input images to manually labeled standard scans; (ii) an enhanced dense CNN with global information exchange and adaptive feature salvaging module to accurately classify each pixel in histopathological scans as benign or malignant; and (iii) a binary grey wolf optimizer (BGWO) to improve the classification process by optimizing the network’s hyperparameters. We evaluate the performance of GreyNet in terms of lesion segmentation and classification on the HAM10000 database. Extensive empirical results demonstrate that GreyNet outperforms existing lesion segmentation methods, achieving improved dice similarity score, volume error, and average processing time of 1.008±0.009, 0.903±0.009%, and 0.079±0.010 s, respectively. Moreover, GreyNet surpasses other skin melanoma classification models, exhibiting improved accuracy, precision, specificity, sensitivity, false negative rate, false positive rate, and Jaccard similarity score (JSS) of 96.5%, 97%, 96.2%, 92.1%, 3.8%, 3%, and 89.5%, respectively. Based on our experimental analysis, we conclude that GreyNet is an efficient tool to aid dermatologists in identifying skin melanoma.

Electromagnetic Waves: Exploring The Fundamental Properties and Impact on Modern Technology and Society

This paper thoroughly investigates electromagnetic waves (EM), highlighting their fundamental characteristics, applications in electrical energy transmission, and mobile phone technology. The study begins by dissecting EM waves’ wave-particle duality and spectrum range, establishing a foundation to understand their significant role in modern technology and society. A key focus is placed on their efficiency in electrical energy transmission, especially relevant in the context of increasing energy demands and the integration of renewable resources for sustainable development. The paper also examines how electromagnetic waves have revolutionized mobile phone technology, encompassing aspects like wireless connectivity, GPS, and near-field communication, profoundly influencing global communication and information exchange. Despite these advancements, the paper acknowledges challenges like health and environmental risks associated with electromagnetic pollution. These complexities underscore the need for balanced advancement in electromagnetic wave applications and technology.

Model Komunikasi Humas dalam Literasi Media Sosial

The development of technology and social media affects the way we interact and access information. Social media, such as Facebook and Twitter, enable the global exchange of information. The phenomenon of virality accelerates the spread of content, creating new dynamics in delivering messages and influencing public opinion. Digital literacy is the key to dealing with the increasingly advanced technological era, especially through social media. This research aims to observe the communication model of the @ppgkemendikbud Instagram account in providing social literacy about Teacher Professional Education (PPG). This research method is descriptive qualitative with data collection through observation and literature study. The results showed that the Ministry of Education's PPG Instagram account with 1,086 uploads and 462 thousand followers plays a role in providing social literacy about PPG. The success of this account lies in its openness without locking, direct message feature that increases interactivity, and regularly uploading the latest information about PPG, national day commemoration, and education seminars. The PR communication model used involves a two-way approach, covering various PPG highlights, so that it is not only a source of information, but also a place to build a community.

Public Danger and Problems of Qualifying the Creation and Operation of Fraudulent Call-Centers

Relevance. The use of technological progress by criminals for criminal purposes is one of the pressing problems today. In this context, modern information and communication technologies deserve special attention, providing the possibility of global communication, exchange and storage of information, learning and knowledge acquisition, optimization and anonymization of financial transactions. Manipulating people's consciousness through the use of information and cognitive technologies is today a common way of committing various types of fraud. These opportunities are fully and actively used in the implementation of such a group form of fraud as the activities of fraudulent call centers. The purpose of the study is to assess the public danger and identify problems in qualifying the creation and operation of fraudulent call centers. Objectives: to identify compliance with the essence of the criminal activity of fraudulent call centers and assess the public danger of this criminal activity;identify problems in classifying such acts and suggest ways to eliminate them. Methodology. When writing the work, the tools of dialectical, systemic and activity-based approaches were used, as well as general logical thinking techniques, general scientific and special scientific methods.Results. It has been established that the underestimation of the public danger of the activities of fraudulent call centers, and, accordingly, the insufficient effectiveness of measures taken today to counter them, are due to ignorance of the fact that the joint criminal activities of fraudsters in this case form a criminal community. Conclusions. This type of criminal activity corresponds to all the characteristics of a criminal community (struct redness; unified leadership; the purpose of creation is to commit one or more serious or especially serious crimes to obtain directly or indirectly financial or other material benefits).In addition, the qualification of the activities of fraudulent call centers under Art.210 of the Criminal Code of the Russian Federation will allow the court to ensure the possibility of confiscation of property obtained as a result of criminal activity.

An Account of Systematic Review of English for Medical Purposes (EMP) Vocabulary Memorization

A high volume of global medical information exchanges has escalated due to the recent pandemic that has hit the world. In addressing international demands of sorts, it is imperative that students improve their English language proficiency, especially for medical purposes. Sufficient vocabulary knowledge is vital for language skills to be employed effectively. However, in China, limited attention has been given to vocabulary learning over the years, especially English for Medical Purposes (EMP) vocabulary since it is very large, complex and filled with jargon. Research shows that students perceive the difficulty of EMP to lie in the learning of vocabulary itself. Faced with problems and challenges in the process of EMP vocabulary learning through memorization technique, theoretical research is urgently required to comprehensively reveal the research status of EMP vocabulary memorization. Therefore, this study adopts the systematic literature review method to evaluate related research on EMP vocabulary memorization in China and abroad from 2011 to 2021. The main characteristics of EMP vocabulary memorization research, the factors that affect EMP vocabulary memorization, and the most popular memory strategies are elaborated in detail in order to integrate the existing fragmented knowledge and demonstrate the development of EMP vocabulary memorization technique(s).

Exploring social media adoption for marketing purpose among healthcare professionals in Gondar town, central Gondar zone: A facility-based cross-sectional survey

Background Social media has become an integral platform for global information exchange and business interactions, emerging as a crucial tool for promoting products and services in the digital age. Despite its worldwide significance, local businesses, especially in Ethiopia, lag behind in leveraging social media for healthcare marketing. The scarcity of studies on social media adoption among healthcare providers in Ethiopia highlights the imperative for comprehensive research. Objective This study, conducted in Gondar Town, focused on private healthcare professionals, aiming to identify the determinants of their behavioral intention and usage behavior in adopting social media marketing. Method A facility-based cross-sectional survey involving 238 health professionals from private healthcare facilities in Gondar Town was conducted between March and June 2023. The study analyzed data using SPSS Version 26 and AMOS Structural Equation Modeling Version 23. Results All participants reported using social media platforms, with Facebook, Telegram, and YouTube being popular choices. Performance expectancy, social influence, facilitation condition, and behavioral intention significantly influenced healthcare professionals’ adoption of social media marketing. Performance expectancy and social influence exhibited the strongest impact on behavioral intention, acting as mediators influencing usage behavior. However, effort expectancy did not significantly influence behavioral intention. Age, gender, experience, and voluntariness showed no significant moderating effects. Conclusion This study contributes valuable insights into social media marketing adoption in the healthcare sector, emphasizing the critical role of various factors in shaping behavioral intention and usage behavior. The findings offer practical implications for private healthcare providers, policymakers, and marketers, guiding strategies to enhance patient communication and engagement through social media in Gondar Town.

Hierarchical attention-guided multiscale aggregation network for infrared small target detection

All man-made flying objects in the sky, ships in the ocean can be regarded as small infrared targets, and the method of tracking them has been received widespread attention in recent years. In search of a further efficient method for infrared small target recognition, we propose a hierarchical attention-guided multiscale aggregation network (HAMANet) in this thesis. The proposed HAMANet mainly consists of a compound guide multilayer perceptron (CG-MLP) block embedded in the backbone net, a spatial-interactive attention module (SiAM), a pixel-interactive attention module (PiAM) and a contextual fusion module (CFM). The CG-MLP marked the width-axis, height-axis, and channel-axis, which can result in a better segmentation effect while reducing computational complexity. SiAM improves global semantic information exchange by increasing the connections between different channels, while PiAM changes the extraction of local key information features by enhancing information exchange at the pixel level. CFM fuses low-level positional information and high-level channel information of the target through coding to improve network stability and target feature utilization. Compared with other state-of-the-art methods on public infrared small target datasets, the results show that our proposed HAMANet has high detection accuracy and a low false-alarm rate.

Co-evolutionary and Elite learning-based bi-objective Poor and Rich Optimization algorithm for scheduling multiple workflows in the cloud

Cloud computing is a cost-effective environment for deploying large-scale scientific applications. However, multi-workflow scheduling has great challenge since users may request a series of applications with different Quality of Service (QoS) at the same time. In this paper, a Co-evolutionary and Elite learning-based bi-objective Poor and Rich Optimization algorithm (CE-PRO) is proposed for scheduling applications to minimize the makespan and cost of each workflow. First, an MPMO framework is combined with PRO to optimize two objectives by two populations, respectively for better balancing the search diversity and convergence speed, where each population is updated by an improved PRO, which adopts the middle-class sub-population and re-defines the update mechanism for rich individuals to enhance search diversity and reduce the possibility of falling into local optima. Second, to restrain each population focusing overly on its respective objective, a global information exchange pool is innovatively designed to save the non-dominated solutions ever found, which will be used back as the shared guiding solutions to foster inter-population communication and co-evolution during an evolutionary process. Third, a hybrid mutation-based Elite Enhancement Strategy (EES) is developed by introducing multiple scales of mutation operations into elite solutions alternatively and iteratively to exploit excellent individuals and explore more trade-off solutions. Extensive experiments are conducted on real world scientific workflows with different types and scales, and the experimental results demonstrate that in most cases, our proposed CE-PRO outperforms its peers in the number of obtained non-dominated solutions, and the solution diversity and quality as well. In particular, the dominance of CE-PRO is superior to its peers by at least 25.62%.

International legal system for combating money laundering and unfair tax competition

Worldwide efforts to eliminate bank secrecy and foster transparency in international currency flows have accelerated significantly in recent years. The identification of tax havens and potentially harmful tax practices and regimes gives rise to a considerable potential for preventing distortions and violations that could undermine the benefits of enhanced capital mobility in today’s global economy. In the light of the aforementioned, the aim of this article is to detail: 1. efforts of the Organization for Economic Cooperation and Development (OECD) to eliminate «unfair tax competition»; 2. efforts of the Financial Action Task Force on Money Laundering (FATF) to reduce international money laundering; 3. steps being taken in the European Union (EU) to combat money laundering and tax evasion. The author analyzes the OECD's efforts to create a firm international platform for global tax information exchange. The Article also touches upon the FATF initiatives to combat money laundering: a) the FATF Forty Recommendations setting out a comprehensive and consistent framework of international standards which countries should implement in order to combat money laundering and terrorist financing; b) identification of jurisdictions which have the substantial and on-going money laundering and terrorist financing risks and strategic deficiencies; c) inclusion of certain anti-money laundering recommendations applicable to business and professions beyond the financial services industry; d) ongoing investigations of compliance with the Forty Recommendations by FATF members and by other states (mutual evaluations); e) helping national governments and financial institutions to ensure adequate and accurate information on the beneficial ownership. In the last part of the Article, the author analyses enforcement measures to combat money laundering adopted in the EU that go further beyond FATF recommendations.

Service-Aware Resource Orchestration in Ultra-Dense LEO Satellite-Terrestrial Integrated 6G: A Service Function Chain Approach

With the rapid expansion of the scale of deployed low earth orbit (LEO) satellites, the ultra-dense LEO satellite-terrestrial integrated network (LTIN) is envisioned as a promising architecture in the sixth-generation (6G) system to implement seamless connectivity and high-speed data rate service. Especially for ultra-remote real-time services with long transmission distance and high delay requirements, the integrated network can guarantee its end-to-end service continuity. However, many challenges have been posed to the efficient resource orchestration for the service delivery, owing to the large scale, heterogeneity and high mobility of the integrated network. For each service, its data needs to go through a series of on-board processing, before being downloaded to the terrestrial network for further applications. To this end, service function chain (SFC), an ordered concatenation of network functions (NFs), is introduced to support service provision. By allocating the constituent NFs over the LTIN, we propose an efficient multiple service delivery scheme to minimize the overall delivery completion latency, while taking into account resource sharing and competition among multiple SFCs. First, we formulate the multiple SFC embedding problem as a noncooperative game that is further proved as the weighted potential game with at least one Nash equilibrium (NE). With the help of the proposed global coordination mechanism, we design two algorithms to obtain the NE. One is the best response (BR) algorithm with faster convergence, while the other is adaptive play (AP) algorithm with more capacity for best solutions. Then, the stochastic learning (SL) algorithm is proposed to adapt to network dynamics and reduce global information exchange. Finally, extensive simulations validate the convergence and effectiveness of the proposed algorithms.