Analysis of Information Propagation in Ethereum Network Using Combined Graph Attention Network and Reinforcement Learning to Optimize Network Efficiency and Scalability

Blockchain technology has revolutionized the way information is propagated in decentralized networks. Ethereum plays a pivotal role in facilitating smart contracts and decentralized applications. Understanding information propagation dynamics in Ethereum is crucial for ensuring network efficiency, security, and scalability. In this study, we propose an innovative approach that utilizes Graph Convolutional Networks (GCNs) to analyze the information propagation patterns in the Ethereum network. The first phase of our research involves data collection from the Ethereum blockchain, consisting of blocks, transactions, and node degrees. We construct a transaction graph representation using adjacency matrices to capture the node embeddings; while our major contribution is to develop a combined Graph Attention Network (GAT) and Reinforcement Learning (RL) model to optimize the network efficiency and scalability. It learns the best actions to take in various network states, ultimately leading to improved network efficiency, throughput, and optimize gas limits for block processing. In the experimental evaluation, we analyze the performance of our model on a large-scale Ethereum dataset. We investigate effectively aggregating information from neighboring nodes capturing graph structure and updating node embeddings using GCN with the objective of transaction pattern prediction, accounting for varying network loads and number of blocks. Not only we design a gas limit optimization model and provide the algorithm, but also to address scalability, we demonstrate the use and implementation of sparse matrices in GraphConv, GraphSAGE, and GAT. The results indicate that our designed GAT-RL model achieves superior results compared to other GCN models in terms of performance. It effectively propagates information across the network, optimizing gas limits for block processing and improving network efficiency

Improved connectivity and cognition due to cognitive stimulation in Alzheimer’s disease

BackgroundDue to the increasing prevalence of Alzheimer’s disease (AD) and the limited efficacy of pharmacological treatment, the interest in non-pharmacological interventions, e.g., cognitive stimulation therapy (CST), to improve cognitive dysfunction and the quality of life of AD patients are on a steady rise.ObjectivesHere, we examined the efficacy of a CST program specifically conceptualized for AD dementia patients and the neural mechanisms underlying cognitive or behavioral benefits of CST.MethodsUsing neuropsychological tests and MRI-based measurements of functional connectivity, we examined the (neuro-) psychological status and network changes at two time points: pre vs. post-stimulation (8 to 12 weeks) in the intervention group (n = 15) who received the CST versus a no-intervention control group (n = 15).ResultsAfter CST, we observed significant improvement in the Mini-Mental State Examination (MMSE), the Alzheimer’s Disease Assessment Scale, cognitive subsection (ADAS-cog), and the behavioral and psychological symptoms of dementia (BPSD) scores. These cognitive improvements were associated with an up-regulated functional connectivity between the left posterior hippocampus and the trunk of the left postcentral gyrus.ConclusionOur data indicate that CST seems to induce short-term global cognition and behavior improvements in mild to moderate AD dementia and enhances resting-state functional connectivity in learning- and memory-associated brain regions. These convergent results prove that even in mild to moderate dementia AD, neuroplasticity can be harnessed to alleviate cognitive impairment with CST

Exogenous and endogenous factors leading to OSS vulnerability ::study on version dependency network

Pursuant to many open-source software (OSS) vulnerability incidents, various research institutes and firms have attempted to either identify the most commonly used OSS components, and examine for potential vulnerabilities and possible investments, or produce tools to detect OSS vulnerabilities by thorough inspection. Although these firms aim to identify vulnerabilities as many as possible, but produce too many alarms, and many false alarms. At the same time, one of the major assumptions in various importance and security studies is that packages keep same dependencies over time, i.e., all dependencies are calculated without considering version information, however this is a wrong assumption, for instance when the gem package “spruz” is scored highly risky, because it was depended on previous versions of some popular packages, “json_pure”. Having included version dependency, “spruz” would not have been scored high importance. Therefore, it is important to investigate package version vulnerability rather than project vulnerability, also discuss research questions as 1) what factors could lead to package vulnerability? also knowing these factors, can one narrow down vulnerability search? For this purpose, we build a version dependency network combining various sources; on the other hand, we collect vulnerabilities from different repositories. We investigate the impact of network exposure and other exogenous and endogenous factors such as contributors count, open issues count, version age and number of forks on latest package version vulnerability

Development strategy and management of AI-based vulnerability detection applications in enterprise software environment

Industries are now struggling with high level of security-risk vulnerabilities in their software environment which mainly originate from open-source dependencies. Industries’ percentage of open source in codebases is about 54% whereas ones with high security risks is about 30% (Synopsys 2018). While there are existing solutions for application security analysis, these typically only detect a limited subset of possible errors based on pre-defined rules. With the availability of open-source vulnerability resources, it is now possible to use data-driven techniques to discover vulnerabilities. Although there are a few AI-based solutions available, but there are some associated challenges: 1) use of artificial intelligence for application security (AppSec) towards vulnerability detection has been very limited and definitely not industry oriented, 2) the strategy to develop, use and manage such AppSec products in enterprises have not been investigated; therefore cybersecurity firms do not use even limited existing solutions. In this study, we aim to address these challenges with some strategies to develop such AppSec, their use management and economic values in enterprise environment

Réseaux, clusters et innovations : 3 essais

[...] Mes travaux portent sur les clusters structurant le réseau et l'innovation car 1) le cluster impacte collectivement plutôt qu’individuellement la sortie du réseau, 2) les couplages intra et inter-cluster représentent la structure même des clusters mais ils influencent différemment l'innovation ou la croissance du cluster, 3) un certain compromis reste à définir entre la structure dense et éparse des différents réseaux. Un cluster est de façon générale défini comme un groupe de choses similaires ou de personnes qui travaillent sur des sujets analogues. Selon le domaine auquel il s’applique, même si l’idée reste la même, la définition s’affine. En sciences des organisations, un cluster représente un regroupement d’entreprises et d’institutions qui interagissent entre-elles par le biais de contrats, d’opérations formelles ou informelles et de réunions occasionnelles afin de contribuer collectivement à un résultat innovant. [...] La thèse est structurée comme suit. Dans l'introduction générale, nous passons en revue la littérature des connaissances existantes qui sert de base pour le cadre conceptuel des documents. Nous définissons ensuite certains concepts utilisés dans les trois articles présentés tels que la structure de réseau complexe (utilisée dans le premier article), l'innovation et les liens de réseau (utilisés principalement dans le deuxième article), et la gestion des connaissances utilisées (dans le troisième article). Dans le premier article, nous discutons les différents mécanismes de formation de liens dictés par les réseaux dirigés permettant de distinguer la distribution des degrés. Dans le deuxième article, nous abordons l'impact de la dynamique de groupe sur l'innovation du groupe de projet OSS. Dans le troisième article, nous nous attachons à l'impact du transfert des connaissances à l'intérieur des groupes sur le transfert des connaissances entre les groupes. L'annexe A permettra de discuter la modélisation analytique de la croissance des réseaux sociaux en utilisant la projection de réseaux multicouches ; l'annexe B sera l’occasion de présenter statistiquement le lien entre les relations intragroupe et les relations intergroupe.[...] However, there is a gap in the literature with regard to the analysis of cluster or group structure as an input and cluster or group innovation as an output, e.g. “impact of network cluster structure on cluster innovation and growth”, i.e. how intra- and inter-cluster coupling, structural holes and tie strength impact cluster innovation and growth; and how intra-cluster density affects inter-cluster coupling; that I address in my thesis.Therefore, I focus on the cluster (or group of individuals) rather than the individual to analyze both network structure and innovation, because 1) clusters represent collective impact on network output rather than individuals’ impact, 2) intra and inter cluster couplings both represent cluster structure but have different impacts on cluster innovation and growth, 3) trade-offs among dense and sparse network cluster structures are different from those associated with networks of individuals. [...] The thesis is structured as follows. In the general introduction, I review the literature of existing knowledge in the field, which serves as a basis for the conceptual framework for the papers. I then define certain concepts used in the papers, such as complex network structure used in the first paper, innovation and network ties mainly used in the second paper, and knowledge management used in the third paper. In the first paper I discuss directed networks’ different link formation mechanisms causing degree distribution distinction. In the second paper, I discuss the impact of group dynamics on OSS project group innovation. In the third paper, I discuss impact of knowledge transfer inside groups onto knowledge transfer between groups. In appendix A, I discuss analytical modeling of social network growth using multilayer network projection; and in appendix B, I discuss statistically how intragroup ties and intergroup ties are related

Cascading impact of cyberattacks on multilayer social networks

Cybercriminals are getting more intelligent with their tactics in cyberattacks; by using a fake social media profile, they are capable of copying a legitimate profile and perform different scale attacks. In social networks, there are other types of cyberattacks such as Compromised Profile, Malicious Links and Content, Social Engineering, and Reconnaissance. Cascading impact in fact is not always caused through sophisticated attacks as observed in the case of SolarWinds by accessing to customer data. There are much simpler examples, one of which is the constant occurrence of business email compromise, or even cascading of cyberattacks in multilayer networks e.g., from social media into business operation. Multilayer networks are ones with multiple kinds of relations in multidimensional settings as an extension of the traditional networks. At the same time, we aim to explore the cascading impact of cyberattacks on multilayer social networks. This means how a cyberattack originated by using a fake social profile will cascade into all parallel multilayer networks. We use dynamic processes in multilayer networks to understand how the cyberattacks are propagated. We use ML algorithms to detect fake and nonfake profiles, e.g. via a dataset in twitter, then use SIR (susceptible, infected or removed) model as a base generating simulated cascades with the goal of comparing them with real ones to assess how realistic this model performs in multilayer networks, e.g. in a dataset including profiles in Google+ -Instagram – Twitter to see how fake profiles are cascaded into parallel social networks

Decentralized crowdsourcing medical data sharing platform to obtain chronological rare data

Researchers have encountered many issues while studying rare illnesses such as lack of information, limited sample sizes, difficulty in diagnosis, and more. However, perhaps the biggest challenge is to recruit a large enough sample size for clinical studies; at the same time, obtaining chronological data for these patients is even more difficult. This has urged us to implement a decentralized crowdsourcing medical data sharing platform to obtain chronological rare data for certain diseases, providing both patients and other stakeholders an easier and more secure way of trading medical data by utilizing blockchain technology. This facilitates the obtention of the most elusive types of health data by dynamically allocating extra financial incentives depending on data scarcity. We also provide a novel framework for medical data cross-validation where the system checks the volunteer reviewer count. The review score depends on the count, and the more the reviewers, the bigger the final score. We also explain how differential privacy is used to protect the privacy of individual medical data while enabling data monetization

Blockchain-based data sharing platform customization with on/off-chain data balancing

Blockchain is widely considered as a promising solution, which can build a secure and efficient environment for data sharing. With more and more people working remotely and privacy becoming a major concern, having a secure and efficient way of sharing data has become a necessity. Blockchain being a decentralized ledger emphasizing cryptography obviously helps with data security and privacy, but the technology can suffer from major constraints in the context of data sharing such as on-chain data volume, storage, network performance, off-chain security, and more. In this study, we are looking at customization as a dynamic or adaptive strategy to determine when/how/what data should be stored on-chain versus off-chain to face the trade-off between performance and security; we explore the relevant research questions and the metrics by implementing a proof-of-concept solution using Hyperledger-fabric and IPFS (Inter Planetary File System). The results show that the on-chain latency increases with rising on-chain data ratio, whereas the off-chain exhibits reduced latency with respect to the on-chain ratio. In conclusion, the balance between on-chain and off-chain data storage in blockchain networks is a nuanced decision that hinges on the nature of the data, resource availability, and the desired trade-off between security and performance. A customized approach, where sensitive data is securely stored on-chain, while other data is managed off-chain for improved throughput, can help achieve the optimal equilibrium, ensuring both data integrity and network efficiency

Graph Theory Analysis Reveals Resting-State Compensatory Mechanisms in Healthy Aging and Prodromal Alzheimer's Disease

Several theories of cognitive compensation have been suggested to explain sustained cognitive abilities in healthy brain aging and early neurodegenerative processes. The growing number of studies investigating various aspects of task-based compensation in these conditions is contrasted by the shortage of data about resting-state compensatory mechanisms. Using our proposed criterion-based framework for compensation, we investigated 45 participants in three groups: (i) patients with mild cognitive impairment (MCI) and positive biomarkers indicative of Alzheimer's disease (AD); (ii) cognitively normal young adults; (iii) cognitively normal older adults. To increase reliability, three sessions of resting-state functional magnetic resonance imaging for each participant were performed on different days (135 scans in total). To elucidate the dimensions and dynamics of resting-state compensatory mechanisms, we used graph theory analysis along with volumetric analysis. Graph theory analysis was applied based on the Brainnetome atlas, which provides a connectivity-based parcellation framework. Comprehensive neuropsychological examinations including the Rey Auditory Verbal Learning Test (RAVLT) and the Trail Making Test (TMT) were performed, to relate graph measures of compensatory nodes to cognition. To avoid false-positive findings, results were corrected for multiple comparisons. First, we observed an increase of degree centrality in cognition related brain regions of the middle frontal gyrus, precentral gyrus and superior parietal lobe despite local atrophy in MCI and healthy aging, indicating a resting-state connectivity increase with positive biomarkers. When relating the degree centrality measures to cognitive performance, we observed that greater connectivity led to better RAVLT and TMT scores in MCI and, hence, might constitute a compensatory mechanism. The detection and improved understanding of the compensatory dynamics in healthy aging and prodromal AD is mandatory for implementing and tailoring preventive interventions aiming at preserved overall cognitive functioning and delayed clinical onset of dementia

Improved connectivity and cognition due to cognitive stimulation in Alzheimer’s disease

Background: Due to the increasing prevalence of Alzheimer’s disease (AD) and the limited efficacy of pharmacological treatment, the interest in nonpharmacological interventions, e.g., cognitive stimulation therapy (CST), to improve cognitive dysfunction and the quality of life of AD patients are on a steady rise. Objectives: Here, we examined the efficacy of a CST program specifically conceptualized for AD dementia patients and the neural mechanisms underlying cognitive or behavioral benefits of CST. Methods: Using neuropsychological tests and MRI-based measurements of functional connectivity, we examined the (neuro-) psychological status and network changes at two time points: pre vs. post-stimulation (8 to 12 weeks) in the intervention group (n = 15) who received the CST versus a no-intervention control group (n = 15). Results: After CST, we observed significant improvement in the Mini-Mental State Examination (MMSE), the Alzheimer’s Disease Assessment Scale, cognitive subsection (ADAS-cog), and the behavioral and psychological symptoms of dementia (BPSD) scores. These cognitive improvements were associated with an up-regulated functional connectivity between the left posterior hippocampus and the trunk of the left postcentral gyrus. Conclusion: Our data indicate that CST seems to induce short-term global cognition and behavior improvements in mild to moderate AD dementia and enhances resting-state functional connectivity in learning- and memory associated brain regions. These convergent results prove that even in mild to moderate dementia AD, neuroplasticity can be harnessed to alleviate cognitive impairment with CST