Social learning theory as a model for illegitimate peer-to-peer use and the effects of implementing a legal music downloading service on peer-to-peer music piracy

In an attempt to both provide added services to students and help curb the growing problem of music piracy on college campuses, many universities have implemented legal digital music services. The Rochester Institute of Technology (RIT) was one of these universities, where the Cdigix Ctrax service is provided to students. In order to evaluate the effectiveness of such a strategy, Information Technology Services (ITS) at RIT requested the services of Dr. Samuel McQuade, who administered a Webbased survey to all Ctrax users and an equal number of randomly selected non-Ctrax using students at RIT. In total, 447 students responded to the survey. This thesis represents a secondary analysis of the data gathered from that survey within the context of social learning. The findings reveal that social learning theory provides a useful framework for explaining illegitimate P2P at RIT and potentially on college campuses across the nation

An agent-driven semantical identifier using radial basis neural networks and reinforcement learning

Due to the huge availability of documents in digital form, and the deception possibility raise bound to the essence of digital documents and the way they are spread, the authorship attribution problem has constantly increased its relevance. Nowadays, authorship attribution,for both information retrieval and analysis, has gained great importance in the context of security, trust and copyright preservation. This work proposes an innovative multi-agent driven machine learning technique that has been developed for authorship attribution. By means of a preprocessing for word-grouping and time-period related analysis of the common lexicon, we determine a bias reference level for the recurrence frequency of the words within analysed texts, and then train a Radial Basis Neural Networks (RBPNN)-based classifier to identify the correct author. The main advantage of the proposed approach lies in the generality of the semantic analysis, which can be applied to different contexts and lexical domains, without requiring any modification. Moreover, the proposed system is able to incorporate an external input, meant to tune the classifier, and then self-adjust by means of continuous learning reinforcement.Comment: Published on: Proceedings of the XV Workshop "Dagli Oggetti agli Agenti" (WOA 2014), Catania, Italy, Sepember. 25-26, 201

Preliminary specification and design documentation for software components to achieve catallaxy in computational systems

This Report is about the preliminary specifications and design documentation for software components to achieve Catallaxy in computational systems. -- Die Arbeit beschreibt die Spezifikation und das Design von Softwarekomponenten, um das Konzept der Katallaxie in Grid Systemen umzusetzen. Eine Einführung ordnet das Konzept der Katallaxie in bestehende Grid Taxonomien ein und stellt grundlegende Komponenten vor. Anschließend werden diese Komponenten auf ihre Anwendbarkeit in bestehenden Application Layer Netzwerken untersucht.Grid Computing

Decentralized Adaptive Helper Selection in Multi-channel P2P Streaming Systems

In Peer-to-Peer (P2P) multichannel live streaming, helper peers with surplus bandwidth resources act as micro-servers to compensate the server deficiencies in balancing the resources between different channel overlays. With deployment of helper level between server and peers, optimizing the user/helper topology becomes a challenging task since applying well-known reciprocity-based choking algorithms is impossible due to the one-directional nature of video streaming from helpers to users. Because of selfish behavior of peers and lack of central authority among them, selection of helpers requires coordination. In this paper, we design a distributed online helper selection mechanism which is adaptable to supply and demand pattern of various video channels. Our solution for strategic peers' exploitation from the shared resources of helpers is to guarantee the convergence to correlated equilibria (CE) among the helper selection strategies. Online convergence to the set of CE is achieved through the regret-tracking algorithm which tracks the equilibrium in the presence of stochastic dynamics of helpers' bandwidth. The resulting CE can help us select proper cooperation policies. Simulation results demonstrate that our algorithm achieves good convergence, load distribution on helpers and sustainable streaming rates for peers

Social Learning Theory and Digital Piracy: Explaining Uploading Behaviors of Digital Pirates

Digital piracy has received significant attention in criminological research but almost no studies have explored illegal uploading and how it may differ from illegal downloading. It is important to examine what theories can explain illegal uploading behaviors and their related factors to develop more effective policies to address digital piracy. This dissertation examined whether Akers’ (1998) social learning theory could explain engagement in digital piracy, both illegal downloading and uploading behavior. Additionally, this research examined the relationship between reciprocity and digital piracy. Questionnaires were administered to 398 university students and 315 visitors to several online communities using a combination of random and nonrandom sampling techniques. Confirmatory factor analysis and a series of structural equation models were used for analysis. Social learning theory was modeled as a second-order latent factor with latent factors for reciprocity and both outcomes while controlling for multiple covariates. Social learning theory was positively related to self-reported illegal downloading behavior and self-reported illegal uploading behavior. Perceptions of reciprocity had a positive direct effect on illegal uploading behavior but did not have a significant direct effect on illegal downloading behavior. Perceptions of reciprocity partially mediated the relationship between social learning and illegal uploading behavior. Self-control was not related to illegal downloading and uploading behaviors, but did have significant indirect effects through social learning. The main contributions of this dissertation were the application of social learning theory to explain illegal uploading and the empirical evidence supporting reciprocity. Possible directions for future research and policy implications are discussed

Systematizing Decentralization and Privacy: Lessons from 15 Years of Research and Deployments

Decentralized systems are a subset of distributed systems where multiple authorities control different components and no authority is fully trusted by all. This implies that any component in a decentralized system is potentially adversarial. We revise fifteen years of research on decentralization and privacy, and provide an overview of key systems, as well as key insights for designers of future systems. We show that decentralized designs can enhance privacy, integrity, and availability but also require careful trade-offs in terms of system complexity, properties provided, and degree of decentralization. These trade-offs need to be understood and navigated by designers. We argue that a combination of insights from cryptography, distributed systems, and mechanism design, aligned with the development of adequate incentives, are necessary to build scalable and successful privacy-preserving decentralized systems

An architectural framework for self-configuration and self-improvement at runtime

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Peer-to-Peer File Sharing WebApp: Enhancing Data Security and Privacy through Peer-to-Peer File Transfer in a Web Application

Peer-to-peer (P2P) networking has emerged as a promising technology that enables distributed systems to operate in a decentralized manner. P2P networks are based on a model where each node in the network can act as both a client and a server, thereby enabling data and resource sharing without relying on centralized servers. The P2P model has gained considerable attention in recent years due to its potential to provide a scalable, fault-tolerant, and resilient architecture for various applications such as file sharing, content distribution, and social networks.In recent years, researchers have also proposed hybrid architectures that combine the benefits of both structured and unstructured P2P networks. For example, the Distributed Hash Table (DHT) is a popular hybrid architecture that provides efficient lookup and search algorithms while maintaining the flexibility and adaptability of the unstructured network.To demonstrate the feasibility of P2P systems, several prototypes have been developed, such as the BitTorrent file-sharing protocol and the Skype voice-over-IP (VoIP) service. These prototypes have demonstrated the potential of P2P systems for large-scale applications and have paved the way for the development of new P2P-based systems

Peer to Peer Information Retrieval: An Overview

Peer-to-peer technology is widely used for file sharing. In the past decade a number of prototype peer-to-peer information retrieval systems have been developed. Unfortunately, none of these have seen widespread real- world adoption and thus, in contrast with file sharing, information retrieval is still dominated by centralised solutions. In this paper we provide an overview of the key challenges for peer-to-peer information retrieval and the work done so far. We want to stimulate and inspire further research to overcome these challenges. This will open the door to the development and large-scale deployment of real-world peer-to-peer information retrieval systems that rival existing centralised client-server solutions in terms of scalability, performance, user satisfaction and freedom