Abstract

Abstract

Abstract
P2P networks endowed individuals with the means to easily and efficiently distribute digital media over the Internet, but user legal liability issues may be raised as they also facilitate the unauthorized distribution and reproduction of copyrighted material. Traditional P2P file sharing systems focus on performance and scalability, disregarding any privacy or legal issues that may arise from their use. Lacking alternatives, and unaware of the privacy issues that arise from relaying traffic of insecure applications, users have adopted anonymity systems for P2P file sharing. This work aims at hiding user content interests from malicious peers through plausible deniability. The Mistrustful P2P model is built on the concept of mistrusting all the entities participating in the P2P network, hence its name. It provides a deterministic and configurable privacy protection that relies on cover content downloads to hide user content interests, has no trust requirements, and introduces several mechanisms to prevent user legal liability and reduce network overhead while enabling timely content downloads. We extend previous work on the Mistrustful P2P model by discussing its legal and ethical framework, assessing its feasibility for more use cases, providing a security analysis, comparing it against a traditional P2P file sharing model, and further defining and improving its main mechanisms.

Abstract

Abstract
Purpose: The purpose of this paper is to highlight possible hidden risks when allocating multi-skilled human resources to teams working in a multi-project environment. Are allocation strategies maximizing the use of skills for each project, the only way to improve the chances of all projects being successful? What are the risks in this strategy? What are the available alternatives? Design/methodology/approach: Simulation was used for different allocation strategies to evaluate, using two different metrics, the staffing of human resources in different projects. Three categories of companies were studied, and for each typology, virtual companies were created and several scenarios of collaborators, projects and tasks were simulated to evaluate the staffing process. Findings: It is shown that for different simulations, different allocation strategies and metrics are possible for evaluation and that there is no golden rule of staffing in organizations with multiple projects and with multiple skills collaborators. The staffing is very much dependent on the context of the company. Practical implications: The numerical method provides general managers with a useful tool to enable a better distribution of staff collaborators in teams handling multiple projects that require multi-skilled human resources. This method can also be used to evaluate training needs and hiring strategies, as it presents an overview of all human resources skills and motivations. Originality/value: For academics, the methodology developed enables the study of characteristics of human resources, skills and motivations, which are interesting for team formation. To practitioners, the numerical method is a practical tool for staffing in multiple skills and multiple projects. This tool can also diagnose each company situation regarding current collaborators’ skills and motivations, serving as a tool for training and for hiring.

Abstract
In this paper, an optimal scheduling of thermal and wind power plants is presented by using a stochastic programming approach to cover the uncertainties of the forecasted generation of wind farms. Uncertainties related to wind forecast error, consequently wind generation outage power and also system load demand are modeled through scenario generation. Then, with regard to day-ahead and real-time energy markets and taking into account the relevant constraints, the thermal unit commitment problem is solved considering wind energy injection into the system. Besides, in order to assess impacts of Demand Response (DR) on the problem, a load reduction demand response model has been applied in the base model. In this approach, self and cross elasticity is used for modeling the customers' behavior modeling. The results indicate that the DR Programs (DRPs) improves the market efficiency especially in peak hours when the thermal Gencos become critical suppliers and the combination of DRPs and wind farm can be so efficient.