Robustness of a Distributed Knowledge Management Model

In globalizing competitive markets knowledge exchange between business organizations requires incentive mechanisms to ensure tactical purposes while strategic purposes are subject to joint organization and other forms of contractual obligations. Where property of knowledge (e.g. patents and copyrights) and contractbased knowledge exchange do not obtain network effectiveness because of prohibitive transaction costs in reducing uncertainty, we suggest a robust model for peer produced knowledge within a distributed setting. The peer produced knowledge exchange model relies upon a double loop knowledge conversion with symmetric incentives in a network since the production of actor specific knowledge makes any knowledge appropriation by use of property rights by the actors irrelevant. Without property rights in knowledge the actor network generates opportunity for incentive symmetry over a period of time. The model merges specific knowledge with knowledge from other actors into a decision support system specific for each actor in the network in recognition of actor role differences. The article suggests a set of 9 static and 5 dynamic propositions for the model to maintain symmetric incentives between different actor networks. The model is proposed for business networks

Specifications and Development of Interoperability Solution dedicated to Multiple Expertise Collaboration in a Design Framework

This paper describes the specifications of an interoperability platform based on the PPO (Product Process Organization) model developed by the French community IPPOP in the context of collaborative and innovative design. By using PPO model as a reference, this work aims to connect together heterogonous tools used by experts easing data and information exchanges. After underlining the growing needs of collaborative design process, this paper focuses on interoperability concept by describing current solutions and their limits. Then a solution based on the flexibility of the PPO model adapted to the philosophy of interoperability is proposed. To illustrate these concepts, several examples are more particularly described (robustness analysis, CAD and Product Lifecycle Management systems connections)

Task allocation in dynamic networks of satellites

The management of distributed satellite systems requires the coordination of a large number of heterogeneous spacecraft. Task allocation in such a system is complicated by limited communication and individual satellite dynamics. Previous work has shown that task allocation using a market-based mechanism can provide scalable and efficient management of static networks; in this paper we extend this work to determine the impact of dynamic topologies. We develop a Keplerian mobility model to describe the topology of the communication network over time. This movement model is then used in simulation to show that the task allocation mechanism does not show a significant decrease in effectiveness from the static case, reflecting the suitability distributed market-based control to the highly dynamic environment

ADEPT2 - Next Generation Process Management Technology

If current process management systems shall be applied to a broad spectrum of applications, they will have to be significantly improved with respect to their technological capabilities. In particular, in dynamic environments it must be possible to quickly implement and deploy new processes, to enable ad-hoc modifications of single process instances at runtime (e.g., to add, delete or shift process steps), and to support process schema evolution with instance migration, i.e., to propagate process schema changes to already running instances. These requirements must be met without affecting process consistency and by preserving the robustness of the process management system. In this paper we describe how these challenges have been addressed and solved in the ADEPT2 Process Management System. Our overall vision is to provide a next generation process management technology which can be used in a variety of application domains

DeepMarks: A Digital Fingerprinting Framework for Deep Neural Networks

This paper proposes DeepMarks, a novel end-to-end framework for systematic fingerprinting in the context of Deep Learning (DL). Remarkable progress has been made in the area of deep learning. Sharing the trained DL models has become a trend that is ubiquitous in various fields ranging from biomedical diagnosis to stock prediction. As the availability and popularity of pre-trained models are increasing, it is critical to protect the Intellectual Property (IP) of the model owner. DeepMarks introduces the first fingerprinting methodology that enables the model owner to embed unique fingerprints within the parameters (weights) of her model and later identify undesired usages of her distributed models. The proposed framework embeds the fingerprints in the Probability Density Function (pdf) of trainable weights by leveraging the extra capacity available in contemporary DL models. DeepMarks is robust against fingerprints collusion as well as network transformation attacks, including model compression and model fine-tuning. Extensive proof-of-concept evaluations on MNIST and CIFAR10 datasets, as well as a wide variety of deep neural networks architectures such as Wide Residual Networks (WRNs) and Convolutional Neural Networks (CNNs), corroborate the effectiveness and robustness of DeepMarks framework

Some Remarks about the Complexity of Epidemics Management

Recent outbreaks of Ebola, H1N1 and other infectious diseases have shown that the assumptions underlying the established theory of epidemics management are too idealistic. For an improvement of procedures and organizations involved in fighting epidemics, extended models of epidemics management are required. The necessary extensions consist in a representation of the management loop and the potential frictions influencing the loop. The effects of the non-deterministic frictions can be taken into account by including the measures of robustness and risk in the assessment of management options. Thus, besides of the increased structural complexity resulting from the model extensions, the computational complexity of the task of epidemics management - interpreted as an optimization problem - is increased as well. This is a serious obstacle for analyzing the model and may require an additional pre-processing enabling a simplification of the analysis process. The paper closes with an outlook discussing some forthcoming problems