Network Immunization with Distributed Autonomy - Oriented Entities ( Supplementary File )

An Object Oriented

We discuss a distributed object-oriented system written in LISP that implements KNOs (KNowledge acquisition, dissemination and manipulation Objects). The system emphasizes advanced features like object autonomy, mobility and dynamic inheritance. The objects are active, independent entities that can travel in a local area network and protect themselves from external aggressions. A dynamic inheri...

Improving Aglets with Strong Agent Mobility through the IBM JikesRVM

— Agents are problem-solving entities that, thanks to characteristics such as autonomy, reactivity, proactivity and sociality, together with mobility, can be used to develop complex and distributed systems. In particular, mobility enables agents to migrate among several hosts, becoming active entities of networks. Java is today one of the most exploited languages to build mobile agent systems, ...

Design and Implementation of Virtual Private Services

Large scale distributed applications such as electronic commerce and online marketplaces (e.g., auction services) combine network access with multiple storage and computational elements. The distributed responsibility for resource control creates new security and privacy issues, caused by the complexity of the operating environment. In particular, policies at multiple layers and locations force...

Middleware Platform for a Biologically-inspired Architecture Supporting Autonomous and Adaptive Network Applications

This paper describes and empirically evaluates the middleware platform in a new network architecture called the Bio-Networking Architecture. The Bio-Networking Architecture is inspired by the observation that the biological systems (e.g. bee colonies) have already developed mechanisms necessary to achieve future network requirements such as autonomy, scalability, adaptability, and simplicity. I...

Material discovery by combining stochastic surface walking global optimization with a neural network† †Electronic supplementary information (ESI) available: Derivation for the gradient of J σ with respect to NN parameters. DFT calculation setups. Parameters of atom-centered symmetry functions for generating TiO2 NN potential. Comparison of the performance of symmetry functions with different numbers of cutoff radii. Comparison of the computational cost of NN and DFT calculations. Comparison of structural properties of experimentally known TiO2 phase using NN and DFT PES. XYZ coordination of structures. See DOI: 10.1039/c7sc01459g Click here for additional data file.