A Novel QoS provisioning Scheme for OBS networks

This paper presents Classified Cloning, a novel QoS provisioning mechanism for OBS networks carrying real-time applications (such as video on demand, Voice over IP, online gaming and Grid computing). It provides such applications with a minimum loss rate while minimizing end-to-end delay and jitter. ns-2 has been used as the simulation tool, with new OBS modules having been developed for performance evaluation purposes. Ingress node performance has been investigated, as well as the overall performance of the suggested scheme. The results obtained showed that new scheme has superior performance to classical cloning. In particular, QoS provisioning offers a guaranteed burst loss rate, delay and expected value of jitter, unlike existing proposals for QoS implementation in OBS which use the burst offset time to provide such differentiation. Indeed, classical schemes increase both end-to-end delay and jitter. It is shown that the burst loss rate is reduced by 50% reduced over classical cloning

Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments

In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course. Top participants were invited to describe their algorithms. In this work, we present eight solutions that used deep reinforcement learning approaches, based on algorithms such as Deep Deterministic Policy Gradient, Proximal Policy Optimization, and Trust Region Policy Optimization. Many solutions use similar relaxations and heuristics, such as reward shaping, frame skipping, discretization of the action space, symmetry, and policy blending. However, each of the eight teams implemented different modifications of the known algorithms.Comment: 27 pages, 17 figure

When the signal is in the noise: Exploiting Diffix's Sticky Noise

Anonymized data is highly valuable to both businesses and researchers. A large body of research has however shown the strong limits of the de-identification release-and-forget model, where data is anonymized and shared. This has led to the development of privacy-preserving query-based systems. Based on the idea of "sticky noise", Diffix has been recently proposed as a novel query-based mechanism satisfying alone the EU Article~29 Working Party's definition of anonymization. According to its authors, Diffix adds less noise to answers than solutions based on differential privacy while allowing for an unlimited number of queries. This paper presents a new class of noise-exploitation attacks, exploiting the noise added by the system to infer private information about individuals in the dataset. Our first differential attack uses samples extracted from Diffix in a likelihood ratio test to discriminate between two probability distributions. We show that using this attack against a synthetic best-case dataset allows us to infer private information with 89.4% accuracy using only 5 attributes. Our second cloning attack uses dummy conditions that conditionally strongly affect the output of the query depending on the value of the private attribute. Using this attack on four real-world datasets, we show that we can infer private attributes of at least 93% of the users in the dataset with accuracy between 93.3% and 97.1%, issuing a median of 304 queries per user. We show how to optimize this attack, targeting 55.4% of the users and achieving 91.7% accuracy, using a maximum of only 32 queries per user. Our attacks demonstrate that adding data-dependent noise, as done by Diffix, is not sufficient to prevent inference of private attributes. We furthermore argue that Diffix alone fails to satisfy Art. 29 WP's definition of anonymization. [...

Deep Reinforcement Learning for Resource Management in Network Slicing

Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices. In order to provide better-performing and cost-efficient services, network slicing involves challenging technical issues and urgently looks forward to intelligent innovations to make the resource management consistent with users' activities per slice. In that regard, deep reinforcement learning (DRL), which focuses on how to interact with the environment by trying alternative actions and reinforcing the tendency actions producing more rewarding consequences, is assumed to be a promising solution. In this paper, after briefly reviewing the fundamental concepts of DRL, we investigate the application of DRL in solving some typical resource management for network slicing scenarios, which include radio resource slicing and priority-based core network slicing, and demonstrate the advantage of DRL over several competing schemes through extensive simulations. Finally, we also discuss the possible challenges to apply DRL in network slicing from a general perspective.Comment: The manuscript has been accepted by IEEE Access in Nov. 201

A synthetic biology approach for consistent production of plant-made recombinant polyclonal antibodies against snake venom toxins

Antivenoms developed from the plasma of hyperimmunized animals are the only effective treatment available against snakebite envenomation but shortage of supply contributes to the high morbidity and mortality toll of this tropical disease. We describe a synthetic biology approach to affordable and cost-effective antivenom production based on plant-made recombinant polyclonal antibodies (termed pluribodies). The strategy takes advantage of virus superinfection exclusion to induce the formation of somatic expression mosaics in agroinfiltrated plants, which enables the expression of complex antibody repertoires in a highly reproducible manner. Pluribodies developed using toxin-binding genetic information captured from peripheral blood lymphocytes of hyperimmunized camels recapitulated the overall binding activity of the immune response. Furthermore, an improved plant-made antivenom (plantivenom) was formulated using an in vitro selected pluribody against Bothrops asper snake venom toxins and has been shown to neutralize a wide range of toxin activities and provide protection against lethal venom doses in mice.Fil: Julve Parreño, Jose Manuel. Universidad Politécnica de Valencia; EspañaFil: Huet, Estefanía. Universidad Politécnica de Valencia; EspañaFil: Fernández del Carmen, Asun. Universidad Politécnica de Valencia; EspañaFil: Segura, Alvaro. Universidad de Costa Rica; Costa RicaFil: Venturi, Micol. Universidad Politécnica de Valencia; EspañaFil: Gandía, Antoni. Universidad Politécnica de Valencia; EspañaFil: Pan, Wei-Song. Universidad Politécnica de Valencia; EspañaFil: Albaladejo, Irene. Universidad Politécnica de Valencia; EspañaFil: Forment, Javier. Universidad Politécnica de Valencia; EspañaFil: Pla, Davinia. Instituto de Biomedicina de Valencia; EspañaFil: Wigdorovitz, Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Genética; ArgentinaFil: Calvete, Juan J.. Instituto de Biomedicina de Valencia; EspañaFil: Gutiérrez, Carlos. Universidad de Las Palmas de Gran Canaria; EspañaFil: Gutiérrez, José María. Universidad de Costa Rica; Costa RicaFil: Granell, Antonio. Universidad Politécnica de Valencia; EspañaFil: Orzáez, Diego. Universidad Politécnica de Valencia; Españ

An Evolutionary Learning Approach for Adaptive Negotiation Agents

Developing effective and efficient negotiation mechanisms for real-world applications such as e-Business is challenging since negotiations in such a context are characterised by combinatorially complex negotiation spaces, tough deadlines, very limited information about the opponents, and volatile negotiator preferences. Accordingly, practical negotiation systems should be empowered by effective learning mechanisms to acquire dynamic domain knowledge from the possibly changing negotiation contexts. This paper illustrates our adaptive negotiation agents which are underpinned by robust evolutionary learning mechanisms to deal with complex and dynamic negotiation contexts. Our experimental results show that GA-based adaptive negotiation agents outperform a theoretically optimal negotiation mechanism which guarantees Pareto optimal. Our research work opens the door to the development of practical negotiation systems for real-world applications