See Maass and Shine (doi: ) for a scientific commentary on this article. Entorhinal cortex is affected early in Alzheimer’s disease and is critical for navigation. Using immersive virtual reality, Howett et al. reveal navigational deficits in biomarker-positive patients with mild cognitive impairment. Navigational deficits are more sensitive and specific to Alzheimer’s disease risk than a battery of reference cognitive tests.

This article presents three methods to forecast accurately the amount of traffic in TCP=IP based networks: a novel neural network ensemble approach and two important adapted time series methods (ARIMA and Holt-Winters). In order to assess their accuracy, several experiments were held using real-world data from two large Internet service providers. In addition, different time scales (5 min, 1 h and 1 day) and distinct forecasting lookaheads were analysed. The experiments with the neural ensemble achieved the best results for 5 min and hourly data, while the Holt-Winters is the best option for the daily forecasts. This research opens possibilities for the development of more efficient traffic engineering and anomaly detection tools, which will result in financial gains from better network resource management.

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Abstract-Comparing graphs to determine the level of underlying structural similarity between them is a widely encountered problem in computer science. It is particularly relevant to the study of Internet topologies, such as the generation of synthetic topologies to represent the Internet's AS topology. We derive a new metric that enables exactly such a structural comparison, the weighted spectral distribution. We then apply this metric to three aspects of the study of the Internet's AS topology. (i) we use it to quantify the effect of changing the mixing properties of a simple synthetic network generator. (ii) we use this quantitative understanding to examine the evolution of the Internet's AS topology over approximately 7 years, finding that the distinction between the Internet core and periphery has blurred over time.(iii) we use the metric to derive optimal parameterizations of several widely used AS topology generators with respect to a large-scale measurement of the real AS topology.

The forecast of Internet traffic is an important issue that has received few attention from the computer networks field. By improving this task, efficient traffic engineering and anomaly detection tools can be created, resulting in economic gains from better resource management. This paper presents a Neural Network Ensemble (NNE) for the prediction of TCP/IP traffic using a Time Series Forecasting (TSF) point of view. Several experiments were devised by considering real-world data from two large Internet Service Providers. In addition, different time scales (e.g. every five minutes and hourly) and forecasting horizons were analyzed. Overall, the NNE approach is competitive when compared with other TSF methods (e.g. Holt-Winters and ARIMA).Paulo Cortez is with the

Abstract-Although direct reciprocity (Tit-for-Tat) contribution systems have been successful in reducing freeloading in peerto-peer overlays, it has been shown that, unless the contribution network is dense, they tend to be slow (or may even fail) to converge [1]. On the other hand, current indirect reciprocity mechanisms based on reputation systems tend to be susceptible to sybil attacks, peer slander and whitewashing.In this paper we present PledgeRoute, an accounting mechanism for peer contributions that is based on social capital. This mechanism allows peers to contribute resources to one set of peers and use this contribution to obtain services from a different set of peers, at a different time. PledgeRoute is completely decentralised, can be implemented in both structured and unstructured peer-to-peer systems, and it is resistant to the three kinds of attacks mentioned above.To achieve this, we model contribution transitivity as a routing problem in the contribution network of the peer-to-peer overlay, and we present arguments for the routing behaviour and the sybilproofness of our contribution transfer procedures on this basis. Additionally, we present mechanisms for the seeding of the contribution network, and a combination of incentive mechanisms and reciprocation policies that motivate peers to adhere to the protocol and maximise their service contributions to the overlay.

The dominant application in today's Internet is content streaming, which is increasingly relying on caches to meet the stringent conditions on the latency between content servers and end-users. These systems routinely face the challenges of limited bandwidth capacities and network server failures, which degrade caching performance. In this paper, we study the problem of optimally allocating content over a resilient caching network, in which each cache may fail under some situations. Given content request rates and multiple routing paths, we formulate an optimization problem to maximize the expected caching gain, i.e., the reduction of latency due to intermediate caching. The offline version of this problem is NP-hard. We first propose a centralized, offline algorithm and show that a solution with (1-1/e) approximation ratio to the optimal can be constructed. We then propose a distributed ascent algorithm based on the concave relaxation of the expected gain. Informed by the results of our analysis, we finally propose a distributed resilient caching algorithm (DR-Cache) that is simple and adaptive to network failures. We show numerically that DR-Cache significantly outperforms other candidate algorithms under synthetic requests, as well as real world traces over a class of network topologies.

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