1,057 research outputs found
The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting
The numerous recent breakthroughs in machine learning (ML) make imperative to
carefully ponder how the scientific community can benefit from a technology
that, although not necessarily new, is today living its golden age. This Grand
Challenge review paper is focused on the present and future role of machine
learning in space weather. The purpose is twofold. On one hand, we will discuss
previous works that use ML for space weather forecasting, focusing in
particular on the few areas that have seen most activity: the forecasting of
geomagnetic indices, of relativistic electrons at geosynchronous orbits, of
solar flares occurrence, of coronal mass ejection propagation time, and of
solar wind speed. On the other hand, this paper serves as a gentle introduction
to the field of machine learning tailored to the space weather community and as
a pointer to a number of open challenges that we believe the community should
undertake in the next decade. The recurring themes throughout the review are
the need to shift our forecasting paradigm to a probabilistic approach focused
on the reliable assessment of uncertainties, and the combination of
physics-based and machine learning approaches, known as gray-box.Comment: under revie
Mitigating Adversarial Gray-Box Attacks Against Phishing Detectors
Although machine learning based algorithms have been extensively used for
detecting phishing websites, there has been relatively little work on how
adversaries may attack such "phishing detectors" (PDs for short). In this
paper, we propose a set of Gray-Box attacks on PDs that an adversary may use
which vary depending on the knowledge that he has about the PD. We show that
these attacks severely degrade the effectiveness of several existing PDs. We
then propose the concept of operation chains that iteratively map an original
set of features to a new set of features and develop the "Protective Operation
Chain" (POC for short) algorithm. POC leverages the combination of random
feature selection and feature mappings in order to increase the attacker's
uncertainty about the target PD. Using 3 existing publicly available datasets
plus a fourth that we have created and will release upon the publication of
this paper, we show that POC is more robust to these attacks than past
competing work, while preserving predictive performance when no adversarial
attacks are present. Moreover, POC is robust to attacks on 13 different
classifiers, not just one. These results are shown to be statistically
significant at the p < 0.001 level
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Gray-box inference for structured Gaussian process models
We develop an automated variational inference method for Bayesian structured prediction problems with Gaussian process (gp) priors and linear-chain likelihoods. Our approach does not need to know the details of the structured likelihood model and can scale up to a large number of observations. Furthermore, we show that the required expected likelihood term and its gradients in the variational objective (ELBO) can be estimated efficiently by using expectations over very low-dimensional Gaussian distributions. Optimization of the ELBO is fully parallelizable over sequences and amenable to stochastic optimization, which we use along with control variate techniques to make our framework useful in practice. Results on a set of natural language processing tasks show that our method can be as good as (and sometimes better than, in particular with respect to expected log-likelihood) hard-coded approaches including svm-struct and crfs, and overcomes the scalability limitations of previous inference algorithms based on sampling. Overall, this is a fundamental step to developing automated inference methods for Bayesian structured prediction
Qos-aware fine-grained power management in networked computing systems
Power is a major design concern of today\u27s networked computing systems, from low-power battery-powered mobile and embedded systems to high-power enterprise servers. Embedded systems are required to be power efficiency because most embedded systems are powered by battery with limited capacity. Similar concern of power expenditure rises as well in enterprise server environments due to cooling requirement, power delivery limit, electricity costs as well as environment pollutions.
The power consumption in networked computing systems includes that on circuit board and that for communication. In the context of networked real-time systems, the power dissipation on wireless communication is more significant than that on circuit board. We focus on packet scheduling for wireless real-time systems with renewable energy resources. In such a scenario, it is required to transmit data with higher level of importance periodically. We formulate this packet scheduling problem as an NP-hard reward maximization problem with time and energy constraints. An optimal solution with pseudo polynomial time complexity is presented. In addition, we propose a sub-optimal solution with polynomial time complexity.
Circuit board, especially processor, power consumption is still the major source of system power consumption. We provide a general-purposed, practical and comprehensive power management middleware for networked computing systems to manage circuit board power consumption thus to affect system-level power consumption. It has the functionalities of power and performance monitoring, power management (PM) policy selection and PM control, as well as energy efficiency analysis. This middleware includes an extensible PM policy library. We implemented a prototype of this middleware on Base Band Units (BBUs) with three PM policies enclosed. These policies have been validated on different platforms, such as enterprise servers, virtual environments and BBUs.
In enterprise environments, the power dissipation on circuit board dominates. Regulation on computing resources on board has a significant impact on power consumption. Dynamic Voltage and Frequency Scaling (DVFS) is an effective technique to conserve energy consumption. We investigate system-level power management in order to avoid system failures due to power capacity overload or overheating. This management needs to control the power consumption in an accurate and responsive manner, which cannot be achieve by the existing black-box feedback control. Thus we present a model-predictive feedback controller to regulate processor frequency so that power budget can be satisfied without significant loss on performance.
In addition to providing power guarantee alone, performance with respect to service-level agreements (SLAs) is required to be guaranteed as well. The proliferation of virtualization technology imposes new challenges on power management due to resource sharing. It is hard to achieve optimization in both power and performance on shared infrastructures due to system dynamics. We propose vPnP, a feedback control based coordination approach providing guarantee on application-level performance and underlying physical host power consumption in virtualized environments. This system can adapt gracefully to workload change. The preliminary results show its flexibility to achieve different levels of tradeoffs between power and performance as well as its robustness over a variety of workloads.
It is desirable for improve energy efficiency of systems, such as BBUs, hosting soft-real time applications. We proposed a power management strategy for controlling delay and minimizing power consumption using DVFS. We use the Robbins-Monro (RM) stochastic approximation method to estimate delay quantile. We couple a fuzzy controller with the RM algorithm to scale CPU frequency that will maintain performance within the specified QoS
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