Adaptive Weighing Designs for Keyword Value Computation

Attributing a dollar value to a keyword is an essential part of running any profitable search engine advertising campaign. When an advertiser has complete control over the interaction with and monetization of each user arriving on a given keyword, the value of that term can be accurately tracked. However, in many instances, the advertiser may monetize arrivals indirectly through one or more third parties. In such cases, it is typical for the third party to provide only coarse-grained reporting: rather than report each monetization event, users are aggregated into larger channels and the third party reports aggregate information such as total daily revenue for each channel. Examples of third parties that use channels include Amazon and Google AdSense. In such scenarios, the number of channels is generally much smaller than the number of keywords whose value per click (VPC) we wish to learn. However, the advertiser has flexibility as to how to assign keywords to channels over time. We introduce the channelization problem: how do we adaptively assign keywords to channels over the course of multiple days to quickly obtain accurate VPC estimates of all keywords? We relate this problem to classical results in weighing design, devise new adaptive algorithms for this problem, and quantify the performance of these algorithms experimentally. Our results demonstrate that adaptive weighing designs that exploit statistics of term frequency, variability in VPCs across keywords, and flexible channel assignments over time provide the best estimators of keyword VPCs.

Patient-centred screening for primary immunodeficiency: a multi-stage diagnostic protocol designed for non-immunologists.

Efficient early identification of primary immunodeficiency disease (PID) is important for prognosis, but is not an easy task for non-immunologists. The Clinical Working Party of the European Society for Immunodeficiencies (ESID) has composed a multi-stage diagnostic protocol that is based on expert opinion, in order to increase the awareness of PID among doctors working in different fields. The protocol starts from the clinical presentation of the patient; immunological skills are not needed for its use. The multi-stage design allows cost-effective screening for PID within the large pool of potential cases in all hospitals in the early phases, while more expensive tests are reserved for definitive classification in collaboration with an immunologist at a later stage. Although many PIDs present in childhood, others may present at any age. The protocols presented here are therefore aimed at both adult physicians and paediatricians. While designed for use throughout Europe, there will be national differences which may make modification of this generic algorithm necessary.

Dynamic multi-partitioning for parallel finite element applications

The central product of the DRAMA (Dynamic Re-Allocation of Meshes for parallel Finite Element Applications) project is a library comprising a variety of tools for dynamic re-partitioning of unstructured Finite Element (FE) applications. The input to the DRAMA library is the computational mesh, and corresponding costs, partitioned into sub-domains. The core library functions then perform a parallel computation of a mesh re-allocation that will re-balance the costs based on the DRAMA cost model. We discuss the basic features of this cost model, which allows a general approach to load identification, modelling and imbalance minimisation. Results from crash simulations are presented which show the necessity for multi-phase/multi-constraint partitioning components

Multi-physics-multi-scale simulation and optimisation: the next generation

FEA and CFD analysis is becoming ever more complex with an emerging demand for simulation software technologies that can address ranges of problems that involve combinations of interactions amongst varying physical phenomena over a variety of time and length scales. Computation modelling of such problems requires software technologies that enable the representation of these complex suites of 'physical' interactions. This functionality requires the structuring of simulation modules for specific physical phemonmena so that the coupling can be effectiely represented. These 'multi-physics' and 'multi-scale' computations are very compute intensive and so the simulation software must operate effectively in parallel if it is to be used in this context. Of course the objective of 'multi-physics' and 'multi-scale' simulation is the optimal design of engineered systems so optimistation is an important feature of such classes of simulation. In this presentation, a multi-disciplinary approach to simulation based optimisation is described with some key examples of application to challenging engineering problems.

Choosing the appropriate level of coupling in multi-physics modeling of metallurgical processes

A computational model for the interrelated phenomena in the process of vacuum arc remelting is analyzed and adjusted of optimal accuracy and computation time. The decision steps in this case study are offered as an example how the coupling in models of similar processes can be addressed. Results show dominance of the electromagnetic forces over buoyancy and inertia for the investigated process conditions.

Measuring party organization: The vertical dimension of the multilevel organization of statewide parties in Spain and the UK

Over the past few years, attention to the role of state-wide political parties in multi-level polities has increased in recognition of their linkage function between levels of government, as these parties compete in both state-wide and regional elections across their countries. This article presents a coding scheme designed to describe the relationship between central and regional levels of state-wide parties. It evaluates the involvement of the regional branches in central decision-making and their degree of autonomy in the management of regional party affairs. This coding scheme is applied to state-wide parties in Spain (the socialist PSOE and the conservative Partido Popular) and in the UK (Labour, the Conservatives and the Liberal Democrats). It is an additional tool with which to analyse party organization and it facilitates the comparison of parties across regions and in different countries.

Multi-Modal CTL:Completeness, Complexity, and an Application

We define a multi-modal version of Computation Tree Logic (ctl) by extending the language with path quantifiers E and A where d denotes one of finitely many dimensions, interpreted over Kripke structures with one total relation for each dimension. As expected, the logic is axiomatised by taking a copy of a ctl axiomatisation for each dimension. Completeness is proved by employing the completeness result for ctl to obtain a model along each dimension in turn. We also show that the logic is decidable and that its satisfiability problem is no harder than the corresponding problem for ctl. We then demonstrate how Normative Systems can be conceived as a natural interpretation of such a multi-dimensional ctl logic. © 2009 Springer Science+Business Media B.V.

Multi-Attribute SCADA-Specific Intrusion Detection System for Power Networks

The increased interconnectivity and complexity of supervisory control and data acquisition (SCADA) systems in power system networks has exposed the systems to a multitude of potential vulnerabilities. In this paper, we present a novel approach for a next-generation SCADA-specific intrusion detection system (IDS). The proposed system analyzes multiple attributes in order to provide a comprehensive solution that is able to mitigate varied cyber-attack threats. The multiattribute IDS comprises a heterogeneous white list and behavior-based concept in order to make SCADA cybersystems more secure. This paper also proposes a multilayer cyber-security framework based on IDS for protecting SCADA cybersecurity in smart grids without compromising the availability of normal data. In addition, this paper presents a SCADA-specific cybersecurity testbed to investigate simulated attacks, which has been used in this paper to validate the proposed approach.

Secure Communication in Cellular Networks: The Benefits of Millimeter Wave Mobile Broadband

This paper proposes millimeter wave (mmWave) mobile broadband for achieving secure communication in downlink cellular network. Analog beamforming with phase shifters is adopted for the mmWave transmission. The secrecy throughput is analyzed based on two different transmission modes, namely delay-tolerant transmission and delay-limited transmission. The impact of large antenna arrays at the mmWave frequencies on the secrecy throughput is examined. Numerical results corroborate our analysis and show that mmWave systems can enable significant secrecy improvement. Moreover, it is indicated that with large antenna arrays, multi-gigabit per second secure link at the mmWave frequencies can be reached in the delay-tolerant transmission mode and the adverse effect of secrecy outage vanishes in the delay-limited transmission mode.

Secure D2D Communication in Large-Scale Cognitive Cellular Networks with Wireless Power Transfer

In this paper, we investigate secure device-to-device (D2D) communication in energy harvesting large-scale cognitive cellular networks. The energy constrained D2D transmitter harvests energy from multi-antenna equipped power beacons (PBs), and communicates with the corresponding receiver using the spectrum of the cellular base stations (BSs). We introduce a power transfer model and an information signal model to enable wireless energy harvesting and secure information transmission. In the power transfer model, we propose a new power transfer policy, namely, best power beacon (BPB) power transfer. To characterize the power transfer reliability of the proposed policy, we derive new closed-form expressions for the exact power outage probability and the asymptotic power outage probability with large antenna arrays at PBs. In the information signal model, we present a new comparative framework with two receiver selection schemes: 1) best receiver selection (BRS), and 2) nearest receiver selection (NRS). To assess the secrecy performance, we derive new expressions for the secrecy throughput considering the two receiver selection schemes using the BPB power transfer policies. We show that secrecy performance improves with increasing densities of PBs and D2D receivers because of a larger multiuser diversity gain. A pivotal conclusion is reached that BRS achieves better secrecy performance than NRS but demands more instantaneous feedback and overhead.

Empirical evaluation of multi-device profiling side-channel attacks

Side-channel analysis of cryptographic systems can allow for the recovery of secret information by an adversary even where the underlying algorithms have been shown to be provably secure. This is achieved by exploiting the unintentional leakages inherent in the underlying implementation of the algorithm in software or hardware. Within this field of research, a class of attacks known as profiling attacks, or more specifically as used here template attacks, have been shown to be extremely efficient at extracting secret keys. Template attacks assume a strong adversarial model, in that an attacker has an identical device with which to profile the power consumption of various operations. This can then be used to efficiently attack the target device. Inherent in this assumption is that the power consumption across the devices under test is somewhat similar. This central tenet of the attack is largely unexplored in the literature with the research community generally performing the profiling stage on the same device as being attacked. This is beneficial for evaluation or penetration testing as it is essentially the best case scenario for an attacker where the model built during the profiling stage matches exactly that of the target device, however it is not necessarily a reflection on how the attack will work in reality. In this work, a large scale evaluation of this assumption is performed, comparing the key recovery performance across 20 identical smart-cards when performing a profiling attack.

Design and deployment of secure, robust, and resilient SDN Controllers

The scale of the Software-Defined Network (SDN) Controller design problem has become apparent with the expansion of SDN deployments. Initial SDN deployments were small-scale, single controller environments for research and usecase testing. Today, enterprise deployments requiring multiple controllers are gathering momentum e.g. Google’s backbone network, Microsoft’s public cloud, and NTT’s edge gateway. Third-party applications are also becoming available e.g. HP SDN App Store. The increase in components and interfaces for the evolved SDN implementation increases the security challenges of the SDN controller design. In this work, the requirements of a secure, robust, and resilient SDN controller are identified, stateof-the-art open-source SDN controllers are analyzed with respect to the security of their design, and recommendations for security improvements are provided. This contribution highlights the gap between the potential security solutions for SDN controllers and the actual security level of current controller designs.

Fast Rule Mining over Multi-dimensional Windows

Association rule mining is an indispensable tool for discovering
insights from large databases and data warehouses.
The data in a warehouse being multi-dimensional, it is often
useful to mine rules over subsets of data defined by selections
over the dimensions. Such interactive rule mining
over multi-dimensional query windows is difficult since rule
mining is computationally expensive. Current methods using
pre-computation of frequent itemsets require counting
of some itemsets by revisiting the transaction database at
query time, which is very expensive. We develop a method
(RMW) that identifies the minimal set of itemsets to compute
and store for each cell, so that rule mining over any
query window may be performed without going back to the
transaction database. We give formal proofs that the set of
itemsets chosen by RMW is sufficient to answer any query
and also prove that it is the optimal set to be computed
for 1 dimensional queries. We demonstrate through an extensive
empirical evaluation that RMW achieves extremely
fast query response time compared to existing methods, with
only moderate overhead in pre-computation and storage