Expansible Computable Aggregation Rules

The aggregation operator have been considered from a computable point of view. The important condition that the computation is friendly when portions of data are inserted o deleted to the list of values to aggregate is considered.

Fusion of Static and Temporal Information for Threat Evaluation in Sensor Networks

In many CCTV and sensor network based intelligent surveillance systems, a number of attributes or criteria are used to individually evaluate the degree of potential threat of a suspect. The outcomes for these attributes are in general from analytical algorithms where data are often pervaded with uncertainty and incompleteness. As a result, such individual threat evaluations are often inconsistent, and individual evaluations can change as time elapses. Therefore, integrating heterogeneous threat evaluations with temporal influence to obtain a better overall evaluation is a challenging issue. So far, this issue has rarely be considered by existing event reasoning frameworks under uncertainty in sensor network based surveillance. In this paper, we first propose a weighted aggregation operator based on a set of principles that constraints the fusion of individual threat evaluations. Then, we propose a method to integrate the temporal influence on threat evaluation changes. Finally, we demonstrate the usefulness of our system with a decision support event modeling framework using an airport security surveillance scenario.

Design of a simplified neuro-fuzzy system based on CMAC Architectures

This work presents the design of a fuzzy controller with simplified architecture that use an artificial neural network working as the aggregation operator for several active fuzzy rules. The simplified architecture of the fuzzy controller is used to minimize the time processing used in the closed loop system operation, the basic procedures of fuzzification are simplified to maximum while all the inference procedures are computed in a private way. As consequence, this simplified architecture allows a fast and easy configuration of the simplified fuzzy controller. The structuring of the fuzzy rules that define the control actions is previously computed using an artificial neural network based on CMAC Cerebellar Model Articulation Controller. The operational limits are standardized and all the control actions are previously calculated and stored in memory. For applications, results and conclusions several configurations of this fuzzy controller are considered.

Data-driven fuzzy analysis in quantitative mineral resource assessment

The integration of geo-information from multiple sources and of diverse nature in developing mineral favourability indexes (MFIs) is a well-known problem in mineral exploration and mineral resource assessment. Fuzzy set theory provides a convenient framework to combine and analyse qualitative and quantitative data independently of their source or characteristics. A novel, data-driven formulation for calculating MFIs based on fuzzy analysis is developed in this paper. Different geo-variables are considered fuzzy sets and their appropriate membership functions are defined and modelled. A new weighted average-type aggregation operator is then introduced to generate a new fuzzy set representing mineral favourability. The membership grades of the new fuzzy set are considered as the MFI. The weights for the aggregation operation combine the individual membership functions of the geo-variables, and are derived using information from training areas and L, regression. The technique is demonstrated in a case study of skarn tin deposits and is used to integrate geological, geochemical and magnetic data. The study area covers a total of 22.5 km(2) and is divided into 349 cells, which include nine control cells. Nine geo-variables are considered in this study. Depending on the nature of the various geo-variables, four different types of membership functions are used to model the fuzzy membership of the geo-variables involved. (C) 2002 Elsevier Science Ltd. All rights reserved.

Merging Knowledge Bases in Possibilistic Logic by Lexicographic Aggregation

Belief merging is an important but difficult problem in Artificial Intelligence, especially when sources of information are pervaded with uncertainty. Many merging operators have been proposed to deal with this problem in possibilistic logic, a weighted logic which is powerful for handling inconsistency and deal-ing with uncertainty. They often result in a possibilistic knowledge base which is a set of weighted formulas. Although possibilistic logic is inconsistency tolerant, it suffers from the well-known "drowning effect". Therefore, we may still want to obtain a consistent possibilistic knowledge base as the result of merging. In such a case, we argue that it is not always necessary to keep weighted information after merging. In this paper, we define a merging operator that maps a set of possibilistic knowledge bases and a formula representing the integrity constraints to a classical knowledge base by using lexicographic ordering. We show that it satisfies nine postulates that generalize basic postulates for propositional merging given in [11]. These postulates capture the principle of minimal change in some sense. We then provide an algorithm for generating the resulting knowledge base of our merging operator. Finally, we discuss the compatibility of our merging operator with propositional merging and establish the advantage of our merging operator over existing semantic merging operators in the propositional case.

Parametric aggregation in ordered weighted averaging

Incorporating further information into the ordered weighted averaging (OWA) operator weights is investigated in this paper. We first prove that for a constant orness the minimax disparity model [13] has unique optimal solution while the modified minimax disparity model [16] has alternative optimal OWA weights. Multiple optimal solutions in modified minimax disparity model provide us opportunity to define a parametric aggregation OWA which gives flexibility to decision makers in the process of aggregation and selecting the best alternative. Finally, the usefulness of the proposed parametric aggregation method is illustrated with an application in metasearch engine. © 2011 Elsevier Inc. All rights reserved.

Improving minimax disparity model to determine the OWA operator weights

Determining the Ordered Weighted Averaging (OWA) operator weights is important in decision making applications. Several approaches have been proposed in the literature to obtain the associated weights. This paper provides an alternative disparity model to identify the OWA operator weights. The proposed mathematical model extends the existing disparity approaches by minimizing the sum of the deviation between two distinct OWA weights. The proposed disparity model can be used for a preference ranking aggregation. A numerical example in preference ranking and an application in search engines prove the usefulness of the generated OWA weights.

Ticagrelor, a platelet aggregation inhibitor for the potential prevention and treatment of arterial thrombosis and acute coronary syndromes

Ticagrelor is an orally active ADP P2Y12 receptor antagonist in development by AstraZeneca plc for the reduction of recurrent ischemic events in patients with acute coronary syndromes (ACS). Prior to the development of ticagrelor, thienopyridine compounds, such as clopidogrel, were the focus of research into therapies for ACS. Although the thienopyridines are effective platelet aggregation inhibitors, they are prodrugs and, consequently, exert a slow onset of action. In addition, the variability in inter-individual metabolism of thienopyridine prodrugs has been associated with reduced efficacy in some patients. Ticagrelor is not a prodrug and exhibits a more rapid onset of action than the thienopyridine prodrugs. In clinical trials conducted to date, ticagrelor was a potent inhibitor of ADP-induced platelet aggregation and demonstrated effects that were comparable to clopidogrel. In a phase II, short-term trial, the bleeding profile of participants treated with ticagrelor was similar to that obtained with clopidogrel; however, an increased incidence of dyspnea was observed - an effect that has not been reported with the thienopyridines. Considering the occurrence of dyspnea, and the apparent non-superiority of ticagrelor to clopidogrel, it is difficult to justify a clear benefit to the continued development of ticagrelor. Outcomes from an ongoing phase III trial comparing ticagrelor with clopidogrel in 18,000 patients with ACS are likely to impact on the future development of ticagrelor.

Metadata aggregation : a critical component of research infrastructure for the future

The development of research data management infrastructure and services and making research data more discoverable and accessible to the research community is a key priority at the national, state and individual university level. This paper will discuss and reflect upon a collaborative project between Griffith University and the Queensland University of Technology to commission a Metadata Hub or Metadata Aggregation service based upon open source software components. It will describe the role that metadata aggregation services play in modern research infrastructure and argue that this role is a critical one.

Secure data aggregation in wireless sensor networks

A Wireless Sensor Network (WSN) is a set of sensors that are integrated with a physical environment. These sensors are small in size, and capable of sensing physical phenomena and processing them. They communicate in a multihop manner, due to a short radio range, to form an Ad Hoc network capable of reporting network activities to a data collection sink. Recent advances in WSNs have led to several new promising applications, including habitat monitoring, military target tracking, natural disaster relief, and health monitoring. The current version of sensor node, such as MICA2, uses a 16 bit, 8 MHz Texas Instruments MSP430 micro-controller with only 10 KB RAM, 128 KB program space, 512 KB external ash memory to store measurement data, and is powered by two AA batteries. Due to these unique specifications and a lack of tamper-resistant hardware, devising security protocols for WSNs is complex. Previous studies show that data transmission consumes much more energy than computation. Data aggregation can greatly help to reduce this consumption by eliminating redundant data. However, aggregators are under the threat of various types of attacks. Among them, node compromise is usually considered as one of the most challenging for the security of WSNs. In a node compromise attack, an adversary physically tampers with a node in order to extract the cryptographic secrets. This attack can be very harmful depending on the security architecture of the network. For example, when an aggregator node is compromised, it is easy for the adversary to change the aggregation result and inject false data into the WSN. The contributions of this thesis to the area of secure data aggregation are manifold. We firstly define the security for data aggregation in WSNs. In contrast with existing secure data aggregation definitions, the proposed definition covers the unique characteristics that WSNs have. Secondly, we analyze the relationship between security services and adversarial models considered in existing secure data aggregation in order to provide a general framework of required security services. Thirdly, we analyze existing cryptographic-based and reputationbased secure data aggregation schemes. This analysis covers security services provided by these schemes and their robustness against attacks. Fourthly, we propose a robust reputationbased secure data aggregation scheme for WSNs. This scheme minimizes the use of heavy cryptographic mechanisms. The security advantages provided by this scheme are realized by integrating aggregation functionalities with: (i) a reputation system, (ii) an estimation theory, and (iii) a change detection mechanism. We have shown that this addition helps defend against most of the security attacks discussed in this thesis, including the On-Off attack. Finally, we propose a secure key management scheme in order to distribute essential pairwise and group keys among the sensor nodes. The design idea of the proposed scheme is the combination between Lamport's reverse hash chain as well as the usual hash chain to provide both past and future key secrecy. The proposal avoids the delivery of the whole value of a new group key for group key update; instead only the half of the value is transmitted from the network manager to the sensor nodes. This way, the compromise of a pairwise key alone does not lead to the compromise of the group key. The new pairwise key in our scheme is determined by Diffie-Hellman based key agreement.