Fuzzy Interval-Valued Multi Criteria Based Decision Making for Ranking Features in Multi-Modal 3D Face Recognition

Soodamani Ramalingam, 'Fuzzy interval-valued multi criteria based decision making for ranking features in multi-modal 3D face recognition', Fuzzy Sets and Systems, In Press version available online 13 June 2017. This is an Open Access paper, made available under the Creative Commons license CC BY 4.0 https://creativecommons.org/licenses/by/4.0/This paper describes an application of multi-criteria decision making (MCDM) for multi-modal fusion of features in a 3D face recognition system. A decision making process is outlined that is based on the performance of multi-modal features in a face recognition task involving a set of 3D face databases. In particular, the fuzzy interval valued MCDM technique called TOPSIS is applied for ranking and deciding on the best choice of multi-modal features at the decision stage. It provides a formal mechanism of benchmarking their performances against a set of criteria. The technique demonstrates its ability in scaling up the multi-modal features.Peer reviewedProo

The relationship between IR and multimedia databases

Modern extensible database systems support multimedia data through ADTs. However, because of the problems with multimedia query formulation, this support is not sufficient.\ud \ud Multimedia querying requires an iterative search process involving many different representations of the objects in the database. The support that is needed is very similar to the processes in information retrieval.\ud \ud Based on this observation, we develop the miRRor architecture for multimedia query processing. We design a layered framework based on information retrieval techniques, to provide a usable query interface to the multimedia database.\ud \ud First, we introduce a concept layer to enable reasoning over low-level concepts in the database.\ud \ud Second, we add an evidential reasoning layer as an intermediate between the user and the concept layer.\ud \ud Third, we add the functionality to process the users' relevance feedback.\ud \ud We then adapt the inference network model from text retrieval to an evidential reasoning model for multimedia query processing.\ud \ud We conclude with an outline for implementation of miRRor on top of the Monet extensible database system

Can Beliefs Wrong?

We care what people think of us. The thesis that beliefs wrong, although compelling, can sound ridiculous. The norms that properly govern belief are plausibly epistemic norms such as truth, accuracy, and evidence. Moral and prudential norms seem to play no role in settling the question of whether to believe p, and they are irrelevant to answering the question of what you should believe. This leaves us with the question: can we wrong one another by virtue of what we believe about each other? Can beliefs wrong? In this introduction, I present a brief summary of the articles that make up this special issue. The aim is to direct readers to open avenues for future research by highlighting questions and challenges that are far from being settled. These papers shouldn’t be taken as the last word on the subject. Rather, they mark the beginning of a serious exploration into a set of questions that concern the morality of belief, i.e., doxastic morality

Material Evidence

How do archaeologists make effective use of physical traces and material culture as repositories of evidence? Material Evidence is a collection of 19 essays that take a resolutely case-based approach to this question, exploring key instances of exemplary practice, instructive failures, and innovative developments in the use of archaeological data as evidence. The goal is to bring to the surface the wisdom of practice, teasing out norms of archaeological reasoning from evidence. -/- Archaeologists make compelling use of an enormously diverse range of material evidence, from garbage dumps to monuments, from finely crafted artifacts rich with cultural significance to the inadvertent transformation of landscapes over the long term. Each contributor to Material Evidence identifies a particular type of evidence with which they grapple and considers, with reference to concrete examples, how archaeologists construct evidential claims, critically assess them, and bring them to bear on pivotal questions about the cultural past. -/- Historians, cultural anthropologists, philosophers, and science studies scholars are increasingly interested in working with material "things" as objects of inquiry and as evidence – and they acknowledge on all sides just how challenging this is. One of the central messages of the book is that close analysis of archaeological best practice can yield constructive guidelines for practice that have much to offer practitioners within archaeology and well beyond

An Evidential Fractal Analytic Hierarchy Process Target Recognition Method

Target recognition in uncertain environments is a hot issue, especially in extremely uncertain situation where both the target attribution and the sensor report are not clearly represented. To address this issue, a model which combines fractal theory, Dempster-Shafer evidence theory and analytic hierarchy process (AHP) to classify objects with incomplete information is proposed. The basic probability assignment (BPA), or belief function, can be modelled by conductivity function. The weight of each BPA is determined by AHP. Finally, the collected data are discounted with the weights. The feasibility and validness of proposed model is verified by an evidential classifier case in which sensory data are incomplete and collected from multiple level of granularity. The proposed fusion algorithm takes the advantage of not only efficient modelling of uncertain information, but also efficient combination of uncertain information