Modeling multi-criteria objective functions using fuzzy measures

We introduce the idea of a measure. We describe several important finite integrals useful for obtaining an average value of a collection of argument values weighted by a measure. We particularly look at the case of binary measures and show that all integrals in this case evaluate to the same value. We describe the use of measures in multi-criteria decision making as a way of expressing a decision maker’s objective function in terms of collection of relevant criteria. We look at the role of an integral as a way to evaluate an alternative’s overall satisfaction to their objective function in terms of its satisfaction to the individual criteria. We look at a number of special types of measure based decision objective functions.     

Edge image quality assessment: a new formulation for degraded edge imaging

We discuss a formalism to characterize degraded edge images formed in a diffraction limited system (with circular pupil of unit radius) under conditions of incoherent illumination. We introduce a novel definition of degraded edges and consider this approach to model a basic optical mechanism involved in the perception of visual depth and edge detection. We introduce a degradation parameter to quantize the degree of edge blur. We present a generalization of such a procedure by assuming the Heaviside function to be a systematic generator of degraded edges. We reproduce experimentally the predictions made by the formalism proposed herein.     

Phrase-based correction model for improving handwriting recognition accuracies

We propose a method for increasing word recognition accuracies by correcting the output of a handwriting recognition system. We treat the handwriting recognizer as a black box, such that there is no access to its internals. This enables us to keep our algorithm general and independent of any particular system. We use a novel method for correcting the output based on a “phrase-based” system in contrast to traditional source-channel models. We report the accuracies of two in-house handwritten word recognizers before and after the correction. We achieve highly encouraging results for a large synthetically generated dataset. We also report results for a commercially available OCR on real data.     

Generalized triangular norm and conorm aggregation operators on ordinal spaces

We describe the basic features of the t-norm operator and then introduce a family of t-norm operators that are defined on an ordinal space. We then do the same for the t-conorms. We note the strong limitation that the requirement of associativity places on the t-norm and t-conorm operators. We particularly note how it limits our ability to model different types of reinforcement. We then define a generalization of the t-conorm aggregation operator, which relaxes the requirement of associativity, we denote these operators as GENOR operators. We show that these operators have the same functionality as the t-conorm. We provide some examples of GENOR operators which allow us to control the reinforcement process. We define a related extension for the t-norm, the GENAND operator and provide some examples.     

Motion strategies for exploration and map building under uncertainty with multiple heterogeneous robots

In this paper, we present a multi-robot exploration strategy for map building. We consider an indoor structured environment and a team of robots with different sensing and motion capabilities. We combine geometric and probabilistic reasoning to propose a solution to our problem. We formalize the proposed solution using stochastic dynamic programming (SDP) in states with imperfect information. Our modeling can be considered as a partially observable Markov decision process (POMDP), which is optimized using SDP. We apply the dynamic programming technique in a reduced search space that allows us to incrementally explore the environment. We propose realistic sensor models and provide a method to compute the probability of the next observation given the current state of the team of robots based on a Bayesian approach. We also propose a probabilistic motion model, which allows us to take into account errors (noise) on the velocities applied to each robot. This modeling also allows us to simulate imperfect robot motions, and to estimate the probability of reaching the next state given the current state. We have implemented all our algorithms and simulations results are presented.     

OWA aggregation over a continuous interval argument with applications to decision making

We briefly describe the ordered weighted average (OWA) operator. We discuss its role in decision making under uncertainty. We provide an extension of the OWA operator to the case in which our argument is a continuous valued interval rather than a finite set of values. We look at some examples of this type of aggregation. We show how it can be used in some tasks that arise in decision making. We consider the extension of the continuous interval argument OWA operator to the more general case in which the argument values have importance weights. We use this to introduce the idea of an attitudinal-based expected value associated with a continuous random variable.     

A line-space analysis of light-field representations

We study how to build optimal light-field or plenoptic models. We quantify geometric errors in light-field representations and show how geometric error bounds directly affect rendering artifacts. Artifacts depend on how the light-field is parameterized, stored, reconstructed and rendered. We present the rendering artifacts and relate them to the presence of geometric errors in the four most common light-field implementations.We show how to optimize a light-field model. We take an arbitrary bounded object and construct the best possible representation using each of the four parameterizations. The best representation has the least geometric error bounds. We use two geometric errors, a positional and a directional error. We also quantify pixelation artifacts. Our analysis is useful to decide how to build a light-field model. It helps select a parameterization, build an optimal representation, and choose the samplings of the parameter spaces so that geometric errors and rendering artifacts are minimized.     

A fuzzy approach to analogical reasoning

 We address the problem of the representation of resemblances involved in analogical reasoning. We use fuzzy relations to compare situations. We provide constructive methods to adapt the solution of an already solved situation to a similar new situation according to the degree of resemblance between these two situations. We give a general definition of analogical scheme which can be considered from a more or less constrained point of view.     

PADUA: a protocol for argumentation dialogue using association rules

We describe PADUA, a protocol designed to support two agents debating a classification by offering arguments based on association rules mined from individual datasets. We motivate the style of argumentation supported by PADUA, and describe the protocol. We discuss the strategies and tactics that can be employed by agents participating in a PADUA dialogue. PADUA is applied to a typical problem in the classification of routine claims for a hypothetical welfare benefit. We particularly address the problems that arise from the extensive number of misclassified examples typically found in such domains, where the high error rate is a widely recognised problem. We give examples of the use of PADUA in this domain, and explore in particular the effect of intermediate predicates. We have also done a large scale evaluation designed to test the effectiveness of using PADUA to detect misclassified examples, and to provide a comparison with other classification systems.     

关于无个人公钥的群认证

群认证是一种多方认证机制,研究动态群的成员们如何确认每个成员身份的真实性。这里,群组成员没有个人公钥。本文首先分析了Harn的群认证方案,证明他的方案在有意义的参数下是不安全的。我们还研究了群认证和几个多方签名之间的关系。我们证明基于身份的多重签名可以转换为安全群认证协议,而门限签名方案没有这种特性。我们还证明,如果去掉签名消息的话,Bellare和Neven的基于ID的多重签名方案实际上就是一个安全的群认证协议。     

Video-as-data: Technical and social aspects of a collaborative multimedia application

We studied the use of a collaborative multimedia system for coordinating teamwork among members of a neurosurgical team. We analyze the use of video within the operating room and the use of broadcast audio and video to other locations in the hospital to enable remote neurophysiological monitoring. We describe how the multimedia system was used in a real world work context, including its benefits and problems. We argue that video can be useful as more than just pictures of people talking to one another; video can be a rich tool to enable analysis and problem solving. We discuss privacy problems inherent in collaborative multimedia technology and describe how they played out in the hospital during the course of our study.     

MARKET-BASED APPROACHES TO OPTIMIZATION

We present a general discussion of what constitutes a market-oriented approach to optimization. We demonstrate how a general framework can be used to conceptually improve two well-known approaches from the literature, and discuss computational properties of the different approaches. We also show how existing theory could be adjusted to be directly applicable to the theory of the two approaches, thus proving special theory unnecessary. We want to bring out the pedagogical aspects of market mechanisms, to take full advantage of its potential.     

Bertrand equilibria and efficiency in markets for congestible network services

This paper is motivated by study of the economics of Quality of Service (QoS) of congestible services. We introduce a queueing game framework to study such problems. We consider multiple competing providers, each offering a queued service. Users are sensitive to both access price and expected delay, and pick providers with the smallest price plus delay cost. We study equilibrium of the pricing (Bertrand) game between the congestible network service providers. We establish the existence of a Nash equilibrium under some natural assumptions. We then consider a setting with multiple classes of differentiated service. Differentiated Services (DiffServ) technologies of the Internet that can provide QoS guarantees have failed to catch on, primarily due to economic impediments. Each provider is now modeled as operating a multi-class queue. We provide sufficient conditions for the existence of a Nash equilibrium in the Bertrand (pricing) game between the providers. We characterize the inefficiency (price of anarchy) due to strategic pricing to be 2/3. Surprisingly, the price of anarchy for the multi-class setting is the same as for the single-class setting.     

A framework for computing extrasentential references

We are concerned with developing a computational method for selecting possible antecedents of referring expressions over sentence boundaries. Our stratified model which uses a Λ-categorial language for meaning representation incorporates valuable features of Fregean-type semantics (a la Lewis, Montague, Partee, and others) along with features of situation semantics developed by Barwise and Perry. We consider a series of selected two-sentence stories which we use to illustrate referential interdependencies between sentences. We explain the conditions under which such dependencies arise, explain the conditions under which various translations can be performed, and formalize a set of rules which specify how to compute the reference. We restrict our discussion to two-sentence stories to avoid most of the problems inherent in where to look for the reference, that is, how to determine the proper antecedent. We restrict our considerations in this paper to situations where a reference, if it can be computed at all, has a unique antecedent. Thus we consider examples such as John wants to catch a fish. He (John) wants to eat it. and John interviewed a man. The man killed him (John). We then summarize the transformation which encompasses these rules and relate it to the stratified model. We discuss three aspects of this transformation that merit special attention from the computational viewpoint and summarize the contributions we have made. We also discuss the computational characteristics of the stratified model in general and present our ideas for a computer realization; there is no implementation of the _t"ratified model at this time.     

Dependency treelet translation: the convergence of statistical and example-based machine-translation?

We describe a novel approach to MT that combines the strengths of the two leading corpus-based approaches: Phrasal SMT and EBMT. We use a syntactically informed decoder and reordering model based on the source dependency tree, in combination with conventional SMT models to incorporate the power of phrasal SMT with the linguistic generality available in a parser. We show that this approach significantly outperforms a leading string-based Phrasal SMT decoder and an EBMT system. We present results from two radically different language pairs, and investigate the sensitivity of this approach to parse quality by using two distinct parsers and oracle experiments. We also validate our automated bleu scores with a small human evaluation.     

Quick-VDR: out-of-core view-dependent rendering of gigantic models

We present a novel approach for interactive view-dependent rendering of massive models. Our algorithm combines view-dependent simplification, occlusion culling, and out-of-core rendering. We represent the model as a clustered hierarchy of progressive meshes (CHPM). We use the cluster hierarchy for coarse-grained selective refinement and progressive meshes for fine-grained local refinement. We present an out-of-core algorithm for computation of a CHPM that includes cluster decomposition, hierarchy generation, and simplification. We introduce novel cluster dependencies in the preprocess to generate crack-free, drastic simplifications at runtime. The clusters are used for LOD selection, occlusion culling, and out-of-core rendering. We add a frame of latency to the rendering pipeline to fetch newly visible clusters from the disk and avoid stalls. The CHPM reduces the refinement cost of view-dependent rendering by more than an order of magnitude as compared to a vertex hierarchy. We have implemented our algorithm on a desktop PC. We can render massive CAD, isosurface, and scanned models, consisting of tens or a few hundred million triangles at 15-35 frames per second with little loss in image quality.     

Average curve of n smooth planar curves

We define the Average Curve (AC) of a compatible set of two or more smooth and planar, Jordan curves. It is independent of their order and representation. We compare two variants: the valley AC (vAC), defined in terms of the valley of the field that sums the squared distances to the input curves, and the zero AC (zAC), defined as the zero set of the field that sums the signed distances to the input curves. Our formulation provides an orthogonal projection homeomorphism from the AC to each input curve. We use it to define compatibility. We propose a fast tracing algorithm for computing a polygonal approximation (PAC) of the AC and for testing compatibility. We provide a linear-cost implementation for tracing the PAC of polygonal approximations of smooth input curves. We also define the inflation of the AC and use it to visualize the local variability in the set of input curves. We argue that the AC and its inflation form a natural extension of the Medial Axis Transform to an arbitrary number of curves. We propose extensions to open curves and to weighted averages of curves, which can be used to design animations.     

On stochastic linear programming problems with the quantile criterion

We study qualitative properties of a stochastic linear programming problem with quantile criterion for a wide class of distributions. We show convexity conditions for the criterion function with respect to the strategy, and continuity conditions with respect to the strategy and reliability level. We give sufficient conditions for the existence of a solution. We present a new algorithm for finding a guaranteeing solution of the problem, i.e., an admissible solution for which the quantile criterion function's value turns out to be close to optimal.