Aggregating Ordinal Values Using A Measure Based Median

We introduce the concept of a fuzzy measure μ on a set X. We discuss some of the properties of a fuzzy measure. We provide some notable examples of fuzzy measures. We discuss the important application of using fuzzy measure to provide information about an uncertain variable V. Here the measure of a subset A indicates the anticipation of finding the value of V in A. Our interest here is in finding the best course of action, alternative, in this uncertain environment where each alternative is modeled as a payoff function that associates with an element in X a payoff. Here because of the uncertainty associated with the value of V our concept of best course of action is captured for each alternative by an average like value of its payoff function with respect to the measure μ. Our particular concern here is with the case where the payoffs for each alternative are drawn from a scale, which rather then being numeric, is just ordinal. Here then we become interested in finding an aggregated value of a collection of uncertain ordinal values where the uncertainty is modeled by a measure.     

A semantic approach to interpolation

Craig interpolation is investigated for various types of formulae. By shifting the focus from syntactic to semantic interpolation, we generate, prove and classify a series of interpolation results for first-order logic. A few of these results non-trivially generalize known interpolation results; all the others are new. We also discuss some applications of our results to the theory of institutions and of algebraic specifications, and a Craig–Robinson version of these results.     

A hybrid mechanism for heterogeneous e-procurement involving a combinatorial auction and bargaining

We discuss the design of a hybrid mechanism for e-procurement, which implements a multi-attribute combinatorial auction, followed by a bargaining process to achieve desirable procurement transaction outcomes. For the auction phase of the mechanism, we discuss incentive-compatible bidding strategies for suppliers, and how the buyer should determine the winning suppliers. In the follow-on bargaining phase, the buyer can implement a pricing strategy that views the winning suppliers as though they are in different groups. We develop a model and derive decision conditions for the buyer to formulate procurement strategy in this context. Our most important finding is that, compared with the classical Vickrey–Clarke–Groves mechanism, the proposed mechanism improves the transactional social surplus, by including the possibility of post-auction bargaining. We also consider the likelihood that such a hybrid mechanism will be able to provide sustainable business value so long as there is reasonable symmetry in bargaining power between the buyer and the supplier. We offer some thoughts on how to extend this research with approaches from behavioral economics and experimental methods.     

cxterm: A Chinese terminal emulator for the X window system

Computing involving data in a logo-syllabic oriental language like Chinese is more difficult than in English. Many attempts have been made to develop bilingual or multilingual processing systems.^1–3 Most of them are based on microcomputers like the IBM-PC. With the widespread use of workstations and window systems, much better multi-lingual processing environments can be provided. This paper describes the design and implementation of the program cxterm, a Chinese terminal emulator for the X Window System. We discuss the representation of multi-byte international characters, the problems of Chinese character input and output, the pros and cons of various approaches, and the design decisions for cxterm. A feature of cxterm is its independence of input methods. A user can incorporate new input methods into cxterm at run-time, without changing the program code. We also compare our approach with related work in multi-lingual input/output in X, and describe how cxterm performs better in terms of efficiency, flexibility, and user-friendliness.     

Evaluating and automating the annotation of a learner corpus

The paper describes a corpus of texts produced by non-native speakers of Czech. We discuss its annotation scheme, consisting of three interlinked tiers, designed to handle a wide range of error types present in the input. Each tier corrects different types of errors; links between the tiers allow capturing errors in word order and complex discontinuous expressions. Errors are not only corrected, but also classified. The annotation scheme is tested on a data set including approx. 175,000 words with fair inter-annotator agreement results. We also explore the possibility of applying automated linguistic annotation tools (taggers, spell checkers and grammar checkers) to the learner text to support or even substitute manual annotation.     

Efficient indexing for semantic search

The increasing performance and wider spread use of automated semantic annotation and entity linking platforms has empowered the possibility of using semantic information in information retrieval. While keyword-based information retrieval techniques have shown impressive performance, the addition of semantic information can increase retrieval performance by allowing for more accurate sense disambiguation, intent determination, and instance identification, just to name a few. Researchers have already delved into the possibility of integrating semantic information into practical search engines using a combination of techniques such as using graph databases, hybrid indices and adapted inverted indices, among others. One of the challenges with the efficient design of a search engine capable of considering semantic information is that it would need to be able to index information beyond the traditional information stored in inverted indices, including entity mentions and type relationships. The objective of our work in this paper is to investigate various ways in which different data structure types can be adopted to integrate three types of information including keywords, entities and types. We will systematically compare the performance of the different data structures for scenarios where (i) the same data structure types are adopted for the three types of information, and (ii) different data structure types are integrated for storing and retrieving the three different information types. We report our findings in terms of the performance of various query processing tasks such as Boolean and ranked intersection for the different indices and discuss which index type would be appropriate under different conditions for semantic search.     

一种基于补偿法则的矩的快速算法

由于不变矩对图像的平移放大旋转的不敏感性，因此在模式识别、图像分类、场景匹配等图像处理和分析领域获得越来越广泛的应用．但是，求矩运算过程复杂、计算量大、使它的应用受到限制．基于Delta方法，提出了一种新的基于补偿法则的矩的快速算法．对任意二值图像分解为多条线段，图像的矩就等于所有线段的矩的和．对每一线段，将其左方(或上方)填满．每一线段的矩就等于填充后的线段的矩减去填充线段的矩．这样做的好处在于：一幅图像所有可能横(竖)线段的数目由N^2减少为N．引入一组N大小的数组，将求矩过程中大量重复计算的数据一次计算后存人数组，需要时查数组即得．从而极大地减少了计算量．由于填充后线段规格一致，便于用统一的公式计算且有利于编程．和已有的某些算法仅适用于无凹图像和矩计算结果是近似的相比，该算法计算结果准确，适用于任意复杂的二值图像．列出了已有矩算法运算量的评估，比较而言，所讨论的算法的计算量和用时都优于其他算法．     

A formally verified hybrid system for safe advisories in the next-generation airborne collision avoidance system

The Next-Generation Airborne Collision Avoidance System (ACAS X) is intended to be installed on all large aircraft to give advice to pilots and prevent mid-air collisions with other aircraft. It is currently being developed by the Federal Aviation Administration (FAA). In this paper, we determine the geometric configurations under which the advice given by ACAS X is safe under a precise set of assumptions and formally verify these configurations using hybrid systems theorem proving techniques. We consider subsequent advisories and show how to adapt our formal verification to take them into account. We examine the current version of the real ACAS X system and discuss some cases where our safety theorem conflicts with the actual advisory given by that version, demonstrating how formal hybrid systems proving approaches are helping to ensure the safety of ACAS X. Our approach is general and could also be used to identify unsafe advice issued by other collision avoidance systems or confirm their safety.     

Solving fuzzy equations using evolutionary algorithms and neural nets

 In this paper we use evolutionary algorithms and neural nets to solve fuzzy equations. In Part I we: (1) first introduce our three solution methods for solving the fuzzy linear equation AˉXˉ + Bˉ= Cˉ; for Xˉ and (2) then survey the results for the fuzzy quadratic equations, fuzzy differential equations, fuzzy difference equations, fuzzy partial differential equations, systems of fuzzy linear equations, and fuzzy integral equations; and (3) apply an evolutionary algorithm to construct one of the solution types for the fuzzy eigenvalue problem. In Part II we: (1) first discuss how to design and train a neural net to solve AˉXˉ + Bˉ= Cˉ for Xˉ and (2) then survey the results for systems of fuzzy linear equations and the fuzzy quadratic.     

A Framework for Collaborative, Content-Based and Demographic Filtering

We discuss learning a profile of user interests for recommending information sources such as Web pages or news articles. We describe the types of information available to determine whether to recommend a particular page to a particular user. This information includes the content of the page, the ratings of the user on other pages and the contents of these pages, the ratings given to that page by other users and the ratings of these other users on other pages and demographic information about users. We describe how each type of information may be used individually and then discuss an approach to combining recommendations from multiple sources. We illustrate each approach and the combined approach in the context of recommending restaurants.     

A Historical View of Context

This paper examines a number of the approaches, origins and ideals of context-aware systems design, looking particularly at the way that history influences what we do in our ongoing activity. As a number of sociologists and philosophers have pointed out, past social interaction, as well as past use of the heterogeneous mix of media, tools and artifacts that we use in our everyday activity, influence our ongoing interaction with the people and media at hand. We suggest that ones experience and history is thus part of ones current context, with patterns of use temporally and subjectively combining and interconnecting different media as well as different modes of use of those media. One such mode of use is transparent use, put forward by Weiser as ubicomps design ideal. One theoretical finding is that this design ideal is unachievable or incomplete because transparent and more focused analytical use are interdependent, affecting and feeding into each other through ones experience and history. Using these theoretical points, we discuss a number of context-aware system designs that make good use of history in supporting ongoing user activity.     

Syntactic decision rules for recognition of spoken words and phrases using a stochastic automaton

This study deals with the design of a syntactic decision rule for recognizing an unknown utterance from a set X. The decision rule is expressed as a function of the character string (CS) derived from the test utterance. To obtain the CS, the waveform of the utterance is divided into a large number of frames of roughly equal duration numbered 1, 2,...,n. The ith symbol in the CS is the phonemic symbol obtained by subjecting the ith frame of the waveform to a relatively simple phoneme decision rule, the number of symbols in the CS being n. All the available nonacoustic information such as the lexicon of words in the set X, the possibility of confusion between different phonemes as seen by the phoneme decision rule, etc. is used in the design of the decision rule. The syntactic decision rule can be implemented by a stochastic finite state automaton involving limited memory and computation. The decision rule can also be interpreted as yielding the phrase x which minimizes a distance measure D(x, z) between the phrase x X and the observed CS z. We wili compare this approach with the other approaches such as the Viterbi methods, the distance approaches involving various types of distances, etc.     

A neural architecture for a class of abduction problems

The general task of abduction is to infer a hypothesis that best explains a set of data. A typical subtask of this is to synthesize a composite hypothesis that best explains the entire data from elementary hypotheses which can explain portions of it. The synthesis subtask of abduction is computationally expensive, more so in the presence of certain types of interactions between the elementary hypotheses. In this paper, we first formulate the abduction task as a nonmonotonic constrained-optimization problem. We then consider a special version of the general abduction task that is linear and monotonic. Next, we describe a neural network based on the Hopfield model of computation for the special version of the abduction task. The connections in this network are symmetric, the energy function contains product forms, and the minimization of this function requires a network of order greater than two. We then discuss another neural architecture which is composed of functional modules that reflect the structure of the abduction task. The connections in this second-order network are asymmetric. We conclude with a discussion of how the second architecture may be extended to address the general abduction task.     

A simple index for assessing fuel moisture content

Assessing fuel moisture content to within a reasonable degree of accuracy is an important part of wildland fire management. In this paper we introduce a fuel moisture index that provides a simple and intuitive method for assessing fuel moisture content. The method can be quickly and easily applied in a field setting to provide a dimensionless measure of fuel moisture content. We compare the index with predictions from several models for fuel moisture content and conclude that it provides an equivalent measure of fuel moisture content for a number of fuel types. We go on to briefly discuss how the index could be used to construct a simple and intuitive fire danger index.     

A platform for crowdsourcing the creation of representative,accurate landcover maps

Accurate landcover maps are fundamental to understanding socio-economic and environmental patterns and processes, but existing datasets contain substantial errors. Crowdsourcing map creation may substantially improve accuracy, particularly for discrete cover types, but the quality and representativeness of crowdsourced data is hard to verify. We present an open-sourced platform, DIYlandcover, that serves representative samples of high resolution imagery to an online job market, where workers delineate individual landcover features of interest. Worker mapping skill is frequently assessed, providing estimates of overall map accuracy and a basis for performance-based payments. A trial of DIYlandcover showed that novice workers delineated South African cropland with 91% accuracy, exceeding the accuracy of current generation global landcover products, while capturing important geometric data. A scaling-up assessment suggests the possibility of developing an Africa-wide vector-based dataset of croplands for $2–3 million within 1.2–3.8 years. DIYlandcover can be readily adapted to map other discrete cover types.     

Learning short synfire chains by self-organization

A model of cortical neurons capable of sustaining a low level of spontaneous activity is investigated. Without learning the activity of the network is chaotic. We report on attempts to learn synfire chains in this type of network by introducing a Hebbian learning mechanism and exciting a small set of neurons at random intervals. We discuss the types of instabilities that can arise and prevent the formation of long synfire chains and also discuss various biologically plausible mechanisms which to some extent cure these instabilities.     

Possibility-induced simplified neutrosophic aggregation operators and their application to multi-criteria group decision-making

Abstract

Simplified neutrosophic set (SNS) is an appropriate tool used to express the incompleteness, indeterminacy and uncertainty of the evaluation objects in decision-making process. In this study, we define the concept of possibility SNS including two types of information such as the neutrosophic performance provided from the evaluation objects and its possibility degree using a value ranging from zero to one. Then by extending the existing neutrosophic information, aggregation models for SNSs that cannot be used effectively to fusion the two different information described above, we propose two novel neutrosophic aggregation operators considering possibility, which are named as a possibility-induced simplified neutrosophic weighted arithmetic averaging operator and possibility-induced simplified neutrosophic weighted geometric averaging operator, and discuss their properties. Moreover, we develop a useful method based on the proposed aggregation operators for solving a multi-criteria group decision-making problem with the possibility simplified neutrosophic information, in which the weights of decision-makers and decision criteria are calculated based on entropy measure. Finally, a practical example is utilised to show the practicality and effectiveness of the proposed method.     

A survey on content-centric technologies for the current Internet: CDN and P2P solutions

One of the most striking properties of the Internet is its flexibility to accommodate features it was not conceived for. Among the most significant examples, in this survey we consider the transition of the Internet from a reliable fault-tolerant network for host-to-host communication to a content-centric network, i.e. a network mostly devoted to support efficient generation, sharing and access to content. We survey this research area according to a top-down approach. We present a conceptual framework that encompasses the key building blocks required to support content-centric networking in the Internet. Then we describe in detail the two most important types of content-centric Internet technologies, i.e., Content-Delivery Networks (CDNs) and P2P systems. For each of them, we show how they cover the key building blocks. We then identify the functional components of CDN and P2P content management solutions, and discuss the main solutions proposed in the literature for each of them. We consider different types of content (both real time and non real time), and different networking environments (fixed, mobile, …). Finally, we also discuss the main recent research trends focused on how to design the Future Internet as a native content-centric network.