By all these lovely tokens... Merging conflicting tokenizations

Given the contemporary trend to modular NLP architectures and multiple annotation frameworks, the existence of concurrent tokenizations of the same text represents a pervasive problem in everyday’s NLP practice and poses a non-trivial theoretical problem to the integration of linguistic annotations and their interpretability in general. This paper describes a solution for integrating different tokenizations using a standoff XML format, and discusses the consequences from a corpus-linguistic perspective.     

Remote Collaboration Over Video Data: Towards Real-Time e-Social Science

The design of distributed systems to support collaboration among groups of scientists raises new networking challenges that grid middleware developers are addressing. This field of development work, ‘e-Science’, is increasingly recognising the critical need of understanding the ordinary day-to-day work of doing research to inform design. We have investigated one particular area of collaborative social scientific work – the analysis of video data. Based on interviews and observational studies, we discuss current practices of social scientific work with digital video in three areas: Preparation for collaboration; Control of data and application; and Annotation configurations and techniques. For each, we describe how these requirements feature in our design of a distributed video analysis system as part of the MiMeG project: our security policy and distribution; the design of the control system; and providing freeform annotation over data. Finally, we review our design in light of initial use of the software between project partners; and discuss how we might transform the spatial configuration of the system to support annotation behaviour.     

基于形式概念分析和语义关联规则的目标图像标注

基于目标的图像标注一直是图像处理和计算机视觉领域中一个重要的研究问题.图像目标的多尺度性、多形变性使得图像标注十分困难.目标分割和目标识别是目标图像标注任务中两大关键问题.本文提出一种基于形式概念分析(Formal concept analysis, FCA)和语义关联规则的目标图像标注方法, 针对目标建议算法生成图像块中存在的高度重叠问题, 借鉴形式概念分析中概念格的思想, 按照图像块的共性将其归成几个图像簇挖掘图像类别模式, 利用类别概率分布判决和平坦度判决分别去除目标噪声块和背景噪声块, 最终得到目标语义簇; 针对语义目标判别问题, 首先对有效图像簇进行特征融合形成共性特征描述, 通过分类器进行类别判决, 生成初始目标图像标注, 然后利用图像语义标注词挖掘语义关联规则, 进行图像标注的语义补充, 以避免挖掘类别模式时丢失较小的语义目标.实验表明, 本文提出的图像标注算法既能保证语义标注的准确性, 又能保证语义标注的完整性, 具有较好的图像标注性能.     

What the <0.70, 1.17, 0.99, 1.07> is a Symbol?

The notion of a ‘symbol’ plays an important role in the disciplines of Philosophy, Psychology, Computer Science, and Cognitive Science. However, there is comparatively little agreement on how this notion is to be understood, either between disciplines, or even within particular disciplines. This paper does not attempt to defend some putatively ‘correct’ version of the concept of a ‘symbol.’ Rather, some terminological conventions are suggested, some constraints are proposed and a taxonomy of the kinds of issue that give rise to disagreement is articulated. The goal here is to provide something like a ‘geography’ of the various notions of ‘symbol’ that have appeared in the various literatures, so as to highlight the key issues and to permit the focusing of attention upon the important dimensions. In particular, the relationship between ‘tokens’ and ‘symbols’ is addressed. The issue of designation is discussed in some detail. The distinction between simple and complex symbols is clarified and an apparently necessary condition for a system to be potentially symbol, or token bearing, is introduced.     

Annotations and the collaborative digital library: Effects of an aligned annotation interface on student argumentation and reading strategies

Recent research on annotation interfaces provides provocative evidence that anchored, annotation-based discussion environments may lead to better conversations about a text. However, annotation interfaces raise complicated tradeoffs regarding screen real estate and positioning. It is argued that solving this screen real estate problem requires limiting the number of annotations displayed to users. In order to understand which annotations have the most learning value for students, this paper presents two complementary studies examining the effects of annotations on students performing a reading-to-write task. The first study used think-aloud protocols and a within-subjects methodology, finding that annotations appeared to provoke students to reflect more critically upon the primary text. This effect was particularly strong when students encountered pairs of annotations presenting different viewpoints on the same section of text. Student interviews suggested that annotations were most helpful when they caused the reader to consider and weigh conflicting viewpoints. The second study used a between-subjects methodology and a more naturalistic task to provide complementary evidence that annotations encourage more reflective responses to a text. This study found that students who received annotated materials both perceived themselves and were perceived by instructors as less reliant on unreflective summary strategies than students who received the same content but in a different format. These findings indicate that the learning value of an annotation lies in its ability to provoke students to consider and weigh new perspectives on the primary text. When selected effectively, annotations provide a critical scaffolding that can support students’ critical thinking and argumentation activities. Collaborative digital libraries and applications for the Web 2.0 should be designed with this learning framework in mind.     

Towards the semantic and context-aware management of mobile multimedia

Users of mobile devices can nowadays easily create large quantities of mobile multimedia documents tracing significant events attended, places visited or, simply, moments of their everyday life. However, they face the challenge of organizing these documents in order to facilitate searching through them at a later time and sharing them with other users. We propose using context awareness and semantic technologies in order to improve and facilitate the organization, annotation, retrieval and sharing of personal mobile multimedia documents. Our approach combines metadata extracted and enriched automatically from the users’ context with annotations provided manually by the users and with annotations inferred by applying user-defined rules to context features. These new contextual metadata are integrated into the processes of annotation, sharing and keyword-based retrieval.     

Why Computers Can’t Feel Pain

The most cursory examination of the history of artificial intelligence highlights numerous egregious claims of its researchers, especially in relation to a populist form of ‘strong’ computationalism which holds that any suitably programmed computer instantiates genuine conscious mental states purely in virtue of carrying out a specific series of computations. The argument presented herein is a simple development of that originally presented in Putnam’s (Representation & Reality, Bradford Books, Cambridge in 1988) monograph, “Representation & Reality”, which if correct, has important implications for turing machine functionalism and the prospect of ‘conscious’ machines. In the paper, instead of seeking to develop Putnam’s claim that, “everything implements every finite state automata”, I will try to establish the weaker result that, “everything implements the specific machine Q on a particular input set (x)”. Then, equating Q (x) to any putative AI program, I will show that conceding the ‘strong AI’ thesis for Q (crediting it with mental states and consciousness) opens the door to a vicious form of panpsychism whereby all open systems, (e.g. grass, rocks etc.), must instantiate conscious experience and hence that disembodied minds lurk everywhere.     

Special factors,periodicity, and an application to Sturmian words

Let w be a finite word and n the least non-negative integer such that w has no right special factor of length and its right factor of length n is unrepeated. We prove that if all the factors of another word v up to the length n + 1 are also factors of w, thenv itself is a factor ofw. A similar result for ultimately periodic infinite words is established. As a consequence, some ‘uniqueness conditions’ for ultimately periodic words are obtained as well as an upper bound for the rational exponents of the factors of uniformly recurrent non-periodic infinite words. A general formula is derived for the ‘critical exponent’ of a power-free Sturmian word. In particular, we effectively compute the ‘critical exponent’ of any Sturmian sequence whose slope has a periodic development in a continued fraction. Received: 6 May 1999 / 21 February 2000     

A Meteorological Fuzzy Expert System Incorporating Subjective User Input

We present a fuzzy expert system, MEDEX, for forecasting gale-force winds in the Mediterranean basin. The most successful local wind forecasting in this region is achieved by an expert human forecaster with access to numerical weather prediction products. That forecaster's knowledge is expressed as a set of ‘rules-of-thumb’. Fuzzy set methodologies have proved well suited for encoding the forecaster's knowledge, and for accommodating the uncertainty inherent in the specification of rules, as well as in subjective and objective input. MEDEX uses fuzzy set theory in two ways: as a fuzzy rule base in the expert system, and for fuzzy pattern matching to select dominant wind circulation patterns as one input to the expert system. The system was developed, tuned, and verified over a two-year period, during which the weather conditions from 539 days were individually analyzed. Evaluations of MEDEX performance for both the onset and cessation of winter and summer winds are presented, and demonstrate that MEDEX has forecasting skill competitive with the US Navy's regional forecasting center in Rota, Spain. Received 5 May 1999 / Revised 8 August 2000 / Accepted in revised form 23 April 2001     

A fast algorithm for computing moments of gray images based on NAM and extended shading approach

Computing moments on images is very important in the fields of image processing and pattern recognition. The non-symmetry and anti-packing model (NAM) is a general pattern representation model that has been developed to help design some efficient image representation methods. In this paper, inspired by the idea of computing moments based on the S-Tree coding (STC) representation and by using the NAM and extended shading (NAMES) approach, we propose a fast algorithm for computing lower order moments based on the NAMES representation, which takes O(N) time where N is the number of NAM blocks. By taking three idiomatic standard gray images ‘Lena’, ‘F16’, and ‘Peppers’ in the field of image processing as typical test objects, and by comparing our proposed algorithm with the conventional algorithm and the popular STC representation algorithm for computing the lower order moments, the theoretical and experimental results presented in this paper show that the average execution time improvement ratios of the proposed NAMES approach over the STC approach, and also the conventional approach are 26.63%, and 82.57% respectively while maintaining the image quality.     

Visual and textual fusion for semantically supervised region-based retrieval

This paper presents a unified annotation and retrieval framework, which integrates region annotation with image retrieval for performance reinforcement. To integrate semantic annotation with region-based image retrieval, visual and textual fusion is proposed for both soft matching and Bayesian probabilistic formulations. To address sample insufficiency and sample asymmetry in the annotation classifier training phase, we present a region-level multi-label image annotation scheme based on pair-wise coupling support vector machine (SVM) learning. In the retrieval phase, to achieve semantic-level region matching we present a novel retrieval scheme which differs from former work: the query example uploaded by users is automatically annotated online, and the user can judge its annotation quality. Based on the user’s judgment, two novel schemes are deployed for semantic retrieval: (1) if the user judges the photo to be well annotated, Semantically supervised Integrated Region Matching is adopted, which is a keyword-integrated soft region matching method; (2) If the user judges the photo to be poorly annotated, Keyword Integrated Bayesian Reasoning is adopted, which is a natural integration of a Visual Dictionary in online content-based search. In the relevance feedback phase, we conduct both visual and textual learning to capture the user’s retrieval target. Better annotation and retrieval performance than current methods were reported on both COREL 10,000 and Flickr web image database (25,000 images), which demonstrated the effectiveness of our proposed framework.     

An Ontology for G2G Collaboration in Public Policy Making,Implementation and Evaluation

This paper concerns the development and use of ontologies for electronically supporting and structuring the highest-level function of government: the design, implementation and evaluation of public policies for the big and complex problems that modern societies face. This critical government function usually necessitates extensive interaction and collaboration among many heterogeneous government organizations (G2G collaboration) with different backgrounds, mentalities, values, interests and expectations, so it can greatly benefit from the use of ontologies. In this direction initially an ontology of public policy making, implementation and evaluation is described, which has been developed as part of the project ICTE-PAN of the Information Society Technologies (IST) Programme of the European Commission, based on sound theoretical foundations mainly from the public policy analysis domain and contributions of experts from the public administrations of four European Union countries (Denmark, Germany, Greece and Italy). It is a ‘horizontal’ ontology that can be used for electronically supporting and structuring the whole lifecycle of a public policy in any vertical (thematic) area of government activity; it can also be combined with ‘vertical’ ontologies of the specific vertical (thematic) area of government activity we are dealing with. In this paper is also described the use of this ontology for electronically supporting and structuring the collaborative public policy making, implementation and evaluation through ‘structured electronic forums’, ‘extended workflows’, ‘public policy stages with specific sub-ontologies’, etc., and also for the semantic annotation, organization, indexing and integration of the contributions of the participants of these forums, which enable the development of advanced semantic web capabilities in this area.     

Scope control and grammatical dependencies

This paper develops a semantics with control over scope relations using Vermeulen’s stack valued assignments as information states. This makes available a limited form of scope reuse and name switching. The goal is to have a general system that fixes available scoping effects to those that are characteristic of natural language. The resulting system is called Scope Control Theory, since it provides a theory about what scope has to be like in natural language. The theory is shown to replicate a wide range of grammatical dependencies, including options for, and constraints on, ‘donkey’, ‘binding’, ‘movement’, ‘Control’ and ‘scope marking’ dependencies.     

A Complete Mechanization of Correctness of a String-Preprocessing Algorithm

We report on mechanization of a correctness proof of a string-preprocessing algorithm. This preprocessing algorithm is employed in Boyer-Moore’s pattern matching algorithm. The mechanization is carried out using the PVS system. The correctness proof being mechanized has been formulated in Linear Time Temporal Logic. It consists of fourteen lemmata which are related to safety properties and two additional lemmata dealing with liveness properties. The entire mechanization of the safety and liveness parts has been completed. Several small errors and omissions were found in the handwritten proof and corrected. Yet, the manual correctness proof of the string-preprocessing algorithm was found to satisfy the usual mathematical validity. We conclude that the string-preprocessing algorithm in Boyer-Moore’s pattern matching algorithm, corrected a number of times because of flaws, does not contain any more undiscovered errors. An extended abstract of this work appears in the Proceedings of the 8^th IEEE International Conference on Engineering of Complex Computer Systems (ICECCS ‘02).     

A Multi-Classifier Approach to Fingerprint Classification

Fingerprint classification is a challenging pattern recognition problem which plays a fundamental role in most of the large fingerprint-based identification systems. Due to the intrinsic class ambiguity and the difficulty of processing very low quality images (which constitute a significant proportion), automatic fingerprint classification performance is currently below operating requirements, and most of the classification work is still carried out manually or semi-automatically. This paper explores the advantages of combining the MASKS and MKL-based classifiers, which we have specifically designed for the fingerprint classification task. In particular, a combination at the ‘abstract level’ is proposed for exclusive classification, whereas a fusion at the ‘measurement level’ is introduced for continuous classification. The advantages of coupling these distinct techniques are well evident; in particular, in the case of exclusive classification, the FBI challenge, requiring a classification error ≤ 1% at 20% rejection, was met on NIST-DB14. Received: 06 November 2000, Received in revised form: 25 October 2001, Accepted: 03 January 2002     

Large scale image annotation: learning to rank with joint word-image embeddings

Image annotation datasets are becoming larger and larger, with tens of millions of images and tens of thousands of possible annotations. We propose a strongly performing method that scales to such datasets by simultaneously learning to optimize precision at k of the ranked list of annotations for a given image and learning a low-dimensional joint embedding space for both images and annotations. Our method both outperforms several baseline methods and, in comparison to them, is faster and consumes less memory. We also demonstrate how our method learns an interpretable model, where annotations with alternate spellings or even languages are close in the embedding space. Hence, even when our model does not predict the exact annotation given by a human labeler, it often predicts similar annotations, a fact that we try to quantify by measuring the newly introduced “sibling” precision metric, where our method also obtains excellent results.     

Numerical solution of singular perturbation problems

A finite difference method for Second Order Singular Perturbation Problems is presented. It is based on a mesh selection strategy derived by using sufficient conditions which ensure the well conditioning of tridiagonal matrices. In particular the implementation aspects of the method are discussed. Numerical tests are reported to evidence the effectiveness of this method and its competitiveness with respect to known solvers for BVPs. Work performed within the activities of the project ‘Matematica Computazionale’ supported by MURST 40%.     

On-line service scheduling

This paper is concerned with a scheduling problem that occurs in service systems where customers are classified as ‘ordinary’ and ‘special’. Ordinary customers can be served on any service facility, while special customers can be served only on the flexible service facilities. Customers arrive dynamically over time and their needs become known upon arrival. We assume any service, once started, will be carried out to its completion. In this paper, we study the worst-case performance of service policies used in practice. In particular, we evaluate three classes of service policies: policies with priority, policies without priority, and their combinations. We obtain tight worst-case performance bounds for all service policies considered.