Physically touching and tasting virtual objects enhances the realism of virtual experiences

Experiment 1 explored the impact of physically touching a virtual object on how realistic the virtual environment (VE) seemed to the user. Subjects in a no touch group picked up a 3D virtual image of a kitchen plate in a VE, using a traditional 3D wand. See and touch subjects physically picked up a virtual plate possessing solidity and weight, using a technique called tactile augmentation. Afterwards, subjects made predictions about the properties of other virtual objects they saw but did not interact with in the VE. See and touch subjects predicted these objects would be more solid, heavier, and more likely to obey gravity than the no touch group. In Experiment 2 (a pilot study), subjects physically bit a chocolate bar in one condition, and imagined biting a chocolate bar in another condition. Subjects rated the event more fun and realistic when allowed to physically bite the chocolate bar. Results of the two experiments converge with a growing literature showing the value of adding physical qualities to virtual objects. This study is the first to empirically demonstrate the effectiveness of tactile augmentation as a simple, safe, inexpensive technique with large freedom of motion for adding physical texture, force feedback cues, smell and taste to virtual objects. Examples of practical applications are discussed.Based in part on Physically touching virtual objects using tactile augmentation enhances the realism of virtual environments' by Hunter Hoffman which appeared in the Proceedings of the IEEE Virtual Reality Annual International Symposium '98, Atlanta GA, pp 59–63. ¢ 1998 IEEE.     

Information Gaps as Communication Needs: A New Semantic Foundation for Some Non-Classical Logics

Semantics connected to some information based metaphor are well-known in logic literature: a paradigmatic example is Kripke semantic for Intuitionistic Logic. In this paper we start from the concrete problem of providing suitable logic-algebraic models for the calculus of attribute dependencies in Formal Contexts with information gaps and we obtain an intuitive model based on the notion of passage of information showing that Kleene algebras, semi-simple Nelson algebras, three-valued ukasiewicz algebras and Post algebras of order three are, in a sense, naturally and directly connected to partially defined information systems. In this way wecan provide for these logic-algebraic structures a raison dêetre different from the original motivations concerning, for instance, computability theory.     

Automated Proof Construction in Type Theory Using Resolution

We provide techniques to integrate resolution logic with equality in type theory. The results may be rendered as follows. A clausification procedure in type theory, equipped with a correctness proof, all encoded using higher-order primitive recursion. A novel representation of clauses in minimal logic such that the -representation of resolution steps is linear in the size of the premisses. A translation of resolution proofs into lambda terms, yielding a verification procedure for those proofs. Availability of the power of resolution theorem provers in interactive proof construction systems based on type theory.     

Unification in partially commutative semigroups

Unification algorithms have been constructed for semigroups and commutative semigroups. This paper considers the intermediate case of partially commutative semigroups. We introduce classesN and of such semigroups and justify their use. We present an equation-solving algorithm for any member of the classN. This algorithm is relative to having an algorithm to determine all non-negative solutions of a certain class of diophantine equations of degree 2 which we call -equations. The difficulties arising when attempting to solve equations in members of the class are discussed, and we present arguments that strongly suggest that unification in these semigroups is undecidable.     

A Writing Support Tool with Multiple Views

This paper describes both SuperText,a computer program designed to support productiveexpository writing processes among students at adistance teaching university, and its theoreticaljustification. Being able to write well is animportant communication skill, and the writingprocess can help to build and clarify the writersknowledge. Computers can support this by providing amedium to externalise and record the writersunderstanding. Representations appropriate to thisexternalisation are uninstantiated idea labels,instantiated text units, and a variety ofrelationships between these items. SuperText usesthese representations to support a range of writingstyles. It provides several independent Views thatrepresent the structure of the evolving documentthrough expanding hierarchies, each with a varietyof Presentations. Allied to these Views is a textwork space providing access to a database ofcontinuous text nodes. Taken together, these providean ability to represent global and intermediatestructures of the document well beyond that ofconventional editors. These aspects were all ratedhighly by students participating in a series offield trials of SuperText.     

Shape optimization of elasto-plastic bodies under plane strains: Sensitivity analysis and numerical implementation

Optimal shape design problems for an elastic body made from physically nonlinear material are presented. Sensitivity analysis is done by differentiating the discrete equations of equilibrium. Numerical examples are included.Notation U ^ad set of admissible continuous design parameters - U _h ^ad set of admissible discrete design parameters - function fromU _h ^ad defining shape of body - _h function fromU _h ^ad defining approximated shape of body - vector of nodal values of _h - { _n} sequence of functions tending to - () domain defined by - K bulk modulus - shear modulus - penalty parameter for contact condition - V() space of virtual displacements in() - V _h(_h) finite element approximation ofV() - J cost functional - J _h discretized cost functional - J algebraic form ofJ _h - (u) stress tensor - e(u) strain tensor - K stiffness matrix - f force vector - b(q) term arising from nonlinear boundary conditions - q vector of nodal degrees of freedom - p vector of adjoint state variables - J Jacobian of isoparametric mapping - |J| determinant ofJ - N vector of shape function values on parent element - L matrix of shape function derivatives on parent element - G matrix of Cartesian derivatives of shape functions - X matrix of nodal coordinates of element - D matrix of elastic coefficients - B strain-displacement matrix - _P part of boundary where tractions are prescribed - _u part of boundary where displacements are prescribed - variable part of boundary - strain invariant     

Dimensional properties of graphs and digital spaces

Many applications of digital image processing now deal with three- and higher-dimensional images. One way to represent n-dimensional digital images is to use the specialization graphs of subspaces of the Alexandroff topological space ⁿ (where denotes the integers with the Khalimsky line topology). In this paper the dimension of any such graph is defined in three ways, and the equivalence of the three definitions is established. Two of the definitions have a geometric basis and are closely related to the topological definition of inductive dimension; the third extends the Alexandroff dimension to graphs. Diagrams are given of graphs that are dimensionally correct discrete models of Euclidean spaces, n-dimensional spheres, a projective plane and a torus. New characterizations of n-dimensional (digital) surfaces are presented. Finally, the local structure of the space ⁿ is analyzed, and it is shown that ⁿ is an n-dimensional surface for all n1.     

Design sensitivity analysis of nonlinear dynamic response of structural and mechanical systems

This paper describes a unified variational theory for design sensitivity analysis of nonlinear dynamic response of structural and mechanical systems for shape, nonshape, material and mechanical properties selection, as well as control problems. The concept of an adjoint system, the principle of virtual work and a Lagrangian-Eulerian formulation to describe the deformations and the design variations are used to develop a unified view point. A general formula for design sensitivity analysis is derived and interpreted for usual performance functionals. Analytical examples are utilized to demonstrate the use of the theory and give insights for application to more complex problems that must be treated numerically.Derivatives The comma notation for partial derivatives is used, i.e. G,_u = G/u. An upper dot represents material time derivative, i.e. ü = ²u/t². A prime implies derivative with respect to the time measured in the reference time-domain, i.e. u = du/d.     

Extracting Phonetic Knowledge from Learning Systems: Perceptrons, Support Vector Machines and Linear Discriminants

Speech perception relies on the human ability to decode continuous, analogue sound pressure waves into discrete, symbolic labels (phonemes) with linguistic meaning. Aspects of this signal-to-symbol transformation have been intensively studied over many decades, using psychophysical procedures. The perception of (synthetic) syllable-initial stop consonants has been especially well studied, since these sounds display a marked categorization effect: they are typically dichotomised into voiced and unvoiced classes according to their voice onset time (VOT). In this case, the category boundary is found to have a systematic relation to the (simulated) place of articulation, but there is no currently-accepted explanation of this phenomenon. Categorization effects have now been demonstrated in a variety of animal species as well as humans, indicating that their origins lie in general auditory and/or learning mechanisms, rather than in some phonetic module specialized to human speech processing.In recent work, we have demonstrated that appropriately-trained computational learning systems (neural networks) also display the same systematic behaviour as human and animal listeners. Networks are trained on simulated patterns of auditory-nerve firings in response to synthetic continuua of stop-consonant/vowel syllables varying in place of articulation and VOT. Unlike real listeners, such a software model is amenable to analysis aimed at extracting the phonetic knowledge acquired in training, so providing a putative explanation of the categorization phenomenon. Here, we study three learning systems: single-layer perceptrons, support vector machines and Fisher linear discriminants. We highlight similarities and differences between these approaches. We find that the modern inductive inference technique for small sample sizes of support vector machines gives the most convincing results. Knowledge extracted from the trained machine indicated that the phonetic percept of voicing is easily and directly recoverable from auditory (but not acoustic) representations.     

Brave Mobots Use Representation: Emergence of Representation in Fight-or-Flight Learning

The paper uses ideas from Machine Learning, Artificial Intelligence and Genetic Algorithms to provide a model of the development of a fight-or-flight response in a simulated agent. The modelled development process involves (simulated) processes of evolution, learning and representation development. The main value of the model is that it provides an illustration of how simple learning processes may lead to the formation of structures which can be given a representational interpretation. It also shows how these may form the infrastructure for closely-coupled agent/environment interaction.     

Collaboration and Teamwork: Immersion and Presence in an Online Learning Environment

In the world of OTIS, an online Internet School for occupational therapists, students from four European countries were encouraged to work collaboratively through problem-based learning by interacting with each other in a virtual semi-immersive environment. This paper describes, often in their own words, the experience of European occupational therapy students working together across national and cultural boundaries. Collaboration and teamwork were facilitated exclusively through an online environment, since the students never met each other physically during the OTIS pilot course. The aim of the paper is to explore the observations that (1) there was little interaction between students from different tutorial groups and (2) virtual teamwork developed in each of the cross-cultural tutorial groups. Synchronous data from the students was captured during tutorial sessions and peer-booked meetings and analyzed using the qualitative constructs of immersion, presence and reflection in learning. The findings indicate that immersion was experienced only to a certain extent. However, students found both presence and shared presence, within their tutorial groups, to help collaboration and teamwork. Other evidence suggests that communities of interest were established. Further study is proposed to support group work in an online learning environment. It is possible to conclude that collaborative systems can be designed, which encourage students to build trust and teamwork in a cross cultural online learning environment.This revised version was published online in March 2005 with corrections to the cover dateFunded by the European Union through the TENTelecom programme.     

Explanation-based interpretation of open-textured concepts in logical models of legislation

In this paper we discuss a view of the Machine Learning technique called Explanation-Based Learning (EBL) or Explanation-Based Generalization (EBG) as a process for the interpretation of vague concepts in logic-based models of law.The open-textured nature of legal terms is a well-known open problem in the building of knowledge-based legal systems. EBG is a technique which creates generalizations of given examples on the basis of background domain knowledge. We relate these two topics by considering EBG's domain knowledge as corresponding to statute law rules, and EBG's training example as corresponding to a precedent case.By making the interpretation of vague predicates as guided by precedent cases, we use EBG as an effective process capable of creating a link between predicates appearing as open-textured concepts in law rules, and predicates appearing as ordinary language wording for stating the facts of a case.Standard EBG algorithms do not change the deductive closure of the domain theory. In the legal context, this is only adequate when concepts vaguely defined in some law rules can be reformulated in terms of other concepts more precisely defined in other rules. We call theory reformulation the process adopted in this situation of complete knowledge.In many cases, however, statutory law leaves some concepts completely undefined. We then propose extensions to the EBG standard that deal with this situation of incomplete knowledge, and call theory revision the extended process. In order to fill in knowledge gaps we consider precedent cases supplemented by additional heuristic information. The extensions proposed treat heuristics represented by abstraction hierarchies with constraints and exceptions.In the paper we also precisely characterize the distinction between theory reformulation and theory revision by stating formal definitions and results, in the context of the Logic Programming theory.We offer this proposal as a possible contribution to cross fertilization between machine learning and legal reasoning methods.     

Differential geometry of a parametric family of invertible linear systems—Riemannian metric,dual affine connections,and divergence

A parametric model of systems is regarded as a geometric manifold imbedded in the enveloping manifold consisting of all the linear systems. The present paper aims at establishing a new geometrical method and framework for analyzing properties of manifolds of systems. A Riemannian metric and a pair of dual affine connections are introduced to a system manifold. They are called-connections. The duality of connections is a new concept in differential geometry. The manifold of all the linear systems is-flat so that it admits natural and invariant-divergence measures. Such geometric structures are useful for treating the problems of approximation, identification, and stochastic realization of systems. By using the-divergences, we solve the problem of approximating a given system by one included in a model. For a sequence of-flat nesting models such as AR models and MA models, it is shown that the approximation errors are decomposed additively corresponding to each dimension of the model.     

‘Use’ discourses in system development: Can communication be improved?

This paper aims to provide a basis for renewed talk about use in computing. Four current discourse arenas are described. Different intentions manifest in each arena are linked to failures in translation, different terminologies crossing disciplinary and national boundaries non-reflexively. Analysis of transnational use discourse dynamics shows much miscommunication. Conflicts like that between the Scandinavian System Development School and the usability approach have less current salience. Renewing our talk about use is essential to a participatory politics of information technology and will lead to clearer perception of the implications of letting new systems becoming primary media of social interaction.     

On Solvability of Operator Inclusions: Application to Elliptic Problems with the Neumann Boundary Condition

In studying the surjectivity of set-valued mappings, a modification of the acute-angle lemma (or the equilibrium theorem) is used. This allows one to weaken the coerciveness condition. Some applications to differential equations (inclusions) with Neumann boundary conditions are considered on Sobolev spaces W _p ¹() in which operators are used that are not coercive in the classical sense.     

Sur les générateurs algébriques et linéaires

Résumé Nous étudions certaines propriétés des générateurs algébriques et linéaires. Nous montrons que le langage algébrique E engendré par la grammaire: S aSbSc + d domine tous les langages algébriques par applications séquentielles fidèles. Nous en déduisons que pour tout langage algébrique L et tout générateur algébrique L, il existe une transduction rationnelle fonctionnelle et fidèle telle que L=(L). Ce résultat, qui n'est pas vérifié pour la famille, Lin, des langages algébriques linéaires, nous permet de montrer qu'aucun générateur algébrique n'appartient à la famille EDTOL. Enfin, nous établissons que si L est un générateur linéaire, L # est un générateur séquentiel pour Lin.

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Algebraic and linear generators

Summary We study some properties of algebraic and linear generators. We show that the algebraic language E generated by the grammar: S aSbSc + d dominates every algebraic language by faithful sequential mappings. We deduce that, for every algebraic language L and every algebraic generator L, there exists a faithful rational function such that L=(L). This result, which does not hold for the family of linear languages, permits us to show that no algebraic generator belongs to the family EDTOL. Also, we prove if L is a linear generator then L # is a sequential generator for Lin.

     

Finiteness in Infinite-Valued Łukasiewicz Logic

In this paper we deepen Mundici's analysis on reducibility of the decision problem from infinite-valued ukasiewicz logic to a suitable m-valued ukasiewicz logic _m , where m only depends on the length of the formulas to be proved. Using geometrical arguments we find a better upper bound for the least integer m such that a formula is valid in if and only if it is also valid in _m. We also reduce the notion of logical consequence in to the same notion in a suitable finite set of finite-valued ukasiewicz logics. Finally, we define an analytic and internal sequent calculus for infinite-valued ukasiewicz logic.     

A ‘complexity level’ analysis of immediate vision

This paper demonstrates how serious consideration of the deep complexity issues inherent in the design of a visual system can constrain the development of a theory of vision. We first show how the seemingly intractable problem of visual perception can be converted into a much simpler problem by the application of several physical and biological constraints. For this transformation, two guiding principles are used that are claimed to be critical in the development of any theory of perception. The first is that analysis at the complexity level is necessary to ensure that the basic space and performance constraints observed in human vision are satisfied by a proposed system architecture. Second, the maximum power/minimum cost principle ranks the many architectures that satisfy the complexity level and allows the choice of the best one. The best architecture chosen using this principle is completely compatible with the known architecture of the human visual system, and in addition, leads to several predictions. The analysis provides an argument for the computational necessity of attentive visual processes by exposing the computational limits of bottom-up early vision schemes. Further, this argues strongly for the validity of the computational approach to modeling the human visual system. Finally, a new explanation for the pop-out phenomenon so readily observed in visual search experiments, is proposed.     

Obvious inferences

The notion of obvious inference in predicate logic is discussed from the viewpoint of proof-checker applications in logic and mathematics education. A class of inferences in predicate logic is defined and it is proposed to identify it with the class of obvious logical inferences. The definition is compared with other approaches. The algorithm for implementing the obviousness decision procedure follows directly from the definition.     

A Divide-and-Conquer Learning Approach to Radial Basis Function Networks

This paper presents a new divide-and-conquer based learning approach to radial basis function (RBF) networks, in which a conventional RBF network is divided into several RBF sub-networks. Each of them individually takes an input sub-space as its input. The original networks output then becomes a linear combination of the sub-networks outputs with the coefficients adaptively learned together with the system parameters of each sub-network. Since this approach reduces the structural complexity of a RBF network by describing a high-dimensional modelling problem via several low-dimensional ones, the networks learning speed is considerably improved as a whole with the comparable generalization capability. The empirical studies have shown its outstanding performance on forecasting two real time series as well as synthetic data. Besides, we have found that the performance of this approach generally varies with the different decompositions of the networks input and the hidden layer. We therefore further explore the decomposition rule with the results verified by the experiments.