Automated Deduction Techniques for the Management of Personalized Documents

This work is about a real-world application of automated deduction. The application is the management of documents (such as mathematical textbooks) as they occur in a readily available tool. In this Slicing Information Technology tool, documents are decomposed (sliced) into small units. A particular application task is to assemble a new document from such units in a selective way, based on the user's current interest and knowledge. It is argued that this task can be naturally expressed through logic, and that automated deduction technology can be exploited for solving it. More precisely, we rely on first-order clausal logic with some default negation principle, and we propose a model computation theorem prover as a suitable deduction mechanism. Beyond solving the task at hand as such, with this work we contribute to the quest for arguments in favor of automated deduction techniques in the real world. Also, we argue why we think that automated deduction techniques are the best choice here.     

Estimating camera and object translation in the presence of camera rotation

This paper deals with stereo camera-based estimation of sensor translation in the presence of modest sensor rotation and moving objects. It also deals with the estimation of object translation from a moving sensor. The approach is based on Gabor filters, direct passive navigation, and Kalman filters.Three difficult problems associated with the estimation of motion from an image sequence are solved. (1) The temporal correspondence problem is solved using multi-scale prediction and phase gradients. (2) Segmentation of the image measurements into groups belonging to stationary and moving objects is achieved using the Mahalanobis distance. (3) Compensation for sensor rotation is achieved by internally representing the inter-frame (short-term) rotation in a rigid-body model. These three solutions possess a circular dependency, forming a cycle of perception. A seeding process is developed to correctly initialize the cycle. An additional complication is the translation-rotation ambiguity that sometimes exists when sensor motion is estimated from an image velocity field. Temporal averaging using Kalman filters reduces the effect of motion ambiguities. Experimental results from real image sequences confirm the utility of the approach.Financial support from the Natural Science and Engineering Research Council (NSERC) of Canada is acknowledged.     

Software Quality Evaluation Based on Expert Judgement

A method using expert judgement for the evaluation of software quality is presented. The underlying principle of the approach is the encoding of experts' tacit knowledge into probabilistic measures associated with the achievement level of software quality attributes. An aggregated quality measure is obtained based on preference statements related to the quality attributes. The technical objectives of the paper are to develop of a generic and operationally feasible measurement technique to transform the tacit knowledge of a software expert to a probability distribution depicting his/her uncertainty of the level of achievement related to a quality attribute; to develop rules for the construction of a consensus probability measure based on expert-specific probability measures; to derive a framework for specifying software quality strategy and for evaluating the acceptance of a software produced in a software development process;The above technical developments are used to support group decision-making regarding the launch or implementation decision of a software version; the allocation of resources during the software development process.     

Nonblockability of Switches with the Cayley Graph Structure for Serial Transfer of Data Blocks. Generalized Hypercubes and Multidimensional Grids

A general method of conflictless arbitrary permutation of large data elements that can be divided into a multitude of smaller data blocks was considered for switches structured as the Cayley graphs. The method was specified for arbitrary permutations in the generalized hypercubes and multidimensional grids, and their characteristics were considered.     

Recognition Using Region Correspondences

Recognition systems attempt to recover information about the identity of observed objects and their location in the environment. A fundamental problem in recognition is pose estimation. This is the problem of using a correspondence between some portions of an object model and some portions of an image to determine whether the image contains an instance of the object, and, in case it does, to determine the transformation that relates the model to the image. The current approaches to this problem are divided into methods that use global properties of the object (e.g., centroid and moments of inertia) and methods that use local properties of the object (e.g., corners and line segments). Global properties are sensitive to occlusion and, specifically, to self occlusion. Local properties are difficult to locate reliably, and their matching involves intensive computation.We present a novel method for recognition that uses region information. In our approach the model and the image are divided into regions. Given a match between subsets of regions (without any explicit correspondence between different pieces of the regions) the alignment transformation is computed. The method applies to planar objects under similarity, affine, and projective transformations and to projections of 3-D objects undergoing affine and projective transformations. The new approach combines many of the advantages of the previous two approaches, while avoiding some of their pitfalls. Like the global methods, our approach makes use of region information that reflects the true shape of the object. But like local methods, our approach can handle occlusion.     

Directional derivatives of the maximum function

Directional differentiability of the function (x) = sup{f(x, u), u U} is proved for a class of smooth functions f. The result is applied to study the directional differentiability of the function (x) = sup{f(x, y), y F(x)}, where F is a multivalued mapping.Translated from Kibernetika i Sistemnyi Analiz, No. 2, pp. 171–173, March–April, 1992.     

Minimization of norms and logarithmic norms by diagonal similarities

It is a commonly occurring problem to find good norms · or logarithmic norms (·) for a given matrix in the sense that they should be close to respectively the spectral radius (A) and the spectral abscissa (A). Examples may be the certification thatA is convergent, i.e. (A)A<1 or stable, i.e. (A)(A)<0. Often the ordinary norms do not suffice and one would like to try simple modifications of them such as using an ordinary norm for a diagonally transformed matrix. This paper treats this problem for some of the ordinary norms.

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Minimisierung von Normen und Logarithmischen Normen durch Diagonale Transformationen

Zusammenfassung Ein oft vorkommendes praktisches Problem ist die Konstruktion von guten Normen · und logarithmischen Normen (·) für eine gegebene MatrixA. Mit gut wird dann verstanden, daß A den Spektralradius (A)=max |₁| und (A) die Spektralabszisse (A)=max Re _i gut approximieren. Beispiele findet man für konvergente Matrizen wo (A)A<1 gewünscht ist, und für stabile Matrizen wo (A)(A)<0 zu zeigen ist. Wir untersuchen hier, wie weit man mit Diagonaltransformationen und dengewöhnlichsten Normen kommen kann.

     

Recognition of handwritten musical notes by a modified Neocognitron

A neural network for recognition of handwritten musical notes, based on the well-known Neocognitron model, is described. The Neocognitron has been used for the what pathway (symbol recognition), while contextual knowledge has been applied for the where (symbol placement). This way, we benefit from dividing the process for dealing with this complicated recognition task. Also, different degrees of intrusiveness in learning have been incorporated in the same network: More intrusive supervised learning has been implemented in the lower neuron layers and less intrusive in the upper one. This way, the network adapts itself to the handwriting of the user. The network consists of a 13×49 input layer and three pairs of simple and complex neuron layers. It has been trained to recognize 20 symbols of unconnected notes on a musical staff and was tested with a set of unlearned input notes. Its recognition rate for the individual unseen notes was up to 93%, averaging 80% for all categories. These preliminary results indicate that a modified Neocognitron could be a good candidate for identification of handwritten musical notes.     

On characterizing the “knee” of the Pareto curve based on Normal-Boundary Intersection

This paper deals with the issue of generating one Pareto optimal point that is guaranteed to be in a desirable part of the Pareto set in a given multicriteria optimization problem. A parameterization of the Pareto set based on the recently developed normal-boundary intersection technique is used to formulate a subproblem, the solution of which yields the point of maximum bulge, often referred to as the knee of the Pareto curve. This enables the identification of the good region of the Pareto set by solving one nonlinear programming problem, thereby bypassing the need to generate many Pareto points. Further, this representation extends the concept of the knee for problems with more than two objectives. It is further proved that this knee is invariant with respect to the scales of the multiple objective functions.The generation of this knee however requires the value of each objective function at the minimizer of every objective function (the pay-off matrix). The paper characterizes situations when approximations to the function values comprising the pay-off matrix would suffice in generating a good approximation to the knee. Numerical results are provided to illustrate this point. Further, a weighted sum minimization problem is developed based on the information in the pay-off matrix, by solving which the knee can be obtained.     

The Objective Conception of Context and Its Logic

In this paper, an objective conception of contexts based loosely upon situation theory is developed and formalized. Unlike subjective conceptions, which take contexts to be something like sets of beliefs, contexts on the objective conception are taken to be complex, structured pieces of the world that (in general) contain individuals, other contexts, and propositions about them. An extended first-order language for this account is developed. The language contains complex terms for propositions, and the standard predicate ist that expresses the relation that holds between a context and a proposition just in case the latter is true in the former. The logic for the objective conception features a global classical predicate calculus, a local logic for reasoning within contexts, and axioms for propositions. The specter of paradox is banished from the logic by allowing ist to be nonbivalent in problematic cases: it is not in general the case, for any context c and proposition p, that either ist(c,p) or ist(c, ¬ p). An important representational capability of the logic is illustrated by proving an appropriately modified version of an illustrative theorem from McCarthy's classic Blocks World example.