Manuscript symbols can be stored, recognized and retrieved from an entropic digital memory that is associative and distributed but yet declarative; memory retrieval is a constructive operation, memory cues to objects not contained in the memory are rejected directly without search, and memory operations can be performed through parallel computations. Manuscript symbols, both letters and numerals, are represented in Associative Memory Registers that have an associated entropy. The memory recognition operation obeys an entropy trade-off between precision and recall, and the entropy level impacts on the quality of the objects recovered through the memory retrieval operation. The present proposal is contrasted in several dimensions with neural networks models of associative memory. We discuss the operational characteristics of the entropic associative memory for retrieving objects with both complete and incomplete information, such as severe occlusions. The experiments reported in this paper add evidence on the potential of this framework for developing practical applications and computational models of natural memory.
We developed a proposal of a usability evaluation by the experts of a learning management system (LMS). The instrument is designed on the basis of the general criteria for the heuristic evaluation proposed by Nielsen, as well as on international standards, guides, and recommendations for software quality (ISO 9241 and ISO 9126). We present the results from applying the usability evaluation instrument to Metacampus, an LMS developed by and used at the Virtual University System, University of Guadalajara.
We present a proposal for a computational representation of competence maps that emphasises relationships of inclusion/part-of and specialization/generalization, and a generic approach to the construction of probabilistic relational learner models based on those competence maps, in which conditional probability tables are built on the basis of the kind of relationships between competences and, for the case of inclusion/part-of relationships, on the number of those relationships. We justify the use of noisy-or as a substitute for composite conditional tables produced by a competence being part of many other competences. Preliminary testing of both frameworks, for computational representation of competence maps and the construction of probabilistic graphical models from them, suggest coherence with reality.
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