Given the limited performance of 2D cellular automata in terms of space when the number of documents increases and in terms of visualization clusters, our motivation was to experiment these cellular automata by increasing the size to view the impact of size on quality of results. The representation of textual data was carried out by a vector model whose components are derived from the overall balancing of the used corpus Term Frequency - Inverse Document Frequency (TF - IDF).The WorldNet thesaurus has been used to address the problem of the lemmatization of the words because the representation used in this study is that of the bags of words. Another independent method of the language was used to represent textual records is that of the n-grams. Several measures of similarity have been tested. To validate the classification we have used two measures of assessment based on the recall and precision (f-measure and entropy). The results are promising and confirm the idea to increase the dimension to the problem of the spatiality of the classes. The results obtained in terms of purity class (ie the minimum value of entropy) shows that the number of documents over longer believes the results are better for 3D cellular automata, which was not obvious to 2D the dimension. In terms of spatial navigation, cellular automata provide very good 3D performance visualization than 2D cellular automata.

We apply the spike-and-slab Restricted Boltzmann Machine (ssRBM) to texture modeling. The ssRBM with tiled-convolution weight sharing (TssRBM) achieves or surpasses the state-of-the-art on texture synthesis and inpainting by parametric models. We also develop a novel RBM model with a spike-and-slab visible layer and binary variables in the hidden layer. This model is designed to be stacked on top of the TssRBM. We show the resulting deep belief network (DBN) is a powerful generative model that improves on single-layer models and is capable of modeling not only single high-resolution and challenging textures but also multiple textures.

This note gives a simple analysis of a randomized approximation scheme for matrix multiplication proposed by Sarlos (2006) based on a random rotation followed by uniform column sampling. The result follows from a matrix version of Bernstein's inequality and a tail inequality for quadratic forms in subgaussian random vectors.

There are many new forms of interfacing human users to machines. We persevere here electric mechanical form of interaction between human and machine. The emergence of brain-computer interface allows mind-to-movement systems. The story of the Pied Piper inspired us to devise some new heuristics for interfacing human motor system using brain waves by combining head helmet and LumbarMotionMonitor For the simulation we use java GridGain Brain responses of classified subjects during training indicates that Probe can be the best stimulus to rely on in distinguishing between knowledgeable and not knowledgeable

Most optimization problems in real life applications are often highly nonlinear. Local optimization algorithms do not give the desired performance. So, only global optimization algorithms should be used to obtain optimal solutions. This paper introduces a new nature-inspired metaheuristic optimization algorithm, called Hoopoe Heuristic (HH). In this paper, we will study HH and validate it against some test functions. Investigations show that it is very promising and could be seen as an optimization of the powerful algorithm of cuckoo search. Finally, we discuss the features of Hoopoe Heuristic and propose topics for further studies.

The Xapagy cognitive architecture has been designed with the explicit goal of narrative reasoning: to model and mimic the activities performed by humans when witnessing, reading, recalling, narrating and talking about stories. Xapagy has been developed from scratch, which required us to revisit many of the problems identified in the classic literature of the story understanding. In particular, the Xapagy architecture takes an unusual approach to knowledge representation: the autobiographical narrative is the only source of knowledge, the autobiographical memory is the only memory model and there is no retrieval from long term into working memory. The claim made by this paper is that these design decisions, supported by the shadowing / headless shadows based reasoning mechanism, can yield a system which can successfully perform narrative reasoning. We support the claim by a detailed description of the representation and reasoning model.

Note: To comply with the blind reviewing guidelines, the name of the system in this paper has been changed to SWNN (system with no name) and the name of the language employed by the system to LWNN (language with no name). We are swimming in a sea of stories, coming from printed, audio and visual media as well as delivered by live speech. Even more important is the narrative of our own lives, which includes events which we witness, but also stories we plan, infer, imagine or daydream. Agents interacting with humans will need to become adept on manipulating stories. This includes creating stories from their life experience, recalling or re-narrating stories with various levels of accuracy, predicting future events in stories, expressing surprise and so on.

Many cognitive systems deploy multiple, closed, individually consistent models which can represent interpretations of the present state of the world, moments in the past, possible futures or alternate versions of reality. While they appear under different names, these structures can be grouped under the general term of worlds. The Xapagy architecture is a story-oriented cognitive system which relies exclusively on the autobiographical memory implemented as a raw collection of events organized into world-type structures called {\em scenes}. The system performs reasoning by shadowing current events with events from the autobiography. The shadows are then extrapolated into headless shadows corresponding to predictions, hidden events or inferred relations.

This paper describes the analysis of a selected testbed of Semantic Web ontologies, by a SPARQL query, which determines those ontologies that can be related to the description logic DL, introduced in [4] and studied in [9]. We will see that a reasonable number of them is expressible within such computationally efficient language. We expect that, in a long-term view, a temporalization of description logics, and consequently, of OWL(2), can open new perspectives for the inclusion in this language of a greater number of ontologies of the testbed and, hopefully, of the "real world".

There are several contexts of non-monotonic reasoning where a priority between rules is established whose purpose is preventing conflicts. One formalism that has been widely employed for non-monotonic reasoning is the sceptical one known as Defeasible Logic. In Defeasible Logic the tool used for conflict resolution is a preference relation between rules, that establishes the priority among them. In this paper we investigate how to modify such a preference relation in a defeasible logic theory in order to change the conclusions of the theory itself. We argue that the approach we adopt is applicable to legal reasoning where users, in general, cannot change facts or rules, but can propose their preferences about the relative strength of the rules. We provide a comprehensive study of the possible combinatorial cases and we identify and analyse the cases where the revision process is successful. After this analysis, we identify three revision/update operators and study them against the AGM postulates for belief revision operators, to discover that only a part of these postulates are satisfied by the three operators.