One common use for swarming agents is in social simulation. This paper reports on such a model developed to track protest activities at the May 2012 NATO summit in Chicago. The use of apoptotic stigmergic agents allows the model to run on-line, consuming two kinds of external data and reporting its results in real time.

Patients with schizophrenia are characterized by humor recognition deficit which is connected with their cognitive disorder such as inability to filter out irrelevant stimuli. As soon as patients with schizotypal and affective disorders easily recognize humor, this may be used as a strong diagnostic criterion in clinical practice. On the other hand humor recognition by artificial intellect became a hot question in computer science in a flow of attempts to bring human-computer communication closer to social. It is argued that schizophrenic and computer thinking have common features. Both have lack of social and emotional context understanding. To compare failures in humor recognition made by patients with schizophrenia vs computer may move forward theory and practice of both clinical psychology and computer science.

Semantic role labeling (SRL) is a technique of semantic interpretation of text on the sentence level. In this paper, we present a corpus that is labeled with semantic roles for biological transport events. The corpus was built using domain knowledge provided by ontologies. We also report on a word-chunking approach for identifying semantic roles of biomedical predicates describing transport events. We trained a first-order Conditional Random Fields (CRF) for chunking applications with the traditional role labeling features and also domain-specific features. The results show that the system performance varies between different roles and the performance was not improved for all roles by introducing domain specific features.

The fable of Hansel and Gretel describes the plight of two children over two types of threat; harm to their immediate survival and pain from hunger. The two contexts of self-preservation and feeding are evident from the flow of the story dialog, therefore an automatic re-playing of dialog can be realized by picking sentences from two lists; one containing sentences in the context of self-preservation, the other in the context of feeding. Theory and Internet humor appreciation surveys suggest that humorous sentences in the context of self-preservation have relatively constant preference with respect to age, while in the context of hunger and protection of feeding turf to decline with age, reflecting the reduced need for food with aging. Sentences in the context of sociosexual relationships increased in preference until adulthood then declined with maturity. Also, sentences in parenting context, such as when caring for offspring, society and the environment were found to increase in preference with age and maturity. Therefore in order to construct a recursive Hansel and Gretel dialog for audience of all ages, two lists of sentences are added to feeding: In sociosexual and parenting contexts. The self-preservation list is paired with one of the remaining three, representing three stages of age; youth, adulthood and maturity. The single thread story of Hansel and Gretel serves as a template for recursive dialog, making it possible to create alternative threads and unbound possibilities for plots, thereby duplicating the story structure without repeating the narrative.

Discussion of "Latent variable graphical model selection via convex optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky [arXiv:1008.1290].

Regression, unlike classification, has lacked a comprehensive and effective approach to deal with cost-sensitive problems by the reuse (and not a re-training) of general regression models. In this paper, a wide variety of cost-sensitive problems in regression (such as bids, asymmetric losses and rejection rules) can be solved effectively by a lightweight but powerful approach, consisting of: (1) the conversion of any traditional one-parameter crisp regression model into a two-parameter soft regression model, seen as a normal conditional density estimator, by the use of newly-introduced enrichment methods; and (2) the reframing of an enriched soft regression model to new contexts by an instance-dependent optimisation of the expected loss derived from the conditional normal distribution.

K-nearest neighbors (KNN) method is used in many supervised learning classification problems. Potential Energy (PE) method is also developed for classification problems based on its physical metaphor. The energy potential used in the experiments are Yukawa potential and Gaussian Potential. In this paper, I use both applet and MATLAB program with real life benchmark data to analyze the performances of KNN and PE method in classification problems. The results show that in general, KNN and PE methods have similar performance. In particular, PE with Yukawa potential has worse performance than KNN when the density of the data is higher in the distribution of the database. When the Gaussian potential is applied, the results from PE and KNN have similar behavior. The indicators used are correlation coefficients and information gain.

Controlled natural languages (mostly English-based) recently have emerged as seemingly informal supplementary means for OWL ontology authoring, if compared to the formal notations that are used by professional knowledge engineers. In this paper we present by examples controlled Latvian language that has been designed to be compliant with the state of the art Attempto Controlled English. We also discuss relation with controlled Lithuanian language that is being designed in parallel.

Ontologies and rules are usually loosely coupled in knowledge representation formalisms. In fact, ontologies use open-world reasoning while the leading semantics for rules use non-monotonic, closed-world reasoning. One exception is the tightly-coupled framework of Minimal Knowledge and Negation as Failure (MKNF), which allows statements about individuals to be jointly derived via entailment from an ontology and inferences from rules. Nonetheless, the practical usefulness of MKNF has not always been clear, although recent work has formalized a general resolution-based method for querying MKNF when rules are taken to have the well-founded semantics, and the ontology is modeled by a general oracle. That work leaves open what algorithms should be used to relate the entailments of the ontology and the inferences of rules. In this paper we provide such algorithms, and describe the implementation of a query-driven system, CDF-Rules, for hybrid knowledge bases combining both (non-monotonic) rules under the well-founded semantics and a (monotonic) ontology, represented by a CDF Type-1 (ALQ) theory. To appear in Theory and Practice of Logic Programming (TPLP)

Much work has been done refining and characterizing the receptive fields learned by deep learning algorithms. A lot of this work has focused on the development of Gabor-like filters learned when enforcing sparsity constraints on a natural image dataset. Little work however has investigated how these filters might expand to the temporal domain, namely through training on natural movies. Here we investigate exactly this problem in established temporal deep learning algorithms as well as a new learning paradigm suggested here, the Temporal Autoencoding Restricted Boltzmann Machine (TARBM).