This paper discusses a novel hybrid approach for text categorization that combines a machine learning algorithm, which provides a base model trained with a labeled corpus, with a rule-based expert system, which is used to improve the results provided by the previous classifier, by filtering false positives and dealing with false negatives. The main advantage is that the system can be easily fine-tuned by adding specific rules for those noisy or conflicting categories that have not been successfully trained. We also describe an implementation based on k-Nearest Neighbor and a simple rule language to express lists of positive, negative and relevant (multiword) terms appearing in the input text. The system is evaluated in several scenarios, including the popular Reuters-21578 news corpus for comparison to other approaches, and categorization using IPTC metadata, EUROVOC thesaurus and others. Results show that this approach achieves a precision that is comparable to top ranked methods, with the added value that it does not require a demanding human expert workload to train.

We define sentence generalization and generalization diagrams as a special case of least general generalization (LGG) as applied to linguistic parse trees. Similarity measure between linguistic parse trees is developed as LGG operation on the lists of sub-trees of these trees. The diagrams introduced are representation of mapping between the syntactic generalization level and semantic generalization level. Generalization diagrams are intended as a framework to compute semantic similarity between texts relying on linguistic parse tree data. Such structured approach significantly improves text relevance assessment in a horizontal domain, where ontologies are not available

We introduce a search engine and information retrieval system for providing access to opinion data. Natural language technology of generalization of syntactic parse trees is introduced as a similarity measure between subjects of textual opinions to link them on the fly. Information extraction algorithm for automatic summarization of web pages in the format of Google sponsored links is presented. We outline the usability of the implemented system, integrated opinion delivery environment (IODE).

It has been also demonstrated that emotional In its basic underpinning assumptions, cognitive load states (e.g., negative mood or anxiety) directly influence theory relies on the analogy between the information cognitive task performance and the operation of working processing aspects of evolution by natural selection and memory, while less evidence exists about the effect of the human cognition (Sweller & Sweller, 2006). It considers emotional content of the processed information (e.g., both biological evolution and human cognition as Kensinger & Corkin, 2003).

This paper investigates feature level fusion for enhancing fault detection from vibration signals in an ocean turbine. Changes in vibration signatures from such rotating machinery typically indicate the presence of a problem such as a shift in its orientation or mechanical impact from its environment. We applied feature level fusion to vibration data acquired from two accelerometers attached to a box fan, and then assessed the abilities of twelve well known machine learners to detect changes in state from the raw accelerometer data and from the fused data. Analysis of the performance of these classifiers showed an overall performance improvement in all twelve classifiers in detecting the state of the fan from the fused data versus from the data from the two individual sensor channels.

List of the organizers and conference committees for FLAIRS-24, including special tracks.

Logic programming has developed as a rich field, built over a logical substratum whose main constituent is a nonclassical form of negation, sometimes coexisting with classical negation. The field has seen the advent of a number of alternative semantics, with Kripke-Kleene semantics, the well-founded semantics, the stable model semantics, and the answer-set semantics standing out as the most successful. We show that all aforementioned semantics are particular cases of a generic semantics, in a framework where classical negation is the unique form of negation and where the literals in the bodies of the rules can be `marked' to indicate that they can be the targets of hypotheses. A particular semantics then amounts to choosing a particular marking scheme and choosing a particular set of hypotheses. When a literal belongs to the chosen set of hypotheses, all marked occurrences of that literal in the body of a rule are assumed to be true, whereas the occurrences of that literal that have not been marked in the body of the rule are to be derived in order to contribute to the firing of the rule. Hence the notion of hypothetical reasoning that is presented in this framework is not based on making global assumptions, but more subtly on making local, contextual assumptions, taking effect as indicated by the chosen marking scheme on the basis of the chosen set of hypotheses. Our approach offers a unified view on the various semantics proposed in logic programming, classical in that only classical negation is used, and links the semantics of logic programs to mechanisms that endow rule-based systems with the power to harness hypothetical reasoning.

We solve constraint satisfaction problems through translation to answer set programming (ASP). Our reformulations have the property that unit-propagation in the ASP solver achieves well defined local consistency properties like arc, bound and range consistency. Experiments demonstrate the computational value of this approach.

Bound propagation is an important Artificial Intelligence technique used in Constraint Programming tools to deal with numerical constraints. It is typically embedded within a search procedure (branch and prune) and used at every node of the search tree to narrow down the search space, so it is critical that it be fast. The procedure invokes constraint propagators until a common fixpoint is reached, but the known algorithms for this have a pseudo-polynomial worst-case time complexity: they are fast indeed when the variables have a small numerical range, but they have the well-known problem of being prohibitively slow when these ranges are large. An important question is therefore whether strongly-polynomial algorithms exist that compute the common bound consistent fixpoint of a set of constraints. This paper answers this question. In particular we show that this fixpoint computation is in fact NP-complete, even when restricted to binary linear constraints.

The concept of modeling multiple complex adaptive systems (CAS) as if they were voting processes proposes that an Error Resilient Data Fusion (ERDF) method can help to mitigate the effects of emergent properties in CAS system-of-systems (SoS). The property of emergence in a CAS composed of multiple, multi-modal sensors poses specific problems for fusion processes due to the difficulty in predicting and accounting for sensor performance under disparate environmental conditions. This paper compares the voting and Choquet integral fusion methods in the context of a multi-modal sensor ERDF SoS.