Information about the spread of crop disease is vital in developing countries, and as a result the governments of such countries devote scarce resources to gathering such data. Unfortunately, current surveys tend to be slow and expensive, and hence also tend to gather insufficient quantities of data. In this work we describe three general methods for improving the use of survey resources by performing data collection with mobile devices and by directing survey progress through the application of AI techniques. First, we describe a spatial disease density model based on Gaussian process ordinal regression, which offers a better representation of the disease level distribution, as compared to the statistical approaches typically applied. Second, we show how this model can be used to dynamically route survey teams to obtain the most valuable survey possible given a fixed budget. Third, we demonstrate that the diagnosis of plant disease can be automated using images taken by a camera phone, enabling data collection by survey workers with only basic training. We have applied our methods to the specific challenge of viral cassava disease monitoring in Uganda, for which we have implemented a real-time mobile survey system that will soon see practical use.

PODTGolog [Rens, 2010] is a Golog dialect attempting Broadly speaking, my research concerns combining to deal with partially observable MDP (POMDP) logic of action and POMDP theory in a coherent, environments. PODTGolog has not been given a mathematical theoretically sound language for agent programming.

Most of the text mining tasks, such as clustering, is dominated by statistical approaches that treat text as a bag of words. Semantics in the text is largely ignored in the mining process, and the mining results are often not easily interpretable. One particular challenge faced by such approaches is short text understanding, as short text lacks enough content from which a statistical conclusion can be drawn. For example, traditional topic analysis methods consider topic segments with tens of hundreds of words. Latent topic modeling, such as latent Dirichlet allocation, also requires sufficient words to infer document topic distribution. We enhance machine learning algorithms by first giving the machine a probabilistic knowledgebase that contains as big, rich, and consistent concepts (of worldly facts) as those in our mental world. Then a Bayesian inference mechanism is developed to conceptualize words and short text. We conducted comprehensive tests of our method on conceptualizing set of text terms, as well as clustering Twitter messages (tweets), which are typically approximately ten words long. Compared to latent semantic topic modeling and other four kinds of methods that using WordNet, Freebase and Wikipedia (category links and explicit semantic analysis), we show significant improvements in terms of tweets clustering accuracy.

Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within an application study of a manufacturing system. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures. The system selected is the workshop of SCIMAT clinker, cement factory in Algeria.

In computer science, ontology is a model of a domain in the form of classes and of relationships between these classes. Classes are organized in a graph the arrows of which are semantic relations. Ontology is static because the class hierarchy is fixed. In paleontology, systematic (i.e., the class hierarchies and the class relationships) is complicated by the time variable. Morphological changes over time yield, by natural selection, the emergence of new forms (taxa) differing from the ancestral morph and contemporaneous taxa of the same class hierarchy. Discovering new taxa implies, therefore, the rearrangement of the class hierarchy or the definition of new classes, based on the degree of atypicality of the new morph. Note that this phenomenon occurs in many domains such as physics, biology, linguistics, for example.

This paper presents an automatic extraction and classification approach of opinions in texts. Therefore, we propose a similarity measurement calculating semantically similarities between a word and predefined subgroups of seed words. We have evaluated our approach on the semantic evaluation company “SemEval 2007” corpus, and we obtained promising results: the best value of Precision, 62%; and F1, 61%; as an improvement of 20 % compared to the participant systems.

One of the key challenges in electronic government (e-government) is the development of systems that can be easily integrated and interoperated to provide seamless services delivery to citizens. In recent years, Semantic Web technologies based on ontology have emerged as promising solutions to the above engineering problems. However, current research practicing semantic development in e-government does not focus on the application of available methodologies and platforms for developing government domain ontologies. Furthermore, only a few of these researches provide detailed guidelines for developing semantic ontology models from a government service domain. This research presents a case study combining an ontology building methodology and two state-of-the-art Semantic Web platforms namely Protege and Java Jena ontology API for semantic ontology development in e-government. Firstly, a framework adopted from the Uschold and King ontology building methodology is employed to build a domain ontology describing the semantic content of a government service domain. Thereafter, UML is used to semi-formally represent the domain ontology. Finally, Protege and Jena API are employed to create the Web Ontology Language (OWL) and Resource Description Framework (RDF) representations of the domain ontology respectively to enable its computer processing. The study aims at: (1) providing e-government developers, particularly those from the developing world with detailed guidelines for practicing semantic content development in their e-government projects and (2), strengthening the adoption of semantic technologies in e-government. The study would also be of interest to novice Semantic Web developers who might used it as a starting point for further investigations.

GENE_ARCH is a Generative Design System that combines Pareto Genetic Algorithms with an advanced energy simulation engine. This work explores its integration with a Shape Grammar, acting as GENE_ARCH’s shape generation module. The islamic patio house typology is readdressed in a contemporary context, by improving its energy-efficiency, and rethinking its role in the genesis of high-density urban areas, while respecting its specific spatial organization and cultural grounding. Field work was carried out in Marrakesh, surveying a number of patio houses, becoming the Corpus of Design, from where a shape grammar was generated. The computational implementation of the patio house grammar was done within GENE_ARCH. The resulting program was able to generate new, alternative patio houses designs that were more energy efficient, while respecting the traditional rules captured from the analysis of existing houses. After the computational system was fully implemented, it was possible to realise a large number of experiments. The first experiments kept more restrained rules, thus generating new designs that closer resembled the existing ones. The progressive relaxation of rules and constraints allowed for a larger number of variations to emerge. Analysis of energy results provide insight into the main patterns resulting from the GA search processes.

The identification of the language of the script is an important stage in the process of recognition of the writing. There are several works in this research area, which treat various languages. Most of the used methods are global or statistical. In this present paper, we study the possibility of using the features of scripts to identify the language. The identification of the language of the script by characteristics returns the identification in the case of multilingual documents less difficult. We present by this work, a study on the possibility of using the structural features to identify the Arabic language from an Arabic / Latin text.

Mining association rules is a task of data mining, which extracts knowledge in the form of significant implication relation of useful items (objects) from a database. Mining multilevel association rules uses concept hierarchies, also called taxonomies and defined as relations of type 'is-a' between objects, to extract rules that items belong to different levels of abstraction. These rules are more useful, more refined and more interpretable by the user. Several algorithms have been proposed in the literature to discover the multilevel association rules. In this article, we are interested in the problem of discovering multi-level frequent itemsets under constraints, involving the user in the research process. We proposed a technique for modeling and interpretation of constraints in a context of use of concept hierarchies. Three approaches for discovering multi-level frequent itemsets under constraints were proposed and discussed: Basic approach, "Test and Generate" approach and Pruning based Approach.