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В статье рассматривается синдром белого пятна креветок WSD, вызываемый двуцепочечным ДНК-вирусом семейства Nimaviridae рода Whispovirus, WSSV. Этот вид вируса вызывает повсеместные вспышки данного заболевания, наносящего огромный экономический ущерб хозяйствам по разведению креветок. Попадая в организм, вирус ассоциируется в тканях экто– и мезодермального происхождения, провоцируя возникновение покраснений под хитиновым покровом тела, а затем образование белых пятен по самому покрову. В случае возникновения эпидемии смертность ракообразных составляет 100%. Представляется необходимым изучить возможные способы лечения и профилактики распространения данного вируса для обеспечения полного функционирования креветочных ферм и снижения экономических потерь для производителя., The article studies the white spot disease in shrimp WSD, caused by the double-stranded DNA virus of the Nimaviridae family of the Whispovirus genus, WSSV. This type of virus causes widespread outbreaks of the disease, leading to enormous economic damage to shrimp farming. Once in the body, the virus manifests in the tissues of ectodermal and mesodermic origin, causing redness under the chitin cover of the integument, and white spots on the cover itself. In the event of an epidemic, the mortality of crustaceans would be 100%. It deems to be necessary to study treatment and prevention of the virus spread in order to ensure the full functioning of shrimp farms and to reduce the economic losses to the farmer., Международный научно-исследовательский журнал, Выпуск 6 (120) 2022, Pages 40-45

Word Sense Disambiguation (WSD) aims to predict the correct sense of a word given its context. This problem is of extreme importance in Arabic, as written words can be highly ambiguous, 43% of diacritized words have multiple interpretations and the percentage increases to 72% for non-diacritized words. Nevertheless, most Arabic written text does not have diacritical marks. Gloss-based WSD methods measure the semantic similarity or the overlap between the context of a target word that needs to be disambiguated and the dictionary definition of that word (gloss of the word). Arabic gloss WSD suffers from a lack of context-gloss datasets. In this paper, we present an Arabic gloss-based WSD technique. We utilize the celebrated Bidirectional Encoder Representation from Transformers (BERT) to build two models that can efficiently perform Arabic WSD. These models can be trained with few training samples since they utilize BERT models that were pretrained on a large Arabic corpus. Our experimental results show that our models outperform two of the most recent gloss-based WSDs when we test them against the same test data used to evaluate our model. Additionally, our model achieves an F1-score of 89% compared to the best-reported F1-score of 85% for knowledge-based Arabic WSD. Another contribution of this paper is introducing a context-gloss benchmark that may help to overcome the lack of a standardized benchmark for Arabic gloss-based WSD.

Recently, generative approaches have been used effectively to provide definitions of words in their context. However, the opposite, i.e., generating a usage example given one or more words along with their definitions, has not yet been investigated. In this work, we introduce the novel task of Exemplification Modeling (ExMod), along with a sequence-to-sequence architecture and a training procedure for it. Starting from a set of (word, definition) pairs, our approach is capable of automatically generating high-quality sentences which express the requested semantics. As a result, we can drive the creation of sense-tagged data which cover the full range of meanings in any inventory of interest, and their interactions within sentences. Human annotators agree that the sentences generated are as fluent and semantically-coherent with the input definitions as the sentences in manually-annotated corpora. Indeed, when employed as training data for Word Sense Disambiguation, our examples enable the current state of the art to be outperformed, and higher results to be achieved than when using gold-standard datasets only. We release the pretrained model, the dataset and the software at https://github.com/SapienzaNLP/exmod.

One of the main issues in the present and upcoming decades is the improvement of natural resource use efficiencies. The industrial use of water is preferred as a target for the implementation of sustainable models of water management with the purpose of reducing the use of this resource and generates less waste. This study aims the application of the Water Source Diagram (WSD) method in paper industry, typology associated to high wastewater generation, as a potential strategy to achieve sustainable industrial processes through wastewater reuse. Operational data from paper industry were provided by previous works. Two scenarios of freshwater consumption were generated to model the system. Both cases showed that the freshwater consumption may be reduced over 30.22%, in relation to the baseline of no reuse. The efficiency of the WSD was also compared to other results reported in the literature. Although economic evaluation has not been addressed in this work, the method could be considered as a strategic tool for decision making. Uno de los principales problemas de la presente y las próximas décadas es la mejora de la eficiencia en el uso de los recursos naturales. Se prefiere el uso industrial del agua como objetivo para la implementación de modelos sostenibles de gestión del agua con el propósito de reducir el uso de este recurso y generar menos residuos. Este estudio tiene como objetivo la aplicación del método Diagrama de fuente de agua (DFA) en la industria papelera, tipología asociada a alta generación de aguas residuales, como una estrategia potencial para lograr procesos industriales sostenibles a través de la reutilización de aguas residuales. Los datos operativos de la industria del papel fueron proporcionados por trabajos anteriores. Se generaron dos escenarios de consumo de agua dulce para modelar el sistema. Ambos casos mostraron que el consumo de agua dulce puede reducirse más del 30,22%, en relación con la línea de base de no reutilización. La eficiencia del DFA también se comparó con otros resultados reportados en la literatura. Si bien la evaluación económica no se ha abordado en este trabajo, el método podría considerarse como una herramienta estratégica para la toma de decisiones. Um dos principais problemas nas décadas atuais e nas próximas é a melhoria da eficiência do uso dos recursos naturais. O uso industrial da água é preferido como alvo para a implantação de modelos sustentáveis de gestão da água com o objetivo de reduzir o uso desse recurso e gerar menos resíduos. Este estudo visa à aplicação do método Diagrama de Fontes de Água (DFA) na indústria de papel, tipologia associada à alta geração de efluentes, como uma estratégia potencial para alcançar processos industriais sustentáveis por meio do reuso de efluentes. Os dados operacionais da indústria de papel foram fornecidos por trabalhos anteriores. Dois cenários de consumo de água doce foram gerados para modelar o sistema. Ambos os casos mostraram que o consumo de água doce pode ser reduzido em mais de 30,22%, em relação à linha de base de não reutilização. A eficiência do DFA também foi comparada com outros resultados relatados na literatura. Embora a avaliação econômica não tenha sido abordada neste trabalho, o método pode ser considerado uma ferramenta estratégica para a tomada de decisões.

We explore human judgments on how well individual patterns of 29 target verbs from the Pattern Dictionary of English Verbs describe their random KWICs. We focus on cases where more than one pattern is judged as highly appropriate for a given KWIC and seek to estimate the effect of event participants (arguments) being denotatively similar in two patterns, considering all pair combinations in a given lemma. We compare this effect to the effect of several contextual features of the KWICs, the effect of paired PDEV implicatures implying each other, and the effect of belonging to a given lemma. We show that the lemma effect is still stronger than any feature going across lemmas we have examined so far, so that each verb appears to be a little universe in its own right.

In this thesis work, we focused on the study and development of a system identification using UWB-Ultra-Wide-Band short range radar to detect the objects and particularly the vulnerable road users (VRUs) that have low RCS-Radar Cross Section- such as cyclist and pedestrian. This work is composed of two stages i.e. detection and recognition. In the first approach of detection stage, we have proposed and studied a robust UWB radar detector that works on one dimension 1-D radar data ( A-scan). It relies on a combination of Higher Order Statistics (HOS) and the well-known CA-CFAR (Cell-Averaging Constant False Alarm Rate) detector. This combination is performed by firstly applying the HOS to the received radar signal in order to suppress the noise. After eliminating the noise of the received radar signal, we apply the CA-CFAR detector. By doing this combination, we finally have an UWB radar detector which is robust against the noise and works with the adaptive threshold. In order to enhance the detection performance, we have evaluated the approach of using two dimensions 2-D (B-Scan) radar data. In this 2-D radar approach, we proposed a new method of noise suppression, which works on this B-Scan data. The proposed method is a combination of WSD (Wavelet Shrinkage Denoising) and HOS. To evaluate the performance of this method, we performed a comparative study with the other noise removal methods in literature including Principal Component Analysis (PCA), Singular Value Decomposition (SVD), WSD and HOS. The Signal-to-Noise Ratio (SNR) of the final result has been computed to compare the effectiveness of individual noise removal techniques. It is observed that a combination of WSD and HOS has better capability to remove the noise compared to that of the other applied techniques in the literature; especially it is found that it allows to distinguish efficiency the pedestrian and cyclist over the noise and clutters whereas other techniques are not showing significant result. In the recognition phase, we have exploited the data from the two approaches 1-D and 2-D, obtained from the detection method. In the first 1-D approach, Support Vector Machines (SVM) and Deep Belief Networks (DBN) have been used and evaluated to identify the target based on the radar signature. The results show that the SVM gives good performances for the proposed system where the total recognition accuracy rate could achieve up to 96,24%. In the second approach of this 1-D radar data, the performance of several DBN architectures compose of different layers have been evaluated and compared. We realised that the DBN architecture with four hidden layers performs better than those of with two or three hidden layers. The results show also that this architecture achieves up to 97.80% of accuracy. This result also proves that the performance of DBN is better than that of SVM (96.24%) in the case of UWB radar target recognition system using 1-D radar signature. In the 2-D approach, the Convolutional Neural Network (CNN) has been exploited and evaluated. In this work, we have proposed and investigated three CNN architectures. The first architecture is the modified of Alexnet model, the second is an architecture with three convolutional layers and one fully connected layer, and the third is an architecture with five convolutional layers and two fully connected layers. The performance of these proposed architectures have been evaluated and compared. We found that the third architecture has a good performance where it achieves up to 99.59% of accuracy. Finally, we compared the performances obtained using CNN, DBN and SVM. The results show that CNN gives a better result in terms of accuracy compared to that of DBN and SVM. It allows to classify correctly the UWB radar targets like cyclist and pedestrian.; Dans ce travail de thèse, nous présentons nos travaux qui portent sur l’identification des cibles en général par un radar Ultra-Large Bande (ULB) et en particulier l’identification des cibles dont la surface équivalente radar est faible telles que les piétons et les cyclistes. Ce travail se décompose en deux parties principales, la détection et la reconnaissance. Dans la première approche du processus de détection, nous avons proposé et étudié un détecteur de radar ULB robuste qui fonctionne avec des données radar 1-D (A-scan) à une dimension. Il exploite la combinaison des statistiques d’ordres supérieurs et du détecteur de seuil automatique connu sous le nom de CA-CFAR pour Cell-Averaging Constant False Alarm Rate. Cette combinaison est effectuée en appliquant d’abord le HOS sur le signal reçu afin de supprimer une grande partie du bruit. Puis, après avoir éliminé le bruit du signal radar reçu, nous implémentons le détecteur de seuil automatique CA-CFAR. Ainsi, cette combinaison permet de disposer d’un détecteur de radar ULB à seuil automatique robuste. Afin d’améliorer le taux de détection et aller plus loin dans le traitement, nous avons évalué l’approche des données radar 2-D (B-Scan) à deux dimensions. Dans un premier temps, nous avons proposé une nouvelle méthode de suppression du bruit, qui fonctionne sur des données B-Scan. Il s’agit d’une combinaison de WSD et de HOS. Pour évaluer les performances de cette méthode, nous avons fait une étude comparative avec d’autres techniques de suppression du bruit telles que l’analyse en composantes principales, la décomposition en valeurs singulières, la WSD, et la HOS. Les rapports signal à bruit -SNR- des résultats finaux montrent que les performances de la combinaison WSD et HOS sont meilleures que celles des autres méthodes rencontrées dans la littérature. A la phase de reconnaissance, nous avons exploité les données des deux approches à 1-D et à 2-D obtenues à partir du procédé de détection. Dans la première approche à 1-D, les techniques SVM et le DBN sont utilisées et évaluées pour identifier la cible en se basant sur la signature radar. Les résultats obtenus montrent que la technique SVM donne de bonnes performances pour le système proposé où le taux de reconnaissance global moyen atteint 96,24%, soit respectivement 96,23%, 95,25% et 97,23% pour le cycliste, le piéton et la voiture. Dans la seconde approche à 1-D, les performances de différents types d’architectures DBN composées de différentes couches ont été évaluées et comparées. Nous avons constaté que l’architecture du réseau DBN avec quatre couches cachées est meilleure et la précision totale moyenne peut atteindre 97,80%. Ce résultat montre que les performances obtenues avec le DBN sont meilleures que celles obtenues avec le SVM (96,24%) pour ce système de reconnaissance de cible utilisant un radar ULB. Dans l’approche bidimensionnelle, le réseau de neurones convolutifs a été utilisé et évalué. Nous avons proposé trois architectures de CNN. La première est le modèle modifié d’Alexnet, la seconde est une architecture avec les couches de convolution arborescentes et une couche entièrement connectée, et la troisième est une architecture avec les cinq couches de convolution et deux couches entièrement connectées. Après comparaison et évaluation des performances de ces trois architectures proposées nous avons constaté que la troisième architecture offre de bonnes performances par rapport aux autres propositions avec une précision totale moyenne qui peut atteindre 99,59%. Enfin, nous avons effectué une étude comparative des performances obtenues avec le CNN, DBN et SVM. Les résultats montrent que CNN a les meilleures performances en termes de précision par rapport à DBN et SVM. Cela signifie que l’utilisation de CNN dans les données radar bidimensionnels permet de classer correctement les cibles radar ULB notamment pour les cibles à faible SER et SNR telles que les cyclistes ou les piétons.

In this thesis work, we focused on the study and development of a system identification using UWB-Ultra-Wide-Band short range radar to detect the objects and particularly the vulnerable road users (VRUs) that have low RCS-Radar Cross Section- such as cyclist and pedestrian. This work is composed of two stages i.e. detection and recognition. In the first approach of detection stage, we have proposed and studied a robust UWB radar detector that works on one dimension 1-D radar data ( A-scan). It relies on a combination of Higher Order Statistics (HOS) and the well-known CA-CFAR (Cell-Averaging Constant False Alarm Rate) detector. This combination is performed by firstly applying the HOS to the received radar signal in order to suppress the noise. After eliminating the noise of the received radar signal, we apply the CA-CFAR detector. By doing this combination, we finally have an UWB radar detector which is robust against the noise and works with the adaptive threshold. In order to enhance the detection performance, we have evaluated the approach of using two dimensions 2-D (B-Scan) radar data. In this 2-D radar approach, we proposed a new method of noise suppression, which works on this B-Scan data. The proposed method is a combination of WSD (Wavelet Shrinkage Denoising) and HOS. To evaluate the performance of this method, we performed a comparative study with the other noise removal methods in literature including Principal Component Analysis (PCA), Singular Value Decomposition (SVD), WSD and HOS. The Signal-to-Noise Ratio (SNR) of the final result has been computed to compare the effectiveness of individual noise removal techniques. It is observed that a combination of WSD and HOS has better capability to remove the noise compared to that of the other applied techniques in the literature; especially it is found that it allows to distinguish efficiency the pedestrian and cyclist over the noise and clutters whereas other techniques are not showing significant result. In the recognition phase, we have exploited the data from the two approaches 1-D and 2-D, obtained from the detection method. In the first 1-D approach, Support Vector Machines (SVM) and Deep Belief Networks (DBN) have been used and evaluated to identify the target based on the radar signature. The results show that the SVM gives good performances for the proposed system where the total recognition accuracy rate could achieve up to 96,24%. In the second approach of this 1-D radar data, the performance of several DBN architectures compose of different layers have been evaluated and compared. We realised that the DBN architecture with four hidden layers performs better than those of with two or three hidden layers. The results show also that this architecture achieves up to 97.80% of accuracy. This result also proves that the performance of DBN is better than that of SVM (96.24%) in the case of UWB radar target recognition system using 1-D radar signature. In the 2-D approach, the Convolutional Neural Network (CNN) has been exploited and evaluated. In this work, we have proposed and investigated three CNN architectures. The first architecture is the modified of Alexnet model, the second is an architecture with three convolutional layers and one fully connected layer, and the third is an architecture with five convolutional layers and two fully connected layers. The performance of these proposed architectures have been evaluated and compared. We found that the third architecture has a good performance where it achieves up to 99.59% of accuracy. Finally, we compared the performances obtained using CNN, DBN and SVM. The results show that CNN gives a better result in terms of accuracy compared to that of DBN and SVM. It allows to classify correctly the UWB radar targets like cyclist and pedestrian.; Dans ce travail de thèse, nous présentons nos travaux qui portent sur l’identification des cibles en général par un radar Ultra-Large Bande (ULB) et en particulier l’identification des cibles dont la surface équivalente radar est faible telles que les piétons et les cyclistes. Ce travail se décompose en deux parties principales, la détection et la reconnaissance. Dans la première approche du processus de détection, nous avons proposé et étudié un détecteur de radar ULB robuste qui fonctionne avec des données radar 1-D (A-scan) à une dimension. Il exploite la combinaison des statistiques d’ordres supérieurs et du détecteur de seuil automatique connu sous le nom de CA-CFAR pour Cell-Averaging Constant False Alarm Rate. Cette combinaison est effectuée en appliquant d’abord le HOS sur le signal reçu afin de supprimer une grande partie du bruit. Puis, après avoir éliminé le bruit du signal radar reçu, nous implémentons le détecteur de seuil automatique CA-CFAR. Ainsi, cette combinaison permet de disposer d’un détecteur de radar ULB à seuil automatique robuste. Afin d’améliorer le taux de détection et aller plus loin dans le traitement, nous avons évalué l’approche des données radar 2-D (B-Scan) à deux dimensions. Dans un premier temps, nous avons proposé une nouvelle méthode de suppression du bruit, qui fonctionne sur des données B-Scan. Il s’agit d’une combinaison de WSD et de HOS. Pour évaluer les performances de cette méthode, nous avons fait une étude comparative avec d’autres techniques de suppression du bruit telles que l’analyse en composantes principales, la décomposition en valeurs singulières, la WSD, et la HOS. Les rapports signal à bruit -SNR- des résultats finaux montrent que les performances de la combinaison WSD et HOS sont meilleures que celles des autres méthodes rencontrées dans la littérature. A la phase de reconnaissance, nous avons exploité les données des deux approches à 1-D et à 2-D obtenues à partir du procédé de détection. Dans la première approche à 1-D, les techniques SVM et le DBN sont utilisées et évaluées pour identifier la cible en se basant sur la signature radar. Les résultats obtenus montrent que la technique SVM donne de bonnes performances pour le système proposé où le taux de reconnaissance global moyen atteint 96,24%, soit respectivement 96,23%, 95,25% et 97,23% pour le cycliste, le piéton et la voiture. Dans la seconde approche à 1-D, les performances de différents types d’architectures DBN composées de différentes couches ont été évaluées et comparées. Nous avons constaté que l’architecture du réseau DBN avec quatre couches cachées est meilleure et la précision totale moyenne peut atteindre 97,80%. Ce résultat montre que les performances obtenues avec le DBN sont meilleures que celles obtenues avec le SVM (96,24%) pour ce système de reconnaissance de cible utilisant un radar ULB. Dans l’approche bidimensionnelle, le réseau de neurones convolutifs a été utilisé et évalué. Nous avons proposé trois architectures de CNN. La première est le modèle modifié d’Alexnet, la seconde est une architecture avec les couches de convolution arborescentes et une couche entièrement connectée, et la troisième est une architecture avec les cinq couches de convolution et deux couches entièrement connectées. Après comparaison et évaluation des performances de ces trois architectures proposées nous avons constaté que la troisième architecture offre de bonnes performances par rapport aux autres propositions avec une précision totale moyenne qui peut atteindre 99,59%. Enfin, nous avons effectué une étude comparative des performances obtenues avec le CNN, DBN et SVM. Les résultats montrent que CNN a les meilleures performances en termes de précision par rapport à DBN et SVM. Cela signifie que l’utilisation de CNN dans les données radar bidimensionnels permet de classer correctement les cibles radar ULB notamment pour les cibles à faible SER et SNR telles que les cyclistes ou les piétons.

The article presents the summary of a dataset related to the risks factors of white spot disease (WSD) of farmed shrimp (Penaeus monodon) in Khulna, Bagerhat and Satkhira districts of Bangladesh. This dataset was developed following two consecutive steps. In the first step, participatory rural appraisal tools were applied to get the conceptual framework for data collection regarding lists of farmers and the variables of the risk factors of WSD. In the second step, sampling of farmers, google featured questionnaire development, and mobile phone-assisted survey were carried out. The total surveyed farms were 233 consisting of 21 and 212 semi-intensive and extensive farms, respectively. The data were collected in the form of continuous, nominal and binary variables disaggregated by saline zones. The dataset contains some basic socio-economic data of shrimp farmers, farm characteristics, environmental attributes and disease history of shrimp farms. The dataset also has GPS coordinates of all the surveyed farms individually which are very useful for spatial analysis. In total, the dataset in MS Excel has 46 variables and attached as the supplementary material with this article.

The problem of word sense disambiguation (WSD) is considered in the article. Given a set of synonyms (synsets) and sentences with these synonyms. It is necessary to select the meaning of the word in the sentence automatically. 1285 sentences were tagged by experts, namely, one of the dictionary meanings was selected by experts for target words. To solve the WSD-problem, an algorithm based on a new method of vector-word contexts proximity calculation is proposed. In order to achieve higher accuracy, a preliminary epsilon-filtering of words is performed, both in the sentence and in the set of synonyms. An extensive program of experiments was carried out. Four algorithms are implemented, including a new algorithm. Experiments have shown that in a number of cases the new algorithm shows better results. The developed software and the tagged corpus have an open license and are available online. Wiktionary and Wikisource are used. A brief description of this work can be viewed in slides (https://goo.gl/9ak6Gt). Video lecture in Russian on this research is available online (https://youtu.be/-DLmRkepf58)., Comment: 15 pages, 1 table, 15 figures, accepted in the journal Transactions of Karelian Research Centre of the Russian Academy of Sciences

Word Sense Disambiguation Based on Large Scale Polish CLARIN Heterogeneous Lexical ResourcesLexical resources can be applied in many different Natural Language Engineering tasks, but the most fundamental task is the recognition of word senses used in text contexts. The problem is difficult, not yet fully solved and different lexical resources provided varied support for it. Polish CLARIN lexical semantic resources are based on the plWordNet — a very large wordnet for Polish — as a central structure which is a basis for linking together several resources of different types. In this paper, several Word Sense Disambiguation (henceforth WSD) methods developed for Polish that utilise plWordNet are discussed. Textual sense descriptions in the traditional lexicon can be compared with text contexts using Lesk’s algorithm in order to find best matching senses. In the case of a wordnet, lexico-semantic relations provide the main description of word senses. Thus, first, we adapted and applied to Polish a WSD method based on the Page Rank. According to it, text words are mapped on their senses in the plWordNet graph and Page Rank algorithm is run to find senses with the highest scores. The method presents results lower but comparable to those reported for English. The error analysis showed that the main problems are: fine grained sense distinctions in plWordNet and limited number of connections between words of different parts of speech. In the second approach plWordNet expanded with the mapping onto the SUMO ontology concepts was used. Two scenarios for WSD were investigated: two step disambiguation and disambiguation based on combined networks of plWordNet and SUMO. In the former scenario, words are first assigned SUMO concepts and next plWordNet senses are disambiguated. In latter, plWordNet and SUMO are combined in one large network used next for the disambiguation of senses. The additional knowledge sources used in WSD improved the performance. The obtained results and potential further lines of developments were discussed.

Identifying the sense of a word within a context is a challenging problem and has many applications in natural language processing. This assignment problem is called word sense disambiguation(WSD). Many papers in the literature focus on English language and data. Our dataset consists of 1400 sentences translated to Turkish from the Penn Treebank Corpus. This paper seeks to address and discuss 6 different feature extraction methods and its classification performances using C4.5, Random Forests, Rocchio, Naive Bayes, KNN, Linear and multilayer Perceptron. This paper calls into question how the described features perform on a morphologically rich language (Turkish) with several classifiers. Bir kelimenin geçtiği bağlam içindeki anlamını belirlemek , doğal dil işleme alanında, zorlu ve çokça uygulaması olan bir problemdir. Bu problemin literatürdeki bilinen adı, kelime belirsizlik gidermedir. Bir çok yayın İngiliz dili ve verileri üzerine yoğunlaşmış çalışmalardır. Bu çalışmada kullandığımız veri kümesi, Penn Treebank Corpus'dan derlenmiş ve Türkçe'ye çevrilmiş 1400 cümleden oluşmaktadır. Çalışmamızın amacı 6 farklı öznitelik çıkarım algoritmasının performanslarını farklı sınıflandırıcılarla ölçmektir. Kullandığımız sınıflandırma algoritmaları, C4.5, Random Forests, Rocchio, Naive Bayes, KNN, Linear ve multilayer Perceptron'dır. yayınımızın amacı açıklanan özniteliklerin morfolojik açıdan zengin olan bir dilde (Türkçe), farklı sınıflandırıcılarla verdiği performans ölçmektir. Publisher's Version

Semantic disambiguation or the understanding of natural language are areas within the natural language processing that although they have been widely studied, continue to pose a major challenge. Traditional approaches to semantic disambiguation lie on the assumption that there is a unique and unequivocal semantic underlying each word in a sentence. However, there is a class of constructions in language known as puns, in which lexical-semantic ambiguity is a sought-after effect in the sentence. That is, the speaker or writer pretends that a particular word or other lexical element is interpreted simultaneously with two or more different meanings. In this project we propose to approach the location and disambiguation of double meaning words (puns) in a set of sentences. To do this we will use different vector representations of words obtained from different corpus, and different metrics of similarity will be studied. The data sets belong to the Semeval 2017 international competition and the results can be compared with those published by the competition., La desambiguación semántica o la comprensión del lenguaje natural son ámbitos dentro del procesamiento del lenguaje natural que aunque han sido ampliamente estudiados, siguen suponiendo un reto importante. Los enfoques tradicionales de la desambiguación semántica descansan en la suposición de que existe una única e inequívoca semántica subyacente a cada palabra en una oración. Sin embargo, existe una clase de construcciones en el lenguaje conocidas como juegos de palabras (puns), en los que la ambigüedad léxico-semántica es un efecto buscado en la oración. Es decir, el hablante o escritor pretende que una determinada palabra u otro elemento léxico sea interpretado simultáneamente con dos o más significados distintos. En este proyecto proponemos abordar la localización y desambiguación de palabras con doble sentido (puns) en una serie de oraciones. Para ello usaremos diferentes representaciones vectoriales de las palabras obtenidas a partir de diferentes corpus, y se estudiarán diferentes métricas de similitud. Los conjuntos de datos pertenecen a la competición internacional Semeval 2017 y los resultados se podrán contrastar con los publicados por la competición., [CA] La desambiguació semàntica o la comprensió del llenguatge natural són àmbits dins del processament del llenguatge natural que encara que han estat àmpliament estudiats, segueixen suposant un repte important. Els enfocaments tradicionals de la desambiguació semàntica descansen en la suposició que hi ha una única e inequívoca semàntica subjacent a cada paraula en una oració. No obstant això, hi ha una classe de construccions en el llenguatge conegudes com jocs de paraules (puns), en els quals l’ambigüitat lexicosemàntica és un efecte buscat en l’oració. És a dir, el parlant o escriptor pretén que una determinada paraula o un altre element lèxic sigui interpretat simultàniament amb dos o més significats diferents. En aquest projecte proposem abordar la localització i desambiguació de paraules amb doble sentit (puns) en una sèrie d’oracions. Per a això farem servir diferents representacions vectorials de les paraules obtingudes a partir de diferents corpus, i s’estudiaran diferents mètriques de similitud. Els conjunts de dades pertanyen a la competició internacional Semeval 2017 i els resultats es podran contrastar amb les publicades per la competició.

Annotating large numbers of sentences with senses is the heaviest requirement of current Word Sense Disambiguation. We present Train-O-Matic, a language-independent method for generating millions of sense-annotated training instances for virtually all meanings of words in a language’s vocabulary. The approach is fully automatic: no human intervention is required and the only type of human knowledge used is a WordNet-like resource. Train-O-Matic achieves consistently state-of-the-art performance across gold standard datasets and languages, while at the same time removing the burden of manual annotation. All the training data is available for research purposes at http://trainomatic.org.

Automatic processing of biomedical documents is made difficult by the fact that many of the terms they contain are ambiguous. Word Sense Disambiguation (WSD) systems attempt to resolve these ambiguities and identify the correct meaning. However, the published literature on WSD systems for biomedical documents report considerable differences in performance for different terms. The development of WSD systems is often expensive with respect to acquiring the necessary training data. It would therefore be useful to be able to predict in advance which terms WSD systems are likely to perform well or badly on.This paper explores various methods for estimating the performance of WSD systems on a wide range of ambiguous biomedical terms (including ambiguous words/phrases and abbreviations). The methods include both supervised and unsupervised approaches. The supervised approaches make use of information from labeled training data while the unsupervised ones rely on the UMLS Metathesaurus. The approaches are evaluated by comparing their predictions about how difficult disambiguation will be for ambiguous terms against the output of two WSD systems. We find the supervised methods are the best predictors of WSD difficulty, but are limited by their dependence on labeled training data. The unsupervised methods all perform well in some situations and can be applied more widely.

Display Omitted HighlightsObjective: to develop a method that can disambiguate terms in biomedical text.WSD method exploits similarity and relatedness information extracted from the UMLS.Evaluates the efficacy of similarity and relatedness measures on WSD.IC-based measures obtains higher accuracy than path-based and relatedness measures. IntroductionIn this article, we evaluate a knowledge-based word sense disambiguation method that determines the intended concept associated with an ambiguous word in biomedical text using semantic similarity and relatedness measures. These measures quantify the degree of similarity or relatedness between concepts in the Unified Medical Language System (UMLS). The objective of this work is to develop a method that can disambiguate terms in biomedical text by exploiting similarity and relatedness information extracted from biomedical resources and to evaluate the efficacy of these measure on WSD. MethodWe evaluate our method on a biomedical dataset (MSH-WSD) that contains 203 ambiguous terms and acronyms. ResultsWe show that information content-based measures derived from either a corpus or taxonomy obtain a higher disambiguation accuracy than path-based measures or relatedness measures on the MSH-WSD dataset. AvailabilityThe WSD system is open source and freely available from http://search.cpan.org/dist/UMLS-SenseRelate/. The MSH-WSD dataset is available from the National Library of Medicine http://wsd.nlm.nih.gov.

The term of word sense disambiguation, WSD, is introduced in the context of text document processing. A knowledge based approach is conducted using WordNet lexical ontology, describing its structure and components used for the process of identification of context related senses of each polysemy words. The principal distance measures using the graph associated to WordNet are presented, analyzing their advantages and disadvantages. A general model for aggregation of distances and probabilities is proposed and implemented in an application in order to detect the context senses of each word. For the non-existing words from WordNet, a similarity measure is used based on probabilities of co-occurrences. The module of WSD is proposed for integration in the step of processing documents such as supervised and unsupervised classification in order to maximize the correctness of the classification. Future work is related to the implementation of different domain oriented ontologies.Keywords: WSD, Similarity Measure, WordNet, Ontology, Synset(ProQuest: ... denotes formulae omitted.)1 IntroductionFor the acquisition of knowledge in artificial intelligence, two approaches defined in [1] are used:* transfer process between human to knowledge base, process with a major disadvantage given by the fact that the one who has knowledge cannot easily identify it;* conceptual modeling process by building models in which are placed the new knowledge as they are acquired, this process leading to the appearance of the ontology as a systematic organization of knowledge, data of the reality, leading to the construction of theories upon what it exists.An essential role of ontology is to be reused in multiple applications. Mapping two or more ontologies is called alignment. This task is particularly difficult, the main cause of limitation in extending existing ontologies [1].Direction that follows the ontology is supported by the introduction of artificial intelligence techniques to emulate the mental representation of concepts used, and the interpenetration of these links.The kernel of the ontology is defined as a system 0 = (£, T, C*,dC, ROOT), where:* £ is the lexicon formed out of the terms from the natural language;* C* a set of concepts;* T represents the reference function that maps the set of terms of the lexicon to the set of concepts;* H is the hierarchy of the taxonomy given by the direct, acyclic, transitive and reflexive relation;* ROOT is the starting point upon which the hierarchy is built on.There are two types of ontologies as defined in [1], depending on the area in which they are used:* ontologies for knowledge-based systems are characterized by a relatively small number of concepts, but linked by a large and varied relationships, concepts are grouped into complex conceptual schemes or scenarios and for each concept there can be one or more customizations;* lexicalized ontologies, including a large number of concepts linked by a small number of relationships, like WordNet ontology concepts that are represented by sets of synonymous words, these ontologies are used in human language processing systems.It is introduced the concept of ontology as a knowledge base in the classification of documents, in order to analyze semantic documents by solving the ambiguity of the terms.This integration results in an improvement in the objective function defined for classification techniques used. The main components of an ontology are described, the concepts and relations between them. These components are analyzed, identifying methods of extracting knowledge from within.With the defined relationships between concepts it is created the graph representation seen as a taxonomy of belonging such as "is- a" of the concepts to the more general ones. The senses of a concept are defined, along with the possibility of graph representation of each sense. …

This master thesis investigated automatic methods of Word Sense Disambiguation (WSD) in HTML pages. The hypothesis was that HTML documents provide various disambiguation cues which are not normally present in general text, and which can enhance the quality of WSD. We tested several existing natural language processing toolkits which provide general WSD services, and compared these to our novel algorithms which were designed to take advantage of the HTML cues. The findings showed that our new algorithms outperformed state of the art general WSD implementations. In addition, our algorithm could provide a ranked list of potential disambiguations, which is useful in an example use case where users “tag” key words in a web page with the help of the disambiguating algorithm. INFO390 MASV-INFO

Decline in capture fisheries and sea food demand lead to improve shrimp aquaculture activities. Iran had good experiences on shrimp farming during tow decades. White spot disease collapse the shrimp farming activities in recent years. Although goater were the main site for shrimp culture but this site were affected by white spot disease (WSD). Environmental stressors were the main criteria for attention in this regard. An investigation was carried out to monitor management practices and to find out whether there is any relationship with occurrence of white spot disease and environmental parameters. Five semi-intensive shrimp farms were selected in bahookalat chabahar area (2500 ha). The farms were situated at goater area. Tree ponds from each farm at random were selected for the study. All major environmental parameters such as O2, temperature, salinity. PH, nitrogen were recognized by standard method. Logistic regression were used for relationship of water parameters with occurrence of white spot disease. There were no significant relationship between PH, salinity and nitrogen in ponds and canals. But significant variations were recorded for oxygen (1.58) Temperature (0.89) with occurrence of white spot disease. Pond aeration can use for reduction and prevention of diseases.

For the first time white spot disease (WSD) was reported in shrimp farms of khoozestan province, in southwest of IRAN in 2002. Then in 2005 the neighbor province, boushehr, was contaminated. In 2008 WSD outbreak reported in sistan-bloochestan province in southeast of Iran. In 2015 all of southern shrimp farms of country except Hormozgan, the middle southern province, which has remained free of WSD, are being contaminated. White Spot disease suspended shrimp culture in thousands hectares of shrimp farms. Considering that white spot disease has not been observed in Hormozgan province yet, the question is; to what extent environmental and management factors participated in preventing WSD outbreak or cause WSD outbreak. In this study (20102012), the effects of environmental factors and management, stressors that decrease immune system function of shrimp are discussed. In addition, the role of pathogen as the main factor of outbreak is discussed. The goal of this study is to define environmental parameters and management practices associates with outbreak of white spot disease in affected provinces and discover reasons of being Hormozgan province free of this disease. In this study the role of the local environmental factors and management practice stressors in susceptibility to WSD was determine. Both the effects of environmental factors in water of ponds including total ammonia, nitrogen, dissolved oxygen, pH, salinity, transparency, and temperature and management issues related to biosecurity are studied. There were overlaps on physical and chemical parameter values obtained in clear areas with contaminated areas .Results of the data analysis suggest that lack of association with WSD incidence was 7 times greater than WSD incidence despite of disease outbreak in sistan-bloochestan province, so other sources of white spot disease virus incidence was suspected in affected areas. Histopathological examinations and polymerase chain reaction (PCR) tests during project performance did not reveal white spot disease virus evidences in post larvae examined from khoozestan province stocked in farms but disease outbreak was happened in that farms , so we suspected to management practice include feed , pond preparation and carrier of disease . Recorded values of temperature and salinity in some months during inspection in Hormozgan province specified stressful condition that may lead to WSD outbreak, however the disease did not appear. Therefore the hypothesis that the water physical and chemical conditions are reasons to prevent disease outbreak in Hormozgan province is being rejected. The policy of Hormozgan’s fishery authorities, to replaced Fenneropenaeus indicus with specific pathogen free Litopenaeus vannamei, that is more resistant to some of diseases, before incidence of WSD in farms and to before being endemic in the Hormozgan province, made an advantage compare to affected southern provinces that introduced Litopenaeus vannamei after WSD prevalence to their farms. However it does not guarantee to maintain current trend of being Hormozgan province farms free of white spot disease. Therefore establishing the principals of biosecurity are strongly emphasized. Strategies taken by the proficient authorities in preparation of SPF shrimp broodstock can be the most important factor in preventing WSD. Regarding biosecurity principals purchased feed must be free of shrimp head powder. Construction the new shrimp farms should be as far as it could be away from contaminated areas. Iranian Fisheries Science Research Institute Published

Decline in capture fisheries and sea food demand lead to improve shrimp aquaculture activities. There are valuable capacities for shrimp culture in Guater site in Chabahar which could be mentioned as making employment. White spot disease collapses the shrimp farming activities in recent years. Although Guater were the main site for shrimp culture but this site were affected by white spot disease (WSD). Environmental stressors were the main criteria for attention in this regard. An investigation was carried out to monitor management practices and to find out whether there is any relationship with occurrence of white spot disease and environmental parameters. Desirable climate fluctuation happened in monsoon seasons in Guater area parallel with shrimp culture. Evidence of thermal shock beside presence of pathogens makes suitable condition for evidence of disease. Therefor one of the most important embarks is reducing the risk of this accident by selecting suitable time for post larvae stocking. Based on our study the best time for this purpose in Guater site is from March to June .Other recommended footsteps in relation to reduce outbreaks are: settlement bio security, using immune system simulators, embrace SPF brood stocks.

Die Ottobock Gruppe, Weltmarktf��hrer in der Orthop��dietechnik, stellt seit Jahren technisch hochentwickelte Produkte her und z��hlt zu den f��hrenden Entwicklern in diesem Bereich. In Wien befindet sich einer der bedeutendsten Forschung- und Entwicklungsstandorte der Gruppe, sowie ein wichtiger Fertigungsstandort, in welchem High-Tech Prothesen und Orthesen hergestellt werden. Dabei liegt der Fokus des Unternehmens auf der st��ndigen Verbesserung der Qualit��t, der Prozesse und des Service f��r den Kunden. Durch die nachhaltige Anwendung und Etablierung des Lean-Ansatzes, und dessen gro��er Erfolg im Unternehmen, besteht der Wunsch nach der Einf��hrung weiterer effizienzsteigernder Methoden und verschwendungsvermeidender Prozesse. Aufgrund der steigenden Absatzmengen und der komplexen Variantenvielfalt der Produkte soll durch den Einsatz solcher Methoden die Effizienz gesteigert, Verschwendung vermieden und die Durchlaufzeit reduziert werden. Daher wird in einem Projekt ein kombinierter Ansatz von Wertstromanalyse (VSM) und Methods-Time Measurement (MTM) eingesetzt um weitere Einsparungen und Vereinfachungen zu erzielen. Im Rahmen der Diplomarbeit wird der Wareneingangsprozess bis zur Einlagerung des Materials und die Organisation innerhalb der Abteilungslagerorte aufgenommen. Eine qualitative Bewertung und ein Ausblick f��r zuk��nftige Entwicklungen, werden am Ende der Arbeit erl��utert. Im Weiteren wird in einem Firmeninternen Projekt der Kommissionier-Vorgang einer Produktgruppe mit MTM-UAS analysiert und bewertet. Auf Basis der MTM-Analysen und deren Erkenntnisse, wird ein Soll-konzept f��r den Kommissionier-Prozess erstellt und nochmalig analysiert. Die Ergebnisse werden am Ende der Diplomarbeit pr��sentiert. Es werden zun��chst die Grundlagen der Logistik und des Lean-Managements sowie eine Auswahl an Methode des IE mit speziellem Fokus auf Methods-Time Measurement erl��utert. Zus��tzlich werden die Vorteile eines kombinierten Methodenansatzes aufgezeigt. Anhand eines Praxisprojektes wird die kombinierte Vorgehensweise mit den zuvor beschriebenen Zielen durchgef��hrt., The Ottobock group, the world leaders in orthopaedic technology, produces highly developed technical products and is one of the leading developers in this field. The most significant research and development location, as well as a important production site, is situated in Vienna where high-tech prosthetic and orthotic products are manufactured. The companies focus lies within the permanent improvement of quality, the processes and the service for its customers. Through sustainable use and establishment of the lean-approach, and ist great success in the company, the request to implement further methods to increase efficiency and establish waste-free processes occurs more often. Due to increasing product sales and the rising complexity of the products such new methods should help increase efficiency, reduce waste and decrease lead time. That is why a combined approach of value stream mapping (VSM) and methods-time measurement (MTM) is used to acchieve further economies and simplifications. Within this diploma thesis the process of goods receipt is analysed to the point where the material reaches its storage location. Further, the storage organisation within the departments of the company is documented. A qualitative analysis and future prospects of these two subjects is given at the end oft he thesis. Furthermore, the picking process of a product group is analysed with the method MTM-UAS. With the knowledge oft he MTM-Analysis a new concept for the picking process is propsed and newly analysed. The results are presented at the end of the thesis. At first, basic informations on logistics and lean-management as well as special methods of industrial engineering with focus on methods-time measurement are given. Additionally the benefits of the combined usage of methods is shown. On the basis of an applied project, this procedure is performed with the previously described aims.

La desambiguación del sentido de las palabras es uno de los problemas más importantes del área del procesamiento del lenguaje natural. Es indispensable en la aplicación de diferentes tarea tales como recuperación de información, traducción automática, búsqueda de respuestas y generación automática de resúmenes, entre otras. Resolver el problema de la desambiguación del sentido de las palabras consiste en seleccionar el sentido correcto de una palabra en un contexto específico de un conjunto determinado de sentidos posibles. En esta tesis, se utiliza la tarea léxica completa english-all-words en el idioma inglés del foro de Senseval-2 y el diccionario de sentidos llamado WordNet en la versión 2.1. Para todas las palabras a desambiguar se extraen todos los sentidos del dicho diccionario y se selecciona un sentido correcto. Uno de los mejores métodos que se aplica para esta tarea es el método de rankeo basado en grafos, llamado TextRank [Mihalcea, 2006]. Se propone comparar diferentes medidas de similitud en este método de rankeo de grafos, que son la medida de coseno, la medida de edición y la subsecuencia común más larga (del inglés, Longest Common Subsequence (LCS)). La evaluación se realiza utilizando las medidas de Precisión y Recuerdo.

Decline in capture fisheries and sea food demand lead to improve shrimp aquaculture activities. Iran had good experiences on shrimp farming during tow decades. White spot disease collapse the shrimp farming activities in recent years. Although goater were the main site for shrimp culture but this site were affected by white spot disease (WSD). Environmental stressors were the main criteria for attention in this regard. An investigation was carried out to monitor management practices and to find out whether there is any relationship with occurrence of white spot disease and environmental parameters. Five semi-intensive shrimp farms were selected in bahookalat chabahar area (2500 ha). The farms were situated at goater area. Tree ponds from each farm at random were selected for the study. All major environmental parameters such as O2, temperature, salinity. PH, nitrogen were recognized by standard method. Logistic regression were used for relationship of water parameters with occurrence of white spot disease. There were no significant relationship between PH, salinity and nitrogen in ponds and canals. But significant variations were recorded for oxygen (1.58) Temperature (0.89) with occurrence of white spot disease. Pond aeration can use for reduction and prevention of diseases. Iranian Fisheries Science Research Institute Published

Decline in capture fisheries and sea food demand lead to improve shrimp aquaculture activities. There are valuable capacities for shrimp culture in Guater site in Chabahar which could be mentioned as making employment. White spot disease collapses the shrimp farming activities in recent years. Although Guater were the main site for shrimp culture but this site were affected by white spot disease (WSD). Environmental stressors were the main criteria for attention in this regard. An investigation was carried out to monitor management practices and to find out whether there is any relationship with occurrence of white spot disease and environmental parameters. Desirable climate fluctuation happened in monsoon seasons in Guater area parallel with shrimp culture. Evidence of thermal shock beside presence of pathogens makes suitable condition for evidence of disease.Therefor one of the most important embarks is reducing the risk of this accident by selecting suitable time for post larvae stocking. Based on our study the best time for this purpose in Guater site is from March to June .Other recommended footsteps in relation to reduce outbreaks are : settlement biosecurity , using immune system stimulators , embrace SPF brood stocks. Iranian Fisheries Science Research Institute Published

For the first time white spot disease (WSD) was reported in shrimp farms of khoozestan province, in southwest of IRAN in 2002. Then in 2005 the neighbor province, boushehr, was contaminated. In 2008 WSD outbreak reported in sistan-bloochestan province in southeast of Iran. In 2015 all of southern shrimp farms of country except Hormozgan, the middle southern province, which has remained free of WSD, are being contaminated. White Spot disease suspended shrimp culture in thousands hectares of shrimp farms. Considering that white spot disease has not been observed in Hormozgan province yet, the question is; to what extent environmental and management factors participated in preventing WSD outbreak or cause WSD outbreak. In this study (20102012), the effects of environmental factors and management, stressors that decrease immune system function of shrimp are discussed. In addition, the role of pathogen as the main factor of outbreak is discussed. The goal of this study is to define environmental parameters and management practices associates with outbreak of white spot disease in affected provinces and discover reasons of being Hormozgan province free of this disease. In this study the role of the local environmental factors and management practice stressors in susceptibility to WSD was determine. Both the effects of environmental factors in water of ponds including total ammonia, nitrogen, dissolved oxygen, pH, salinity, transparency, and temperature and management issues related to biosecurity are studied. There were overlaps on physical and chemical parameter values obtained in clear areas with contaminated areas .Results of the data analysis suggest that lack of association with WSD incidence was 7 times greater than WSD incidence despite of disease outbreak in sistan-bloochestan province, so other sources of white spot disease virus incidence was suspected in affected areas. Histopathological examinations and polymerase chain reaction (PCR) tests during project performance did not reveal white spot disease virus evidences in post larvae examined from khoozestan province stocked in farms but disease outbreak was happened in that farms , so we suspected to management practice include feed , pond preparation and carrier of disease. Recorded values of temperature and salinity in some months during inspection in Hormozgan province specified stressful condition that may lead to WSD outbreak, however the disease did not appear. Therefore the hypothesis that the water physical and chemical conditions are reasons to prevent disease outbreak in Hormozgan province is being rejected. The policy of Hormozgan’s fishery authorities, to replaced Fenneropenaeus indicus with specific pathogen free Litopenaeus vannamei, that is more resistant to some of diseases, before incidence of WSD in farms and to before being endemic in the Hormozgan province, made an advantage compare to affected southern provinces that introduced Litopenaeus vannamei after WSD prevalence to their farms. However it does not guarantee to maintain current trend of being Hormozgan province farms free of white spot disease. Therefore establishing the principals of biosecurity are strongly emphasized. Strategies taken by the proficient authorities in preparation of SPF shrimp broodstock can be the most important factor in preventing WSD. Regarding biosecurity principals purchased feed must be free of shrimp head powder. Construction the new shrimp farms should be as far as it could be away from contaminated areas.

Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered an AI-complete problem, that is, a task whose solution is at least as hard as the most difficult problems in artificial intelligence. We introduce the reader to the motivations for solving the ambiguity of words and provide a description of the task. We overview supervised, unsupervised, and knowledge-based approaches. The assessment of WSD systems is discussed in the context of the Senseval/Semeval campaigns, aiming at the objective evaluation of systems participating in several different disambiguation tasks. Finally, applications, open problems, and future directions are discussed.

In this paper we present an unsupervised approach to word sense disambiguation based on evolutionary game theory. In our algorithm each word to be disambiguated is represented as a node on a graph and each sense as a class. The algorithm performs a consistent class assignment of senses according to the similarity information of each word with the others, so that similar words are constrained to similar classes. The dynamics of the system are formulated in terms of a non-cooperative multiplayer game, where the players are the data points to decide their class memberships and equilibria correspond to consistent labeling of the data.

In this paper we present an approach to Word Sense Disambiguation based on Topic Modeling (LDA). Our approach consists of two different steps, where first a binary classifier is applied to decide whether the most frequent sense applies or not, and then another classifier deals with the non most frequent sense cases. An exhaustive evaluation is performed on the Spanish corpus Ancora, to analyze the performance of our two-step system and the impact of the context and the different parameters in the system. Our best experiment reaches an accuracy of 74.53, which is 6 points over the highest baseline. All the software developed for these experiments has been made freely available, to enable reproducibility and allow the re-usage of the software. En este artículo se presenta una aproximación a la Desambiguación del Sentido de las Palabras basada en Modelado de Categorías (LDA). Nuestra aproximación consiste en dos pasos diferenciados, donde primero un clasificador binario se ejecuta para decidir si la heurística del sentido más frecuente se debe aplicar, y posteriormente otro clasificador se encarga del resto de sentidos donde esta heurística no corresponde. Se ha realizado una evaluación exhaustiva en el corpus en español Ancora, para analizar el funcionamiento de nuestro sistema de dos pasos y el impacto del contexto y de diferentes parámetros en dicho sistema. Nuestro mejor experimento alcanza un acierto de 74.53, lo cual es 6 puntos superior al baseline más alto. Todo el software desarrollado para estos experimentos se ha puesto disponible libremente para permitir la reprodubilidad de los experimentos y la reutilización del software.

The contact of diesel fuel with engine subsystems demands a good wear resistance. Lubricity is an important feature for integrity of injection system and the sulphur composites are primarily responsible for lubrication of the injector nozzle. Biodiesel is responsible for partially restoring the lubricity of diesel fuel that presents low levels of sulphur composites and, furthermore, it causes less pollution than diesel fuel. The lubricity is measured through the wear scar diameter following the ASTM D 975 standards. However, the friction and wear with light loads of micro/nanocomponents are highly dependent on surface interactions that can be evaluated by microscopy techniques. This study aimed to measure and to analyze the biodiesel lubricity and their blends (B5, B20) with diesel by observing the wear scars of discs using the scanning electronic microscopy (SEM), atomic force microscopy (AFM) and micro roughness techniques. The fuels performance was evaluated using HFRR tribometer. The tests conditions were based on standard ADTM D-6079-04. The coefficient of friction was measure during the test. After the test, the worn ball and disc were analyzed by SEM, AFM and profilometer. The results showed that the addition of biodiesel in diesel improve the tribological performance of fuel. Also, the just WSD value is not sufficient to evaluate the lubrication ability of a fuel. Analysis of the worn disc surfaces proved to be compatible with WSD number and also more sensitive to these kinds of fuels, showing mainly the form and intensity of the wear.

In this project, we have investigated how to perform rule-based machine translation of sets of keywords between two languages. The goal was to translate an input set, which contains one or more keywords in a source language, to a corresponding set of keywords, with the same number of elements, in the target language. However, some words in the source language may have several senses and may be translated to several, or no, words in the target language. If ambiguous translations occur, the best translation of the keyword should be chosen with respect to the context. In traditional machine translation, a word's context is determined by a phrase or sentences where the word occurs. In this project, the set of keywords represents the context. By investigating traditional approaches to machine translation (MT), we designed and described models for the specific purpose of keyword- translation. We have proposed a solution, based on direct translation for translating keywords between English and Swedish. In the proposed solu- tion, we also introduced a simple graph-based model for solving ambigu- ous translations. I detta projekt har vi undersökt hur man utför regelbaserad maskinöver- sättning av nyckelord mellan två språk. Målet var att översätta en given mängd med ett eller flera nyckelord på ett källspråk till en motsvarande, lika stor mängd nyckelord på målspråket. Vissa ord i källspråket kan dock ha flera betydelser och kan översättas till flera, eller inga, ord på målsprå- ket. Om tvetydiga översättningar uppstår ska nyckelordets bästa över- sättning väljas med hänsyn till sammanhanget. I traditionell maskinö- versättning bestäms ett ords sammanhang av frasen eller meningen som det befinner sig i. I det här projektet representerar den givna mängden nyckelord sammanhanget. Genom att undersöka traditionella tillvägagångssätt för maskinöversätt- ning har vi designat och beskrivit modeller specifikt för översättning av nyckelord. Vi har presenterat en direkt maskinöversättningslösning av nyckelord mellan engelska och svenska där vi introducerat en enkel graf- baserad modell för tvetydiga översättningar.

In this project, we have investigated how to perform rule-based machine translation of sets of keywords between two languages. The goal was to translate an input set, which contains one or more keywords in a source language, to a corresponding set of keywords, with the same number of elements, in the target language. However, some words in the source language may have several senses and may be translated to several, or no, words in the target language. If ambiguous translations occur, the best translation of the keyword should be chosen with respect to the context. In traditional machine translation, a word's context is determined by a phrase or sentences where the word occurs. In this project, the set of keywords represents the context. By investigating traditional approaches to machine translation (MT), we designed and described models for the specific purpose of keyword- translation. We have proposed a solution, based on direct translation for translating keywords between English and Swedish. In the proposed solu- tion, we also introduced a simple graph-based model for solving ambigu- ous translations. I detta projekt har vi undersökt hur man utför regelbaserad maskinöver- sättning av nyckelord mellan två språk. Målet var att översätta en given mängd med ett eller flera nyckelord på ett källspråk till en motsvarande, lika stor mängd nyckelord på målspråket. Vissa ord i källspråket kan dock ha flera betydelser och kan översättas till flera, eller inga, ord på målsprå- ket. Om tvetydiga översättningar uppstår ska nyckelordets bästa över- sättning väljas med hänsyn till sammanhanget. I traditionell maskinö- versättning bestäms ett ords sammanhang av frasen eller meningen som det befinner sig i. I det här projektet representerar den givna mängden nyckelord sammanhanget. Genom att undersöka traditionella tillvägagångssätt för maskinöversätt- ning har vi designat och beskrivit modeller specifikt för översättning av nyckelord. Vi har presenterat en direkt maskinöversättningslösning av nyckelord mellan engelska och svenska där vi introducerat en enkel graf- baserad modell för tvetydiga översättningar.

The aims of this study were to standard and optimize a qPCR protocol with FAM-BHQ1 probe, and to compare its sensitivity against TaqMan qPCR and PCR methods to diagnose shrimp WSD. The FAM-BHQ1 qPCR presented higher clinical sensitivity and showed to be a robust alternative to detect WSSV in clinical samples.

Geotechnical design has traditionally been done using working stress design (WSD). Working stress design identifies limit states for a structure, then protects against their occurrence using factors of safety. Load and resistance factor design (LRFD) uses load and resistance factors instead of factors of safety. LRFD also establishes the geotechnical resistance much more precisely than WSD. This research establishes load and resistance factors using reliability analysis and specifically lays out methods of calculation of resistance for four limit states related to MSE wall design: overturning, sliding, reinforcement pullout and reinforcement rupture.

An approach to the word sense disambiguation (WSD) relaying on the WordNet synsets is proposed. The method uses semantically tagged glosses to perform a process similar to the spreading activation in semantic network, creating ranking of the most probable meanings for word annotation. Preliminary evaluation shows quite promising results. Comparison with the state-of-theart WSD methods indicates that the use of WordNet relations and semantically tagged glosses should enhance accuracy of word disambiguation methods.

Il est connu que les problèmes d'ambiguïté de la langue ont un effet néfaste sur les résultats des systèmes de Recherche d'Information (RI). Toutefois, les efforts de recherche visant à intégrer des techniques de Désambiguisation de Sens (DS) à la RI n'ont pas porté fruit. La plupart des études sur le sujet obtiennent effectivement des résultats négatifs ou peu convaincants. De plus, des investigations basées sur l'ajout d'ambiguïté artificielle concluent qu'il faudrait une très haute précision de désambiguation pour arriver à un effet positif. Ce mémoire vise à développer de nouvelles approches plus performantes et efficaces, se concentrant sur l'utilisation de statistiques de cooccurrence afin de construire des modèles de contexte. Ces modèles pourront ensuite servir à effectuer une discrimination de sens entre une requête et les documents d'une collection. Dans ce mémoire à deux parties, nous ferons tout d'abord une investigation de la force de la relation entre un mot et les mots présents dans son contexte, proposant une méthode d'apprentissage du poids d'un mot de contexte en fonction de sa distance du mot modélisé dans le document. Cette méthode repose sur l'idée que des modèles de contextes faits à partir d'échantillons aléatoires de mots en contexte devraient être similaires. Des expériences en anglais et en japonais montrent que la force de relation en fonction de la distance suit généralement une loi de puissance négative. Les poids résultant des expériences sont ensuite utilisés dans la construction de systèmes de DS Bayes Naïfs. Des évaluations de ces systèmes sur les données de l'atelier Semeval en anglais pour la tâche Semeval-2007 English Lexical Sample, puis en japonais pour la tâche Semeval-2010 Japanese WSD, montrent que les systèmes ont des résultats comparables à l'état de l'art, bien qu'ils soient bien plus légers, et ne dépendent pas d'outils ou de ressources linguistiques. La deuxième partie de ce mémoire vise à adapter les méthodes développées à des applications de Recherche d'Information. Ces applications ont la difficulté additionnelle de ne pas pouvoir dépendre de données créées manuellement. Nous proposons donc des modèles de contextes à variables latentes basés sur l'Allocation Dirichlet Latente (LDA). Ceux-ci seront combinés à la méthodes de vraisemblance de requête par modèles de langue. En évaluant le système résultant sur trois collections de la conférence TREC (Text REtrieval Conference), nous observons une amélioration proportionnelle moyenne de 12% du MAP et 23% du GMAP. Les gains se font surtout sur les requêtes difficiles, augmentant la stabilité des résultats. Ces expériences seraient la première application positive de techniques de DS sur des tâches de RI standard., It is known that the ambiguity present in natural language has a negative effect on Information Retrieval (IR) systems effectiveness. However, up to now, the efforts made to integrate Word Sense Disambiguation (WSD) techniques in IR systems have not been successful. Past studies end up with either poor or unconvincing results. Furthermore, investigations based on the addition of artificial ambiguity shows that a very high disambiguation accuracy would be needed in order to observe gains. This thesis has for objective to develop efficient and effective approaches for WSD, using co-occurrence statistics in order to build context models. Such models could then be used in order to do a word sense discrimination between a query and documents of a collection. In this two-part thesis, we will start by investigating the principle of strength of relation between a word and the words present in its context, proposing an approach to learn a function mapping word distance to count weights. This method is based on the idea that context models made from random samples of word in context should be similar. Experiments in English and Japanese shows that the strength of relation roughly follows a negative power law. The weights resulting from the experiments are then used in the construction of Naïve Bayes WSD systems. Evaluations of these systems in English with the Semeval-2007 English Lexical Sample (ELS), and then in Japanese with the Semeval-2010 Japanese WSD (JWSD) tasks shows that the systems have state-of-the-art accuracy even though they are much lighter and don't rely on linguistic tools or resources. The second part of this thesis aims to adapt the new methods to IR applications. Such applications put heavy constraints on performance and available resources. We thus propose the use of corpus-based latent context models based on Latent Dirichlet Allocation (LDA). The models are combined with the query likelihood Language Model (LM) approach for IR. Evaluating the systems on three collections from the Text REtrieval Conference (TREC), we observe average proportional improvement in the range of 12% in MAP and 23% in GMAP. We then observe that the gains are mostly made on hard queries, augmenting the robustness of the results. To our knowledge, these experiments are the first positive application of WSD techniques on standard IR tasks.

Shrimp farmers have been suffering from loss of production due to White Spot Disease (WSD) in the last decades. A few studies focused on the environmental factors predisposed WSD. Our study aimed to investigate the effects of the different salinities on occurrence of WSD. The treatments were 30 ppt, 40 ppt and 50 ppt in triplicate and corresponding untreated control groups. White Spot Virus (WSV) was intramuscularly injected to the shrimp in whole treatments. PCR and histopathological findings within the measurement of some hemolymph parameters such as Differentiated Hemocyte Count (DHC), Total Hemocyte Count (THC) and Total Plasma Protein (TPP) were carried out. A positive result was observed for the occurrence of the antigen of the WSV obtained from the samples of each treatment of salinity due to the challenging treatments with the WSV. Mortality at the salinity of 30 ppt began at 72 h post inoculation as well as the salinity of 40 ppt, but at 50 ppt it started after 50 h. The Minimum and maximum count of mortality at the salinity of 30, 40 and 50 ppt were 3.5, 8.5 0.5, 4.5 and 1.5, 7.5, respectively. No mortality was observed in the untreated control groups of 30, 40 and 50 ppt during the experiment. It is concluded that the higher and the lower salinity, lesser or greater than the normal condition in exposed to WSV could lead to severe mortality of WSD.

Word sense disambiguation (WSD) is the process of computationally identifying and labeling poly- semous words in context with their correct meaning, known as a sense. WSD is riddled with various obstacles that must be overcome in order to reach its full potential. One of these problems is the aspect of the representation of word meaning. Traditional WSD algorithms make the assumption that a word in a given context has only one meaning and therfore can return only one discrete sense. On the other hand, a novel approach is that a given word can have multiple senses. Studies on graded word sense assignment (Erk et al., 2009) as well as in cognitive science (Hampton, 2007; Murphy, 2002) support this theory. It has therefore been adopted in a novel, paraphrasing system which performs word sense disambiguation by returning a probability distribution over potential paraphrases (in this case synonyms) of a given word. However, it is unknown how well this type of algorithm fares against the traditional one. The current study thus examines if and how it is possible to make a comparison of the two. A method of comparison is evaluated and subsequently rejected. Reasons for this as well as suggestions for a fair and accurate comparison are presented.

In this study two species of algae, Sargassum glaucescens and Padina boergeseni that found plenteously in Persian Gulf and Bushehr coast, were collected and hot water extracts of them were lyophilized. F. indicus (11.32±1.20 g), after two weeks adaptation in Shoghab research station were immersed in seawater (39 ppt and 25±1 ○C) containing hot-water extract of each brown algae, S.glaucescens and P.boergeseni, at 100, 300 and 500 mg/l concentration, Survival rate and immunological parameters (total haemocyte count (THC), total plasma protein (TPP), Phagocytic activity, bacterial clearance efficiency and bactericidal activity) were examined. In addition effect of dietary administration of beta 1, 3 1, 6 glucan on prevention of White Spot Disease and immunological parameters of shrimp were investigated. According to results, immersion in seawater containing 300 and 500 mg/l concentration of algal hot-water extract after 2 and 3 hours or oral administration of beta 1,3 1,6 glucan at level of 10 g/kg diet for 14 days significantly enhanced THC, TPP, Phagocytic activity, bacterial clearance efficiency and bactericidal activity. Immersion in seawater containing 100, 300 and 500 mg/l hot-water extract of S.glaucescens after 3 hours, improved the survival rate of WSSV-infected F. indicus.

International audience; The importance of describing regular polysemy in a lexicon has often been outlined, especially in the field of natural language processing (for a good overview of this issue, see (Ravin and Leacock, 2000)). Unfortunately, no existing broad-coverage semantic lexicon has been built following this relatively recent advice. And since producing a broad coverage semantic lexicon is a very time-consuming task, one has to put this idea into practice on existing lexicons. WordNet is an appropriate lexical semantic resource for running this experiment as it is machine readable and has a wide coverage (Fellbaum, 1998). In this paper, we introduce a method to create regular polysemy patterns from WordNet data and to automatically detect their occurrences in the lexicon.

In this study two species of algae, Sargassum glaucescens and Padina boergeseni that found plenteously in Persian Gulf and Bushehr coast, were collected and hot water extracts of them were lyophilized. F. indicus (11.32±1.20 g), after two weeks adaptation in Shoghab research station were immersed in seawater (39 ppt and 25±1 ○C) containing hot-water extract of each brown algae, S.glaucescens and P.boergeseni, at 100, 300 and 500 mg/l concentration, Survival rate and immunological parameters (total haemocyte count (THC), total plasma protein (TPP), Phagocytic activity, bacterial clearance efficiency and bactericidal activity) were examined. In addition effect of dietary administration of beta 1, 3 1, 6 glucan on prevention of White Spot Disease and immunological parameters of shrimp were investigated. According to results, immersion in seawater containing 300 and 500 mg/l concentration of algal hot-water extract after 2 and 3 hours or oral administration of beta 1,3 1,6 glucan at level of 10 g/kg diet for 14 days significantly enhanced THC, TPP, Phagocytic activity, bacterial clearance efficiency and bactericidal activity. Immersion in seawater containing 100, 300 and 500 mg/l hot-water extract of S.glaucescens after 3 hours, improved the survival rate of WSSV-infected F. indicus. Iranian Fisheries Science Research Institute Published

Este artículo presenta el resultado del estudio de un amplio conjunto de bases de conocimiento multilíngües actualmente disponibles que pueden ser de interés para un gran número de tareas de procesamiento semántico a gran escala. El estudio incluye una amplia gama de recursos derivados de forma manual y automática para el inglés y castellano. Con ello pretendemos mostrar una imagen clara de su estado actual. Para establecer una comparación justa y neutral, la calidad de cada recurso se ha evaluado indirectamente usando el mismo método en dos tareas de resolución de la ambigüedad semántica de las palabras (WSD, del inglés Word Sense Disambiguation). En concreto, las tareas de muestra léxica del inglés del Senseval-3. This report presents a wide survey of publicly available multilingual Knowledge Resources that could be of interest for wide–coverage semantic processing tasks. We also include an empirical evaluation in a multilingual scenario of the relative quality of some of these large-scale knowledge resources. The study includes a wide range of manually and automatically derived large-scale knowledge resources for English and Spanish. In order to establish a fair and neutral comparison, the quality of each knowledge resource is indirectly evaluated using the same method on a Word Sense Disambiguation task (Senseval-3 English Lexical Sample Task). Este trabajo ha sido parcialmente financiado por grupo IXA de la UPV/EHU y los proyectos KNOW (TIN2006-15049-C03-01) y ADIMEN (EHU06/113).

The WSD is the important viral shrimp disease in past decade. The detection of virus in each country was investigated by polymerase chain reaction for sensitivity and accurate. In this research study we collected 23 samples from shrimp suspected to WSD. The DNA from samples collected and extracted and design two kind of primer from VP24 identified in gene bank by DNAsis software. A primer also designs for House keeping gene for positive and negative samples in all examined. The results showed the gene colon for wssv is the similar with others and 97% is consistency. The product of PCR was colon in plasmid and confirmed and this plasmid used for internal control. Iranian Fisheries Science Research Institute Published

Tesis doctoral en Informática Realizada por Manuel García Vega y dirigida por el Doctor L. Alfonso Ureña López (Univ. de Jaén). El acto de defensa de tesis tuvo lugar en Jaén en diciembre de 2006 ante el tribunal formado por los doctores Miguel Toro Bonilla (Univ. de Sevilla), Manuel Palomar Sanz (Univ. de Alicante), Lidia Moreno Boronat (Univ. Politécnica de Valencia), Andrés Montoyo Guijarro (Univ. de Alicante) y María Teresa Martín Valdivia (Univ. de Jaén). La calificación obtenida fue Sobresaliente Cum Laudem por unanimidad. PhD thesis in Computer Science written by Manuel García Vega under the supervision of Dr. L. Alfonso Ureña López (Univ. of Jaén). The author was examined in December 2006 in Jaén by the committee formed by Miguel Toro Bonilla (Univ. of Sevilla), Manuel Palomar Sanz (Univ. of Alicante), Lidia Moreno Boronat (Univ. Politécnica of Valencia), Andrés Montoyo Guijarro (Univ. of Alicante) y María Teresa Martín Valdivia (Univ. of Jaén). The grade obtained was Sobresaliente Cum Laudem. Este trabajo ha sido parcialmente financiado por los proyectos FIT-150500-2002-416, FIT-150500-2003-412 y TIC2003-07158-C04-04

The WSD is the important viral shrimp disease in past decade. The detection of virus in each country was investigated by polymerase chain reaction for sensitivity and accurate. In this research study we collected 23 samples from shrimp suspected to WSD. The DNA from samples collected and extracted and design two kind of primer from VP24 identified in gene bank by DNAsis software. A primer also designs for Housekeeping gene for positive and negative samples in all examined. The results showed the gene colon for wssv is the similar with others and 97% is consistency. The product of PCR was colon in plasmid and confirmed and this plasmid used for internal control.

En este trabajo se presenta un nuevo recurso, diseñado con el objetivo de ser usado en desambiguación semántica de las palabras en español, basado en las relaciones sintagmáticas entre las palabras. Las relaciones sintagmáticas son relaciones entre sentidos dentro de un sintagma o dentro de una oración. En nuestro caso, estas relaciones han sido extraídas de un corpus anotado manualmente, corpus Cast3LB. Este corpus ha sido anotado con información sintáctica (constituyentes y funciones) e información semántica (sentidos de WordNet). De este corpus se han extraído aproximadamente 3000 patrones. Cada patrón muestra la relación sintagmática entre el sentido del verbo y el sentido de sus argumentos dentro de una oración. Sin embargo, estos patrones son excesivamente específicos para ser usados en contextos multilingües o en documentos de dominio general. Para solucionar esto es necesario obtener patrones más generales semánticamente, para lo cual se ha utilizado la ontología SUMO. In this paper, we present a new resource, designed for being used in WSD, based on syntagmatic relations between senses for Spanish. These relations have been extracted from a corpus: the Cast3LB corpus which has been manually annotated with syntactic and semantic information (WordNet senses). From it, approximately 3000 patterns have been extracted. These patterns show the syntagmatic relations between verb senses and its arguments within a sentence. However, these patterns can be too specific to be used in multilingual contexts or in open domain texts. Consequently, it is necessary to obtain more abstract patterns. In order to do so, we have also developed general patterns using semantic classes based on the SUMO ontology. Este trabajo ha sido parcialmente financiado por el proyecto CES-ECE (HUM2004-21127-E) y el proyecto R2D2 (TIC2003-07158-C04-01).

En este artículo presentamos una extensión de una arquitectura integrada diseñada originalmente para Semantic Parsing a WSD. El marco propuesto permitirá que ambas tareas puedan colaborar y llevarse a cabo simultáneamente. Se ha probado la validez y robustez de esta arquitectura contra una tarea de WSD bien definida (el SENSEVAL-II English Lexical Sample) aplicando modelos sintáctico-semánticos adquiridos automáticamente. This paper presents an extension for WSD of an integrated arquitecture designed for Semantic Parsing. In the proposed framework, both tasks could be addressed simultaneously, collaborating between them. The feasibility and robustness of the proposed arquitecture have been proved against a well-defined task on WSD (the SENSEVAL-II English Lexical Sample) using automatically acquired models.