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Sparse representation has been widely applied to multi-focus image fusion in recent years. As a key step, the construction of an informative dictionary directly decides the performance of sparsity-based image fusion. To obtain sufficient bases for dictionary learning, different geometric information of source images is extracted and analysed. The classified image bases are used to build corresponding subdictionaries by principle component analysis. All built subdictionaries are merged into one informative dictionary. Based on constructed dictionary, compressive sampling matched pursuit algorithm is used to extract corresponding sparse coefficients for the representation of source images. The obtained sparse coefficients are fused by Max-L1 fusion rule first, and then inverted to form the final fused image. Multiple comparative experiments demonstrate that the proposed method is competitive with other the state-of-the-art fusion methods.

Face recognition based on deep neural networks has achieved great success, but its application in resource-constrained and unconstrained scenarios, such as vehicle images from traffic monitoring systems, remains challenging. These scenarios involve complex image variations and require effective training models. To address these challenges, we constructed a low-resolution driver face detection and recognition model, named the SPFL-DC framework, tailored for complex environments. Our primary contribution is a novel method that automates dataset construction using triplet loss, guided by license plate information and pre-training on public datasets. This approach enhances the efficiency of dataset construction and the robustness of model predictions. Secondly, our model used a super-resolution technique to process low-resolution images, fusing them with the original low-resolution inputs. This method improved image resolution while minimizing the loss of critical identity information. We conducted experiments on the AR and LFW datasets to demonstrate the effectiveness of our model, showing competitive performance against state-of-the-art methods.

Semantic lexical resources are a mainstay of various Natural Language Processing applications. However, comprehensive and reliable resources are rare and not often freely available. Handcrafted resources are too costly for being a general solution while automatically-built resources need to be validated by experts or at least thoroughly evaluated. We propose in this paper a picture of the current situation with regard to lexical resources, their building and their evaluation. We give an in-depth description of Wiktionary, a freely available and collaboratively built multilingual dictionary. Wiktionary is presented here as a promising raw resource for NLP. We propose a semi-automatic approach based on random walks for enriching Wiktionary synonymy network that uses both endogenous and exogenous data. We take advantage of the wiki infrastructure to propose a validation \"by crowds\". Finally, we present an implementation called WISIGOTH, which supports our approach.

Short texts are typically composed of small number of words, most of which are abbreviations, typos and other kinds of noise. This makes the noise to signal ratio relatively high for this specific category of text. A high proportion of noise in the data is undesirable for analysis procedures as well as machine learning applications. Text normalization techniques are used to reduce the noise and improve the quality of text for processing and analysis purposes. In this work, we propose a combination of statistical and rule-based techniques to normalize short texts. More specifically, we focus our attention on SMS messages. We base our normalization approach on a statistical machine translation system which translates from noisy data to clean data. This system is trained on a small manually annotated set. Then, we study several automatic methods to extract more general rules from the normalizations generated with the statistical machine translation system. We illustrate the proposed methodology by conducting some experiments with a SMS Haitian-Créole data collection. In order to evaluate the performance of our methodology we use several Haitian-Créole dictionaries, the well-known perplexity criteria and the achieved reduction of vocabulary.

Abstract: Dictionary compilation for the African languages in South Africa stands on the eve of the African Renaissance and prospective compilers of dictionaries for African languages are in need of a sound perspective and guidelines in respect of the compilation and choice of examples. The aim of this article is to analyse and evaluate some current approaches towards the handling of examples of usage as a data category in modem dictionaries and to suggest ways in which this information category can be improved by compiling, selecting and shaping examples to render optimal transfer of infonnation and to enhance information retrieval. The emphasis will be on example phrases and sentences given in the dictionary article. Keywords: LEXICOGRAPHY, DICTIONARY, METALEXICOGRAPHY, AFRICAN LANGUAGES, EXAMPLES, AUTHENTIC EXA.MPLES, CONSTRUCTED EXAMPLES Opsomming: Die gebruik van voorbeelde in polifunksionele woordeboeke.Die samestelling van woordeboeke vir die Afrikatale in Suid-Africa staan op die vooraand van dieAfrika Renaissance en voomemende samestellers van woordeboeke vir die Afrikatale benodig 'ngesonde perspektief en riglyne ten opsigte van die samestelling en die keuse van voorbeelde, Diedoel van hierdie artikel is om sommige van die jongste benaderings ten opsigte van die hanteringvan gebruiksvoorbeelde as 'n datakategorie in modeme woordeboeke te analiseer en te evalueer enom metodes aan die hand te doen waarvolgens hierdie inligtingskategorie verbeter kan word deurdie samestelling, keuse en afronding van voorbeelde om optima Ie inligtingsoordrag te bewerkstelligen om inligtingsontsluiting te verbeter, Die klem sal val op voorbeeldfrases en -sinne wat in diewoordeboekartikel aangebied word. Sleutelwoorde: LEKSIKOGRAFIE, WOORDEBOEK, METALEKSIKOGRAFIE, AFRIKATALE, VOORBEELDE, OUTENTIEKE VOORBEELDE, GEMAAKTE VOORBEELDE

This chapter contains sections titled: Why No One Seems to Know What Religion Is Histories of Religion Power and the Religious/Secular Distinction Conclusion References

In its profound questioning of our gender arrangements, The Left Hand of Darkness is of great interest to feminist, although its status as a utopia is debatable. With The Dispossessed, however, [Ursula K. Le Guin] coined the phrase 'An Ambiguous Utopia' for the novel's subtitle. Balancing dystopia, or 'bad place,' against eutopia, or 'good place,' her story alternates between an account of Shevek's visit to Urras and his previous life on Anarres. An infrequently mentioned aspect of Le Guin's anarchic society on Anarres is its exclusive use of the invented language Pravic. Although Le Guin has not gone so far as to document Pravic with a grammar or dictionary, she tells us enough about it to allow it to become part of the social fabric of her utopia, expressing its ideology and vision of reality. Frequent comparisons of how things are said in Pravic with how they are said in Iotic, the major Urrasti language, help to explain Shevek's experiences of culture shock on Urras. He even discovers that Pravic is inadequate for some purposes: 'Pravic was not a good swearing language. It is hard to swear when sex is not dirty and blasphemy does not exist' (p. 208). In her next utopias novel, Always Coming Home, Le Guin attempts to represent all conceivable aspects of an imaginary world, including its languages. Always Coming Home purports to be an illustrated collection of texts from a future utopian society, the Kesh, translated and presented to us by an anthropologist who goes by the name of Pandora. It both resembles and differs from [Monique Wittig]'s and [Suzette Haden Elgin]'s work. Like Brouillon pour un dictionnaire des amantes, it inmates a 'non-fictional' genre and does not insist on a linear reading, but rather invites the reader to browse in it at will. Among the texts which comprise a supplementary section called 'the back of the book' are a Kesh glossary and alphabet, like Elgin's First Grammar and Dictionary of Laadan, but not as fully developed. Instead, information about Kesh syntax and vocabulary is also sprinkled throughout the body of the work, sometimes relegated to a note, and sometimes found in the context of a cultural explanation. For example, kinship terms are defined in the section titled 'Kinfolk' (pp. 424-429), definition of medical terms are found in 'Some Notes on Medical Practices' (pp. 471-477) and six Kesh words which may be translated as 'love' are found in a section titled 'Love' (p. 493). In this way, Le Guin integrates her invented language more fully into the operations of her imaginary society. This is in contract to Elgin, whose separation of invented language from fiction renders her basic premise somewhat suspect. Although Stone Telling's story is her own, we are never allowed to forget that it, too, is filtered through another consciousness. The presence of Pandora as editor and translator is never far away, stepping in to interpose between us readers and kesh voices. Thus Terter Abhao's dramatic divorce from Willow provides Pandora with an occasion to discuss a translation problem: When Willow places all Abhao's belongings outside the door to signify he is no longer welcome, he does not understand what has happened. Someone else must explain to him 'that a man may come and go as he likes and a woman may take him back or not as she likes, but the house is hers, and if she shuts the door he may not open it.' When he responds, 'but she belongs to me, the child belongs to me,' he is ridiculed by a Kesh woman who 'began to do the Blood Clown turkey-gobble around him, shouting, 'The hammar menstruates to me! They pleat the courage to her.' These expressions are called 'reversal words,' which Pandora explains in a note: Kesh grammar makes no provision for a relation of ownership between living beings. A language in which the verb 'to have' is an intransitive and in which 'to be rich' is the same words as 'give' is likely to turn its foreign speaker and translator, into a clown all too often' (p. 40).