Abstract: A system and method perform cross-modal information retrieval, by generating a graph representing the set of media objects. Each node of the graph corresponds to a media object and is labeled with a set of features corresponding to a text part of the respective media object. Each edge between two nodes represents a similarity between a media part of the two nodes. A first relevance score is computed for each media object of the set of media objects that corresponds to a text-based score. A second relevance score is computed for each media object by inputting the graph into a graph neural network. The first relevance score and the second relevance score are combined to obtain a final ranking score for each media object.

Abstract: A system and method perform cross-modal information retrieval, by generating a graph representing the set of media objects. Each node of the graph corresponds to a media object and is labeled with a set of features corresponding to a text part of the respective media object. Each edge between two nodes represents a similarity between a media part of the two nodes. A first relevance score is computed for each media object of the set of media objects that corresponds to a text-based score. A second relevance score is computed for each media object by inputting the graph into a graph neural network. The first relevance score and the second relevance score are combined to obtain a final ranking score for each media object.

Abstract: Generating a pedestrian tour includes: receiving a query from a user device via a network, the query indicative of a request for a pedestrian tour and including a location and preferences including a target length for the pedestrian tour; obtaining a graph of a geographical area around the location of the user device, the graph including nodes indicative of path crossings and arcs indicative of paths connecting pairs of the nodes; determining scores for the arcs based on characteristics of the arcs, respectively, and the preferences; selecting connecting ones of the arcs based on the scores of the connecting ones of the arcs, lengths of the selected connecting ones of the arcs, and the target length; adding the selected connecting ones of the arcs to the pedestrian tour to initialize the pedestrian tour; and transmitting the pedestrian tour to the user device via the network for display.

Abstract: Documents of a set of documents are represented by bag-of-words (BOW) vectors. L labeled topics are provided, each labeled with a word list comprising words of a vocabulary that are representative of the labeled topic and possibly a list of relevant documents. Probabilistic classification of the documents generates for each labeled topic a document vector whose elements store scores of the documents for the labeled topic and a word vector whose elements store scores of the words of the vocabulary for the labeled topic. Non-negative matrix factorization (NMF) is performed to generate a document-topic model that clusters the documents into k topics where k>L. NMF factors representing L topics of the k topics are initialized to the document and word vectors for the L labeled topics. In some embodiments the NMF factors representing the L topics initialized to the document and word vectors are frozen, that is, are not updated by the NMF after the initialization.

Abstract: Processing methods and systems are provided for representing documents relative to importance of words in the document. A processor comprising a weighting model of word importance in a document in a collection relative to an importance of the word in other documents in the collection computes a deviation of distribution of the word from a probability distribution of the word in other documents in the collection, where the deviation distribution is weighted in accordance with a concavity control function. A concavity control parameter is adjustable relative to word frequency.

Abstract: Documents of a set of documents are represented by bag-of-words (BOW) vectors. L labeled topics are provided, each labeled with a word list comprising words of a vocabulary that are representative of the labeled topic and possibly a list of relevant documents. Probabilistic classification of the documents generates for each labeled topic a document vector whose elements store scores of the documents for the labeled topic and a word vector whose elements store scores of the words of the vocabulary for the labeled topic. Non-negative matrix factorization (NMF) is performed to generate a document-topic model that clusters the documents into k topics where k>L. NMF factors representing L topics of the k topics are initialized to the document and word vectors for the L labeled topics. In some embodiments the NMF factors representing the L topics initialized to the document and word vectors are frozen, that is, are not updated by the NMF after the initialization.

Abstract: A multimedia information retrieval system includes a storage and an electronic processing device. The latter is configured to perform a process including: computing values of a pairwise similarity measure quantifying pairwise similarity of documents of a multimedia reference repository; storing the computed values in the storage; performing an initial information retrieval process respective to the multimedia reference repository to return a set of initial repository documents; and identifying a set of top ranked documents of the multimedia reference repository based at least on the stored computed values pertaining to the set of initial repository documents.

Abstract: A method for estimating average occupancy of a set of stalls based on incomplete occupancy data includes acquiring payment data for the set of stalls for each of a set of times and acquiring occupancy data for the set of stalls for each of the set of times, the occupancy data being acquired from a reporting subset consisting of fewer than all of the stalls. An estimate of a time integral of a function of occupancy is computed, based on the payment data and occupancy data and information is output based on the estimate.

Abstract: Cross-lingual information retrieval is disclosed, comprising: translating a received query from a source natural language into a target natural language; performing a first information retrieval operation on a corpus of documents in the target natural language using the translated query to retrieve a set of pseudo-feedback documents in the target natural language; re-translating the received query from the source natural language into the target natural language using a translation model derived from the set of pseudo-feedback documents in the target natural language; and performing a second information retrieval operation on the corpus of documents in the target natural language using the re-translated query to retrieve an updated set of documents in the target natural language.

Abstract: Processing methods and systems are provided for representing documents relative to importance of words in the document. A processor comprising a weighting model of word importance in a document in a collection relative to an importance of the word in other documents in the collection computes a deviation of distribution of the word from a probability distribution of the word in other documents in the collection, where the deviation distribution is weighted in accordance with a concavity control function. A concavity control parameter is adjustable relative to word frequency.

Abstract: A method for information retrieval includes querying a multimedia collection with a first component of a multimedia query to generate first comparison measures between the first component of the query and respective objects in the collection for a first media type. The collection is queried with a second component of the multimedia query to generate second comparison measures between the second component of the query and respective objects in the collection for a second media type. An aggregated score for each of a set of objects in the collection is computed by applying an aggregating function in which a first confidence weighting is applied to the first comparison measure and a second confidence weighting is applied to the second comparison measure. The first confidence weighting is independent of the second comparison measure. The second confidence weighting is dependent on the first comparison measure.

Abstract: A translation system and method suited to use in Cross Language Information Retrieval employ a retrieval-based scoring function for reranking candidate translations. The method includes translating an input source language query to generate a set of the candidate translations in a target language. The candidate translations are scored with the scoring function, which allows them to be reranked, and an optimal one or more selected for use in querying a domain-specific collection of documents in the target language. The scoring function applies weights to features extracted from the candidate translations. The weights have been learned on features extracted from translated queries, each generated by translation of an original query into the target language, and a measure of information retrieval performance of each the translated queries. One or more of the features used is a domain-specific feature which relies on a corpus of documents in the domain of interest.

Abstract: A multimedia information retrieval system includes a storage and an electronic processing device. The latter is configured to perform a process including: computing values of a pairwise similarity measure quantifying pairwise similarity of documents of a multimedia reference repository; storing the computed values in the storage; performing an initial information retrieval process respective to the multimedia reference repository to return a set of initial repository documents; and identifying a set of top ranked documents of the multimedia reference repository based at least on the stored computed values pertaining to the set of initial repository documents.

Abstract: Cross-lingual information retrieval is disclosed, comprising: translating a received query from a source natural language into a target natural language; performing a first information retrieval operation on a corpus of documents in the target natural language using the translated query to retrieve a set of pseudo-feedback documents in the target natural language; re-translating the received query from the source natural language into the target natural language using a translation model derived from the set of pseudo-feedback documents in the target natural language; and performing a second information retrieval operation on the corpus of documents in the target natural language using the re-translated query to retrieve an updated set of documents in the target natural language