281,117 research outputs found
Achieving Secure and Efficient Cloud Search Services: Cross-Lingual Multi-Keyword Rank Search over Encrypted Cloud Data
Multi-user multi-keyword ranked search scheme in arbitrary language is a
novel multi-keyword rank searchable encryption (MRSE) framework based on
Paillier Cryptosystem with Threshold Decryption (PCTD). Compared to previous
MRSE schemes constructed based on the k-nearest neighbor searcha-ble encryption
(KNN-SE) algorithm, it can mitigate some draw-backs and achieve better
performance in terms of functionality and efficiency. Additionally, it does not
require a predefined keyword set and support keywords in arbitrary languages.
However, due to the pattern of exact matching of keywords in the new MRSE
scheme, multilingual search is limited to each language and cannot be searched
across languages. In this pa-per, we propose a cross-lingual multi-keyword rank
search (CLRSE) scheme which eliminates the barrier of languages and achieves
semantic extension with using the Open Multilingual Wordnet. Our CLRSE scheme
also realizes intelligent and per-sonalized search through flexible keyword and
language prefer-ence settings. We evaluate the performance of our scheme in
terms of security, functionality, precision and efficiency, via extensive
experiments
On the learning of vague languages for syntactic pattern recognition
The method of the learning of vague languages which represent distorted/ambiguous patterns is proposed in the paper. The goal of the method is to infer the quasi-context-sensitive string grammar which is used in our model as the generator of patterns. The method is an important component of the multi-derivational model of the parsing of vague languages used for syntactic pattern recognition
Formal Validation of Pattern Matching code
When addressing the formal validation of generated software, two main
alternatives consist either to prove the correctness of compilers or
to directly validate the generated code. Here, we focus on directly
proving the correctness of compiled code issued from powerful
pattern matching constructions typical of ML like languages or
rewrite based languages such as ELAN, MAUDE or Tom.
In this context, our first contribution is to define a general
framework for anchoring algebraic pattern-matching capabilities
in existing languages like C, Java or ML. Then, using a just enough
powerful intermediate language, we formalize the behavior of compiled
code and define the correctness of compiled code with respect to
pattern-matching behavior. This allows us to prove the equivalence of
compiled code correctness with a generic first-order proposition whose
proof could be achieved via a proof assistant or an automated theorem
prover. We then extend these results to the multi-match situation
characteristic of the ML like languages.
The whole approach has been implemented on top of the Tom compiler
and used to validate the syntactic matching code of the Tom compiler
itself
Automata with Modulo Counters and Nondeterministic Counter Bounds
We introduce and investigate Nondeterministically Bounded Modulo Counter
Automata (NBMCA), which are two-way multi-head automata that comprise a
constant number of modulo counters, where the counter bounds are nondeterministically
guessed, and this is the only element of nondeterminism. NBMCA are
tailored to recognising those languages that are characterised by the existence of
a specific factorisation of their words, e. g., pattern languages. In this work, we
subject NBMCA to a theoretically sound analysis
Neural overlap of L1 and L2 semantic representations across visual and auditory modalities : a decoding approach/
This study investigated whether brain activity in Dutch-French bilinguals during semantic access to concepts from one language could be used to predict neural activation during access to the same concepts from another language, in different language modalities/tasks. This was tested using multi-voxel pattern analysis (MVPA), within and across language comprehension (word listening and word reading) and production (picture naming). It was possible to identify the picture or word named, read or heard in one language (e.g. maan, meaning moon) based on the brain activity in a distributed bilateral brain network while, respectively, naming, reading or listening to the picture or word in the other language (e.g. lune). The brain regions identified differed across tasks. During picture naming, brain activation in the occipital and temporal regions allowed concepts to be predicted across languages. During word listening and word reading, across-language predictions were observed in the rolandic operculum and several motor-related areas (pre- and postcentral, the cerebellum). In addition, across-language predictions during reading were identified in regions typically associated with semantic processing (left inferior frontal, middle temporal cortex, right cerebellum and precuneus) and visual processing (inferior and middle occipital regions and calcarine sulcus). Furthermore, across modalities and languages, the left lingual gyrus showed semantic overlap across production and word reading. These findings support the idea of at least partially language- and modality-independent semantic neural representations
Lessons learned in multilingual grounded language learning
Recent work has shown how to learn better visual-semantic embeddings by
leveraging image descriptions in more than one language. Here, we investigate
in detail which conditions affect the performance of this type of grounded
language learning model. We show that multilingual training improves over
bilingual training, and that low-resource languages benefit from training with
higher-resource languages. We demonstrate that a multilingual model can be
trained equally well on either translations or comparable sentence pairs, and
that annotating the same set of images in multiple language enables further
improvements via an additional caption-caption ranking objective.Comment: CoNLL 201
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Unifying cross-linguistic and within-language patterns of finiteness marking in MOSAIC
MOSAIC, a model that has already simulated cross-linguistic differences in the occurrence of the Optional Infinitive phenomenon, is applied to the simulation of the pattern of finiteness marking within Dutch. This within-language pattern, which includes verb placement, low rates of Optional Infinitives in Wh-questions and the correlation between finiteness marking and subject provision, has been taken as evidence for the view that children have correctly set the clause structure and inflectional parameters for their language. MOSAIC, which employs no built-in linguistic knowledge, clearly simulates the pattern of results as a function of its utterance-final bias, the same mechanism that is responsible for its successful simulation of the crosslinguistic data. These results suggest that both the crosslinguistic and within–language pattern of finiteness marking can be understood in terms of the interaction between a simple resource-limited learning mechanism and the distributional statistics of the input to which it is exposed. Thus, these phenomena do not provide any evidence for abstract or innate knowledge on the part of the child
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