Search results

The purpose of this paper is to propose an automatic method to generate semantic annotations of football transfer in the news. The current automatic news integration systems on the Web are constantly faced with the challenge of diversity, heterogeneity of sources. The approaches for information representation and storage based on syntax have some certain limitations in news searching, sorting, organizing and linking it appropriately. The models of semantic representation are promising to be the key to solving these problems.

The approach of the author leverages Semantic Web technologies to improve the performance of detection of hidden annotations in the news. The paper proposes an automatic method to generate semantic annotations based on named entity recognition and rule-based information extraction. The authors have built a domain ontology and knowledge base integrated with the knowledge and information management (KIM) platform to implement the former task (named entity recognition). The semantic extraction rules are constructed based on defined language models and the developed ontology.

The proposed method is implemented as a part of the sport news semantic annotations-generating prototype BKAnnotation. This component is a part of the sport integration system based on Web Semantics BKSport. The semantic annotations generated are used for improving features of news searching – sorting – association. The experiments on the news data from SkySport (2014) channel showed positive results. The precisions achieved in both cases, with and without integration of the pronoun recognition method, are both over 80 per cent. In particular, the latter helps increase the recall value to around 10 per cent.

This is one of the initial proposals in automatic creation of semantic data about news, football news in particular and sport news in general. The combination of ontology, knowledge base and patterns of language model allows detection of not only entities with corresponding types but also semantic triples. At the same time, the authors propose a pronoun recognition method using extraction rules to improve the relation recognition process.

The purpose of this paper is to enhance the quality of new reducing sentence in sentence-generation-based summarizing method by establishing consequence relationship between two action, state or process Vietnamese sentences.

First, types of pairs of Vietnamese sentences based on presupposition about the consequence relationship is classified: the verb indicating action or state at the first sentence is considered as the consequence of the verb indicating action, state or process at the second sentence. Then main predicates in Discourse Representation Structure – a logical form which represents the semantic of a given pair of sentences – is analyzed and inner- and inter-sentential relationships are determined. The next step is to generate the syntactic structure of the new reducing sentence. Finally, a combination with the built set of lexicons is done to complete the new meaning-summarizing Vietnamese sentence.

This method makes the new meaning-summarizing Vietnamese sentence satisfy two requirements: summarize the semantic of the given pair of Vietnamese sentences and have naturalism in common Vietnamese communication. In addition, it is possible to extend the method and apply for the purpose of summarizing the more complex Vietnamese paragraphs as well as paragraphs in other languages.

At the first step, only inter-sentential consequence relationship is considered and this is applied to the limit types of pairs of Vietnamese sentences which have a simple structure.

This study presents improvements in sentence-generation-based summarization method to enhance the quality of new meaning-summarizing Vietnamese sentences. This method proves effective in summarizing the considered pairs of sentences.

The purpose of this paper is to determine the effectiveness of using fully homomorphic encryption (FHE) to preserve the privacy of biometric data in an authentication system. Biometrics offers higher accuracy for personal recognition than traditional methods because of its properties. Biometric data are permanently linked with an individual and cannot be revoked or cancelled, especially when biometric data are compromised, leading to privacy issues.

By reviewing current approaches, FHE is considered as a promising solution for the privacy issue because of its ability to perform computations in the encrypted domain. The authors studied the effectiveness of FHE in biometric authentication systems. In doing so, the authors undertake the study by implementing a protocol for biometric authentication system using iris.

The security analysis of the implementation scheme demonstrates the effectiveness of FHE to protect the privacy of biometric data, as unlimited operations can be performed in the encrypted domain, and the FHE secret key is not shared with any other party during the authentication protocol.

The use of malicious model in the design of the authentication protocol to improve the privacy, packing methods and use of low-level programming language to enhance performance of the system needs to be further investigated.

The main contributions of this paper are the implementation of a privacy-preserving iris biometric authentication protocol adapted to lattice-based FHE and a sound security analysis of authentication and privacy.

The purpose of this paper is to propose a method of calculating the sentiment value of a tweet based on the emoticon role.

Classification of emoticon roles as four types showing “emphasis”, “assuagement”, “conversion” and “addition”, with roles determined based on the respective relations to sentiment of sentences and emoticons.

Clustering of users of four types based on emoticon sentiment.

Formalization, using regression analysis, of the relation of sentiment between sentences and emoticons in all roles.

The purpose of this paper is to propose a Web application system for visualizing Twitter users based on temporal changes in the impressions received from the tweets posted by the users on Twitter.

The system collects a specified user’s tweets posted during a specified period using Twitter API, rates each tweet based on three distinct impressions using an impression mining system, and then generates pie and line charts to visualize results of the previous processing using Google Chart API.

Because there are more news articles featuring somber topics than those featuring cheerful topics, the impression mining system, which uses impression lexicons created from a newspaper database, is considered to be more effective for analyzing negative tweets.

The system uses Twitter API to collect tweets from Twitter. This suggests that the system cannot collect tweets of the users who maintain private timelines. According to our questionnaire, about 30 per cent of Twitter users’ timelines are private. This is one of the limitations to using the system.

The system enables people to grasp the personality of Twitter users by visualizing the impressions received from tweets the users normally post on Twitter. The target impressions are limited to those represented by three bipolar scales of impressions: “Happy/Sad”, “Glad/Angry” and “Peaceful/Strained”. The system also enables people to grasp the context in which keywords are used by visualizing the impressions from tweets in which the keywords were found.

This paper aims to give an overview on a number of selected applications in comparison to a previous evaluation conducted two years ago, as well as performing an analysis on several new applications. Mobile messaging and VoIP applications for smartphones have seen a massive surge in popularity, which has also sparked the interest in research related to their security and privacy protection, leading to in-depth analyses of specific applications or vulnerabilities.

The evaluation methods mostly focus on known vulnerabilities in connection with authentication and validation mechanisms but also describe some newly identified attack vectors.

The results show a positive trend for new applications, which are mostly being developed with security and privacy features, whereas some of the older applications have shown little progress or have even introduced new vulnerabilities. In addition, this paper shows privacy implications of smartphone messaging that are not even solved by today’s most sophisticated “secure” smartphone messaging applications, as well as discusses methods for protecting user privacy during the creation of the user network.

Currently, there is no perfect solution available; thus, further research on this topic needs to be conducted.

In addition to conducting a security evaluation of existing applications together with newly designed messengers that were designed with a security background in mind, several methods for protecting user privacy were discussed. Furthermore, some new attack vectors were discussed.

This paper aims to investigate the methods for the prediction of tags on a textual corpus that describes diverse data sets based on short messages; as an example, the authors demonstrate the usage of methods based on hotel staff inputs in a ticketing system as well as the publicly available StackOverflow corpus. The aim is to improve the tagging process and find the most suitable method for suggesting tags for a new text entry.

The paper consists of two parts: exploration of existing sample data, which includes statistical analysis and visualisation of the data to provide an overview, and evaluation of tag prediction approaches. The authors have included different approaches from different research fields to cover a broad spectrum of possible solutions. As a result, the authors have tested a machine learning model for multi-label classification (using gradient boosting), a statistical approach (using frequency heuristics) and three similarity-based classification approaches (nearest centroid, k-nearest neighbours (k-NN) and naive Bayes). The experiment that compares the approaches uses recall to measure the quality of results. Finally, the authors provide a recommendation of the modelling approach that produces the best accuracy in terms of tag prediction on the sample data.

The authors have calculated the performance of each method against the test data set by measuring recall. The authors show recall for each method with different features (except for frequency heuristics, which does not provide the option to add additional features) for the dmbook pro and StackOverflow data sets. k-NN clearly provides the best recall. As k-NN turned out to provide the best results, the authors have performed further experiments with values of k from 1–10. This helped us to observe the impact of the number of neighbours used on the performance and to identify the best value for k.

The value and originality of the paper are given by extensive experiments with several methods from different domains. The authors have used probabilistic methods, such as naive Bayes, statistical methods, such as frequency heuristics, and similarity approaches, such as k-NN. Furthermore, the authors have produced results on an industrial-scale data set that has been provided by a company and used directly in their project, as well as a community-based data set with a large amount of data and dimensionality. The study results can be used to select a model based on diverse corpora for a specific use case, taking into account advantages and disadvantages when applying the model to your data.