Abstract: Aspect Based Sentiment Analysis is a type of fine-grained sentiment analysis. It is popular in both industry and academic communities, since it provides more detailed information on the user generated text in product reviews or social network. We propose a novel framework based on neural network to determine the polarity of a review given a specific target. Not only the words close to the target but also the words far from the target determine the polarity of the review given a certain target, so we use self attention to solve the problem of long distance dependence. Briefly, we do multiple linear mapping on the review, do multiple attention and combine them to attend to the information from different representation sub-spaces. Besides, we use domain embedding to get close to the real word embedding in a certain domain, since the meaning of the same word may be different in different situation. Moreover, we use position embedding to underline the target and pay more attention to the words that are close to the target to get better performance on the task. We validate our model on four benchmarks, they are SemEval 2014 restaurant dataset, SemEval 2014 laptop dataset, SemEval 2015 restaurant dataset and SemEval 2016 restaurant dataset. The final results show that our model is effective and strong, which brings a 0.74% boost averagely based on the previous state-of-the-art work.

Abstract: Law enforcement agencies and private security companies work to prevent, detect and counteract any threat with the resources they have, including alarms and video surveillance. Even so, there are still terrorist attacks or shootings in schools in which armed people move around a venue exercising violence and generating victims, showing the limitations of current systems. For example, they force security agents to monitor continuously all the images coming from the installed cameras, and potential victims nearby are not aware of the danger until someone triggers a general alarm, which also does not give them information on what to do to protect themselves. In this article we present a project that is being developed to apply the latest technologies in early threat detection and optimal response. The system is based on the automatic processing of video surveillance images to detect weapons and a mobile app that serves both for detection through the analysis of mobile device sensors, and to send users personalised and dynamic indications. The objective is to react in the shortest possible time and minimise the damage suffered.

Abstract: This paper is devoted to new aspects of Natural Language Interface to Relational Database (NLIDB) integration into third party corporate environments related to control data access. Because there is no schema information in the input NL-query and the different relational database management system (RDBMS) requires different meta-data types and rules to control data access, developers meet a problem addressed to automatic data access control in the case of NL-interface implementation to relational databases. In the paper we suggest a comprehensive approach which takes into account permissions throughout the pipeline of transforming NL-query into SQL-query with an intermediate SPARQL representation. Our integration solutions based on well-known Ontology Based Data Access (OBDA) approach, which gives us the opportunity to adapt the proposed solutions to the specifics of data access control in different RDBMS. Suggested approach has been implemented within intelligent service named Reply and tested in the real-world projects.

Abstract: Cross-modal retrieval has attracted increasing attention in recent years. Deep supervised hashing methods have been widely used for cross-modal similarity retrieval on large-scale datasets, because the deep architectures can generate more discriminative feature representations. Traditional hash methods adopt a symmetric way to learn the hash function for both query points and database points. However, those methods take an immense amount of work and time for model training, which is inefficient with the explosive growth of data volume. To solve this issue, we propose an Asymmetric Deep Cross-modal Hashing (ADCH) method to perform more effective hash learning by simultaneously preserving the semantic similarity and the underlying data structures. More specifically, ADCH treats the query points and database points in an asymmetric way. Furthermore, to provide more similarity information, a detailed definition for cross-modal similarity matrix is also proposed. The training of ADCH takes less time than traditional symmetric deep supervised hashing methods. Extensive experiments on two widely used datasets show that the proposed approach achieves the state-of-the-art performance in cross-modal retrieval.

Abstract: In Dial-a-Ride Problem (DARP) customers request from an operator a transportation service from a pick-up to a drop-off place. Depending on the formulation, the problem can address several constraints, being associated with problems such as door-to-door transportation for elderly / disabled people or occasional private drivers. This paper addresses the latter case where a private drivers company transports passengers in a heterogeneous fleet of saloons, estates, people carriers and minibuses. The problem is formulated as a multiple objective DARP which tries to minimize the total distances, the number of empty seats, and the wage differential between the drivers. To solve the problem a Non-dominated Sorting Genetic Algorithm-II is hybridized with a local search. Results for daily scheduling are shown.