Alberto Rivas

Relationship recommender system in a business and employment-oriented social network

Abstract In the last ten years, social networks have had a great influence on people’s lifestyles and have changed, above all, the way users communicate and relate. This is why, one of the main lines of research in the field of social networks focuses on finding and analyzing possible connections between users. These developments allow users to expand on their network of contacts without having to search among the total set of users. However, there are many types of social networks which attract users with specific needs, these needs influence on the type of contacts users are looking for. Our article proposes a relationship recommender system for a business and employment-oriented social network. The presented system functions by extracting relevant information from the social network which it then uses to adequately recommend new contacts and job offers to users. The recommender system uses information gathered from job offer descriptions, user profiles and users’ actions. Then, different metrics are applied in order to discover new ties that are likely to convert into relationships.

A Hash Based Image Matching Algorithm for Social Networks

One of the main research trends over the last years has focused on knowledge extraction from social networks users. One of the main difficulties of this analysis is the lack of structure of the information and the multiple formats in which it can appear. The present article focuses on the analysis of the information provided by different users in image form. The problem that is intended to be solved is the detection of equal images (although they may have minimal transformations, such as a watermark), which allows establishing links between users who publish the same images. The solution proposed in the article is based on the comparison of hashes, which allows certain transformations that can be made to an image from a computational point of view.

The Use of Drones in Spain: Towards a Platform for Controlling UAVs in Urban Environments

Rapid advances in technology make it necessary to prepare our society in every aspect. Some of the most significant technological developments of the last decade are the UAVs (Unnamed Aerial Vehicles) or drones. UAVs provide a wide range of new possibilities and have become a tool that we now use on a daily basis. However, if their use is not controlled, it could entail several risks, which make it necessary to legislate and monitor UAV flights to ensure, inter alia, the security and privacy of all citizens. As a result of this problem, several laws have been passed which seek to regulate their use; however, no proposals have been made with regards to the control of airspace from a technological point of view. This is exactly what we propose in this article: a platform with different modes designed to control UAVs and monitor their status. The features of the proposed platform provide multiple advantages that make the use of UAVs more secure, such as prohibiting UAVs’ access to restricted areas or avoiding collisions between vehicles. The platform has been successfully tested in Salamanca, Spain.

Social computing for image matching

One of the main technological trends in the last five years is mass data analysis. This trend is due in part to the emergence of concepts such as social networks, which generate a large volume of data that can provide added value through their analysis. This article is focused on a business and employment-oriented social network. More specifically, it focuses on the analysis of information provided by different users in image form. The images are analyzed to detect whether other existing users have posted or talked about the same image, even if the image has undergone some type of modification such as watermarks or color filters. This makes it possible to establish new connections among unknown users by detecting what they are posting or whether they are talking about the same images. The proposed solution consists of an image matching algorithm, which is based on the rapid calculation and comparison of hashes. However, there is a computationally expensive aspect in charge of revoking possible image transformations. As a result, the image matching process is supported by a distributed forecasting system that enables or disables nodes to serve all the possible requests. The proposed system has shown promising results for matching modified images, especially when compared with other existing systems.

Image Matching Algorithm Based on Hashes Extraction

Nowadays, the rise of social networks and the continuous storage of large of information are topical issue. But the main problem is not the storage itself, is the ability to process most of this information, so that it is not stored in vain. In this way, using the shared images within the scope of social networks, possible relationships between users could be identified. From this idea arises the present work, which focuses on identifying similar images even if they have been modified (applying color filters, rotations or even watermarks). The solution involves preprocessing to eliminate possible filters and then apply hashing techniques, just to obtain hashes that are unique for each image and allow the comparison of an abstract but effective way for the user.

Case-Based Reasoning and Agent Based Job Offer Recommender System

The large amounts of information that social networks contain, makes it necessary for them to provide guides and aids that improve users’ experience in the system. In addition to search and filtering tools, users should be presented with the content they wish to obtain before they take any action to find it. To be able to recommend content to users, it is necessary to analyse their profiles and determine what type of content they want to view. The present work is focused on an employability oriented social network for which a job offer recommender system is proposed, following the model of a multi-agent system. The recommendation system has a hybrid approach, consisting of a CBR system and an argumentation framework. The CBR system is capable of deciding, on the basis of a series of metrics and similar cases stored in the system, whether a job offer is likely to be recommended to a user. Besides, the argumentation framework extends the system with an argumentation CBR, through which old and similar cases can be obtained from the CBR system. Finally, based on the different solutions proposed by the agents and the experience gained from past cases, a process of discussion among agents is established. Here, a debate is held in which a final decision is reached, giving the best recommendation to the proposed problem.

The Right to Honour on Social Networks: Detection and Classifications of Users

It is clear that social networks have come to stay. In recent years they have become the media par excellence. The users who participate in them help to spread information quickly and easily so that everyone can benefit. Users are becoming more inclined to voice their opinions, networks are willing to listen and technology has an enormous outreach. A priori, something that seems a great advantage can become a big problem when the news spread violates an individual’s right to honour. This paper proposes a tool that detects and collects information from users who publish or disseminate offensive information to an individual. It establishes parameters that determine the level of damage certain individuals can make on social media and makes a ranking that is based on their characteristics and publications. This proposal is an example of the infinite possibilities that automatic data collection and processing provide us with. Without these technologies it would have been impossible to protect the rights of individuals on social networks, due to the large number of users.

Detection of Cattle Using Drones and Convolutional Neural Networks

Multirotor drones have been one of the most important technological advances of the last decade. Their mechanics are simple compared to other types of drones and their possibilities in flight are greater. For example, they can take-off vertically. Their capabilities have therefore brought progress to many professional activities. Moreover, advances in computing and telecommunications have also broadened the range of activities in which drones may be used. Currently, artificial intelligence and information analysis are the main areas of research in the field of computing. The case study presented in this article employed artificial intelligence techniques in the analysis of information captured by drones. More specifically, the camera installed in the drone took images which were later analyzed using Convolutional Neural Networks (CNNs) to identify the objects captured in the images. In this research, a CNN was trained to detect cattle, however the same training process could be followed to develop a CNN for the detection of any other object. This article describes the design of the platform for real-time analysis of information and its performance in the detection of cattle.

Semantic Analysis System for Industry 4.0

The sensorization of machines used in industries (Industry 4.0) and the ability to connect them to a data network, have changed the way companies maintain and optimize the performance of their machines. Each one is capable of generating large volumes of data daily, big data methodologies can now be applied to these data in order to extract knowledge, this was an impossible task not so long ago. However, in many cases sensorization and data analysis are not enough to detect faults or alarms and once they occur, an operator must fix them manually. The purpose of this paper is to use a semantic analyzer, based primarily on a case-based reasoning system which extracts information from the reports written by operators about the faults they resolved in machines. Thus, when a fault or alarm occurs and there are previous reports about this machine, the developed system independently proposes a solution and there is no need for an operator to identify the problem. To do this, a text analysis platform has been created, it applies case-based reasoning to report the causes of the problem. In the majority of cases, the proposed system can successfully resolve the problem and it is not necessary to revise the machine in order to detect a malfunction and also simplifies the repair process by providing the operator with a glossary of key terms based on the history of repair reports.

Human-Computer Interaction in Currency Exchange

Technology has changed the way in which humans interact with each other. Many activities that before required human interaction can now be performed using a machine. It can be said that the way in which human beings relate has changed altogether. One of the main difficulties in communication between humans occurs when they do not speak the same language. This problem becomes more acute in situations where the understanding of both parties is essential, for example during economic transactions. Computing can help improve this process by promoting understanding between the customer and the seller, contributing to better consumer experience. This paper presents a case study in which human-machine interfaces are used in the exchange of currency at airports, where customers are of multiple nationalities and the vendor does not always speak the same language as the customer. The system was evaluated at a real airport and very positive results were obtained.