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Due to companies’ awareness of the real value of data, the amount of data they handle has increased significantly in recent years [20]. In order to obtain value from the data collected in each company, Big Data and Data Analytics techniques have to be applied. In carrying out this analysis in order to obtain valuable information for the company, several issues related to the computing power of the machines often emerge due to the high volume of data collected. In this paper we emphasis the importance of processing data effectively through data compression and an approach that can help to achieve this. In particular, we have used Bayesian networks to perform data compression without missing useful information.

Quantum computing is set to revolutionize the world of software engineering. Currently, quantum paradigms and languages are being developed that are bringing the physical principles of quantum information closer to solving problems in multiple application domains. These paradigms and languages will continue to evolve and bring quantum computing closer to software engineering, and to an increasingly massive use of it. Unfortunately, as quantum software engineering advances in bringing these new capabilities closer to widespread industry exploitation, attackers will turn the spotlight on quantum software vulnerabilities, on what will be the new battlefront in the world of cybersecurity. In this paper, we approach this security from scratch, starting with an introduction to the current state of quantum programming languages. Then, some current hardware limitations are presented and how these affect quantum software engineering. Followed, we make an overview on the analysis of the quantum languages in terms of security together with some of the research made up to date. As a conclusion, we express the necessity to avoid the many times made mistake of not including security considerations from the first steps in the design of computing paradigms. An error that is repeated a second time is not an error; it is, indeed, a choice.

Phishing is a negative technique that is used to steel private and confidential information over the web. In the present work author proposed a hybrid similarity of Cosine and Soft Cosine to calculate the similarity between the user query and repository as an anti-phishing approach. The proposed work model uses a multiclass learning method called Feed Forward Back Propagation Neural Network. The model evaluation results with 100 to 3000 test files shows that the hybrid model is able to detect the phishing attack with an average precision of 71% and is highly effective.

El actual momento en el que la industria ha adoptado el camino de la digitalizacion, existen diversas implicaciones tecnicas, tecnologicas y legales que merecen ser analizadas. En este contexto, el presente articulo presenta una reflexion abierta sobre los retos de Ciber-Seguridad en la Industria Conectada, presentandolos como oportunidades de cara al futuro. Palabras clave: Seguridad industrial, Ciber-seguridad, Industria 4.0

The problem of transport is one of the fundamental problems in today's large cities and one of the fundamental pillars on which work is being done under the paradigm of smart cities. Although the use of public transport is encouraged in cities, there are always problems related to private transport parking availability. This work focuses on the development of an algorithm for monitoring parking spaces in open-air car parks, which is effective and simple from the point of view of the costs required for its deployment and robust in detecting the state of the car park. To this end, an algorithm based on Deep Learning has been developed for processing the status images of different car parks, obtaining real-time information on the real status of the car park, without the need for extensive deployment of devices in the car park. The proposed solution has been shown to outperform other image-based solutions for the same problem.

In this manuscript, we derive a non-parametric version of the Fisher kernel. We obtain this original result from the Non-negative Matrix Factorization with the Kullback-Leibler divergence. By imposing suitable normalization conditions on the obtained factorization, it can be assimilated to a mixture of densities, with no assumptions on the distribution of the parameters. The equivalence between the Kullback-Leibler divergence and the log-likelihood leads to kernelization by simply taking partial derivatives. The estimates provided by this kernel, retain the consistency of the Fisher kernel.

In this study, we propose a deeper analysis on the algorithmic treatment of financial time series, with a focus on Forex markets’ applications. The relevant aspects of the paper refers to a more beneficial data arrangement, proposed into a two-dimensional objects and to the application of a Temporal Convolutional Neural Network model, representing a more than valid alternative to Recurrent Neural Networks. The results are supported by expanding the comparison to other more consolidated deep learning models, as well as with some of the most performing Machine Learning methods. Finally, a financial framework is proposed to test the real effectiveness of the algorithms.

The Probabilistic Latent Semantic Analysis has been related with the Singular Value Decomposition. Several problems occur when this comparative is done. Data class restrictions and the existence of several local optima mask the relation, being a formal analogy without any real significance. Moreover, the computational difficulty in terms of time and memory limits the technique applicability. In this work, we use the Nonnegative Matrix Factorization with the Kullback–Leibler divergence to prove, when the number of model components is enough and a limit condition is reached, that the Singular Value Decomposition and the Probabilistic Latent Semantic Analysis empirical distributions are arbitrary close. Under such conditions, the Nonnegative Matrix Factorization and the Probabilistic Latent Semantic Analysis equality is obtained. With this result, the Singular Value Decomposition of every nonnegative entries matrix converges to the general case Probabilistic Latent Semantic Analysis results and constitutes the unique probabilistic image. Moreover, a faster algorithm for the Probabilistic Latent Semantic Analysis is provided.

The use of gestures is one of the principal ways of communication among human beings when other forms, such as speech, are not possible. Taking this as a basis, the use of gestures has become also a principal form of human machine interaction in many different fields, ranging from advanced industrial setups where robots are commanded by gestures, to the use of hands to remotely control multimedia devices present at home.

Tras la publicacion en el ejemplar de nuestra Revista DYNA correspondiente a septiembre/octubre de 2017 de BLOCKCHAIN: Retos y oportunidades mas alla del bitcoin, se han suscitado, por algunos de nuestros lectores, varias preguntas que hemos trasladado a los autores y que estos, amablemente, nos han respondido de forma asequible, incluso para los no especializados. Dada la actualidad y puntos de vista diversos generados alrededor del tema, nos abstenemos de cualquier juicio de valor respecto a los citados productos.

In recent years, Internet of Things has being used in several fields of modern life. The possibility of having an interconnection between all the related objects through data opens up a world of research in different fields. Among these fields, IoT technology is considered to make a significant impact in casting industry and the whole casting process. Moreover, the casting industry is an exceptionally critical field since it directly provides elements to other industries. Therefore, castings are subjected to a series of very rigorous quality controls which must be validated by the entire manufacturing process. Considering that the casting process has to be interconnected in order to foresee the state of the each piece before it is produced, it is vitally important that all sub-processes feed back into the system to learn from errors. In this work, we presented a new methodology for the detection and categorization of the imperfections on the surface of the castings. Among these defects, we particularly focused on inclusions, cold laps and misruns. To this end, we first compared several features extracted from the obtained images in order to highlight the regions of the casting that may be affected. And then, we applied several machine-learning techniques to classify the regions. The final results were carried to the starting point of the process to use them in the models to predict the quality of new castings.

The possibilities offered by the management of huge quantities of equipment and/or networks is attracting a growing number of developers of malware. In this paper, we propose a working methodology for the detection of malicious traffic, based on the analysis of the flow of packets circulating on the network. This objective is achieved through the parameterization of the characteristics of these packages to be analyzed later with supervised learning techniques focused on traffic labeling, so as to enable a proactive response to the large volume of information handled by current filters.

The most popular virtual currency (bitcoin) has become so popular because it relies on the concept of Distributed Ledger, realized by means of a technology known as BlockChain. This technology removes the need for third parties (such as authorities) that authorize transactions; in fact, it is a peer-to-peer network where consensus to transactions is given by peers. Furthermore, the distributed nature of BlockChain ensures a high-level of robustness to attacks. However, since its birth (a decade ago) many evolutions have been introduced: now, we distinguish between permissionless and permissioned BlockChains, the concept of smart contract is now supported, and various platforms are available. The contribution of this paper is to provide novices with the current trends, in particular by discussing the adoption of BlockChain technology in financial services. This work is licensed under aCreative Commons Attribution-NonCommercial 4.0 International License.

There are several industries that supplies key elements to other industries where they are critical. Hence, foundry castings are subject to very strict safety controls to assure the quality of the manufactured castings. In the last years, the use of computer vision technologies to control the surface quality. In particular, we have focused our work on inclusions, cold laps and misruns. We propose a new methodology that detects and categorises imperfections on the surface. To this end, we compared several features extracted from the images to highlight the regions of the casting that may be affected and, then, we applied several machine-learning techniques to classify the regions. Despite Deep Learning techniques have a very good performance in this problems, they need a huge dataset to get this results. In this case, due to the size of the dataset (which is a real problem in a real environment), we have use traditional machine learning techniques. Our experiments shows that this method obtains high precision rates, in general, and our best results are a 96,64% of accuracy and 0.9763 of area under ROC curve.

BlockChain technology has imposed a new perspective in the area of data management, i.e., the possibility of realizing immutable and distributed ledgers. Furthermore, the introduction of the concept of smart contract has further extended the potential applicability of this potentially disruptive technology. Although BlockChain was developed to support virtual currencies and is usually associated with them, novel platforms are under development, that are not at all related to the original application context.

This study is focused on a decentralised intelligent transportation systems with distributed intelligence based on classification techniques. The rationale behind this architecture is to offer a fully distributed, flexible and scalable system. The architecture encompasses the entire process of capture and management of available road data, enabling the generation of services to promote transportation efficiency. Besides that, thanks to the embedded classification techniques, the system is capable of predicting and reacting to certain events, facing them in an appropriate way. The aim of this work is to demonstrate how the system works in two different real-world use cases. To achieve this objective, how the architecture acts to deal with some incidences is proven. In addition, both use cases serve to show the effective communication between the different components of the system. Besides this, this work demonstrates the fundamental role played by the artificial intelligence techniques working in the system. The well-known C4.5 algorithm has been used for the accurate prediction of traffic congestion and pollution level. The authors explain in this work the reasons for using this classification technique, and the previous experiments performed.

Malware packing is a common technique employed to hide malicious code and to avoid static analysis. In order to fully inspect the contents of the executable, unpacking techniques must be applied. Unfortunately, generic unpacking is computationally expensive. For this reason, it is important to filter binaries in order to correctly handle them. In previous work, we proposed the adoption of anomaly detection for the classification of packed and not packed binaries using features based on the Portable Executable structure. In this paper, we extend this work and thoroughly evaluate the method with a different dataset and two different feature sets, rendering new conclusions. While anomaly detection is reaffirmed as a sound method for the discrimination of packed and not packed binaries, Portable Executable structure based features present limitations to distinguish custom packed files from not packed files.

Foundry is an important industry that supplies key castings to other industries where they are critical. Hence, foundry castings are subject to very strict safety controls to assure the quality of the manufactured castings. One of the type of flaws that may appear in the castings are defects on the surface; in particular, our work focuses in inclusions, cold laps and misruns. We propose a new approach that detects imperfections on the surface using a segmentation method that marks the regions of the casting that may be affected by some of these defects and, then, applies machine-learning techniques to classify the regions in correct or in the different types of faults. We show that this method obtains high precision rates. Keywords: machine vision, machine learning, defect categorisation.

The Connected Industry is already a reality. The accelerated evolution of the different technological currents converges in this last decade in a synergic effect that is destined in our days to constitute by itself what some gurus already call the Fourth Industrial Revolution.

The recent outbreak of indie games has popularized volumetric terrains to a new level, although video games have used them for decades. These terrains contain geological data, such as materials or cave systems. To improve the exploration experience and due to the large amount of data needed to construct volumetric terrains, industry uses procedural methods to generate them. However, they use their own methods, which are focused on their specific problem domains, lacking customization features. Besides, the evaluation of the procedural terrain generators remains an open issue in this field since no standard metrics have been established yet. In this paper, we propose a new approach to procedural volumetric terrains. It generates completely customizable volumetric terrains with layered materials and other features (e.g., mineral veins, underground caves, material mixtures and underground material flow). The method allows the designer to specify the characteristics of the terrain using intuitive parameters. Additionally, it uses a specific representation for the terrain based on stacked material structures, reducing memory requirements. To overcome the problem in the evaluation of the generators, we propose a new set of metrics for the generated content.

The use of mobile phones has increased because they offer nearly the same functionality as a personal computer. In addition, the number of applications available for Android-based mobile devices has increased. Google offers programmers the opportunity to upload and sell applications in the Android Market, but malware writers upload their malicious code there. In light of this background, we present here manifest analysis for malware detection in Android MAMA, a new method that extracts several features from the Android manifest of the applications to build machine learning classifiers and detect malware.

Malware can be defined as any type of malicious code that has the potential to harm a computer or network. The volume of malware is growing faster every year and poses a serious global security threat. Consequently, malware detection has become a critical topic in computer security. Currently, signature-based detection is the most widespread method used in commercial antivirus. In spite of the broad use of this method, it can detect malware only after the malicious executable has already caused damage and provided the malware is adequately documented. Therefore, the signature-based method consistently fails to detect new malware. In this paper, we propose a new method to detect unknown malware families. This model is based on the frequency of the appearance of opcode sequences. Furthermore, we describe a technique to mine the relevance of each opcode and assess the frequency of each opcode sequence. In addition, we provide empirical validation that this new method is capable of detecting unknown malware.

Un Modelo Predictivo de Control (MPC) es un sistema que permite controlar una planta de produccion. Gracias a este tipo de sistemas, es posible hacer que la produccion llegue a cero defectos. Para alcanzar este objetivo, este tipo de sistemas esta compuesto por varias fases. Una de las mas criticas es la que se encarga de la prediccion en un instante futuro. Actualmente, la investigacion en este campo esta relacionada con los MPC lineales, aunque el proceso pueda no serlo. Anteriormente, se presentaron diferentes experimentos que probaban que la fase de prediccion, normalmente representada por una formula matematica, podia ser representada mediante modelos de aprendizaje automatico. Sin embargo, la utilizacion de clasificadores unitarios introduce algunas limitaciones. En este trabajo, se extienden las investigaciones anteriores para proponer un metodo general para la prediccion de defectos o caracteristicas a traves de la utilizacion de la combinacion de diferentes metodos con un meta-clasificador, eliminando la necesidad de seleccion del mejor de ellos. Finalmente, se comparan los resultados obtenidos mostrando que esta nueva aproximacion permite obtener mejores resultados tanto en terminos de precision como en tasas de error.

Run-time packing is a technique employed by malware authors in order to conceal e.g., encrypt malicious code and recover it at run-time. In particular, some run-time packers only decrypt individual regions of code on demand, re-encrypting them again when they are not running. This technique is known as shifting decode frames and it can greatly complicate malware analysis. The first solution that comes to mind to analyze these samples is to apply multi-path exploration to trigger the unpacking of all the code regions. Unfortunately, multi-path exploration is known to have several limitations, such as its limited scalability for the analysis of real-world binaries. In this paper, we propose a set of domain-specific optimizations and heuristics to guide multi-path exploration and improve its efficiency and reliability for unpacking binaries protected with shifting decode frames.

The use of the social web has brought a series of changes in the way how content is created. In particular, social news sites link stories and the different users can comment them. In this paper, we propose a new method based on different features extracted from the text able to categorise the comments. To this end, we use a combination of statistical, syntactic and opinion features and machine-learning classifiers to classify a comment within three different categorisation types: the focus of the comment, the type of information contained in the comment and the controversy level of the comment. We validate our approach with data from 'Meneame', a popular Spanish social news site.

Spam has become a major issue in computer security because it is a channel for threats such as computer viruses, worms and phishing. Many solutions feature machine-learning algorithms trained using statistical representations of the terms that usually appear in the e-mails. Still, these methods require a training step with labelled data. Dealing with the situation where the availability of labelled training instances is limited slows down the progress of filtering systems and offers advantages to spammers. Currently, many approaches direct their efforts into Semi-Supervised Learning (SSL). SSL is a halfway method between supervised and unsupervised learning, which, in addition to unlabelled data, receives some supervision information such as the association of the targets with some of the examples. Collective Classification for Text Classification poses as an interesting method for optimising the classification of partially-labelled data. In this way, we propose here, for the first time, Collective Classification algorithms for spam filtering to overcome the amount of unclassified e-mails that are sent every day.

Spam has become a major issue in computer security because it is a channel for threats such as computer viruses, worms, and phishing. More than 86% of received e-mails are spam. Historical approaches to combating these messages, including simple techniques such as sender blacklisting or the use of e-mail signatures, are no longer completely reliable. Many current solutions feature machine-learning algorithms trained using statistical representations of the terms that most commonly appear in such e-mails. However, these methods are merely syntactic and are unable to account for the underlying semantics of terms within messages. In this paper, we explore the use of semantics in spam filtering by introducing a pre-processing step of Word Sense Disambiguation (WSD). Based upon this disambiguated representation, we apply several well-known machine-learning models and show that the proposed method can detect the internal semantics of spam messages.