A survey of diversity-oriented optimization

The concept of diversity plays a crucial role in many optimization approaches: On the one hand, diversity can be formulated as an essential goal, such as in level set approximation or multiobjective optimization where the aim is to find a diverse set of alternative feasible or, respectively, Pareto optimal solutions. On the other hand, diversity maintenance can play an important role in algorithms that ultimately searc

Desafios para a implantação de soluções de integração de aplicações empresariais em provedores de computação em nuvem

Nos últimos anos o campo de estudos conhecido como Integração de Aplicações Empresariais tem desempenhado um importante papel ao proporcionar metodologias, técnicas e ferramentas para que as empresas possam desenvolver soluções de integração, visando reutilizar suas aplicações e dar suporte às novas demandas que surgem com a evolução dos seus processos de negócio. A Computação em Nuvem é parte de uma nova realidade, na qual tanto pequenas como grandes empresas têm a sua disposição uma infraestrutura de TI de alta capacidade, a um baixo custo, na qual podem implantar e executar suas soluções de integração. O modelo de cobrança adotado pelos provedores de Computação em Nuvem se baseia na quantidade de recursos computacionais consumidos por uma solução de integração. Tais recursos podem ser conhecidos, basicamente, de duas formas distintas: a partir da execução real de uma solução de integração em um motor de orquestração, ou a partir da simulação do modelo conceitual que descreve a solução sem que a mesma tenha que ser previamente implementada. Ainda, é desejável que os provedores proporcionem modelos conceituais que descrevam detalhadamente a variabilidade de serviços e as restrições entre eles. A revisão da literatura técnica e científica evidencia que não existem metodologias, técnicas e ferramentas para estimar a demanda de recursos computacionais consumidos por soluções de integração, a partir de seus modelos conceituais. Além disso, os provedores de Computação em Nuvem não possuem ou disponibilizam os modelos conceituais dos serviços que possam ser contratados. Tais questões constituem a base para que se possa estabelecer um processo e desenvolver ferramentas de apoio a tomada de decisão para a implantação de soluções de integração de aplicações empresariais em provedores de Computação em Nuvem

Characterising Enterprise Application Integration Solutions as Discrete-Event Systems

It is not difficult to find an enterprise which has a software ecosystem composed of applications that were built using different technologies, data models, operating systems, and most often were not designed to exchange data and share functionalities. Enterprise Application Integration provides methodologies and tools to design and implement integration solutions. The state-of-the-art integration technologies provide a domain-specific language that enables the design of conceptual models for integration solutions. The analysis of integration solutions to predict their behaviour and find possible performance bottlenecks is an important activity that contributes to increase the quality of the delivered solutions, however, software engineers follow a costly, risky, and time-consuming approach. Integration solutions shall be understood as a discrete-event system. This chapter introduces a new approach based on simulation to take advantage of well-established techniques and tools for discrete-event simulation, cutting down cost, risk, and time to deliver better integration solutions

Deobfuscating Leetspeak With Deep Learning to Improve Spam Filtering

The evolution of anti-spam filters has forced spammers to make greater efforts to bypass filters in order to distribute content over networks. The distribution of content encoded in images or the use of Leetspeak are concrete and clear examples of techniques currently used to bypass filters. Despite the importance of dealing with these problems, the number of studies to solve them is quite small, and the reported performance is very limited. This study reviews the work done so far (very rudimentary) for Leetspeak deobfuscation and proposes a new technique based on using neural networks for decoding purposes. In addition, we distribute an image database specifically created for training Leetspeak decoding models. We have also created and made available four different corpora to analyse the performance of Leetspeak decoding schemes. Using these corpora, we have experimentally evaluated our neural network approach for decoding Leetspeak. The results obtained have shown the usefulness of the proposed model for addressing the deobfuscation of Leetspeak character sequences

An automatic generation of textual pattern rules for digital content filters proposal, using grammatical evolution genetic programming

AbstractThis work presents a conceptual proposal to address the problem of intensive human specialized resources that are nowadays required for the maintenance and optimized operation of digital contents filtering in general and anti-spam filtering in particular. The huge amount of spam, malware, virus, and other illegitimate digital contents distributed through network services, represents a considerable waste of physical and technical resources, experts and end users time, in continuous maintenance of anti-spam filters and deletion of spam messages, respectively. The problem of cumbersome and continuous maintenance required to keep anti-spam filtering systems updated and running in an efficient way, is addressed in this work by the means of genetic programming grammatical evolution techniques, for automatic rules generation, having SpamAssassin anti-spam system and SpamAssassin public corpus as the references for the automatic filtering customization

Multi-objective evolutionary optimization for dimensionality reduction of texts represented by synsets

Despite new developments in machine learning classification techniques, improving the accuracy of spam filtering is a difficult task due to linguistic phenomena that limit its effectiveness. In particular, we highlight polysemy, synonymy, the usage of hypernyms/hyponyms, and the presence of irrelevant/confusing words. These problems should be solved at the pre-processing stage to avoid using inconsistent information in the building of classification models. Previous studies have suggested that the use of synset-based representation strategies could be successfully used to solve synonymy and polysemy problems. Complementarily, it is possible to take advantage of hyponymy/hypernymy-based to implement dimensionality reduction strategies. These strategies could unify textual terms to model the intentions of the document without losing any information ( e.g. , bringing together the synsets “viagra”, “ciallis”, “levitra” and other representing similar drugs by using “virility drug” which is a hyponym for all of them). These feature reduction schemes are known as lossless strategies as the information is not removed but only generalised. However, in some types of text classification problems (such as spam filtering) it may not be worthwhile to keep all the information and let dimensionality reduction algorithms discard information that may be irrelevant or confusing. In this work, we are introducing the feature reduction as a multi-objective optimisation problem to be solved using a Multi-Objective Evolutionary Algorithm (MOEA). Our algorithm allows, with minor modifications, to implement lossless (using only semantic-based synset grouping), low-loss (discarding irrelevant information and using semantic-based synset grouping) or lossy (discarding only irrelevant information) strategies. The contribution of this study is two-fold: (i) to introduce different dimensionality reduction methods (lossless, low-loss and lossy) as an optimization problem that can be solved using MOEA and (ii) to provide an experimental comparison of lossless and low-loss schemes for text representation. The results obtained support the usefulness of the low-loss method to improve the efficiency of classifiers.Agencia Estatal de Investigación | Ref. TIN2017-84658-C2-1-RAgencia Estatal de Investigación | Ref. TIN2017-84658-C2-2-RXunta de Galicia | Ref. ED431C 2022/03-GRCEusko Jaurlaritza | Ref. IT1676-22Fundação para a Ciência e a Tecnologia | Ref. UIDB/04466/2020Fundação para a Ciência e a Tecnologia | Ref. UIDP/04466/202

A cloud-based integration platform for enterprise application integration: A Model-Driven Engineering approach

This article addresses major information systems integration problems, approaches, technologies, and tools within the context of Model-Driven Software Engineering. The Guaraná integration platform is introduced as an innovative platform amongst state-of-the-art technologies available for enterprises to design and implement integration solutions. In this article, we present its domain-specificmodeling language and its industrial cloud-based web development platform, which supports the design and implementation of integration solutions. A real-world case study is described and analyzed; then, we delve into its design and implementation, to finally disclose ten measures that empirically help estimating the amount of effort involved in the development of integration solutions

A proposal of Infrastructure-as-a-Service providers pricing model using linear regression

The increasing demand for companies to reduce the IT infrastructure (on-premise) are driving the adoption of a type of cloud computing category known as Infrastructure-as-a-Service (IaaS) to provide virtualized computing resources over the Internet. However, the choice of an instance of virtual machine whose configuration is able to meet the demands of the company is a complex task, especially concerning the price charged by providers. The lack of transparency of the mechanism of definition of the prices adopted by providers makes difficult the decision-making process, considering the influence of several factors on the final price of the instances, among them the geographical location of the data center. In view of this problem, this work presents a new proposal of price modeling of instances using multiple linear regression model, including the geographical location of the data center as one of variables of the model. To verify the accuracy of the regression model proposed, the calculated prices were compared to real prices charged by IaaS providers Amazon EC2, Google Cloud Platform e Microsoft Azure

Improving the drug discovery process by using multiple classifier systems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Machine learning methods have become an indispensable tool for utilizing large knowledge and data repositories in science and technology. In the context of the pharmaceutical domain, the amount of acquired knowledge about the design and synthesis of pharmaceutical agents and bioactive molecules (drugs) is enormous. The primary challenge for automatically discovering new drugs from molecular screening information is related to the high dimensionality of datasets, where a wide range of features is included for each candidate drug. Thus, the implementation of improved techniques to ensure an adequate manipulation and interpretation of data becomes mandatory. To mitigate this problem, our tool (called D2-MCS) can split homogeneously the dataset into several groups (the subset of features) and subsequently, determine the most suitable classifier for each group. Finally, the tool allows determining the biological activity of each molecule by a voting scheme. The application of the D2-MCS tool was tested on a standardized, high quality dataset gathered from ChEMBL and have shown outperformance of our tool when compare to well-known single classification models