A Novel Scheme for Intelligent Recognition of Pornographic Images

Harmful contents are rising in internet day by day and this motivates the essence of more research in fast and reliable obscene and immoral material filtering. Pornographic image recognition is an important component in each filtering system. In this paper, a new approach for detecting pornographic images is introduced. In this approach, two new features are suggested. These two features in combination with other simple traditional features provide decent difference between porn and non-porn images. In addition, we applied fuzzy integral based information fusion to combine MLP (Multi-Layer Perceptron) and NF (Neuro-Fuzzy) outputs. To test the proposed method, performance of system was evaluated over 18354 download images from internet. The attained precision was 93% in TP and 8% in FP on training dataset, and 87% and 5.5% on test dataset. Achieved results verify the performance of proposed system versus other related works

Exploiting multimedia in creating and analysing multimedia Web archives

The data contained on the web and the social web are inherently multimedia and consist of a mixture of textual, visual and audio modalities. Community memories embodied on the web and social web contain a rich mixture of data from these modalities. In many ways, the web is the greatest resource ever created by human-kind. However, due to the dynamic and distributed nature of the web, its content changes, appears and disappears on a daily basis. Web archiving provides a way of capturing snapshots of (parts of) the web for preservation and future analysis. This paper provides an overview of techniques we have developed within the context of the EU funded ARCOMEM (ARchiving COmmunity MEMories) project to allow multimedia web content to be leveraged during the archival process and for post-archival analysis. Through a set of use cases, we explore several practical applications of multimedia analytics within the realm of web archiving, web archive analysis and multimedia data on the web in general

Network Infrastructures in the Dark Web

With the appearance of the Internet, open to everyone in 1991, criminals saw a big opportunity in moving their organisations to the World Wide Web, taking advantage of these infrastructures as it allowed higher mobility and scalability. Later on, in the year 2000, the first system appeared, creating what is known today as the Dark Web. This layer of the World Wide Web became quickly the option to go when criminals wanted to sell and deliver content such as match-fixing, children pornography, drugs market, guns market, etc. This obscure side of the Dark Web, makes it a relevant topic to study in order to tackle this huge network and help to identify these malicious activities and actors. In this master thesis, it is shown through the study of two datasets from the Dark Web, that we are surrounded by capable technologies that can be applied to these types of problems in order to increase our knowledge about the data and reveal interesting characteristics in an interactive and useful way. One dataset has 10 000 relations from domains living in the Dark Web, and the other dataset has thousands of data from just 11 specific domains from the Dark Web. We reveal detailed information about each dataset by applying di↵erent analysis and data mining algorithms. For the first dataset we studied domains availability patterns with temporal analysis, we categorised domains with machine learning neural networks and we reveal the network topology and nodes relevance with social networks analysis and core-periphery model. Regarding the second dataset, we created a cross matching information web graph and applied a name entity recognition algorithm which ended in a tool for identifying entities within dark web’s domains. All of these approaches culminated in an interactive web application where we publicly not only display the entire research but also the tools developed along with the project (https://darkor.org).Com o surgimento da Internet, aberta a todos em 1991, os criminosos viram uma grande oportunidade em passar as suas organizações para a World Wide Web, aproveitando-se assim dessas infraestruturas que permitiam uma maior mobilidade e escalabilidade. Mais tarde, no ano 2000, surgiu o primeiro sistema, criando o que hoje é conhecido como a Dark Web. Essa camada da World Wide Web tornou-se rapidamente a opção a seguir quando os criminosos queriam vender e entregar conteúdo como combinação de resultados, pornografia infantil, mercado de drogas, mercado de armas, etc. Este lado obscuro da Dark Web, torna-a num tema relevante de estudo a fim de ajudar a identificar atividades e atores maliciosos. Nesta dissertação de mestrado é mostrado, através do estudo de dois conjuntos de dados da Dark Web, que estamos rodeados de tecnologias que podem ser aplicadas neste tipo de problemas de forma a aumentar o nosso conhecimento sobre os dados e revelar características interessantes de forma interativa e útil. Um conjunto de dados tem 10 000 relações de domínios que vivem na Dark Web enquanto que o outro conjunto de dados tem milhares de dados de apenas 11 domínios específicos da Dark Web. Neste estudo revelamos informações detalhadas sobre cada conjunto de dados aplicando diferentes análises e algoritmos de data mining. Para o primeiro conjunto de dados, estudamos padrões de disponibilidade de domínios com análise temporal, categorizamos domínios com o auxílio de redes neuronais e revelamos a topologia da rede e a relevância dos nós com análise de redes sociais e a aplicação de um modelo núcleo-periferia. Em relação ao segundo conjunto de dados, criamos um grafo da rede com cruzamento de dados e aplicamos um algoritmo de reconhecimento de entidades que resultou em uma ferramenta para identificar entidades dentro dos domínios da Dark Web estudados. Todas estas abordagens culminaram em uma aplicação web interativa onde exibimos publicamente não apenas todo o estudo, mas também as ferramentas desenvolvidas ao longo do projeto (https://darkor.org)

Indian Monuments Classification using Support Vector Machine

Recently, Content-Based Image Retrieval is a widely popular and efficient searching and indexing approach used by knowledge seekers. Use of images by e-commerce sites, by product and by service industries is not new nowadays. Travel and tourism are the largest service industries in India. Every year people visit tourist places and upload pictures of their visit on social networking sites or share via the mobile device with friends and relatives. Classification of the monuments is helpful to hoteliers for the development of a new hotel with state of the art amenities, to travel service providers, to restaurant owners, to government agencies for security, etc.. The proposed system had extracted features and classified the Indian monuments visited by the tourists based on the linear Support Vector Machine (SVM). The proposed system was divided into 3 main phases: preprocessing, feature vector creation and classification. The extracted features are based on Local Binary Pattern, Histogram, Co-occurrence Matrix and Canny Edge Detection methods.  Once the feature vector had been constructed, classification was   performed using Linear SVM. The Database of 10 popular Indian monuments was generated with 50 images for each class. The proposed system is implemented in MATLAB and achieves very high accuracy. The proposed system was also tested on other popular benchmark databases

Visual Perception System for Aerial Manipulation: Methods and Implementations

La tecnología se evoluciona a gran velocidad y los sistemas autónomos están empezado a ser una realidad. Las compañías están demandando, cada vez más, soluciones robotizadas para mejorar la eficiencia de sus operaciones. Este también es el caso de los robots aéreos. Su capacidad única de moverse libremente por el aire los hace excelentes para muchas tareas que son tediosas o incluso peligrosas para operadores humanos. Hoy en día, la gran cantidad de sensores y drones comerciales los hace soluciones muy tentadoras. Sin embargo, todavía se requieren grandes esfuerzos de obra humana para customizarlos para cada tarea debido a la gran cantidad de posibles entornos, robots y misiones. Los investigadores diseñan diferentes algoritmos de visión, hardware y sensores para afrontar las diferentes tareas. Actualmente, el campo de la robótica manipuladora aérea está emergiendo con el objetivo de extender la cantidad de aplicaciones que estos pueden realizar. Estas pueden ser entre otras, inspección, mantenimiento o incluso operar válvulas u otras máquinas. Esta tesis presenta un sistema de manipulación aérea y un conjunto de algoritmos de percepción para la automatización de las tareas de manipulación aérea. El diseño completo del sistema es presentado y una serie de frameworks son presentados para facilitar el desarrollo de este tipo de operaciones. En primer lugar, la investigación relacionada con el análisis de objetos para manipulación y planificación de agarre considerando diferentes modelos de objetos es presentado. Dependiendo de estos modelos de objeto, se muestran diferentes algoritmos actuales de análisis de agarre y algoritmos de planificación para manipuladores simples y manipuladores duales. En Segundo lugar, el desarrollo de algoritmos de percepción para detección de objetos y estimación de su posicione es presentado. Estos permiten al sistema identificar objetos de cualquier tipo en cualquier escena para localizarlos para efectuar las tareas de manipulación. Estos algoritmos calculan la información necesaria para los análisis de manipulación descritos anteriormente. En tercer lugar. Se presentan algoritmos de visión para localizar el robot en el entorno al mismo tiempo que se elabora un mapa local, el cual es beneficioso para las tareas de manipulación. Estos mapas se enriquecen con información semántica obtenida en los algoritmos de detección. Por último, se presenta el desarrollo del hardware relacionado con la plataforma aérea, el cual incluye unos manipuladores de bajo peso y la invención de una herramienta para realizar tareas de contacto con superficies rígidas que sirve de estimador de la posición del robot. Todas las técnicas presentadas en esta tesis han sido validadas con extensiva experimentación en plataformas reales.Technology is growing fast, and autonomous systems are becoming a reality. Companies are increasingly demanding robotized solutions to improve the efficiency of their operations. It is also the case for aerial robots. Their unique capability of moving freely in the space makes them suitable for many tasks that are tedious and even dangerous for human operators. Nowadays, the vast amount of sensors and commercial drones makes them highly appealing. However, it is still required a strong manual effort to customize the existing solutions to each particular task due to the number of possible environments, robot designs and missions. Different vision algorithms, hardware devices and sensor setups are usually designed by researchers to tackle specific tasks. Currently, aerial manipulation is being intensively studied to allow aerial robots to extend the number of applications. These could be inspection, maintenance, or even operating valves or other machines. This thesis presents an aerial manipulation system and a set of perception algorithms for the automation aerial manipulation tasks. The complete design of the system is presented and modular frameworks are shown to facilitate the development of these kind of operations. At first, the research about object analysis for manipulation and grasp planning considering different object models is presented. Depend on the model of the objects, different state of art grasping analysis are reviewed and planning algorithms for both single and dual manipulators are shown. Secondly, the development of perception algorithms for object detection and pose estimation are presented. They allows the system to identify many kind of objects in any scene and locate them to perform manipulation tasks. These algorithms produce the necessary information for the manipulation analysis described in the previous paragraph. Thirdly, it is presented how to use vision to localize the robot in the environment. At the same time, local maps are created which can be beneficial for the manipulation tasks. These maps are are enhanced with semantic information from the perception algorithm mentioned above. At last, the thesis presents the development of the hardware of the aerial platform which includes the lightweight manipulators and the invention of a novel tool that allows the aerial robot to operate in contact with static objects. All the techniques presented in this thesis have been validated throughout extensive experimentation with real aerial robotic platforms