Problem Given the number of times in which an unknown event has happened and failed: Required the chance that the probability of its happening in a single trial lies somewhere between any two degrees of probability that can be named. SECTION 1 Definition 1. Several events are inconsistent, when if one of them happens, none of the rest can. 2. Two events are contrary when one, or other of them must; and both together cannot happen. 3. An event is said to fail, when it cannot happen; or, which comes to the same thing, when its contrary has happened. 4. An event is said to be determined when it has either happened or failed. 5. The probability of any event is the ratio between the value at which an expectation depending on the happening of the event ought to be computed, and the value of the thing expected upon it’s 2 happening.

An essay towards solving a problem in the doctrine of chances. [Facsimil]

In recent years online social networks have suffered an increase in sexism, racism, and other types of aggressive and cyberbullying behavior, often manifesting itself through offensive, abusive, or hateful language. Past scientific work focused on studying these forms of abusive activity in popular online social networks, such as Facebook and Twitter. Building on such work, we present an eight month study of the various forms of abusive behavior on Twitter, in a holistic fashion. Departing from past work, we examine a wide variety of labeling schemes, which cover different forms of abusive behavior. We propose an incremental and iterative methodology that leverages the power of crowdsourcing to annotate a large collection of tweets with a set of abuse-related labels. By applying our methodology and performing statistical analysis for label merging or elimination, we identify a reduced but robust set of labels to characterize abuse-related tweets. Finally, we offer a characterization of our annotated dataset of 80 thousand tweets, which we make publicly available for further scientific exploration.

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Large scale crowdsourcing and characterization of twitter abusive behavior

Definitions and basic properties of the Voronoi diagram generalizations of the Voronoi diagram algorithms for computing Voronoi diagrams poisson Voronoi diagrams spatial interpolation models of spatial processes point pattern analysis locational optimization through Voronoi diagrams.

Spatial tessellations. Concepts and Applications of Voronoi diagrams

As the social networking sites get more popular, spammers target these sites to spread spam posts. Twitter is one of the most popular online social networking sites where users communicate and interact on various topics. Most of the current spam filtering methods in Twitter focus on detecting the spammers and blocking them. However, spammers can create a new account and start posting new spam tweets again. So there is a need for robust spam detection techniques to detect the spam at tweet level. These types of techniques can prevent the spam in real time. To detect the spam at tweet level, often features are defined, and appropriate machine learning algorithms are applied in the literature. Recently, deep learning methods are showing fruitful results on several natural language processing tasks. We want to use the potential benefits of these two types of methods for our problem. Toward this, we propose an ensemble approach for spam detection at tweet level. We develop various deep learning models based on convolutional neural networks (CNNs). Five CNNs and one feature-based model are used in the ensemble. Each CNN uses different word embeddings (Glove, Word2vec) to train the model. The feature-based model uses content-based, user-based, and n-gram features. Our approach combines both deep learning and traditional feature-based models using a multilayer neural network which acts as a meta-classifier. We evaluate our method on two data sets, one data set is balanced, and another one is imbalanced. The experimental results show that our proposed method outperforms the existing methods.

A Neural Network-Based Ensemble Approach for Spam Detection in Twitter

Most existing techniques for spam detection on Twitter aim to identify and block users who post spam tweets. In this paper, we propose a semi-supervised spam detection (S3D) framework for spam detection at tweet-level. The proposed framework consists of two main modules: spam detection module operating in real-time mode and model update module operating in batch mode. The spam detection module consists of four lightweight detectors: 1) blacklisted domain detector to label tweets containing blacklisted URLs; 2) near-duplicate detector to label tweets that are near-duplicates of confidently prelabeled tweets; 3) reliable ham detector to label tweets that are posted by trusted users and that do not contain spammy words; and 4) multiclassifier-based detector labels the remaining tweets. The information required by the detection module is updated in batch mode based on the tweets that are labeled in the previous time window. Experiments on a large-scale data set show that the framework adaptively learns patterns of new spam activities and maintain good accuracy for spam detection in a tweet stream.

Semi-Supervised Spam Detection in Twitter Stream

Twitter has become one of the most used social networks. And, as happens with every popular media, it is prone to misuse. In this context, spam in Twitter has emerged in the last years, becoming an important problem for the users. In the last years, several approaches have appeared that are able to determine whether an user is a spammer or not. However, these blacklisting systems cannot filter every spam message and a spammer may create another account and restart sending spam. In this paper, we propose a content-based approach to filter spam tweets. We have used the text in the tweet and machine learning and compression algorithms to filter those undesired tweets.

Twitter Content-Based Spam Filtering

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.

Procedural approach to volumetric terrain generation

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.

Machine-learning-based surface defect detection and categorisation in high-precision foundry

The volumetric approach for terrain representation is a technique used by several video-games and other graphic applications to manage both surface and geological data from the virtual world. To enhance the exploration experience and due to the amount of data required by this approach, volumetric terrains are usually generated with procedural methods. Nevertheless, one of the main issues of those methods is the generation of cave systems with playable features and a natural appearance. In this paper we propose a new method to generate playable cave systems for 2D and 3D volumetric terrains, based on Voronoi diagrams and Delaunay triangulations. Our approach is completely customizable by the designer by a set of parameters directly related to the cave itself avoiding technical concepts. Additionally, the method runs in a completely independent way, with no interactive steps.

Procedural Playable Cave Systems Based on Voronoi Diagram and Delaunay Triangulation

Internet is a powerful source of information. However, some of the information that is available in the Internet, cannot be shown to every type of public. For instance, pornography is not desirable to be shown to children. To this end, several algorithms for text filtering have been proposed that employ a Vector Space Model representation of the webpages. Nevertheless, these type of filters can be surpassed using different attacks. In this paper, we present the first adult content filtering tool that employs compression algorithms to represent data that is resilient to these attacks. We show that this approach enhances the results of classic VSM models.

Aitor Santamaría-Ibirika

Papers

Twitter Content-Based Spam Filtering

Procedural approach to volumetric terrain generation

Machine-learning-based surface defect detection and categorisation in high-precision foundry

Procedural Playable Cave Systems Based on Voronoi Diagram and Delaunay Triangulation

Adult Content Filtering through Compression-Based Text Classification