Identifying Clickbait: A Multi-Strategy Approach Using Neural Networks

Online media outlets, in a bid to expand their reach and subsequently increase revenue through ad monetisation, have begun adopting clickbait techniques to lure readers to click on articles. The article fails to fulfill the promise made by the headline. Traditional methods for clickbait detection have relied heavily on feature engineering which, in turn, is dependent on the dataset it is built for. The application of neural networks for this task has only been explored partially. We propose a novel approach considering all information found in a social media post. We train a bidirectional LSTM with an attention mechanism to learn the extent to which a word contributes to the post's clickbait score in a differential manner. We also employ a Siamese net to capture the similarity between source and target information. Information gleaned from images has not been considered in previous approaches. We learn image embeddings from large amounts of data using Convolutional Neural Networks to add another layer of complexity to our model. Finally, we concatenate the outputs from the three separate components, serving it as input to a fully connected layer. We conduct experiments over a test corpus of 19538 social media posts, attaining an F1 score of 65.37% on the dataset bettering the previous state-of-the-art, as well as other proposed approaches, feature engineering or otherwise.Comment: Accepted at SIGIR 2018 as Short Pape

Evolutionary Stable Strategies Depending on Population Density

The concept of evolutionary stable strategies is extended to include density dependence. Dynamical stability is shown to follow for two-strategy games and for symmetric payoff matrices. It is conjectured that stability also results for general multi-strategy games

Permissive Controller Synthesis for Probabilistic Systems

We propose novel controller synthesis techniques for probabilistic systems modelled using stochastic two-player games: one player acts as a controller, the second represents its environment, and probability is used to capture uncertainty arising due to, for example, unreliable sensors or faulty system components. Our aim is to generate robust controllers that are resilient to unexpected system changes at runtime, and flexible enough to be adapted if additional constraints need to be imposed. We develop a permissive controller synthesis framework, which generates multi-strategies for the controller, offering a choice of control actions to take at each time step. We formalise the notion of permissivity using penalties, which are incurred each time a possible control action is disallowed by a multi-strategy. Permissive controller synthesis aims to generate a multi-strategy that minimises these penalties, whilst guaranteeing the satisfaction of a specified system property. We establish several key results about the optimality of multi-strategies and the complexity of synthesising them. Then, we develop methods to perform permissive controller synthesis using mixed integer linear programming and illustrate their effectiveness on a selection of case studies

On the expected number of equilibria in a multi-player multi-strategy evolutionary game

In this paper, we analyze the mean number $E(n,d)$ of internal equilibria in a general $d$-player $n$-strategy evolutionary game where the agents' payoffs are normally distributed. First, we give a computationally implementable formula for the general case. Next we characterize the asymptotic behavior of $E(2,d)$, estimating its lower and upper bounds as $d$ increases. Two important consequences are obtained from this analysis. On the one hand, we show that in both cases the probability of seeing the maximal possible number of equilibria tends to zero when $d$ or $n$ respectively goes to infinity. On the other hand, we demonstrate that the expected number of stable equilibria is bounded within a certain interval. Finally, for larger $n$ and $d$, numerical results are provided and discussed.Comment: 26 pages, 1 figure, 1 table. revised versio

The impact of a multi-strategy academic writing handbook on Emergent bilinguals’ cross-curricular writing competences

La escritura académica en una segunda lengua puede ser uno de los requerimientos más complejos en la educación superior debido a los elementos lingüísticos, estratégicos y procedimentales que esta abarca al igual que los procesos cognitivos superiores que involucra. A pesar de su presencia permanente en la academia, los profesores no han encontrado aún una forma apropiada para enseñar y evaluar la escritura que garantice el progreso de los estudiantes y el apoyo continuo a lo largo de su proceso de aprendizaje. De esta manera, este estudio de caso de métodos mixtos apunta a diseñar y evaluar la efectividad de un Manual de Referencia para la Escritura Académica (MREA) que pretende proveer la asistencia constante que los estudiantes necesitan para solidificar su conocimiento de escritura y el material pedagógico apropiado que los docentes requieren para unificar los prácticas de enseñanza y evaluación de la escritura; este manual está fundamentado en los enfoques de la escrita como proceso y basada en el género, análisis de errores y evaluación..

Constructing Multi-Strategy Fund of Hedge Funds

This paper aims to develop a systematic allocation methodology to combine multi-strategy hedge funds within a structure of fund of funds in a risk-controlled manner. This is particularly important since the traditional mean-variance optimization proves ineffective in addressing hedge fund return distributions that are asymmetric in nature. Moreover, unstable correlations among various hedge fund strategies also pose a challenge to a meaningful optimization to combine various hedge fund strategies. This paper attempts to suggest some practical ways to overcome both these obstacles

Tradeoffs in the utility of learned knowledge

Planning systems which make use of domain theories can produce more accurate plans and achieve more goals as the quality of their domain knowledge improves. MTR, a multi-strategy learning system, was designed to learn from system failures and improve domain knowledge used in planning. However, augmented domain knowledge can decrease planning efficiency. We describe how improved knowledge that becomes expensive to use can be approximated to yield calculated tradeoffs in accuracy and efficiency