Factors and processes in children's transitive deductions

Transitive tasks are important for understanding how children develop socio-cognitively. However, developmental research has been restricted largely to questions surrounding maturation. We asked 6-, 7- and 8-year-olds (N = 117) to solve a composite of five different transitive tasks. Tasks included conditions asking about item-C (associated with the marked relation) in addition to the usual case of asking only about item-A (associated with the unmarked relation). Here, children found resolving item-C much easier than resolving item-A, a finding running counter to long-standing assumptions about transitive reasoning. Considering gender perhaps for the first time, boys exhibited higher transitive scores than girls overall. Finally, analysing in the context of one recent and well-specified theory of spatial transitive reasoning, we generated the prediction that reporting the full series should be easier than deducing any one item from that series. This prediction was not upheld. We discuss amendments necessary to accommodate all our earlier findings

A strategy for trust propagation along the more trusted paths

The main goal of social networks are sharing and exchanging information among users. With the rapid growth of social networks on the Web, the most of interactions are conducted among unknown individuals. On the other hand, with increasing the biased behaviors in online communities, ability to assess the level of trustworthiness of a person before interacting with him has an important influence on users' decisions. Trust inference is a method used for this purpose. This paper studies propagating trust values along trust relationships in order to estimate the reliability of an anonymous person from the point of view of the user who intends to trust him/her. It describes a new approach for predicting trust values in social networks. The proposed method selects the most reliable trust paths from a source node to a destination node. In order to select the optimal paths, a new relation for calculating trustable coefficient based on previous performance of users in the social network is proposed. In ciao dataset there is a column called helpfulness. Helpfulness values represent previous performance of users in the social network. Advantages of this algorithm is its simplicity in trust calculation, using a new entity in dataset and its improvement in accuracy. The results of the experiments on Ciao dataset indicate that accuracy of the proposed method in evaluating trust values is higher than well-known methods in this area including TidalTrust, MoleTrust methods

Using Twitter trust network for stock market analysis

Online social networks are now attracting a lot of attention not only from their users but also from researchers in various fields. Many researchers believe that the public mood or sentiment expressed in social media is related to financial markets. We propose to use trust among users as a filtering and amplifying mechanism for the social media to increase its correlation with financial data in the stock market. Therefore, we used the real stock market data as ground truth for our trust management system. We collected stock-related data (tweets) from Twitter, which is a very popular Micro-blogging forum, to see the correlation between the Twitter sentiment valence and abnormal stock returns for eight firms in the S&P 500. We developed a trust management framework to build a user-to-user trust network for Twitter users. Compared with existing works, in addition to analyzing and accumulating tweets’ sentiment, we take into account the source of tweets – their authors. Authors are differentiated by their power or reputation in the whole community, where power is determined by the user-to-user trust network. To validate our trust management system, we did the Pearson correlation test for an eight months period (the trading days from 01/01/2015 through 08/31/2015). Compared with treating all the authors equally important, or weighting them by their number of followers, our trust network based reputation mechanism can amplify the correlation between a specific firm’s Twitter sentiment valence and the firm’s stock abnormal returns. To further consider the possible auto-correlation property of abnormal stock returns, we constructed a linear regression model, which includes historical stock abnormal returns, to test the relation between the Twitter sentiment valence and abnormal stock returns. Again, our results showed that by using our trust network power based method to weight tweets, Twitter sentiment valence reflect abnormal stock returns better than treating all the authors equally important or weighting them by their number of followers

Exploring the Combination of Dempster-Shafer Theory and Neural Network for Predicting Trust and Distrust

In social media, trust and distrust among users are important factors in helping users make decisions, dissect information, and receive recommendations. However, the sparsity and imbalance of social relations bring great difficulties and challenges in predicting trust and distrust. Meanwhile, there are numerous inducing factors to determine trust and distrust relations. The relationship among inducing factors may be dependency, independence, and conflicting. Dempster-Shafer theory and neural network are effective and efficient strategies to deal with these difficulties and challenges. In this paper, we study trust and distrust prediction based on the combination of Dempster-Shafer theory and neural network. We firstly analyze the inducing factors about trust and distrust, namely, homophily, status theory, and emotion tendency. Then, we quantify inducing factors of trust and distrust, take these features as evidences, and construct evidence prototype as input nodes of multilayer neural network. Finally, we propose a framework of predicting trust and distrust which uses multilayer neural network to model the implementing process of Dempster-Shafer theory in different hidden layers, aiming to overcome the disadvantage of Dempster-Shafer theory without optimization method. Experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework

Análisis y desarrollo de medidas de centralidad aplicadas a la formación de metales vítreos.

La presente Tesis Doctoral tiene como objetivo general contribuir a la comprensión de la formación de metales vítreos a través del uso de redes complejas, mediante la implementación de una nueva medida de centralidad adaptada. Para lograr este objetivo general, el inicio del presente estudio se centra en la introducción y estudio de las medidas de centralidad existentes, así como la implementación de las mismas, en casos de estudio como pueden ser las redes urbanas. A continuación, se propone una nueva medida de centralidad y la adaptación de la misma para posteriormente implementarla en el caso particular de formación de metales vítreos, objetivo principal de la presente Tesis Doctoral. La primera parte de la presente Tesis se centra en el estudio de las medidas de centralidad clásicas para la comprensión de las mismas así como de su utilización. En esta parte introductoria del estudio se aplican estas medidas de centralidad clásicas en redes urbanas con el objetivo de dilucidar mejoras concretas y eficientes de restauración que proporcionen un aumento sustancial en la accesibilidad en una ciudad histórica. El uso de las medidas clásicas de centralidad resulta ser muchas veces insuficiente para el estudio de problemas específicos como es el caso de la formación de metales vítreos. Por ello, en la actualidad existen diferentes medidas de centralidad adaptadas a cada problema concreto, siendo unas más generales y otras más específicas. En esta tesis se propone una medida de centralidad adaptada, con cierta generalidad para poder ser aplicada a diferentes ámbitos, pero pensada especialmente para resolver el problema de la comprensión de la formación de los metales vítreos. La medidas de centralidad realizadas con la medida clásica de intermediación o betweenness (CBT), están basados en los caminos más cortos y aleatorios, es decir, miden la importancia global de un nodo como nodo intermedio o de transición, pero tienen la característica común de no tener en cuenta la densidad de clúster de cada nodo. Para solucionar esta carencia se ha propuesto una nueva medida de centralidad basada en los caminos aleatorios de retorno tipo betweenness (2RW). Desde el punto de vista de las redes densas, está medida realiza una cuantificación de la importancia de un nodo a través de las relaciones entre cuatro nodos diferentes conectados. En el análisis de la implementación de la nueva medida de centralidad de la red, ésta se ha aplicado desde una perspectiva orientada a clasificar nodos, reforzando comunidades densas mediante la evaluación de grafos y utilizando una matriz de probabilidad de transición de dos posibles caminos. Por tanto, se ha desarrollado una nueva medida de centralidad 2RW que combina la idea de la centralidad betweenness y el algoritmo de predicción de enlaces Return Random Walk. En concreto, la métrica propuesta aumenta la posición del ranking de relevancia en la clasificación de los nodos que pertenecen a clústeres densos con un grado medio superior al del resto de clústeres, observando que funciona mejor en redes densas. Además, podemos detectar la debilidad de una red comparando el método CBT con nuestra propuesta (2RW). La aplicación de la medida de centralidad inicial propuesta (2RW) no se adapta completamente al problema planteado inicialmente debido fundamentalmente a la direccionalidad natural del mismo y a la falta de comprensión del comportamiento de cada nodo dentro de la red, es decir, su papel o rol dentro de la misma. Por tanto, en la presente Tesis se ha propuesto el Algoritmo de Centralidad de intermediación aleatorio bidireccional dirigido (D2RWBT), un nuevo modelo de centralidad para redes dirigidas. Mediante este modelo se han obtenido un ranking de los nodos en redes dirigidas para describir su relevancia dentro de la red como nodos de transición, teniendo en cuenta el comportamiento de los mismos en la red. Más en detalle, el modelo describe un nodo mediante cuatro índices que proporcionan información sobre la densidad de su clúster/comunidad (denso o disperso), la fuerza de sus conexiones, la importancia relativa y absoluta en la red, o la relevancia como nodo intra o interclúster. De la aplicación de la nueva medida de centralidad (D2RWBT) para la comprensión de la formación de los metales vítreos a través de una de las variables de mayor relevancia, la temperatura de transición vítrea reducida (Trg), se han obtenido resultados que han sido ratificados por una buena correlación entre estos y la obtención real de vidrios metálicos en recientes investigaciones. En concreto se ha podido extraer la relevancia de los elementos químicos que componen las redes densas y los elementos que forman redes dispersas, así como comparar estos resultados con los obtenidos con la medida clásica de centralidad clásica betweenness. Además, se ha obtenido el rol de los elementos químicos formadores de metales vítreos dentro de la red y cuáles de ellos desempeñan funciones semejantes dentro de la misma y, por tanto, pueden ser posibles elementos de sustitución. Como otra parte del estudio final se han analizado los elementos con funciones de relevancia en la composición de ambos tipos de redes. Por último, se han cotejado los resultados y se ha realizado una síntesis de la compresión del problema de formación de metales vítreos abordando el objetivo general descrito.Ingeniería, Industria y Construcció

A Trust Management Framework for Decision Support Systems

In the era of information explosion, it is critical to develop a framework which can extract useful information and help people to make “educated” decisions. In our lives, whether we are aware of it, trust has turned out to be very helpful for us to make decisions. At the same time, cognitive trust, especially in large systems, such as Facebook, Twitter, and so on, needs support from computer systems. Therefore, we need a framework that can effectively, but also intuitively, let people express their trust, and enable the system to automatically and securely summarize the massive amounts of trust information, so that a user of the system can make “educated” decisions, or at least not blind decisions. Inspired by the similarities between human trust and physical measurements, this dissertation proposes a measurement theory based trust management framework. It consists of three phases: trust modeling, trust inference, and decision making. Instead of proposing specific trust inference formulas, this dissertation proposes a fundamental framework which is flexible and can be adapted by many different inference formulas. Validation experiments are done on two data sets: the Epinions.com data set and the Twitter data set. This dissertation also adapts the measurement theory based trust management framework for two decision support applications. In the first application, the real stock market data is used as ground truth for the measurement theory based trust management framework. Basically, the correlation between the sentiment expressed on Twitter and stock market data is measured. Compared with existing works which do not differentiate tweets’ authors, this dissertation analyzes trust among stock investors on Twitter and uses the trust network to differentiate tweets’ authors. The results show that by using the measurement theory based trust framework, Twitter sentiment valence is able to reflect abnormal stock returns better than treating all the authors as equally important or weighting them by their number of followers. In the second application, the measurement theory based trust management framework is used to help to detect and prevent from being attacked in cloud computing scenarios. In this application, each single flow is treated as a measurement. The simulation results show that the measurement theory based trust management framework is able to provide guidance for cloud administrators and customers to make decisions, e.g. migrating tasks from suspect nodes to trustworthy nodes, dynamically allocating resources according to trust information, and managing the trade-off between the degree of redundancy and the cost of resources

Multiple-Criteria Decision Making

Decision-making on real-world problems, including individual process decisions, requires an appropriate and reliable decision support system. Fuzzy set theory, rough set theory, and neutrosophic set theory, which are MCDM techniques, are useful for modeling complex decision-making problems with imprecise, ambiguous, or vague data.This Special Issue, “Multiple Criteria Decision Making”, aims to incorporate recent developments in the area of the multi-criteria decision-making field. Topics include, but are not limited to:- MCDM optimization in engineering;- Environmental sustainability in engineering processes;- Multi-criteria production and logistics process planning;- New trends in multi-criteria evaluation of sustainable processes;- Multi-criteria decision making in strategic management based on sustainable criteria