Efficiency, investment and bank lending in transition and emerging economies

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis studies the economic development in transition and emerging economies with focus on three particular economic issues: production efficiency, physical investment rate and bank lending under bank ownership perspective. The thesis chooses to study transition and emerging economies because they have undergone many important reform processes that may be thought of as experiments of different policy choices which lead to different economic outcomes. The thesis contributes to the literature in several ways. First, it adds to the literature on institutional economics and transition economies by confirming the significant role of institutional quality for efficiency and investment in a panel of transition economies. Better institutions are associated with higher efficiency levels and investment rates in transition economies. Given that investment is one of the key determinants of growth this means good institutions are important for growth in transition economies. Second, the thesis finds that banks of different ownership respond in remarkably different ways to monetary policies, which has important implication for the transmission and effectiveness of monetary policy. It also finds an asymmetric effect of monetary policy on bank lending with regard to the monetary conditions: in easy regime bank lending may not be affected my monetary tightening. This result calls for duly consideration of the ownership structure of the banking system when monetary policy and its effect on credit are studied. In summary, the thesis highlights the importance of institutional settings for economic development in transition and emerging economies

Reinforcement Learning in Stock Trading

Using machine learning techniques in financial markets, particularly in stock trading, attracts a lot of attention from both academia and practitioners in recent years. Researchers have studied different supervised and unsupervised learning techniques to either predict stock price movement or make decisions in the market. In this paper we study the usage of reinforcement learning techniques in stock trading. We evaluate the approach on real-world stock dataset. We compare the deep reinforcement learning approach with state-of-the-art supervised deep learning prediction in real-world data. Given the nature of the market where the true parameters will never be revealed, we believe that the reinforcement learning has a lot of potential in decision-making for stock trading

Studying machine learning techniques for intrusion detection systems

Intrusion detection systems (IDSs) have been studied widely in the computer security community for a long time. The recent development of machine learning techniques has boosted the performance of the intrusion detection systems significantly. However, most modern machine learning and deep learning algorithms are exhaustive of labeled data that requires a lot of time and effort to collect. Furthermore, it might be late until all the data is collected to train the model. In this study, we first perform a comprehensive survey of existing studies on using machine learning for IDSs. Hence we present two approaches to detect the network attacks. We present that by using a tree-based ensemble learning with feature engineering we can outperform state-of-the-art results in the field. We also present a new approach in selecting training data for IDSs hence by using a small subset of training data combined with some weak classification algorithms we can improve the performance of the detector while maintaining the low running cost

Évaluation de la confiance dans la collaboration à large échelle

Large-scale collaborative systems wherein a large number of users collaborate to perform a shared task attract a lot of attention from both academic and industry. Trust is an important factor for the success of a large-scale collaboration. It is difficult for end-users to manually assess the trust level of each partner in this collaboration. We study the trust assessment problem and aim to design a computational trust model for collaborative systems. We focused on three research questions. 1. What is the effect of deploying a trust model and showing trust scores of partners to users? We designed and organized a user-experiment based on trust game, a well-known money-exchange lab-control protocol, wherein we introduced user trust scores. Our comprehensive analysis on user behavior proved that: (i) showing trust score to users encourages collaboration between them significantly at a similar level with showing nick- name, and (ii) users follow the trust score in decision-making. The results suggest that a trust model can be deployed in collaborative systems to assist users. 2. How to calculate trust score between users that experienced a collaboration? We designed a trust model for repeated trust game that computes user trust scores based on their past behavior. We validated our trust model against: (i) simulated data, (ii) human opinion, and (iii) real-world experimental data. We extended our trust model to Wikipedia based on user contributions to the quality of the edited Wikipedia articles. We proposed three machine learning approaches to assess the quality of Wikipedia articles: the first one based on random forest with manually-designed features while the other two ones based on deep learning methods. 3. How to predict trust relation between users that did not interact in the past? Given a network in which the links represent the trust/distrust relations between users, we aim to predict future relations. We proposed an algorithm that takes into account the established time information of the links in the network to predict future user trust/distrust relationships. Our algorithm outperforms state-of-the-art approaches on real-world signed directed social network datasetsLes systèmes collaboratifs à large échelle, où un grand nombre d’utilisateurs collaborent pour réaliser une tâche partagée, attirent beaucoup l’attention des milieux industriels et académiques. Bien que la confiance soit un facteur primordial pour le succès d’une telle collaboration, il est difficile pour les utilisateurs finaux d’évaluer manuellement le niveau de confiance envers chaque partenaire. Dans cette thèse, nous étudions le problème de l’évaluation de la confiance et cherchons à concevoir un modèle de confiance informatique dédiés aux systèmes collaboratifs. Nos travaux s’organisent autour des trois questions de recherche suivantes. 1. Quel est l’effet du déploiement d’un modèle de confiance et de la représentation aux utilisateurs des scores obtenus pour chaque partenaire ? Nous avons conçu et organisé une expérience utilisateur basée sur le jeu de confiance qui est un protocole d’échange d’argent en environnement contrôlé dans lequel nous avons introduit des notes de confiance pour les utilisateurs. L’analyse détaillée du comportement des utilisateurs montre que: (i) la présentation d’un score de confiance aux utilisateurs encourage la collaboration entre eux de manière significative, et ce, à un niveau similaire à celui de l’affichage du surnom des participants, et (ii) les utilisateurs se conforment au score de confiance dans leur prise de décision concernant l’échange monétaire. Les résultats suggèrent donc qu’un modèle de confiance peut être déployé dans les systèmes collaboratifs afin d’assister les utilisateurs. 2. Comment calculer le score de confiance entre des utilisateurs qui ont déjà collaboré ? Nous avons conçu un modèle de confiance pour les jeux de confiance répétés qui calcule les scores de confiance des utilisateurs en fonction de leur comportement passé. Nous avons validé notre modèle de confiance en relativement à: (i) des données simulées, (ii) de l’opinion humaine et (iii) des données expérimentales réelles. Nous avons appliqué notre modèle de confiance à Wikipédia en utilisant la qualité des articles de Wikipédia comme mesure de contribution. Nous avons proposé trois algorithmes d’apprentissage automatique pour évaluer la qualité des articles de Wikipédia: l’un est basé sur une forêt d’arbres décisionnels tandis que les deux autres sont basés sur des méthodes d’apprentissage profond. 3. Comment prédire la relation de confiance entre des utilisateurs qui n’ont pas encore interagi ? Etant donné un réseau dans lequel les liens représentent les relations de confiance/défiance entre utilisateurs, nous cherchons à prévoir les relations futures. Nous avons proposé un algorithme qui prend en compte les informations temporelles relatives à l’établissement des liens dans le réseau pour prédire la relation future de confiance/défiance des utilisateurs. L’algorithme proposé surpasse les approches de la littérature pour des jeux de données réels provenant de réseaux sociaux dirigés et signé

Numerical solution of the problems for plates on some complex partial internal supports

In the recent works, Dang and Truong proposed an iterative method for solving some problems of plates on one, two and three line partial internal supports (LPISs), and a cross internal support. In nature they are problems with strongly mixed boundary conditions for biharmonic equation. For this reason the method combines a domain decomposition technique with the reduction of the order of the equation from four to two. In this study, the method is developed for plates on internal supports of more complex configurations. Namely, we examine the cases of symmetric rectangular and H-shape supports, where the computational domain after reducing to the first quadrant of the plate is divided into three subdomains. Also, we consider the case of asymmetric rectangular support where the computational domain needs to be divided into 9 subdomains. The problems under consideration are reduced to sequences of weak mixed boundary value problems for the Poisson equation, which are solved by difference method. The performed numerical experiments show the effectiveness of the iterative method

Enhancing stability and robustness in online machine shop scheduling:A multi-agent system and negotiation-based approach for handling machine downtime in industry 4.0

Autonomous factories require high levels of adaptability, flexibility, and resilience to react to uncertainties on the shop floor, such as machine downtime. This paper proposes a negotiation-based, partial rescheduling method, combined with an existing multi-agent system, to swap jobs between machines. The negotiations are restricted to machines within the same work center, giving rise to a partial reschedule. A learning algorithm is also utilized, allowing machines to individually learn how to evaluate proposed bids from other machines and adapt the bids to their current environment. The main objective is to minimize the mean weighted tardiness of all jobs. Computational results indicate an improvement of 10–30 tardiness, compared to continuous rescheduling and complete rescheduling methods. In addition, a decrease of 70–80 sensitivity analysis and analysis of the partial reschedule

Utilizing attack enumerations to study SDN/NFV vulnerabilities

International audienceSeveral cybersecurity attack enumerations area available today. These enumerations present lists of known attack patterns (CAPEC), security weaknesses (CWE) or cybersecurity vulnerabilities (CVE). These enumerations are being developed separately and manually. In this paper, we present our efforts in determine the relations between enumerations automatically. We rely on text-based, graph-based and recommendation-based approaches. Then we present of using the prediction in recommending related attacks to SDN/NFV security issues. Experimental results showed that we can predict the relations at high AU C and F − 1 scores. Furthermore, the results gave us some insights about how the enumerations are created and linked, and some suggestions to improve the process in the future