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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
Ćvaluation de la confiance dans la collaboration Ć large Ć©chelle
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 systeĢmes collaboratifs aĢ large eĢchelle, ouĢ un grand nombre dāutilisateurs collaborent pour reĢaliser une taĢche partageĢe, attirent beaucoup lāattention des milieux industriels et acadeĢmiques. Bien que la confiance soit un facteur primordial pour le succeĢs dāune telle collaboration, il est difficile pour les utilisateurs finaux dāeĢvaluer manuellement le niveau de confiance envers chaque partenaire. Dans cette theĢse, nous eĢtudions le probleĢme de lāeĢvaluation de la confiance et cherchons aĢ concevoir un modeĢle de confiance informatique deĢdieĢs aux systeĢmes collaboratifs. Nos travaux sāorganisent autour des trois questions de recherche suivantes. 1. Quel est lāeffet du deĢploiement dāun modeĢle de confiance et de la repreĢsentation aux utilisateurs des scores obtenus pour chaque partenaire ? Nous avons concĢ§u et organiseĢ une expeĢrience utilisateur baseĢe sur le jeu de confiance qui est un protocole dāeĢchange dāargent en environnement controĢleĢ dans lequel nous avons introduit des notes de confiance pour les utilisateurs. Lāanalyse deĢtailleĢe du comportement des utilisateurs montre que: (i) la preĢsentation dāun score de confiance aux utilisateurs encourage la collaboration entre eux de manieĢre significative, et ce, aĢ un niveau similaire aĢ celui de lāaffichage du surnom des participants, et (ii) les utilisateurs se conforment au score de confiance dans leur prise de deĢcision concernant lāeĢchange moneĢtaire. Les reĢsultats suggeĢrent donc quāun modeĢle de confiance peut eĢtre deĢployeĢ dans les systeĢmes collaboratifs afin dāassister les utilisateurs. 2. Comment calculer le score de confiance entre des utilisateurs qui ont deĢjaĢ collaboreĢ ? Nous avons concĢ§u un modeĢle de confiance pour les jeux de confiance reĢpeĢteĢs qui calcule les scores de confiance des utilisateurs en fonction de leur comportement passeĢ. Nous avons valideĢ notre modeĢle de confiance en relativement aĢ: (i) des donneĢes simuleĢes, (ii) de lāopinion humaine et (iii) des donneĢes expeĢrimentales reĢelles. Nous avons appliqueĢ notre modeĢle de confiance aĢ WikipeĢdia en utilisant la qualiteĢ des articles de WikipeĢdia comme mesure de contribution. Nous avons proposeĢ trois algorithmes dāapprentissage automatique pour eĢvaluer la qualiteĢ des articles de WikipeĢdia: lāun est baseĢ sur une foreĢt dāarbres deĢcisionnels tandis que les deux autres sont baseĢs sur des meĢthodes dāapprentissage profond. 3. Comment preĢdire la relation de confiance entre des utilisateurs qui nāont pas encore interagi ? Etant donneĢ un reĢseau dans lequel les liens repreĢsentent les relations de confiance/deĢfiance entre utilisateurs, nous cherchons aĢ preĢvoir les relations futures. Nous avons proposeĢ un algorithme qui prend en compte les informations temporelles relatives aĢ lāeĢtablissement des liens dans le reĢseau pour preĢdire la relation future de confiance/deĢfiance des utilisateurs. Lāalgorithme proposeĢ surpasse les approches de la litteĢrature pour des jeux de donneĢes reĢels provenant de reĢseaux sociaux dirigeĢs et signeĢ
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
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 ā 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
CURRENT SITUATION OF STUDENTSā PSYCHOLOGICAL STATE BEFORE PRACTICAL COURSESā FINAL EXAMS
The article aimed to determine the factors influencing studentsā psychological state before the final exam of practical courses. The article used conventional scientific research methods in sports and physical training combined with psychological tests studied on fifty students at Ho Chi Minh City University of Physical Education and Sports (UPES). After reviewing related studies and consulting with experts, four tests were employed to assess the psychological state before the test of the research subjects. The results showed that students with a good psychological state to take the exam had good test results. Conversely, students with a feverish or lethargic state will have poor test results. The research results serve as the basis for proposing measures to adjust the psychological state before the exam, contributing to improving the learning results of students.Ā Article visualizations
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