The 2001 Economic Crisis of Turkey and Holding Companies’ Diversification Strategies: Does Bank Ownership Make Difference?

The objectives of the study is to search whether there are any difference in holding companies’ diversification strategies before and after 2001 crisis in Turkey. In this context, diversification strategies are adopted by the holding companies is tried to be determined. The holding companies included in study divided into two groups one of them is before the economic crisis of 2001 having bank and continue in the same way after 2001. The study tests whether having bank is advantageous in diversification strategies or not.  A quantitative research is used for reaching on the data. The data related with holding companies which is obtained by Borsa Istanbul. Holding companies and industries they performed and the industry relationships are tried to be explained. The study concludes that 2001 economic crisis enhance the diversification especially for bank owner holding companies due to the fact that the crisis provides opportunities which can be easily used by the holding companies that have strong banks. The result implicates that bank owner holdings’ risk appetite and capabilities increased after the 2001 crisis. Keywords: Borsa Istanbul, Diversification, Economic Crisis, Holding Companie

Studies of Chemosensory Responses in the Pharynx of the Sea Lamprey (Petromyzon marinus)

The sea lamprey is a basal vertebrate, and the oldest extant species with taste buds. The objective of this thesis was to study the chemosensory responses from the sea lamprey’s pharynx, which is located caudal to oral cavity and medial to seven lateral brachiopores. The pharynx contains taste buds that detect and respond to tastants to inform lampreys about gustatory factors in the environment. The location of these taste buds along the lateral surface of the pharynx was first determined using scanning electron microscopy. Then using electrophysiology, our recordings from these pharyngeal regions containing taste buds picked up responses from the nerve fibers after stimulation using a variety of compounds such as sucrose, NaCl, alanine, arginine, denatonium, ATP and bile acids. Each region of the pharynx between the seven brachiopores was tested and was responsive to the application of a carefully picked set of taste stimulants, indicating no regional differences in the chemosensory properties of the pharynx. Additionally, larval responses to samples collected from the natural stream burrowing sites were tested and were stimulatory to the larval lamprey’s pharynx. This thesis provides insight into the tastants and gustatory system of a basal vertebrate that posseses a taste system that derived from ancestral jawless vertebrates

Privacy-preserving top-N recommendation on horizontally partitioned data

Collaborative filtering techniques are widely used by many E-commerce sites for recommendation purposes. Such techniques help customers by suggesting products to purchase using other users’ preferences. Today’s top-recommendation schemes are based on market basket data, which shows whether a customer bought an item or not. Data collected for recommendation purposes might be split between different parties. To provide better referrals and increase mutual advantages, such parties might want to share data. Due to privacy concerns, however, they do not want to disclose data. This paper presents a scheme for binary ratings-based top-N recommendation on horizontally partitioned data, in which two parties own disjoint sets of users ’ ratings for the same items while preserving data owners ’ privacy. If data owners want to produce referrals using the combined data while preserving their privacy, we propose a scheme to provide accurate top-N recommendations without exposing data owners’ privacy. We conducted various experiments to evaluate our scheme and analyzed how different factors affect the performance using the experiment result

A Trust-based Recommender System over Arbitrarily Partitioned Data with Privacy

Recommender systems are effective mechanisms for recommendations about what to watch, read, or taste based on user ratings about experienced products or services. To achieve higher quality recommendations, e-commerce parties may prefer to collaborate over partitioned data. Due to privacy issues, they might hesitate to work in pairs and some solutions motivate them to collaborate. This study examines how to estimate trust-based predictions on arbitrarily partitioned data in which two parties have ratings for similar sets of customers and items. A privacy- preserving scheme is proposed, and it is justified that it efficiently offers trust-based predictions on partitioned data while preserving privacy

Volume CXIV, Number 4, November 7, 1996

Objective: Turner syndrome (TS) is a chromosomal disorder caused by complete or partial X chromosome monosomy that manifests various clinical features depending on the karyotype and on the genetic background of affected girls. This study aimed to systematically investigate the key clinical features of TS in relationship to karyotype in a large pediatric Turkish patient population.Methods: Our retrospective study included 842 karyotype-proven TS patients aged 0-18 years who were evaluated in 35 different centers in Turkey in the years 2013-2014.Results: The most common karyotype was 45,X (50.7%), followed by 45,X/46,XX (10.8%), 46,X,i(Xq) (10.1%) and 45,X/46,X,i(Xq) (9.5%). Mean age at diagnosis was 10.2±4.4 years. The most common presenting complaints were short stature and delayed puberty. Among patients diagnosed before age one year, the ratio of karyotype 45,X was significantly higher than that of other karyotype groups. Cardiac defects (bicuspid aortic valve, coarctation of the aorta and aortic stenosis) were the most common congenital anomalies, occurring in 25% of the TS cases. This was followed by urinary system anomalies (horseshoe kidney, double collector duct system and renal rotation) detected in 16.3%. Hashimoto's thyroiditis was found in 11.1% of patients, gastrointestinal abnormalities in 8.9%, ear nose and throat problems in 22.6%, dermatologic problems in 21.8% and osteoporosis in 15.3%. Learning difficulties and/or psychosocial problems were encountered in 39.1%. Insulin resistance and impaired fasting glucose were detected in 3.4% and 2.2%, respectively. Dyslipidemia prevalence was 11.4%.Conclusion: This comprehensive study systematically evaluated the largest group of karyotype-proven TS girls to date. The karyotype distribution, congenital anomaly and comorbidity profile closely parallel that from other countries and support the need for close medical surveillance of these complex patients throughout their lifespa

AN INTELLIGENT SOFTWARE DEFINED NETWORKING CONTROLLER COMPONENT TO DETECT AND MITIGATE DENIAL OF SERVICE ATTACKS

Despite many advantages of software defined networking (SDN) such as manageability, scalability, and performance, it has inherent security threats. In particular, denial of service (DoS) attacks are major threats to SDN. The controller’s processing and communication abilities are overwhelmed by DoS attacks. The capacity of the flow tables in the switching device is exhausted due to excess flows created by the controller because of malicious packets. DoS attacks on the controller cause the network performance to drop to a critical level. In this paper, a new SDN controller component was proposed to detect and mitigate DoS attacks in the SDN controller. POX layer three controller component was used for underlying a testbed for PacketIn messages. Any packet from the host was incremented to measure the rate of packet according to its device identification and its input port number. Considering the rate of packets received by the controller and threshold set, malicious packets could be detected and mitigated easily. A developed controller component was tested in a Mininet simulation environment with an hping3 tool to build artificial DoS attacks. Using the enhanced controller component, DoS packets were prevented from accessing the controller and thus, the data plane (switching devices) was prevented from being filled with unwanted flows

AN INTELLIGENT SOFTWARE DEFINED NETWORKING CONTROLLER COMPONENT TO DETECT AND MITIGATE DENIAL OF SERVICE ATTACKS

Despite many advantages of software defined networking (SDN) such as manageability, scalability, and performance, it has inherent security threats. In particular, denial of service (DoS) attacks are major threats to SDN. The controller’s processing and communication abilities are overwhelmed by DoS attacks. The capacity of the flow tables in the switching device is exhausted due to excess flows created by the controller because of malicious packets. DoS attacks on the controller cause the network performance to drop to a critical level. In this paper, a new SDN controller component was proposed to detect and mitigate DoS attacks in the SDN controller. POX layer three controller component was used for underlying a testbed for PacketIn messages. Any packet from the host was incremented to measure the rate of packet according to its device identification and its input port number. Considering the rate of packets received by the controller and threshold set, malicious packets could be detected and mitigated easily. A developed controller component was tested in a Mininet simulation environment with an hping3 tool to build artificial DoS attacks. Using the enhanced controller component, DoS packets were prevented from accessing the controller and thus, the data plane (switching devices) was prevented from being filled with unwanted flows

Privacy-preserving collaborative filtering

With the evolution of the Internet, collaborative filtering (CF) techniques are becoming increasingly popular. Such techniques are widely used by many E-commerce companies to suggest products to customers, based on like-minded customers\u27 preferences; but they fail to protect users\u27 privacy. Data from customers is gathered for recommendation purposes; however, collecting high quality data is not an easy task, due to privacy concerns. Since many users are worried about their privacy, they sometimes refuse to contribute their data; or occasionally they might decide to give false information. Producing accurate referrals based on insufficient data and/or false data is impossible. CF systems provide referrals on existing databases comprised of ratings recorded from groups of people evaluating various items; sometimes, however, the systems\u27 ratings are split among different parties. To provide better filtering services, the parties may wish to share their data; but they may not want to disclose information of a private nature. We propose to use randomization techniques to protect users\u27 privacy while still producing accurate referrals. We investigate providing predictions on memory- and model-based CF algorithms with privacy assured by using randomized perturbation techniques (RPT) in Chapter 3 and Chapter 4, respectively; while we show how to produce recommendations without violating users\u27 privacy using randomized response techniques (RRT) in Chapter 5. In Chapter 6, we explore inconsistently disguised data-based CF with privacy provided by the RPT. When data owners wish to provide recommendations using a joint database without disclosing sensitive data to each other, they may achieve accurate privacy-preserving referrals on integrated data with schemes we have proposed. We investigate performing filtering tasks; including prediction generations and top- N recommendations, on vertically or horizontally partitioned data, with privacy preservation, in Chapter 7 and Chapter 8. We conduct various experiments using well-known existing data sets to evaluate the overall performance of our approaches. Furthermore, we analyze the effects of different parameters on accuracy and privacy. Finally, in Chapter 9, we present our results and explain future directions

ABSTRACT SVD-based Collaborative Filtering with Privacy ∗

Collaborative filtering (CF) techniques are becoming increasingly popular with the evolution of the Internet. Such techniques recommend products to customers using similar users’ preference data. The performance of CF systems degrades with increasing number of customers and products. To reduce the dimensionality of filtering databases and to improve the performance, Singular Value Decomposition (SVD) is applied for CF. Although filtering systems are widely used by E-commerce sites, they fail to protect users ’ privacy. Since many users might decide to give false information because of privacy concerns, collecting high quality data from customers is not an easy task. CF systems using these data might produce inaccurate recommendations. In this paper, we discuss SVD-based CF with privacy. To protect users ’ privacy while still providing recommendations wit

Privacy-preserving top-N recommendation on horizontally partitioned data

Collaborative filtering techniques are widely used by many E-commerce sites for recommendation purposes. Such techniques help customers by suggesting products to purchase using other users ’ preferences. Today’s top-recommendation schemes are based on market basket data, which shows whether a customer bought an item or not. Data collected for recommendation purposes might be split between different parties. To provide better referrals and increase mutual advantages, such parties might want to share data. Due to privacy concerns, however, they do not want to disclose data. This paper presents a scheme for binary ratings-based top-N recommendation on horizontally partitioned data, in which two parties own disjoint sets of users ’ ratings for the same items while preserving data owners ’ privacy. If data owners want to produce referrals using the combined data while preserving their privacy, we propose a scheme to provide accurate top-N recommendations without exposing data owners ’ privacy. We conducted various experiments to evaluate our scheme and analyzed how different factors affect the performance using the experiment results. 1