Despite recommender systems being useful, for some applications it is hard to accumulate all the required information needed for the recommendation. In today‟s ubiquitous environment, mobile devices with different characteristics are widely available. Our work focuses on the recommendation service built on mobile environment to support tourists‟ traveling need. When tourists visit a new attraction, their recommender systems can exchange data with the attraction system to help obtain rating information of people with similar tastes. Such asynchronous rating exchange mechanisms allow a tourist to receive ratings from other people even though they may not collocate at the same time. We proposed four data exchange methods between a user and an attraction system. Our recommendation mechanism incorporates other users‟ opinions to provide recommendations once the user has collected enough ratings. Every method is compared under four conditions which attraction systems carry different amount of existing data. Then we compare these methods under different amount of existing rating data and shed the light on their advantages and disadvantages. Finally, we compare our proposed asynchronous methods with other synchronous data exchange methods proposed previously.

Accompanying with the Internet growth explosion, more and more information disseminates on the Web. The large amount of information, however, causes the information overload problem that disturbs users who desire to search and find useful information online. Information retrieval and information filtering arise to compensate for the searching and comprehending ability of the users. Recommender systems as one of the information filtering techniques emerge when users cannot describe their requirements precisely as keywords. Collaborative filtering (CF) compares novel information with common interests shared by a group of people to make the recommendations. One of its methods, the Model-based CF, generates predicted recommendation based on the model learned from the past opinions of the users. However, two issues on model-based CF should be addressed. First, data quality of the rating matrix input can affect the prediction performance. Second, most current models treat the data class as the nominal scale instead of ordinal nature in ratings. The objective of this research is thus to propose a model-based CF algorithm that considers data reliability and data scale in the model. Three experiments are conducted accordingly, and the results show our proposed method outperforms other counterparts especially under data of mild sparsity degree and of large scale. These results justify the feasibility of our proposed method in real applications.

Traditional remote sensing image classification has primarily relied on image spectral information and texture information was ignored or not fully utilized. Existing remote sensing software packages have very limited functionalities with respect to texture information extraction and utilization. This research focuses on the use of multi-scale image texture analysis techniques using Gabor filter banks and Wavelet transformations. Gabor filter banks model texture as irradiance patterns in an image over a limited range of spatial frequencies and orientations. Using Gabor filters, each image texture can be differentiated with respect to its dominant spatial frequency and orientation. Wavelet transformations are useful for decomposition of an image into a set of images based on an orthonormal basis. Dyadic transformations are applied to generate a multi-scale image pyramid which can be used for texture analysis. The analysis of texture is carried out using both artificial textures and remotely sensed image corresponding to natural scenes. This research has shown that texture can be extracted and incorporated in conventional classification algorithms to improve the accuracy of classified results. The applicability of Gabor filter banks and Wavelets is explored for classifying and segmenting remote sensing imagery for geographical applications. A qualitative and quantitative comparison between statistical texture indicators and multi-scale texture indicators has been performed. Multi-scale texture indicators derived from Gabor filter banks have been found to be very effective due to the nature of their configurability to target specific textural frequencies and orientations in an image. Wavelet transformations have been found to be effective tools in image texture analysis as they help identify the ideal scale at which texture indicators need to be measured and reduce the computation time taken to derive statistical texture indicators. A robust set of software tools for texture analysis has been developed using the popular .NET and ArcObjects. ArcObjects has been chosen as the API of choice, as these tools can be seamlessly integrated into ArcGIS. This will aid further exploration of image texture analysis by the remote sensing community.

U.S. Treasury Securities are crucially important in many areas of finance. However, zero-coupon yields are not observable in the market. Even though published zero- coupon yields exist, they are sometimes not available for certain research topics or for high frequency. Recently, high frequency data analysis has become popular, and the GovPX database is a good source of tick data for U.S. Treasury securities from which we can construct zero-coupon yield curves. Therefore, we try to t zero- coupon yield curves from low frequency and high frequency data from GovPX by three different methods: the Nelson-Siegel method, the Svensson method, and the cubic spline method. Then, we try to retest the expectations hypothesis (EH) with new zero-coupon yields that are made from GovPX data by three methods using the Campbell and Shiller regression, the Fama and Bliss regression, and the Cochrane and Piazzesi regression. Regardless of the method used (the Nelson-Siegel method, the Svensson method, or the cubic spline method), the expectations hypothesis cannot be rejected in the period from June 1991 to December 2006 for most maturities in many cases. We suggest the possible explanation for the test result of the EH. Based on the overreaction hypothesis, the degree of the overreaction of spread falls over time. Thus, our result supports that the evidence of rejection of the EH has weaken over time. Also, we introduce a new estimation method for the stochastic volatility model of the short-term interest rates. Then, we compare our method with the existing method. The results suggest that our new method works well for the stochastic volatility model of short-term interest rates.

Coherent optical signal processing systems typically require dynamic, low-loss phase changes of an optical signal. Waveform generation employing phase modulation is an important application area. In particular, laser radar systems have been shown to perform better with non-linear frequency chirps. This work shows how dynamically tunable integrated optical ring resonators are able to produce such phase changes to a signal in an effective manner and offer new possibilities for the detection of phase-modulated optical signals. When designing and fabricating dynamically tunable integrated optical ring resonators for any application, system level requirements must be taken into account. For frequency chirped laser radar systems, the primary system level requirements are good long range performance and fine range resolution. These mainly depend on the first sidelobe level and mainlobe width of the autocorrelation of the chirp. Through simulation, the sidelobe level and mainlobe width of the autocorrelation of the non-linear frequency modulated chirp generated by a series of integrated optical ring resonators is shown to be significantly lower than the well-known tangent-FM chirp. Proof-of-concept experimentation is also important to verify simulation assumptions. A proof-of-concept experiment employing thermally tunable Silicon-Nitride integrated optical ring resonators is shown to generate non-linear frequency modulated chirp waveforms with peak instantaneous frequencies of 28 kHz. Besides laser radar waveform generation, three other system level applications of dynamically tunable integrated optical ring resonators are explored in this work. A series of dynamically tunable integrated optical ring resonators is shown to produce constant dispersion which can then help extract complex spectral information. Broadband photonic RF phase shifting for beam steering of a phased array antenna is also shown using dynamically tunable integrated optical ring resonators. Finally all-optical pulse compression for laser radar using dynamically tunable integrated optical ring resonators is shown through simulation and proof-of-concept experimentation.

One-class collaborative filtering is a special type of collaborative filtering methods that aims to deal with datasets that lack counter-examples. In this work, we introduced social networks as a new data source to the one-class collaborative filtering (OCCF) methods and sought ways to benefit from them when dealing with OCCF problems. We divided our research into two parts. In the first part, we proposed different weighting schemes based on social graphs for some well known OCCF algorithms. One of the weighting schemes we proposed outperformed our baselines for some of the datasets we used. In the second part, we focused on the dataset differences in order to find out why our algorithm performed better on some of the datasets. We compared social graphs with the graphs of users and their neighbors generated by the k-NN algorithm. Our research showed that social graphs generated from a specialized domain better improves the recommendation performance than the social graphs generated from a more generic domain.

Recommender systems are information retrieval tools helping users in their information seeking tasks and guiding them in a large space of possible options. Many hybrid recommender systems are proposed so far to overcome shortcomings born of pure content-based (PCB) and pure collaborative filtering (PCF) systems. Most studies on recommender systems aim to improve the accuracy and efficiency of predictions. In this thesis, we propose an online hybrid recommender strategy (CBCFdfc) based on content boosted collaborative filtering algorithm which aims to improve the prediction accuracy and efficiency. CBCFdfc combines content-based and collaborative characteristics to solve problems like sparsity, new item and over-specialization. CBCFdfc uses fuzzy clustering to keep a certain level of prediction accuracy while decreasing online prediction time. We compare CBCFdfc with PCB and PCF according to prediction accuracy metrics, and with CBCFonl (online CBCF without clustering) according to online recommendation time. Test results showed that CBCFdfc performs better than other approaches in most cases. We, also, evaluate the effect of user-specified parameters to the prediction accuracy and efficiency. According to test results, we determine optimal values for these parameters. In addition to experiments made on simulated data, we also perform a user study and evaluate opinions of users about recommended movies. The results that are obtained in user evaluation are satisfactory. As a result, the proposed system can be regarded as an accurate and efficient hybrid online movie recommender.

In this thesis, we present a content based packet filtering Architecture in Linux using Deterministic Finite Automata and iptables framework. New generation firewalls and intrusion detection systems not only filter or inspect network packets according to their header fields but also take into account the content of payload. These systems use a set of signatures in the form of regular expressions or plain strings to scan network packets. This scanning phase is a CPU intensive task which may degrade network performance. Currently, the Linux kernel firewall scans network packets separately for each signature in the signature set provided by the user. This approach constitutes a considerable bottleneck to network performance. We implement a content based packet filtering architecture and a multiple string matching extension for the Linux kernel firewall that matches all signatures at once, and show that we are able to filter network traffic by consuming constant bandwidth regardless of the number of signatures. Furthermore, we show that we can do packet filtering in multi-gigabit rates.

The task of the recommendation systems is to recommend items that are relevant to the preferences of users. Two main approaches in recommendation systems are collaborative filtering and content-based filtering. Collaborative filtering systems have some major problems such as sparsity, scalability, new item and new user problems. In this thesis, a hybrid recommendation system that is based on content-boosted collaborative filtering approach is proposed in order to overcome sparsity and new item problems of collaborative filtering. The content-based part of the proposed approach exploits semantic similarities between items based on a priori defined ontology-based metadata in movie domain and derived feature-weights from content-based user models. Using the semantic similarities between items and collaborative-based user models, recommendations are generated. The results of the evaluation phase show that the proposed approach improves the quality of recommendations.

With the increasing amount of data on web, people start to need tools which will help them to deal with the most significant ones among the thousands. The idea of a system which recommends items to its users emerged to fulfill this inevitable need. But most of the recommender systems make recommendations for individuals. On the other hand, some people need recommendation for items which they will use or for activities which they will attend together. Group recommenders serve for these purposes. Group recommenders diverge from individual recommenders such that they need to aggregate members of the group in a joint model, and in order to do so, they need a user satisfaction function. There are two different aggregation methods and a few different satisfaction functions for group recommendation process. Reading groups domain is a new domain for group recommenders. In this thesis we propose a web based group recommender system which is called BoRGo: Book Recommender for Reading Groups , for reading groups domain. BoRGo uses a new information filtering technique and present a media for post recommendation processes. We present comparative evaluation results of this new technique in this thesis.