Consumer-generated reviews play an important role in online purchase decisions for many consumers. However, the quality and helpfulness of online reviews varies significantly. In addition, the helpfulness of different consumer-generated reviews is not disclosed to consumers unless they carefully analyze the overwhelming number of available contents. Therefore, it is of vital importance to develop predictive models that can evaluate online product reviews efficiently and then display the most useful reviews to consumers, in order to assist them in making purchase decisions. This thesis examines the problem of building computational models for predicting whether a consumer-generated review is helpful based on consumers' online votes on other reviews (where a consumer's vote on a review is either HELPFUL or UNHELPFUL), with the aim of suggesting the most suitable products and vendors to consumers.In particular, we propose in this thesis three different helpfulness prediction approaches for consumer-generated reviews. Our entropy-based approach is relatively simple and suitable for applications requiring simple recommendation engine with fully-voted reviews. However, our entropy-based approach, as well as the existing approaches, lack a general framework and are all limited to utilizing fully-voted reviews. We therefore present a probabilistic helpfulness prediction framework to overcome these limitations. To demonstrate the versatility and flexibility of this framework, we propose an EM-based model and a logistic regression-based model. We show that the EM-based model can utilize reviews voted by a very small number of voters as the training set, and the logistic regression-based model is suitable for real-time helpfulness predicting of consumer-generated reviews. To our best knowledge, this is the first framework for modeling review helpfulness and measuring the goodness of models. Although this thesis primarily considers the problem of review helpfulness prediction, the presented probabilistic methodologies are, in general, applicable for developing recommender systems that make recommendation based on other forms of user-generated contents.

This thesis is concerned with the channel assignment (CA) problem in multi-channel multi-interface wireless mesh networks (M2WNs). First, for M2WNs with general topologies, we rigorously demonstrate using the combinatorial principle of inclusion/exclusion that the CA solution space can be quantified, indicating that its cardinality is greatly influenced by the number of radio interfaces installed on each router. Based on this analysis, a novel scheme is developed to construct a new reduced search space, represented by a lattice structure, that is searched more efficiently for a CA solution. The elements in the reduced lattice-based space, labeled Solution Structures (SS), represent groupings of feasible CA solutions satisfying the radio constraints at each node. Two algorithms are presented for searching the lattice structure. The first is a greedy algorithm that finds a good SS in polynomial time, while the second provides a user-controlled depthfirst search for the optimal SS. The obtained SS is used to construct an unconstrained weighted graph coloring problem which is then solved to satisfy the soft interference constraints. For the special class of full M2WNs (fM2WNs), we show that an optimal CA solution can only be achieved with a certain number of channels; we denote this number as the characteristic channel number and derive upper and lower bounds for that number as a function of the number of radios per router. Furthermore, exact values for the required channels for minimum interference are obtained when certain relations between the number of routers and the radio interfaces in a given fM2WN are satisfied. These bounds are then employed to develop closed-form expressions for the minimum channel interference that achieves the maximum throughput for uniform traffic on all communication links. Accordingly, a polynomial-time algorithm to find a near-optimal solution for the channel assignment problem in fM2WN is developed. Experimental results confirm the obtained theoretical results and demonstrate the performance of the proposed schemes.

The goal of this thesis is to find provably correct methods for detecting conflicts between XACML rules. A conflict occurs when one rule permits a request and another denies that same request. As XACML deals with access control, we can help prevent unwanted access by verifying that it contains rules that do not have unintended conflicts. In order to help with this, we propose an algorithm to find these conflicts then use the Coq Proof Assistant to prove correctness of this algorithm. The algorithm takes a rule set specified in XACML and returns a list of pairs of indices denoting which rules conflict. It is then up to the policy writer to see if the conflicts are intended, or if they need modifying. Since we will prove that this algorithm is sound and complete, we can be assured that the list we obtain is complete and only contains true conflicts.

The root is the primary lexical unit of Ontological terms, which carries the most significant aspects of semantic content and cannot be reduced into small constituents. It is the key of ontological term structure. After the identification of root, we can easily get the meaning of terms. According to the meaning, it’s helpful to identify the other parts of terms, such as the relation, definition and so on. We have generated a general classification model to identify the roots of terms in this master thesis. There are four features defined in our classification model: the Token, the POS, the Length and the Position. Implementation is followed using Java and algorithm is followed using Naïve Bayes. We implemented and evaluated the classification model using Gene Ontology (GO). The evaluation results showed that our framework and model were effective.

In solving noise-free and noisy optimization problems, candidate initialization and sampling play a key role, but are not deeply investigated. It is of interest to know if the entire search space has the same quality for candidate-solutions during solving different type of optimization problems. In this thesis, a comprehensive investigation is conducted in order to clear those doubts, and to examine the effects of variant sampling methods on solving challenging optimization problems, such as large-scale, noisy, and multi-modal problems. As a result, the search space is segmented by using seven segmentation schemes, namely: Center-Point, Center-Based, Modula-Opposite, Quasi-Opposite, Quasi-Reflection, Supper- Opposite, and Opposite-Random. The introduced schemes are studied using Monte-Carlo simulation, on various types of noise-free optimization problems, and ultimately ranked based on their performance in terms of probability of closeness, average distance to unknown solution, number of solutions found, and diversity. Based on the results of the experiments, high-ranked schemes are selected and utilized on well-known metaheuristic algorithms, as case studies. Two categories of case studies are targeted; one for a singlesolution- based metaheuristic (S-metaheuristic) and another one for a population based metaheuristic (P-metaheuristic). A high-ranked single-solution-based scheme is utilized to accelerate Simulated Annealing (SA) algorithm, as a noise-free S-metaheuristic case study. Similarly, for noise-free P-metaheuristic case study, an effective population-based algorithm, Differential Evolution (DE), has been utilized. The experiments confirm that the new algorithms outperform the parent algorithm (DE) on large-scale problems. In the same direction, with regards to solving noisy problems more efficiently, a Shaking-based sampling method is introduced, in which the original noise is tackled by adding an additional noise into the search process. As a case study, the Shaking-based sampling is utilized on the DE algorithm, from which two variant algorithms have been developed and showed impressive performance in comparison to the classical DE, in tackling noisy largescale problems. This thesis has created an opportunity for a comprehensive investigation on search space segmentation schemes and proposed new sampling methods. The current study has provided a guide to use appropriate sampling schemes for a given types of problems such as noisy, large-scale and multi-modal optimization problems. Furthermore, this thesis questions the effectiveness of uniform-random sampling method, which is widely used in of S-Metaheuristic and P-Metaheuristic algorithms. / UOIT

This thesis presents the design, implementation, and fabrication of a pattern-guided equalizer in a 65nm CMOS process. By counting the occurrence of 6 out of 16 4-bit patterns in the received data and utilizing their spectral content, the signal is equalized separately at fN and fN/2, where fN is half the bit rate. The design was packaged using a 64 pin Quad Flat No leads (QFN) package. Two different channels were used and the equalizer was able to open the eye for both 13dB and 17dB of attenuation at the Nyquist frequency. The adaptation performance was determined by measuring the vertical and horizontal eye openings for all possible equalizer coefficients. Measured results at 6Gb/s confirm that the adaptation engine opens a closed eye to within 2.6% of optimal vertical opening and 7% of optimal horizontal eye opening while consuming 16.8mW from a 1.2V supply.

This thesis presents the design, implementation, and fabrication of a pattern-guided equalizer in a 65nm CMOS process. By counting the occurrence of 6 out of 16 4-bit patterns in the received data and utilizing their spectral content, the signal is equalized separately at fN and fN/2, where fN is half the bit rate. The design was packaged using a 64 pin Quad Flat No leads (QFN) package. Two different channels were used and the equalizer was able to open the eye for both 13dB and 17dB of attenuation at the Nyquist frequency. The adaptation performance was determined by measuring the vertical and horizontal eye openings for all possible equalizer coefficients. Measured results at 6Gb/s confirm that the adaptation engine opens a closed eye to within 2.6% of optimal vertical opening and 7% of optimal horizontal eye opening while consuming 16.8mW from a 1.2V supply.

Optical Burst Switching (OBS) is an emerging technology for bearing bursty IP traffic directly over Wavelength Division Multiplexing (WDM) links. In OBS network, a key challenge is to reduce the data loss rate with efficient scheduling algorithms. In this work, we first propose a novel traffic aggregation algorithm, namely Tree-based Burst Aggregation (TBA), which aggregates bursts that are routed within a common tree topology into a composite burst and switch them as a single unit whenever possible. Then we propose another set of algorithms are batch scheduling using interval graphs in the core nodes. The algorithms effectively consider the strong correlations among the multiple bursts, and employ the proposed interval graphs and min-cost circular flow techniques to achieve optimized network performance in terms of data loss rate in OBS networks.

The Discrete Wavelet Transform (DWT) is a popular tool in the field of image and video compression applications. Because of its multi-resolution representation capability, the DWT has been used effectively in applications such as transient signal analysis, computer vision, texture analysis, cell detection, and image compression. Daubechies wavelets are one of the popular transforms in the wavelet family. Daubechies filters provide excellent spatial and spectral locality-properties which make them useful in image compression.<p> In this thesis, we present an efficient implementation of a shared hardware core to compute two 8-point Daubechies wavelet transforms. The architecture is based on a new two-level folded mapping technique, an improved version of the Algebraic Integer Quantization (AIQ). The scheme is developed on the factorization and decomposition of the transform coefficients that exploits the symmetrical and wrapping structure of the matrices. The proposed architecture is parallel, pipelined, and multiplexed. Compared to existing designs, the proposed scheme reduces significantly the hardware cost, critical path delay and power consumption with a higher throughput rate.<p> Later, we have briefly presented a new mapping scheme to error-freely compute the Daubechies-8 tap wavelet transform, which is the next transform of Daubechies-6 in the Daubechies wavelet series. The multidimensional technique maps the irrational transformation basis coefficients with integers and results in considerable reduction in hardware and power consumption, and significant improvement in image reconstruction quality.

<p>Because of the slow access time of disk storage, storage management is crucial to the performance of many large scale computer systems. This thesis studies performance issues in buffer cache management and disk layout management, two important components of storage management. </p><p>The buffer cache stores popular disk pages in memory to speed up the access to them. Buffer cache management algorithms used in real systems often have many parameters that require careful hand-tuning to get good performance. A self-tuning algorithm is proposed to automatically tune the page cleaning activity in the buffer cache management algorithm by monitoring the I/O activities of the buffer cache. This algorithm achieves performance comparable to the best manually tuned system.</p><p>The global data structure used by the buffer cache management algorithm is protected by a lock. Access to this lock can cause contention which can significantly reduce system throughput in multi-processor systems. Current solutions to eliminate lock contention decrease the hit ratio of the buffer cache, which causes poor performance when the system is I/O-bound. A new approach, called the multi-region cache, is proposed. This approach eliminates lock contention, maintains the hit ratio of the buffer cache, and incurs little overhead. Moreover, this approach can be applied to most buffer cache management algorithms.</p><p>Disk layout management arranges the layout of pages on disks to improve the disk I/O efficiency. The typical disk layout approach, called Overwrite, is optimized for sequential I/Os from a single file. Interleaved writes from multiple users can significantly decrease system throughput in large scale systems using Overwrite. Although the Log-structured File System (LFS) is optimized for such workloads, its garbage collection overhead can be expensive. In modern and future disks, because of the much faster improvement of disk transfer bandwidth over disk positioning time, LFS performs much better than Overwrite in most workloads, unless the disk is close to full. A new disk layout approach, called HyLog, is proposed. HyLog achieves performance comparable to the best of existing disk layout approaches in most cases.