Faculty of Computer Science (Fredericton)

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Design pattern as a service for service-oriented systems
Design pattern as a service for service-oriented systems
by Eltaher Mohamed El-Shanta, Software systems nowadays face many more challenges than ever before due to the intrinsic high complexity of systems and increasing demands from organizations. While patterns enable reuse of abstract design and architectural knowledge, abstractions documented as patterns do not directly yield reusable code. Software design patterns in particular are essential building blocks for almost any software system. In an effort to enable the use and reuse of implemented software design patterns, we propose a methodology to implement software design patterns as pattern services to make building pattern-based software applications considerably easier and faster. We describe the conceptual architecture and steps of the proposed methodology, and then we explain the implementation stages of a pattern as a service. After that, we demonstrate how the proposed methodology can be applied to Service-Oriented Architecture (SOA) patterns. To create a platform for managing pattern services, we design a Pattern as a Service (PaaS) system that functions as the platform for developing, storing, integrating, deploying, and managing pattern services and pattern-based applications. Furthermore, we describe a prototypical implementation of the PaaS system and the implementation of two case study applications, namely, an Online Discussion Group (ODG) and Online Stock Market Ticker (OSMT) that make use of the Observer pattern service and use the prototypical PaaS system as their platform. Then we perform some evaluation procedures on the proposed methodology both analytically and experimentally, and we give some concrete test results. Finally, we attach an appendix to this thesis in which we apply the methodology to the 23 design patterns introduced by Gamma et al. (1995). In it, we describe the important contents of each resulting pattern service.
Designing and realizing the USB interface in the thermal conductivity instrument
Designing and realizing the USB interface in the thermal conductivity instrument
by Ning Ju, The Thermal Conductivity Instrument (TCi) is a state-of-the-art scientific instrument for analyzing thermo-physical properties of materials. The product includes multiple development elements, mechanical design, electronics hardware and firmware design, software application on the PC side and scientific aspects in thermal conductivity test. In our research we provide a way to expand the Universal Serial Bus (USB) interface function for TCi inside which the microprocessor has no USB module. Various approaches are considered including: Device firmware, USB firmware, USB driver ( windows OS side) and Device application software. To be specific, what is discussed and described in this thesis is a whole process about how to develop a USB support product which means our solution can be applied to many products rather than just the TCi.
Determining if this word is used like that word
Determining if this word is used like that word
by Milton King, Determining the meaning of a word in context is an important task for a variety of natural language processing applications such as translating between languages, summarizing paragraphs, and phrase completion. Usage similarity (USim) is an approach to describe the meaning of a word in context that does not rely on a sense inventory -- a set of dictionary-like definitions. Instead, pairs of usages of a target word are rated in terms of their similarity on a scale. In this thesis, we evaluate unsupervised approaches to USim based on embeddings for words, contexts, and sentences, and achieve state-of-the-art results over two USim datasets. We further consider supervised approaches to USim, and find that they can increase the performance of our models. We look into a more detailed evaluation, observing the performance on different parts-of-speech as well as the change in performance when using different features. Our models also do competitively well in two word sense induction tasks, which involve clustering instances of a word based on the meaning of the word in context., M.C.S. University of New Brunswick, Faculty of Computer Science, 2017.
Diachronically like-minded user community detection
Diachronically like-minded user community detection
by Hossein Fani, Diachronically like-minded user community detectionStudy of users’ behaviour, interests, and influence is of interest within the realm of online social networks due to its wide range of applications, such as personalized recommendations and marketing campaigns. However, the proposed approaches are not always scalable to a large number of users and a huge amount of user-generated content. Community-level studies are introduced to facilitate scalability, among other characteristics, highlighting the main properties of the network at a higher collective level. Prior work is mainly focused on the identification of online communities that are formed based on shared links and/or similar content. However, there is little literature on detecting communities that simultaneously share topical and temporal similarities. To extract diachronically like-minded user communities who have similar temporal dispositions according to their topics of interest from social content, we put forward two approaches: i) multivariate time series analysis, and ii) neural embeddings. In the former approach, we model users’ temporal topics of interest through multivariate time series, and inter-user affinities are calculated based on pairwise cross-correlation. While simple and effective, this approach suffers from sparsity in multivariate time series. In the latter method, however, each user is mapped to a dense embedding space and inter-user affinities are calculated based on pairwise cosine similarity. While the objective of these two proposed approaches is to identify user communities up until the present; in the last step of this thesis, we propose two approaches to identify future communities, i.e., community prediction: i) Granger regression, and ii) temporal latent space modeling. In Granger regression, we propose to consider both the temporal evolution of users’ interests as well as inter-user influence through the notion of causal dependency. In the latter method, however, we assume that each user lies in an unobserved latent space, and similar users in the latent space are more likely to be members of the same user community. The model allows each user to adjust her location in the latent space as her topics of interest evolve over time. Empirically, we demonstrate that our proposed approaches, when evaluated on a Twitter dataset, outperform existing methods under two application scenarios, namely news recommendation and user prediction., Electronic Only.
Diskless data analytics on distributed coordination systems
Diskless data analytics on distributed coordination systems
by Dayal Dilli, A distributed system contains software programs, applications and data resources dispersed across independent computers connected through a communication network. Distributed coordination systems are file-system like distributed meta-data stores that ensure consistency between processes of the distributed system. The challenge in this area is to perform processing fast enough on data that is continuously changing. The focus of this research is to reduce the disk bound time of a chosen distributed coordination system called Apache Zookeeper. By reducing the disk dependency, the performance will be improved. The shortcoming of this approach is that data is volatile on failures. The durability of the data is provided by replicating the data and restoring it from other nodes in the distributed ensemble. On average, a 30 times write performance improvement has been achieved with this approach.
Diskless data analytics on distributed coordination systems
Diskless data analytics on distributed coordination systems
by Dayal Dilli, A distributed system contains software programs, applications and data resources dispersed across independent computers connected through a communication network. Distributed coordination systems are file-system like distributed meta-data stores that ensure consistency between processes of the distributed system. The challenge in this area is to perform processing fast enough on data that is continuously changing. The focus of this research is to reduce the disk bound time of a chosen distributed coordination system called Apache Zookeeper. By reducing the disk dependency, the performance will be improved. The shortcoming of this approach is that data is volatile on failures. The durability of the data is provided by replicating the data and restoring it from other nodes in the distributed ensemble. On average, a 30 times write performance improvement has been achieved with this approach., Electronic Only. (UNB thesis number) Thesis 9336. (OCoLC) 961212958, M.C.S., University of New Brunswick, Faculty of Computer Science, 2014.
Distributed modular ontology reasoning
Distributed modular ontology reasoning
by Li Ji, This thesis proposes algorithms for a distributed reasoning system over interface-based modular ontologies. The thesis research includes three parts: (1) The algorithm designs for distributed modular ontology reasoning, including TBox (see Glossary 5.) and ABox (see Glossary 1.) reasoning of concept, negated concept, disjunction, conjunction, subsumption, and role queries; (2) The distributed modular ontology reasoning system, comprising system functionality, architecture and functionality realization; and (3) A case study and experiments for evaluating the distributed modular ontology reasoning compared to monolithic ontology reasoning.
Divisible load scheduling on multi-level processor trees
Divisible load scheduling on multi-level processor trees
by Mark Lord, Divisible Load Theory (DLT) is an effective tool for blueprinting data-intensive computational problems. Heuristic algorithms have been proposed in the past to solve for a DLS (Divisible Load Schedule) with result collection on heterogeneous star networks. However scheduling on heterogeneous multi-level trees with result collection is still an open problem. In this thesis, new heuristic algorithms for scheduling divisible loads on heterogeneous multi-level trees (single- and two-installment) including result collection are presented. Experiments are performed on both random networks and cluster networks. Results show that scheduling using multi-level trees produces lower solution times compared to the traditional star network in the majority of cases, however efficiency of resources in multi-level trees tends to be lower, i.e., more processors were used. Cluster results with multi-level trees are found to outperform the star when there are enough clusters available to provide good overlap of communication and computation. Experiments on random networks with varying levels of heterogeneity of resources show that multi-level trees outperform star networks in the majority of cases. Experiments were conducted comparing schedules with and without latency costs. The results from all schedules where latency was considered had signifiantly lower solution times and higher efficiency of resources. Overall, scheduling on single-installment multi-level trees in either clusters or random networks had the lowest solution times, but the star had highest efficiency of resources., Degree name on title page is mislabeled as "Master of Computer Science In the Graduate Academic Unit of in the Graduate Academic Unit of Computer Science". Also, pagination is wrong. The last page of the front matter (second page of List of Figures) is paginated with an Arabic number 1 (one) instead of a Roman number viii (eight). i.e. Page viii is labeled as page 1, page 1 is labeled as page 2, …, page 89 (last page) is labeled as page 90. Electronic Only. (UNB thesis number) Thesis 8661 (OCoLC) 960871143, M.C.S., University of New Brunswick, Faculty of Computer Science, 2011.
Domain generation algorithm (DGA) detection
Domain generation algorithm (DGA) detection
by Shubhangi Upadhyay, Domain name plays a crucial role today, as it is designed for humans to refer the access point they need and there are certain characteristics that every domain name has which justifies their existence. A technique was developed to algorithmically generate domain names with the idea to solve the problem of designing domain names manually. DGAs are immune to static prevention methods like blacklisting and sinkholing. Attackers deploy highly sophisticated tactics to compromise end-user systems to gain control as a target for malware to spread. There have been multiple attempts made using lexical feature analysis, domain query responses by blacklist or sinkholing, and some of these techniques have been really efficient as well. In this research the idea to design a framework to detect DGAs even in real network traffic, using features studied from legitimate domain names in static and real traffic, by considering feature extraction as the key of the framework we propose. The detection process consists of detection, prediction and classification attaining a maximum accuracy of 99% even without using neural networks or deep learning techniques.
Dynamic monitor allocation in the IBM J9 virtual machine
Dynamic monitor allocation in the IBM J9 virtual machine
by Marcel Dombrowski, With the Java language and sandboxed environments becoming more and more popular, research needs to be conducted into improving the performance of these environments while decreasing their memory footprints. This thesis focuses on a dynamic approach for growing monitors for objects in order to reduce the memory footprint and improve the execution time of the IBM Java Virtual Machine. According to the Java Language Specification every object needs to have the ability to be used for synchronization. This new approach grows monitors only when required. The impact of this approach on performance and memory has been evaluated using different benchmarks and future work is also discussed. On average, a performance increase of 0.6% and a memory reduction of about 5% has been achieved with this approach.
Efficient and privacy-preserving AdaBoost classification framework for mining healthcare data over outsourced cloud
Efficient and privacy-preserving AdaBoost classification framework for mining healthcare data over outsourced cloud
by Mahtab Davoudi, In recent years, the analysis and mining of electronic health records (EHRs) with the aid of machine learning (ML) algorithms have become a popular approach to improve the quality of patient care and increase the productivity and efficiency of healthcare delivery. A sufficient amount of data is needed to have robust and more accurate decision-making systems with machine learning algorithms. Due to the high volume of EHRs, many frameworks require outsourcing their data to cloud servers. However, cloud servers are not fully trusted. Moreover, releasing sensitive raw data might put individuals at risk. For example, in Canada, the University of Ontario Institute of Technology (UOIT) in collaboration with IBM, has implemented an online real-time analytic platform, Artemis¹. The Artemis framework is a storage of patients' raw physiological and clinical information and is also used for online real-time analysis and data mining. While utilizing patients' sensitive healthcare data contributes to more accurate diagnoses, it raises security and privacy breaches. In 2019, 25 million patients were the victims of the American Medical Collection Agency (AMCA) data breach². As a result, preserving the privacy of sensitive health records is a pressing issue. A practical solution to ensure the security and privacy of the extreme volume of healthcare data is outsourcing encrypted data to the cloud servers. However, encryption increases the computational cost significantly. As noted earlier, the rapid growth of Machine Learning (ML) and big data have become ubiquitous. However, adversaries may abuse the healthcare data outsourced to the cloud servers without encryption. Thus a Privacy-Preserving (PP) model is required. Researchers have proposed various PP ML models with the aim of different privacy techniques. Nonetheless, time efficiency in PP ML frameworks matters. In comparison to existing ML models, AdaBoost is a fast, simple, and versatile yet highly accurate classifier. Privacy-Preserving techniques can restore the balance between data usage and data privacy. An inefficient privacy technique, by contrast, requires intensive computational power. To address these challenges, we conduct studies and experiments to propose an efficient and privacy-preserving classification framework for mining outsourced encrypted healthcare data. This thesis covers the AdaBoost learning process, classification, Homomorphic Encryption (HE), and Paillier cryptosystem algorithm. The experimental results prove the accuracy and demonstrates the efficiency of our framework. ¹ http://hir.uoit.ca/cms/?q=node/24 ² https://healthitsecurity.com/news/the-10-biggest-healthcare-data-breaches-of-2019-so-far

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