Browsing by Author "Bagheri, Ebrahim"

Now showing 1 - 10 of 10
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    A framework for developing adaptive service compositions
    (University of New Brunswick, 2018) Bashari, Mahdi; Du, Weichang; Bagheri, Ebrahim
    This thesis proposes a framework for automatic generation of self-healing service composition which can recover from functional and non-functional failures. To this end, it firstly proposes an automated method for generation of service composition which enables a user to build a service composition by selecting a set of desired features and secondly it proposes a method for adapting the generated service composition to recover autonomously from service failures or non-functional constraint violations. The proposed service composition method uses software product line engineering concepts to build a repository of features and link them to their corresponding services. Using this repository, it uses AI planning to build a work flow of service interactions based on the requirements. It then uses concepts from partial-order-planning to optimize the generated work flow. Eventually, the generated work flow is converted to structured and executable BPEL code. The proposed adaptation method extends the composition software product line to become a dynamic software product line. The proposed dynamic software product line is capable of re-selecting features of a running service composition to continue service with limited features to recover from a service failure or a violation of critical non-functional requirements. A method has been proposed which uses linear regression to determine the effect of features on the non-functional properties of service composition. Knowing how each feature affects non-functional requirements, a method has been proposed which reduces the problem of finding an alternate set of features which recovers service composition from service failure or non-functional requirement violation to a pseudo-boolean optimization problem, which can then be solved. An online tool-suite realizing the proposed framework has been implemented and the usability, effectiveness, and reliability of the proposed framework have been evaluated with extensive experiments.
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    Astrolabe: A Collaborative Multi-Perspective Goal-Oriented Risk Analysis Methodology
    Bagheri, Ebrahim; Ghorbani, Ali, A.
    The intention of this paper is to introduce a risk analysis methodology, called Astrolabe. Astrolabe is based on the key idea of mining system risks from their origins and sources in order to both align the current standpoint of the system with its intentions and identify any vulnerabilities or hazards threatening its being. Astrolabe adopts concepts from organizational theory and software requirement analysis. The aim of Astrolabe is to guide risk analysis through its phases so that a near complete investigation of system risks is performed. The concepts driving the Astrolabe methodology have been defined in a metamodel that has been introduced in this paper. Keywords: Risk Analysis, Goal-Oriented Modeling, Risk Lifecycle
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    Causal studies on users’ behavioral choices in social networks
    (University of New Brunswick, 2022-08) Falavarjani, Seyed Amin Mirlohi; Bagheri, Ebrahim; Ghorbani, Ali A.
    Causal inference is an essential topic across many domains, such as statistics, computer science, education, and economics, to name a few. The existence and convenience of obtaining appropriate observational data and the rapidly developing area of Big Data has enabled us to estimate causal effects between phenomena that was not previously possible. Scientists refer to causality as cause and effect where the cause, which can be an event, process, state, or object, is responsible for producing the effect, which is another event, process, state, or object. Causal inference is the process of determining a conclusion about a causal connection based on the conditions of the occurrence of an effect. This thesis proposes approaches to explore the potential causal effects of users’ different offline behaviors such as exercising, dining, shopping, and traveling on their alignment with social beliefs and emotions in online platforms. Additionally, this thesis examines whether being aligned with society is contagious. Concretely, this thesis considers the potential causal effects of users’ offline activities on their online social behavior. The objective of our work is to understand whether the activities that users are involved with in their real daily life, which place them within or away from social situations, have any direct causal impact on their behavior in online social networks. This work is motivated by the theory of normative social influence, which argues that individuals may show behaviors or express opinions that conform to those of the community for the sake of being accepted or from fear of rejection or isolation. Our main findings can be summarized as follows (1) a change in users’ offline behavior that affects the level of users’ exposure to social situations, e.g., starting to go to the gym or discontinuing frequenting bars, can have a causal impact on users’ online topical interests and sentiments; and (2) the causal relations between users’ socially situated offline activities and their online social behavior can be used to build effective predictive models of users’ online topical interests and sentiments. We further expand the state of the art by exploring the impact of social contagion on users’ social alignment, i.e., whether the decision to socially align oneself with the general opinion of the users on the social network is contagious to one’s connections on the network or not. This is an important problem as it explores whether users will make decisions to socially align themselves with others depending on whether their social network connections decide to socially align or not. The novelty of our work include: (1) unlike earlier work, our work is among the first to explore the contagiousness of the concept of social alignment on social networks; (2) our work adopts an instrumental variable approach to determine reliable causal relations between observed social contagion effects on the social network; (3) our work expands beyond the mere presence of contagion in social alignment and also explores the role of population heterogeneity on social alignment contagion. We find that a user’s decision to socially align or distance from social topics and sentiments influences the social alignment decisions of their connections on the social network.
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    Diachronically like-minded user community detection
    (University of New Brunswick, 2020) Fani, Hossein; Du, Weichang; Bagheri, Ebrahim
    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.
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    Difficulty adjustment algorithms for preventing proof-of-work mining attacks
    (University of New Brunswick, 2022-08) Azimy, Hamid; Ghorbani, Ali A.; Bagheri, Ebrahim
    Bitcoin mining is the process of generating new blocks in the blockchain. This process is vulnerable to different types of attacks. One of the most famous attacks in this category is selfish mining, introduced by Eyal and Sirer [21] in 2014. Selfish mining is a very well-known attack and many studies tried to analyze, mitigate, or extend it. This attack is essentially a strategy that a sufficiently powerful miner can follow to obtain more revenue than its fair share. To put it simply, it works by slowing down the network and wasting the hash power of both attackers and honest miners but wasting honest miners’ hash power more. This attack is not exclusive to Bitcoin and can be performed on many proof-of-work blockchains and cryptocurrencies (e.g. Ethereum) and it is observed in a few cases on other altcoins (Monacoin). The reason that selfish mining is effective in Bitcoin is the difficulty adjustment algorithm in Bitcoin. Because after the difficulty adjustment, the selfish miner will benefit from higher relative revenue. This is the point that is not well-studied in the literature and we try to address it in this thesis. However, the difficulty adjustment algorithm is an essential part of the Bitcoin protocol and cannot be removed. In this thesis, we analyze the profitability of selfish mining concerning time and also the presence of other selfish miners. We also propose a family of alternative difficulty adjustment algorithms including Zeno’s DAA, Zeno’s Max DAA, and Zeno’s Parametric DAA, that discourage selfish mining, while allowing the Bitcoin network to remain scalable (by adjusting the difficulty of the network). We analyze our proposed solutions, using two methods: mathematical analysis and simulation analysis. Then, we present the results and discuss the effectiveness of our proposed solutions. Based on our analysis, our proposed algorithms effectively increase the profitability waiting time for the attackers to almost double its original value. For example, for a miner with 40% of the network’s hash power, it extends the waiting time from four weeks to more than eleven weeks. This will discourage attackers from performing their malicious activities. We also show that our proposed algorithm allows the network to scale while it increases the waiting time.
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    Indexing infrastructure for semantics full-text search
    (University of New Brunswick, 2019) Lashkari, Fatemeh; Ghorbani, Ali; Bagheri, Ebrahim
    The increasing effectiveness and wide spread use of automated entity linking platforms has enabled search techniques to adopt semantic-enabled methods such as sense disambiguation, intent determination, and instance identification within the search process. Researchers have already delved into the possibility of integrating semantic information into practical search engines, a paradigm known as semantic full-text search. However, the practical and efficient incorporation of semantic information within search indices is still an open challenge. In this thesis, we proposed two indexing approaches for building efficient and effective semantic full-text indices. In the first approach, we remain faithful to the traditional form of building search indices where the index key of the index is guaranteed to be present in each of the indexed documents. As such, we will assume that the documents related to each of keyword, semantic entity, semantic type, do in fact explicitly contain this information. For this reason, the first proposed indexing mechanism is referred to Explicit Semantic Full-text Index. We propose various representation data structures and their effective integration strategies for building the explicit semantic full-text index. Furthermore, we introduce algorithms for performing query processing tasks such as Boolean and rank union and intersection on the proposed indices. In the second approach, we relax the traditional condition of search indices and allow documents associated with an index key to be semantically similar to the index key as opposed to explicitly including the key. We refer to this indexing strategy as the Implicit Semantic Full-text Index. We propose a mechanism to embedd keyword, semantic entity, semantic type information within a homogeneous representation space and hence be indexed in the same indexing data structure. Based on our experiments, we find that when neural embeddings are used to build inverted indices; hence, relaxing the requirement to explicitly observe the posting list key in the indexed document, (a) retrieval efficiency will increase compared to a standard inverted index, hence reducing the index size and query processing time, and at the same time (b) retrieval effectiveness retains competitive performance compared to the baseline in terms of retrieving a reasonable number of relevant documents from the indexed corpus.
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    Non-functional properties in software product lines: a framework for developing quality-centric software products
    (University of New Brunswick, 2017) Noorian, Mahdi; Du, Weichang; Bagheri, Ebrahim
    Software Product Line Engineering (SPLE) is a discipline that facilitates a systematic reuse-based software development and is founded on the idea of building software products using a set of core assets rather than developing individual software systems from scratch. Feature models are among the widely used artifacts for SPL development that mostly capture functional and operational variability of a system. Researchers have argued that connecting intentional variability models such as gldoal models with feature variability models in a target domain can enrich feature models with valuable quality and non-functional information. Interrelating goal models and feature models has already been proposed in the literature for capturing non-functional properties in software product lines; however, the manual integration process is cumbersome and tedious. In addition, as one of the main artifacts of the software product line, a feature model represents the possible configuration space and can be customized based on the stakeholders' needs and goals. Considering the complexity of the variabilities represented by feature models in addition to the diversity of the stakeholders' expectations, the configuration process can be viewed as a complex optimization problem. In this thesis, we propose a framework for developing quality-centric software products in the SPL context. We developed the Quality-centric Feature Model (QcFM) method as a basis for bringing non-functional properties into feature models via connecting feature and goal models in the domain engineering phase. Based on this, we then developed the Quality-centric Configuration Process (QcCP) method for configuring software product line feature models. The approach is mainly grounded on two theoretical parts. First, in domain engineering, we integrate feature and goal models through a semantic-enabled process to build a comprehensive domain model. Then, in the application engineering phase, we conduct a semi-automated process to configure the product line according to the stakeholders' functional and non-functional requirements and preferences. The key contributions of this thesis are: (i) a semi-automated framework for semantically integrating feature and goal model elements using a semantics-enabled text analysis process; (ii) a method to assist domain analysts to decide on selecting and connecting the related elements in a feature and goal models in such a way that feature models can be extended with domain non-functional properties; (iii) a configuration process by means of a feature model staged configuration approach such that stakeholders' functional and non-functional requirements can be captured using domain level goal models; and (iv) the formalization of the configuration problem in the form of an integer linear program to develop a semi-automated configuration process.
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    Ontology-based recommendation of academic papers
    (University of New Brunswick, 2014) Al-Wakel, Esraa; Ghorbani, Ali; Bagheri, Ebrahim
    In an era when recommender systems aspire to reduce information overload, we analyze how recommender systems can be implemented to overcome current limitations. This thesis presents a novel framework for a semantic recommender system that not only copes with existing problems but also presents a strategy that computes customized recommendations using a variety of tools including semantic contents. To this end, we have identified the need for developing semantic recommender systems, which are able to extend existing systems and perform a semantic search in an effort to find the most suitable scientific papers in the field of Computer Science. For this purpose, we developed three different semantic recommender techniques rooted in annotation systems and its semantic matching components. The techniques, which are entitled REI, REII, and REIII, are based on GATE, Alchemy API, and a combination of both tools. These recommender techniques are capable of exploring an annotated database in an attempt to trace and rank the most relevant documents in a particular query. Precision and recall are subsequently measured and compared to a similar query conducted in Google Scholar indicating that this research is promising and can improve on current semantics-based recommender systems.
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    Ontology-based recommendation of academic papers
    (University of New Brunswick, 2014) Al-Wakel, Esraa Hassan Jawad; Ghorbani, Ali; Bagheri, Ebrahim
    In an era when recommender systems aspire to reduce information overload, we analyze how recommender systems can be implemented to overcome current limitations. This thesis presents a novel framework for a semantic recommender system that not only copes with existing problems but also presents a strategy that computes customized recommendations using a variety of tools including semantic contents. To this end, we have identified the need for developing semantic recommender systems, which are able to extend existing systems and perform a semantic search in an effort to find the most suitable scientific papers in the field of Computer Science. For this purpose, we developed three different semantic recommender techniques rooted in annotation systems and its semantic matching components. The techniques, which are entitled REI, REII, and REIII, are based on GATE, Alchemy API, and a combination of both tools. These recommender techniques are capable of exploring an annotated database in an attempt to trace and rank the most relevant documents in a particular query. Precision and recall are subsequently measured and compared to a similar query conducted in Google Scholar indicating that this research is promising and can improve on current semantics-based recommender systems.
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    Semantic annotation of quantitative textual content
    (University of New Brunswick, 2016) Ghashghaei, Mehrnaz; Bagheri, Ebrahim; Ghorbani, Ali
    Semantic annotation techniques provide the basis for linking textual content with concepts in well grounded knowledge bases. In spite of their many application areas, current semantic annotation systems have some limitations. One of the prominent limitations of such systems is that none of the existing semantic annotator systems are able to identify and disambiguate quantitative (numerical) content. In textual documents such as Web pages, specially technical contents, there are many quantitative information such as product specifications that need to be semantically qualified. In this thesis, we propose an approach for annotating quantitative values in short textual content. In our approach, we identify numeric values in the text and link them to an existing property in a knowledge base. Based on this mapping, we are then able to find the concept that the property is associated with, whereby identifying both the concept and the specific property of that concept that the numeric value belongs to. Our experiments show that our proposed approach is able to reach an accuracy of over 70% for semantically annotating quantitative content.