M.Phil / MS
Permanent URI for this collectionhttps://repository.cuilahore.edu.pk/handle/123456789/36
This collection archives the complete set of theses produced by students of the COMSATS University Islamabad, Lahore Campus.
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Item Online Trust in Teen Communities(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2017) Alia Samreen; FA15-RCS-015; LHR TP 6833; Dr. Adnan AhmadConsistently, individuals from everywhere throughout the world connect, share and exchange data over Internet. We communicate online to share individual data, discover answers to inquiries, direct money related exchanges, play social recreations and keep up proficient and individual connections. “Online Social Network (OSN) is a set of personal contacts through which the people maintains their social identity and receive emotional support, material aid, services, information and new social contacts” [1] like Facebook, Orkut etc. Online Social Network gives a virtual situation where people can share their stories, pictures, recordings, and take part in chat rooms for online chatting with their friends and associates. In the last decade, Online Social Networks (OSN) have expanded ubiquitously. More people use OSN to share their interests and make associations, [1] likewise, OSN help people to overcome geographical impediments. Despite of its advantages OSN has also raised some privacy concerns for users. Many people have lost their jobs [86], lives [89], become bullied [4], hacked [4] etc. because of privacy issues of OSN. The situation is even worst for teens who get emotionally, socially and physically compromised due to interactions over online social networks. This is because of the fragile psychology of teens as they are more inclined to pulls of fascination [4,38], emotionally immature [3], more inclined to sensual offers [3,5] and easily grasped by fraudulent [3]. Thus, there is a need of an application that can evaluate, maintain and exhibit the community trust over acquaintances for teens. Trust is an essential part of connections in online social networks whether it is for security, deciding information access, or recommender systems [8]. In this thesis, the student has analyzed issues of teenagers and the factors that affect the formation of end user trust in online environments. After exploring the factors, the student has proposed a framework portraying various stages and their elements important to build trust among people. On the basis of framework, a reputation model is proposed, which xi ensures that a stranger or an acquaintance is trustworthy or not. Our proposed method basically judges people on the basis of their reputation, trustworthiness, honesty, benevolence, integrity, compatibility, confidence etc. The method is also implemented using dot net framework and SQL server. The student generates a new app on Facebook App. Then, the student gets Facebook App Key and App Secret Key. After that, in dot net framework the student gets built in Facebook Configuration Dll. Then, the student creates an instance of Facebook Dll Class and set its App Key and App Secret Key. Then, ask the User Current Session Token Context to be used by Facebook. Then, it will check for Permissions assigned to us by the User on FaceBook. The application was also tested and evaluated by teens on the basis of usefulness, ease of use, ease of learning, satisfaction, and system capabilities. The questionnaire was validated through inter-item and inter-variable correlation which appeared to be sufficiently significant. Moreover, Cronbach’s Alpha test was carried out for internal consistency which also appears to be statistically significant. The average users’ response over the variable and their items suggests that teens are satisfied with the proposed approach and it really helped them to identify trustworthy and well reputed friendsItem A Methodological Exploration of BPM Community(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2017) Saman R. Abdi; FA15-RCS-007; LHR TP 6831; Dr. Abid SohailThe study is motivated to explore the collaboration between the authors, the patterns which are being used by the authors and the areas on which the authors are working. The end product of the research is a set of generic guidelines for the field of business process management. Business process management conference, which was one of the most famous conference is been used to develop the dataset for the study. Business process management conference started since 2003 so the papers since 2003 till 2016 were collected to create the dataset. The collaboration of authors is been studied by applying certain filters over the raw data of business process management conference. The research pattern of different authors was been studied individually from each paper and later they were all combined by identifying the common or similar aspects of the processes to form a set of frameworks. The study has created a generic set of five frameworks. Each for a specific domain of business process management i.e. business process similarity, business process abstraction, business process model redesign, business process monitoring, and business process mining. The study also provides some key facts about the most collaborative authors and the domains on which the authors or the research community is mainly focusing.Item A Structural Variants Process Models Collection for Process Similarities Evaluations(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2017) Rimsha Anam; FA15-RCS-017; LHR TP 6822; Dr. Abid SohailOrganizations having years of involvement in business process management, regularly keep up business process model repository containing hundreds or even a large number of process models. The process models in their repositories typically evolve from different sources and are created by various stakeholders. A new process models may be by expanding or refining existing models. Therefore, process models have a tendency to collect duplicate process models which, if left unconsolidated, may develop autonomously and cause irregularities. Similarly, it frequently happens that many organizations manage the business process models that have similar objectives but involve different business unit which may differentiate one another. The similarity (Correspondence) of process models can be explored through label, structure and behavior. This study is mainly focusing structural similarity measurement. The variations in the process model though structural correspondences have already been proposed in literature. But, majority of them are conceptual i.e. not providing the implication and verification on process model collections. Moreover, the previous studies on structural changes are not yet defining any proper classification for the structural variations. The first contribution of this study is the provision process model design classification through structural variations. We have tried to explore the availability of BPMN dataset and found some existences, one of which is SAP business process repository but, that was not available publically. So, as a contribution the complete repository was created with 600 different variants of 150 original models. The original models were taken from a verified source of our research group available dataset. Furthermore, the regenerated models were once verified from domain experts and finally included. The rigor evaluations were performed on dataset both manually and automated.Item Cross Genre Author Profiling Using Semantic Tagger Based Approach(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2017) Humaira Muqdes; FA15-RCS-023; LHR TP 7000; Dr. Rao Muhammad Adeel NawabAuthor profiling is a task to predict one or more traits of an author (e.g. age, gender etc.), from his/her written text. The collaborative environments have significantly increased the chance of fake author profiles, particularly over the social media. The field of automatically detecting an author’s profile from written text has potential applications in marketing, forensics, security, detecting fake profiling and harassment cases. This research work aims to explore the problem of cross genre author profiling, in which training dataset is in one genre and test dataset is in another genre. We explored three different methods for cross-genre author profiling: (1) stylometry based approach, (2) content based approach, (3) semantic tags based approach and (4) combination of previous three approaches. As far as we are aware semantic tags based approach and combination of approaches has not been previously used for cross genre author profiling problem. Evaluation was carried out using four benchmark author profiling corpora: (1) PAN-AP-14 Social Media Corpus, (2) PAN-AP-14 Hotel Reviews corpus, (3) PAN-AP-14 Blogs Corpus and (4) PAN-AP-16 Twitter Corpus. Accuracy was used an evaluation measure. Above mentioned techniques were applied for both same and cross genre problems. For same genre, best results were obtained using content based technique on PAN-AP 14-Hotel Reviews Corpus (Accuracy =55.26) for Age (Accuracy= 66.59) for gender content based techniques for PAN-AP-Blogs Corpus (Accuracy = 67.57) for age, Semantic tagger based technique (Accuracy = 78.38) for gender. Content based technique on PAN-AP-14-social media Corpus (Accuracy =55.26) for Age (Accuracy= 78.95) for gender. Content based technique on PAN-AP-16-Twitter Corpus (Accuracy =54.76) for Age (Accuracy= 74.42) for gender. For Cross Genre Training Corpus PAN-14-Blogs and testing Corpus PAN-14-Hotel reviews for age (Accuracy = 56.96) for gender training Corpus is PAN-16-Twitter (Accuracy= 58.97). Content Based Technique for Training Corpus PAN-14-Hotel reviews and testing Corpus PAN-14-Blogs for age (Accuracy = 40.81) for gender Stylistic based for training Corpus is PAN-14-Blogs (Accuracy= 59.86). Content based technique for Training Corpus PAN-14-Social Media and testing Corpus PAN-14-Blogs for age (Accuracy = 40.81) for gender Content based technique training Corpus is PAN-14-Blogs (Accuracy= 59.86). Stylistic based technique for PAN-16- Twitter training corpus and testing corpus PAN-14-Blogs (Accuracy = 40.81) for age. Content based technique for gender where testing corpus is PAN-14-Blogs (61.22) for gender.Item Semantics Of Delegation In Federated Cloud(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2017) Numan Ahmad; FA15-RCS-005; LHR TP 6915; Dr. Adnan AhmadIn federated cloud different cloud service provider allow each other to use their unused resources at some cost when their own resources are not enough. Or they collaborate with each other by delegating different services which other CSP (cloud service providers) are not providing. Before delegating they need to get pre-approval from some administrative authority which is time consuming task and can create administrative bottleneck. There should be a mechanism in federated cloud that allow the user to delegate permissions without taking pre-approval i.e. Ad-hoc Delegation. In this thesis, we explored the semantic of decentralized ad-hoc delegation in federated cloud and proposed a framework that explains the essential characteristics for delegation in federated cloud. We also developed a model, based on framework, that explore the semantics of decentralized ah-hoc delegation in federated cloud. Moreover, the model is formulated through Colored Petri Net using CPN-tools to verify it’s working.Item Developing an Urdu Lemmatize(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2017) Muntaha Iqbal; FA15-RCS-010; LHR TP 6821Lemmatization is a process of obtaining root form of a given word. Lemmatizer is an important part of Natural Language Processing (NLP) toolkit and is essential for many NLP systems e.g. Information Retrieval (IR), plagiarism and text reuse detection, Information Extraction (IE), Machine Translation (MT), Word Sense Disambiguation (WSD) etc. Urdu is a widely spoken language in the world, but very less work has been done on developing basic NLP tools for this language, one of them is Urdu lemmatizer. Since Urdu is a morphologically rich language and has words with many inflectional and derivational forms, development of an efficient lemmatizer is a challenging task and it will be useful for many Urdu NLP applications. This research work aims to develop an efficient lemmatizer for Urdu language. For this purpose, we explored two main approaches: (1) dictionary lookup approach and (2) rule based approach. Note that we also tried combinations of these two approaches. For the first approach, we build a large dictionary of 131,539 entries. In this dictionary, 3,026 are exceptional words, 22,181 are multi-word expression, and 18,267 are Named Entities (NEs) for person and 79,418 are Named Entities (NEs) for locations. Remaining 8,647 are variants of most frequent 5,000 Urdu language words. For the second approach, we extracted a total of 215 rules (67 are prefix, 128 are postfixes) from raw Urdu text. We also developed a benchmark test dataset of 1,000 Urdu words to evaluate our proposed approaches for Urdu lemmatization task. The proposed lemmatizer was evaluated using six different approaches: (1) Dictionary Lookup Approach (DLA), (2) Rule Based Approach (RBA), (3) Dictionary (DIC) + Exceptional Word List (EWL) Approach, (4) Rule Based + Exceptional Word List Approach, (5) Rule Based + Final Dictionary Approach and (6) Final Dictionary + Rule Based Approach. Evaluation was carried out using Accuracy measure. Results showed that Dictionary + Rule Based Approach gave highest Accuracy of 82.9%. We also developed a prototype system, which takes an Urdu word as input and outputs its lemmaItem Software Measurements at Small and Medium Enterprises(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2017) Muhammad Noman; FA14-MSCS-018; LHR TP 6871; Dr. Ghulam RasoolSoftware measurement processes are crucial to recognize, assess, identify and measure software products and resources. However, the successful measurement processes implementation is a challenging task in small and medium enterprises (SMEs). The main goal of this thesis is to propose a measurement framework to assess the significant factors that may influence the successful measurement processes implementation in Software SMEs. A multi-method research technique is used to perform this research. A systematic mapping study is conducted to extract the significant factors, challenges, and limitations that may affect the successful implementation of measurement processes in software organizations. A search string is prepared and, applied to different databases (IEEE, ACM, Springer etc) to identify the relevant studies. In total, 24 primary studies are finalized after applying inclusion and exclusion criteria. All the primary studies are explored comprehensively and, identified success factors are categorized in seven major constructs. A measurement framework is proposed for adaptation of measurement processes in software SMEs based on the findings of the mapping study. A total of seven constructs (success factors) are defined in the proposed framework. The proposed framework is evaluated using an online survey. An online survey is conducted from the software professionals in software organizations of Pakistan. In total, 217 responses were collected and a data set is prepared. Structural equation modeling (SEM) technique is used to evaluate the data set. Statistical tools (AMOS and SPSS) are used for factor analysis and hypothesis testing. The statistical results shows that the proposed framework is acceptable due to satisfactory values of model fit indices and use of reliable items in the path diagram. In hypotheses testing, two of the seven constructs (External Forces, Management Commitment) are found to have a significant effect on successful measurement processes in software organizations. The proposed framework can be extended by adding more factors and it can also be verified in software industries of different countries.Item Development of Large Scale English-Urdu Machine Translation Corpus for Statistical and Neural Machine Translation Systems(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2019) Moodser Hussain; FA17-RCS-005; LHR TP 5623; Dr. Rao Muhammad Adeel NawabThe process of automatically converting the text of one natural language into an other is known as Machine Translation (MT). The two state-of-the-art machine translation techniques are Statistical Machine Translation (SMT) and Neural Ma chine Translation (NMT). In both MT techniques, a large-scale parallel aligned corpus of source & target language text is the most basic requirement to develop quality models. For the Urdu language, there are minimal parallel resources de veloped until now, and these existing resources are not enough to produce some quality translation systems. This research study is majorly focusing on: (1) the development of a large-scale semi-automatically aligned English-Urdu parallel corpus of more than 150k sen tences by collecting the data from different domains. (2) the compilation of large sized monolingual data for English and Urdu language by merging existing cor pora. (3) the development of a character-separated parallel corpus for supervised transliteration (by using Wikipedia titles) (4) the evaluation of impact generated by language model and monolingual corpus on translation quality (by using differ ent data and word-order in language models training). (5) development of Phrase based SMT (PB-SMT) and Recurrent Neural Network-based NMT (RNN-NMT) systems to evaluate and compare their performance on the proposed parallel cor pus. (6) the usage of supervised transliteration and ‘minimum error rate training’ to enhance the quality of SMT. To evaluate the performance of MT systems, ‘Bilingual Evaluation Understudy (BLEU)’ is used as an evaluation measure. The best-achieved scores for English to-Urdu translations are 37.39 and 32.70 by using PBSMT and RNN-NMT, re spectively. While for Urdu-to-English translations, PBSMT and RNN-NMT yield the best score of 28.20 and 30.63 respectivelyItem Mono- and Cross-lingual Semantic Word Similarity for Urdu Language(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2019) Ghazeefa Fatima; FA17-RCS-016; LHR TP 5778; Dr. Muhammad Salman KhanThe process of finding a degree of similarity between words is called semantic word similarity. Machine Translation and information retrieval are common and well distinguishes applications of Semantic Word Similarity. Because of its applications, finding similarity between words is a crucial task and it becomes an important problem of Natural Language Processing. Semantic Word Similarity has two basic types: (1) Mono-lingual Semantic Word Similarity (2) Cross-lingual Semantic Word Similarity. When both words are in the same language it’s called Mono-lingual Semantic Word Similarity, on the other hand, when the both words come across two different languages it’s called Cross-lingual Semantic Word Similarity. Semantic similarity can be detected at many levels i.e. word, sentence, phrase or document level. Urdu is the national language of Pakistan. 11 million people speak it as mother tongue and 105 million people as a second language [1]. According to Riaz [2], it has been estimated that the Urdu language has 300 million speakers in the overall world. The aim of this thesis is to develop mono- and cross-lingual semantic word similarity datasets and models for the Urdu language. The purpose of this study is threefold (1) Create Monolingual Semantic Word Similarity dataset (2) Develop Cross-lingual Semantic Word Similarity dataset (3) Create new models and evaluate pre-trained models using newly created gold standard mono- and cross-lingual corpora. In the first step, we will translate SemEval-500 dataset into Urdu language using Google, Bing and other translation resources. After translation, a linguistic expert verifies the translation and make corrections as required. The verified dataset then assigned to 12 annotators for annotation by following the guidelines provided in SemEval-2014 task 3 [3]. In the second step, the newly created dataset and X SemEval-500 dataset are combined to create a cross-lingual dataset. This dataset is also annotated by 12 annotators by following the same guidelines. We use intra-class correlation coefficient for inter annotator agreement of both mono- and cross-lingual dataset and got r=0.9 with p < .001 for each dataset. In the third step, we create new models for Monolingual semantic Word Similarity and Cross-lingual Semantic Word Similarity. We evaluate these newly created models and pre-trained models using our mono and cross-lingual gold standard datasets. We use person correlation as evaluation measure. In case of monolingual models, we got correlation of 0.54 for corpus based techniques. Fasttext pre-trained Cbow and Skip-gram models got correlation of 0.53 and 0.63 respectively. Ur-Mono based trained fasttext Cbow and Skip-gram models got maximum correlation of 0.63 and 0.67 respectively. For cross-lingual semantic word similarity, we applied Translation Plus Monoligual analysis technique, in which after translation of word pairs, multiple WordNet measures are applied. Path Similarity measure outperform other measures with correlation of 0.70 while using Google translation. In case of Bing translation, Path Similarity and Leacock measure outperform other measures with correlation of 0.71 and 0.71 respectivelyItem Multi-label Document Classification for Urdu Language(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2019) Madiha Rasool; FA17-RCS-010; LHR TP 5779; Dr. Muhammad Salman KhanMulti-label document classification is a conventional supervised machine learning task where each document is associated with multiple labels to correctly categorize the meaning of context. All natural languages present this computational linguistic task for a wide series of research domains, such as Natural Language processing, document categorization and text mining. Many benchmark corpora have been developed for multi-label classification task, but most are for English and other European languages. However, there is a dearth of annotated benchmark corpus and methods for the multi-label document classification task for the Urdu language. To fill this gap, we proposed a large benchmark corpus and methods for the multi-label classification of Urdu documents. The proposed corpus contains 600 documents from the field of journalism in the following domains: sports, national, foreign, showbiz and business. The proposed corpus has been manually annotated with USAS (UCREL Semantic Analysis System) scheme which provides 21 major labels and 232 sub labels for fine grained annotation. Each document in the proposed dataset was annotated with minimum two labels and maximum six labels to present a detailed analysis of the context as a supervised multi-label document classification task. To demonstrate how our proposed dataset can be used for the development and evaluation of Urdu multi-label document classification system, three different techniques were applied: (1) Multi-Word Expression (MWE) N-gram (2) Single-Word Expression (SWE) N-gram and (3) Most Frequent Word (MFW) N-gram. Seven different multi-label classifiers were applied on features extracted using three MWE, SWE and MFW methods. Evaluation was carried out using accuracy, exact match, hamming loss and F1 measures. Results show that the (F1 is 0.545) gives best results by using MFW technique on CC and MCC classifier