TOWARDS BUILDING INTELLIGENT COLLABORATIVE PROBLEM SOLVING SYSTEMS

Historically, Collaborative Problem Solving (CPS) systems were more focused on Human Computer Interaction (HCI) issues, such as providing good experience of communication among the participants. Whereas, Intelligent Tutoring Systems (ITS) focus both on HCI issues as well as leveraging Artificial Intelligence (AI) techniques in their intelligent agents. This dissertation seeks to minimize the gap between CPS systems and ITS by adopting the methods used in ITS researches. To move towards this goal, we focus on analyzing interactions with textual inputs in online learning systems such as DeepTutor and Virtual Internships (VI) to understand their semantics and underlying intents. In order to address the problem of assessing the student generated short text, this research explores firstly data driven machine learning models coupled with expert generated as well as general text analysis features. Secondly it explores method to utilize knowledge graph embedding for assessing student answer in ITS. Finally, it also explores a method using only standard reference examples generated by human teacher. Such method is useful when a new system has been deployed and no student data were available.To handle negation in tutorial dialogue, this research explored a Long Short Term Memory (LSTM) based method. The advantage of this method is that it requires no human engineered features and performs comparably well with other models using human engineered features.Another important analysis done in this research is to find speech acts in conversation utterances of multiple players in VI. Among various models, a noise label trained neural network model performed better in categorizing the speech acts of the utterances.The learners\u27 professional skill development in VI is characterized by the distribution of SKIVE elements, the components of epistemic frames. Inferring the population distribution of these elements could help to assess the learners\u27 skill development. This research sought a Markov method to infer the population distribution of SKIVE elements, namely the stationary distribution of the elements.While studying various aspects of interactions in our targeted learning systems, we motivate our research to replace the human mentor or tutor with intelligent agent. Introducing intelligent agent in place of human helps to reduce the cost as well as scale up the system

9th Annual Symposium of School of Science, Engineering and Health

This symposium continues a strong tradition of annual events designed to showcase student and faculty innovation, creativity and productivity. However, it is the first that we are holding as a newly re-organized School of Science, Engineering and Health

Representation Learning for Words and Entities

This thesis presents new methods for unsupervised learning of distributed representations of words and entities from text and knowledge bases. The first algorithm presented in the thesis is a multi-view algorithm for learning representations of words called Multiview Latent Semantic Analysis (MVLSA). By incorporating up to 46 different types of co-occurrence statistics for the same vocabulary of english words, I show that MVLSA outperforms other state-of-the-art word embedding models. Next, I focus on learning entity representations for search and recommendation and present the second method of this thesis, Neural Variational Set Expansion (NVSE). NVSE is also an unsupervised learning method, but it is based on the Variational Autoencoder framework. Evaluations with human annotators show that NVSE can facilitate better search and recommendation of information gathered from noisy, automatic annotation of unstructured natural language corpora. Finally, I move from unstructured data and focus on structured knowledge graphs. I present novel approaches for learning embeddings of vertices and edges in a knowledge graph that obey logical constraints.Comment: phd thesis, Machine Learning, Natural Language Processing, Representation Learning, Knowledge Graphs, Entities, Word Embeddings, Entity Embedding

Image Annotation and Topic Extraction Using Super-Word Latent Dirichlet

This research presents a multi-domain solution that uses text and images to iteratively improve automated information extraction. Stage I uses local text surrounding an embedded image to provide clues that help rank-order possible image annotations. These annotations are forwarded to Stage II, where the image annotations from Stage I are used as highly-relevant super-words to improve extraction of topics. The model probabilities from the super-words in Stage II are forwarded to Stage III where they are used to refine the automated image annotation developed in Stage I. All stages demonstrate improvement over existing equivalent algorithms in the literature

Deep Learning for Semantic Video Understanding

The field of computer vision has long strived to extract understanding from images and videos sequences. The recent flood of video data along with massive increments in computing power have provided the perfect environment to generate advanced research to extract intelligence from video data. Video data is ubiquitous, occurring in numerous everyday activities such as surveillance, traffic, movies, sports, etc. This massive amount of video needs to be analyzed and processed efficiently to extract semantic features towards video understanding. Such capabilities could benefit surveillance, video analytics and visually challenged people. While watching a long video, humans have the uncanny ability to bypass unnecessary information and concentrate on the important events. These key events can be used as a higher-level description or summary of a long video. Inspired by the human visual cortex, this research affords such abilities in computers using neural networks. Useful or interesting events are first extracted from a video and then deep learning methodologies are used to extract natural language summaries for each video sequence. Previous approaches of video description either have been domain specific or use a template based approach to fill detected objects such as verbs or actions to constitute a grammatically correct sentence. This work involves exploiting temporal contextual information for sentence generation while working on wide domain datasets. Current state-of- the-art video description methodologies are well suited for small video clips whereas this research can also be applied to long sequences of video. This work proposes methods to generate visual summaries of long videos, and in addition proposes techniques to annotate and generate textual summaries of the videos using recurrent networks. End to end video summarization immensely depends on abstractive summarization of video descriptions. State-of- the-art neural language & attention joint models have been used to generate textual summaries. Interesting segments of long video are extracted based on image quality as well as cinematographic and consumer preference. This novel approach will be a stepping stone for a variety of innovative applications such as video retrieval, automatic summarization for visually impaired persons, automatic movie review generation, video question and answering systems

ENHANCING LITERATURE REVIEW METHODS - TOWARDS MORE EFFICIENT LITERATURE RESEARCH WITH LATENT SEMANTIC INDEXING

Nowadays, the facilitated access to increasing amounts of information and scientific resources means that more and more effort is required to conduct comprehensive literature reviews. Literature search, as a fundamental, complex, and time-consuming step in every literature research process, is part of many established scientific methods. However, it is still predominantly supported by search techniqus based on conventional term-matching methods. We address the lack of semantic approaches in this context by proposing an enhancement of established literature review methods. For this purpose, we followed design science research (DSR) principles in order to develop artifacts and implement a prototype of our Tool for Semantic Indexing and Similarity Quries (TSISQ) based on the core concepts of latent semantic indexing (LSI). Its applicability is demonstrated and evaluated in a case study. Results indicate that the presented approach can help save valuable time in finding basic literature in a desired research field or increasing the comprehensiveness of a review by efficiently identifying sources that otherwise would not have been taken into account. The target audience for our findings includes researchers who need to efficiently gain an overview of a specific research field, deepen their knowledge or refine the theoretical foundations of their research

Cloud service discovery and analysis: a unified framework

Over the past few years, cloud computing has been more and more attractive as a new computing paradigm due to high flexibility for provisioning on-demand computing resources that are used as services through the Internet. The issues around cloud service discovery have considered by many researchers in the recent years. However, in cloud computing, with the highly dynamic, distributed, the lack of standardized description languages, diverse services offered at different levels and non-transparent nature of cloud services, this research area has gained a significant attention. Robust cloud service discovery approaches will assist the promotion and growth of cloud service customers and providers, but will also provide a meaningful contribution to the acceptance and development of cloud computing. In this dissertation, we have proposed an automated cloud service discovery approach of cloud services. We have also conducted extensive experiments to validate our proposed approach. The results demonstrate the applicability of our approach and its capability of effectively identifying and categorizing cloud services on the Internet. Firstly, we develop a novel approach to build cloud service ontology. Cloud service ontology initially is built based on the National Institute of Standards and Technology (NIST) cloud computing standard. Then, we add new concepts to ontology by automatically analyzing real cloud services based on cloud service ontology Algorithm. We also propose cloud service categorization that use Term Frequency to weigh cloud service ontology concepts and calculate cosine similarity to measure the similarity between cloud services. The cloud service categorization algorithm is able to categorize cloud services to clusters for effective categorization of cloud services. In addition, we use Machine Learning techniques to identify cloud service in real environment. Our cloud service identifier is built by utilizing cloud service features extracted from the real cloud service providers. We determine several features such as similarity function, semantic ontology, cloud service description and cloud services components, to be used effectively in identifying cloud service on the Web. Also, we build a unified model to expose the cloud service’s features to a cloud service search user to ease the process of searching and comparison among a large amount of cloud services by building cloud service’s profile. Furthermore, we particularly develop a cloud service discovery Engine that has capability to crawl the Web automatically and collect cloud services. The collected datasets include meta-data of nearly 7,500 real-world cloud services providers and nearly 15,000 services (2.45GB). The experimental results show that our approach i) is able to effectively build automatic cloud service ontology, ii) is robust in identifying cloud service in real environment and iii) is more scalable in providing more details about cloud services.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 201