Ali Shariq Imran

Semantic Tags for Lecture Videos

In an effort to develop effective multi-media learning objects (MLO), we propose a framework to extract and associate semantic tags to temporally segmented instructional videos. These tags serve for the purpose of efficient indexing and retrieval system. We create these semantic tags from potential keywords extracted from the lecture transcript. The keywords undergo a series of refinement process to select few but meaningful set of tags. We use word similarity measure using visual ness and word sense disambiguation to select the tags from candidate keywords. These tags are finally associated with video segments in which they appear based on timestamp. Each video segment represents a key idea or a topic. We also evaluated the objective keyword selection criteria to subjective test with some interesting results.

SEMCON: Semantic and contextual objective metric

This paper proposes a new objective metric called the SEMCON to enrich existing concepts in domain ontologies for describing and organizing multimedia documents. The SEMCON model exploits the document contextually and semantically. The preprocessing module collects a document and partitions that into several passages. Then a morpho-syntatic analysis is performed on the partitioned passages and a list of nouns as part-of-speech (POS) is extracted. An observation matrix based on statistical features is then computed followed by computing the contextual score. The semantics is then incorporated by computing a semantic similarity score between two terms - term (noun) that is extracted from a document and term that already exists in the ontology as a concept Eventually, an overall objective score is computed by adding contextual score with semantic score. Subjective experiments are conducted to evaluate the performance of the SEMCON model. The model is compared with state-of-the-art tf*idf and χ2 (Chi square) using FI measure. The experimental results show that SEMCON achieved an improved accuracy of 10.64 % over the tf*idf and 13.04 % over the χ2.

HIP – A Technology-Rich and Interactive Multimedia Pedagogical Platform

Technology enhanced learning is a key part of learning and teaching in most of the higher education. It not only provides easy access to pedagogical content of interest with few clicks, but it is a great way to acquire knowledge at ones doorstep. Many universities are providing distance and blended education programs through eLearning platforms, learning management systems (LMS) and smart tools, along side traditional lectures for on campus students. The use of recorded lecture videos and audios, lecture notes, presentation slides, handouts, etc., are commonly used to disseminate knowledge via various eLearning platforms. While these platforms are a good way to reach out to off-campus students, they often lack a two-way communication between a student and a teacher, and the interactivity with the content. The lack of real-time interactivity and right communication channel make online courses less effective. To address this problem we propose the use of an intelligent pedagogical media called hyper interactive presenter (HIP).

A non-reference perceptual quality metric based on visual attention model for videos

The Human Visual System (HVS) tends to focus on specific regions of viewed images or video frames, this is done effortlessly, instantly and unconsciously. These are called salient regions and form a saliency map, which could be used to improve a number of image and video processing techniques. In this paper, we propose a novel non-reference objective video quality metric based on the saliency map to improve the estimation of the perceived video quality. This metric estimates the degree of blur and blockiness in each video frame from the impaired video only, and uses it with the saliency map to derive a weighting function. The latter is used to modulate the contribution of the pixel differences to the final quality score. The salient regions of the videos are automatically computed using our video saliency model. A psychophysical experiment is conducted to estimate the perceived quality of the impaired videos. The results of this subjective test are compared to the scores obtained with the proposed objective metric. The objective and subjective scores are found to be highly correlated, which shows that our metric correctly estimates the perceived quality of a video.

SEMCON: A Semantic and Contextual Objective Metric for Enriching Domain Ontology Concepts

This paper presents a novel concept enrichment objective metric combining contextual and semantic information of terms extracted from the domain documents. The proposed metric is called SEMCON which stands for semantic and contextual objective metric. It employs a hybrid learning approach utilizing functionalities from statistical and linguistic ontology learning techniques. The metric also introduced for the first time two statistical features that have shown to improve the overall score ranking of highly relevant terms for concept enrichment. Subjective and objective experiments are conducted in various domains. Experimental results F1 from computer domain show that SEMCON achieved better performance in contrast to tf*idf, and LSA methods, with 12.2%, 21.8%, and 24.5% improvement over them respectively. Additionally, an investigation into how much each of contextual and semantic components contributes to the overall task of concept enrichment is conducted and the obtained results suggest that a balanced weight gives the best performance.

An Improved Concept Vector Space Model for Ontology Based Classification

This paper proposes an improved concept vector space (ICVS) model which takes into account the importance of ontology concepts. Concept importance shows how important a concept is in an ontology. This is reflected by the number of relations a concept has to other concepts. Concept importance is computed automatically by converting the ontology into a graph initially and then employing one of the Markov based algorithms. Concept importance is then aggregated with concept relevance which is computed using the frequency of concept occurrences in the dataset. In order to demonstrate the applicability of our proposed model and to validate its efficacy, we conducted experiments on document classification using concept based vector space model. The dataset used in this paper consists of 348 documents from the funding domain. The results show that the proposed model yields higher classification accuracy comparing to traditional concept vector space (CVS) model, ultimately giving better document classification performance. We also used different classifiers in order to check for the classification accuracy. We tested CVS and ICVS on Naive Bayes and Decision Tree classifiers and the results show that the classification performance in terms of F1 measure is improved when ICVS is used on both classifiers.

Blackboard content classification for lecture videos

In this paper, we propose a novel approach to understand the high level semantics of instructional video by identifying mid-level features from the lecture content. The lecture content in instructional videos can be divided into text, equations and figures. In unscripted lecture video, these visual contents can be useful visual cues to understand the high level semantics. For example, it could help us achieve efficient structuring and indexing of multimedia learning material. To understand the high level semantics from the content itself is however not a trivial task. To this end, we propose visual content classification system (VCCS) for multimedia lecture videos. We propose hybrid approach by combining support vector machine (SVM) and optical character recognition (OCR) to classify visual content into figures, text and equations. The initial results show overall classification accuracy above 85 percent.

Towards Understanding the MOOC Trend: Pedagogical Challenges and Business Opportunities

Undoubtedly, MOOCs have the potential to introduce a new wave of technological innovation in learning. In spite of the great interest among the educators and the general public MOOCs have generated, there are some challenges that MOOCs might face when it comes to examining and determining the best pedagogical approaches that MOOCs should be based on. Moreover, MOOCs are facing also challenges towards building a consistent business model. The main objective of this paper is to shed more light on the MOOCs phenomenon, by analyzing and discussing some benefits and drawbacks of MOOCs from the pedagogical and business perspectives. Therefore, in this paper we provide an in-depth analysis of MOOCs challenges and opportunities towards determining pedagogical innovations. We also analyze current trends of MOOCs expansion to create new educational markets by overpassing the bricks-and-mortar educational institutions. To do so, we conduct a SWOT analysis on MOOCs. Finally, we provide possible directions and insights for future research to better understand how MOOCs can be improved to lead to greater innovations in the higher education landscape to answer the needs of a knowledge-based economy.

Lecture content classification tool

In this paper, we address the problem of content classification for chalkboard images. Unlike document images, classifying chalkboard content into different categories is a challenging task. The task gets even tougher with varying handwriting styles and arbitrary drawings. We therefore, propose a tool with a set of functions to distinguish equations from text and figures. A hybrid solution is proposed, consisting of, the state of the art support vector machine (SVM) and optical character recognition (OCR) for this purpose. Prior to feature extraction and classification, some preprocessing steps are performed to remove noise and to enhance the chalk contrast. Our initial experiment shows promising results of above 85% accuracy for chalkboard images. We later on applied our algorithm to MNIST database of handwritten digits, our created handwritten lower-case and upper-case characters and basic mathematical operators and obtained 96% accuracy.

A visual attention based reference free perceptual quality metric

In this paper we study image distortions and impairments that affect the perceived quality of blackboard lectures images. We also propose a novel reference free image quality evaluation metric that correlates well with the perceived image quality. The perceived quality of images of blackboard lecture contents is mostly affected by the presence of noise, blur and compression artifacts. Therefore, the importance of these impairments are estimated and used in the proposed quality metric. In this context there is no reference, distortion free, image; thus we propose to evaluate the image perceived quality based on the features extracted from its content. The proposed objective metric estimates the blockliness and blur artifacts in the salient regions of the lecture images. The use of a visual saliency models allows the metric to focus only on the distortions in perceptually important regions of the images; hence mimicking the human visual system in its perception of image quality. The experimental results show a very good correlation between the objective quality scores obtained by our metric and the mean opinion scores obtained via psychophysical experiments. The obtained objective scores are also compared to those of the PSNR.