Iterative Usability Evaluation for an Online Educational Web Portal

Online education is a popular paradigm for promoting continuing education for adult learners. However, only a handful of studies have addressed usability issues in the online education environment. Particularly, few studies have integrated the multifaceted usability evaluation into the lifecycle of developing such an environment. This paper will show the integration of usability evaluation into the development process of an online education center. Multifaceted usability evaluation methods were applied at four different stages of the MU Extension web portal’s development. These methods were heuristic evaluation, focus group interview and survey, think-aloud interviewing, and multiple-user simultaneous testing. The results of usability studies at each stage enhanced the development team’s understanding of users’ difficulties, needs, and wants, which served to guide web developers’ subsequent decisions.

Content Adaptation in Mobile Learning Environments

In this paper, the authors present their proposal for adaptation of educational contents of learning objects to a particular mobile device and a specific learner. Content adaptation in mobile learning objects implies user adaptation and device adaptation, and requires additional metadata categories in comparison with SCORM 2004. This learning object content model, ALMA (A Learning content Model Adaptation), inherits from the SCORM standard a subset of metadata categories, and extends it with three top level metadata categories for content adaptation, i.e., Knowledge, Use, and Mobile Device Requirements (Castillo & Ayala, 2008). For user adaptation, the authors developed NORIKO (NOn-monotonic Reasoning for Intelligent Knowledge awareness and recommendations On the move), a belief system based on DLV, a programming system based on Answer Set Programming paradigm. For device adaptation the authors designed CARIME (Content Adapter of Resources In Mobile learning Environments), which uses transcoding and transrating to adapt media content to suit the device characteristics.

An Image Clustering and Feedback-Based Retrieval Framework

Most existing object-based image retrieval systems are based on single object matching, with its main limitation being that one individual image region (object) can hardly represent the user’s retrieval target, especially when more than one object of interest is involved in the retrieval. Integrated Region Matching (IRM) has been used to improve the retrieval accuracy by evaluating the overall similarity between images and incorporating the properties of all the regions in the images. However, IRM does not take the user’s preferred regions into account and has undesirable time complexity. In this article, we present a Feedback-based Image Clustering and Retrieval Framework (FIRM) using a novel image clustering algorithm and integrating it with Integrated Region Matching (IRM) and Relevance Feedback (RF). The performance of the system is evaluated on a large image database, demonstrating the effectiveness of our framework in catching users’ retrieval interests in object-based image retrieval.

Multimedia Databases and Data Management

The exponential growth of the technological advancements has resulted in high-resolution devices, such as digital cameras, scanners, monitors, and printers, which enable the capturing and displaying of multimedia data in high-density storage devices. Furthermore, more and more applications need to live with multimedia data. However, the gap between the characteristics of various media types and the application requirements has created the need to develop advanced techniques for multimedia data management and the extraction of relevant information from multimedia databases. Though many research efforts have been devoted to the areas of multimedia databases and data management, it is still far from maturity. The purpose of this article is to discuss how the existing techniques, methodologies, and tools addressed relevant issues and challenges to enable a better understanding in multimedia databases and data management. The focuses include: (1) how to develop a formal structure that can be used to capture the distinguishing content of the media data in a multimedia database (MMDB) and to form an abstract space for the data to be queried; (2) how to develop advanced content analysis and retrieval techniques that can be used to bridge the gaps between the semantic meaning and low-level media characteristics to improve multimedia information retrieval; and (3) how to develop query mechanisms that can handle complex spatial, temporal, and/or spatio-temporal relationships of multimedia data to answer the imprecise and incomplete queries issued to an MMDB.

Robust Duplicate Detection of 2D and 3D Objects

In this paper, the authors analyze their graph-based approach for 2D and 3D object duplicate detection in still images. A graph model is used to represent the 3D spatial information of the object based on the features extracted from training images to avoid explicit and complex 3D object modeling. Therefore, improved performance can be achieved in comparison to existing methods in terms of both robustness and computational complexity. Different limitations of this approach are analyzed by evaluating performance with respect to the number of training images and calculation of optimal parameters in a number of applications. Furthermore, effectiveness of object duplicate detection algorithm is measured over different object classes. The authors’ method is shown to be robust in detecting the same objects even when images with objects are taken from different viewpoints or distances.

Building Tag-Aware Groups for Music High-Order Ranking and Topic Discovery

In popular music information retrieval systems, users have the opportunity to tag musical objects to express their personal preferences, thus providing valuable insights about the formulation of user groups/communities. In this article, the authors focus on the analysis of social tagging data to reveal coherent groups characterized by their users, tags and music objects (e.g., songs and artists), which allows for the expression of discovered groups in a multi-aspect way. For each group, this study reveals the most prominent users, tags, and music objects using a generalization of the popular web-ranking concept in the social data domain. Experimenting with real data, the authors’ results show that each Tag-Aware group corresponds to a specific music topic, and additionally, a three way ranking analysis is performed inside each group. Building Tag-Aware groups is crucial to offer ways to add structure in the unstructured nature of tags.

Board Game Supporting Learning Prim’s Algorithm and Dijkstra’s Algorithm

The concept of minimum spanning tree algorithms in data structure is difficult for students to learn and to imagine without practice. Usually, learners need to diagram the spanning trees with pen to realize how the minimum spanning tree algorithm works. In this paper, the authors introduce a competitive board game to motivate students to learn the concept of minimum spanning tree algorithms. They discuss the reasons why it is beneficial to combine graph theories and board game for the Dijkstra and Prim minimum spanning tree theories. In the experimental results, this paper demonstrates the board game and examines the learning feedback for the mentioned two graph theories. Advantages summarizing the benefits of combining the graph theories with board game are discussed.

Fast Caption Alignment for Automatic Indexing of Audio

For large archives of audio media, just as with text archives, indexing is important for allowing quick and accurate searches. Similar to text archives, audio archives can use text for indexing. Generating this text requires using transcripts of the spoken portions of the audio. From them, an alignment can be made that allows users to search for specific content and immediately view the content at the position where the search terms were spoken. Although previous research has addressed this issue, the solutions align the transcripts only in real-time or greater. In this paper, the authors propose AutoCap. It is capable of producing accurate audio indexes in faster than real-time for archived audio and in real-time for live audio. In most cases it takes less than one quarter the original duration for archived audio. This paper discusses the architecture and evaluation of the AutoCap project as well as two of its applications.

Synthetic Video Generation for Evaluation of Sprite Generation

Sprite generation is the process of aligning, warping, and blending of pixels that belong to an object in a video. The evaluation of the correctness of a sprite is usually accomplished by a combination of objective and subjective evaluations. Availability of ground-truth image would help mere objective evaluation. In this paper, the authors present video generation from an image based on various camera motion parameters to be used as ground-truth for the sprite evaluation. This paper introduces a framework for evaluation of sprite generation algorithms. Experiments under the proposed framework were performed on the synthetic videos of different camera motion patterns to reveal the components of the sprite generation algorithm to be improved.

Ontology Instance Matching Based MPEG-7 Resource Integration

Heterogeneous multimedia contents are annotated by a sharable formal conceptualization, often called ontology, and these contents, regardless of their media, become sharable resources/instances. Integration of the sharable resources and acquisition of diverse knowledge is getting researchers’ attention at a rapid pace. In this regard, MPEG-7 standard convertible to semantic Resource Description Framework (RDF) evolves for containing structured data and knowledge sources. In this paper, the authors propose an efficient approach to integrate the multimedia resources annotated by the standard of MPEG-7 schema using ontology instance matching techniques. MPEG-7 resources are usually specified explicitly by their surrounding MPEG-7 schema entities, e.g., concepts and properties, in conjunction with other linked resources. Therefore, resource integration needed schema matching as well. In this approach, the authors obtained the schema matching using their scalable ontology alignment algorithm and collected the semantically linked resources, referred to as the Semantic Link Cloud (SLC) collectively for each of the resources. Techniques were addressed to solve several data heterogeneity: value transformation, structural transformation and logical transformation. These experiments show the strength and efficiency of the proposed matching approach.