A Novel Approach to Multimedia Ontology Engineering for Automated Reasoning over Audiovisual LOD Datasets

Multimedia reasoning, which is suitable for, among others, multimedia content analysis and high-level video scene interpretation, relies on the formal and comprehensive conceptualization of the represented knowledge domain. However, most multimedia ontologies are not exhaustive in terms of role definitions, and do not incorporate complex role inclusions and role interdependencies. In fact, most multimedia ontologies do not have a role box at all, and implement only a basic subset of the available logical constructors. Consequently, their application in multimedia reasoning is limited. To address the above issues, VidOnt, the very first multimedia ontology with SROIQ(D) expressivity and a DL-safe ruleset has been introduced for next-generation multimedia reasoning. In contrast to the common practice, the formal grounding has been set in one of the most expressive description logics, and the ontology validated with industry-leading reasoners, namely HermiT and FaCT++. This paper also presents best practices for developing multimedia ontologies, based on my ontology engineering approach

Towards robust and reliable multimedia analysis through semantic integration of services

Thanks to ubiquitous Web connectivity and portable multimedia devices, it has never been so easy to produce and distribute new multimedia resources such as videos, photos, and audio. This ever-increasing production leads to an information overload for consumers, which calls for efficient multimedia retrieval techniques. Multimedia resources can be efficiently retrieved using their metadata, but the multimedia analysis methods that can automatically generate this metadata are currently not reliable enough for highly diverse multimedia content. A reliable and automatic method for analyzing general multimedia content is needed. We introduce a domain-agnostic framework that annotates multimedia resources using currently available multimedia analysis methods. By using a three-step reasoning cycle, this framework can assess and improve the quality of multimedia analysis results, by consecutively (1) combining analysis results effectively, (2) predicting which results might need improvement, and (3) invoking compatible analysis methods to retrieve new results. By using semantic descriptions for the Web services that wrap the multimedia analysis methods, compatible services can be automatically selected. By using additional semantic reasoning on these semantic descriptions, the different services can be repurposed across different use cases. We evaluated this problem-agnostic framework in the context of video face detection, and showed that it is capable of providing the best analysis results regardless of the input video. The proposed methodology can serve as a basis to build a generic multimedia annotation platform, which returns reliable results for diverse multimedia analysis problems. This allows for better metadata generation, and improves the efficient retrieval of multimedia resources

Differences in Increasing The Ability of Reasoning in Problem Based Learning Model and Computer-Based Group Investigation

This study was conducted to determine whether there were significant differences between students 'reasoning abilities taught using the PBL model and with the Computer Multimedia Assisted GI, and to determine whether or not there was an influence of interaction between learning models and students' initial abilities on mathematical reasoning abilities . This research is a comparative research with treatment. The population in this study were students of MTs Al-Washliyah 28 Sergai. The variables in this study are mathematical reasoning abilities by collecting data using a questionnaire and tests of mathematical reasoning abilities as well as student achievement tests. The analysis prerequisite test uses the Kolmogorov-Smirnov normality test with a sig (2-tailed) value for the Computer Multimedia Assisted PBL class is 0.200*>0.005 and the sig (2-tailed) value for the computer multimedia-assisted GI class is 0.132>0.005. Hypothesis testing uses 2 path analysis of variance (Anova) with SPSS aids. The results showed that there were significant differences between students' reasoning abilities taught with the PBL and with the Computer Multimedia Assisted GI with a sig value of 0,0000.05

Video semantic content analysis framework based on ontology combined MPEG-7

The rapid increase in the available amount of video data is creating a growing demand for efficient methods for understanding and managing it at the semantic level. New multimedia standard, MPEG-7, provides the rich functionalities to enable the generation of audiovisual descriptions and is expressed solely in XML Schema which provides little support for expressing semantic knowledge. In this paper, a video semantic content analysis framework based on ontology combined MPEG-7 is presented. Domain ontology is used to define high level semantic concepts and their relations in the context of the examined domain. MPEG-7 metadata terms of audiovisual descriptions and video content analysis algorithms are expressed in this ontology to enrich video semantic analysis. OWL is used for the ontology description. Rules in Description Logic are defined to describe how low-level features and algorithms for video analysis should be applied according to different perception content. Temporal Description Logic is used to describe the semantic events, and a reasoning algorithm is proposed for events detection. The proposed framework is demonstrated in sports video domain and shows promising results

Pengembangan Multimedia Pembelajaran “Scraperat” Untuk Meningkatkan Kemampuan Penalaran Matematis Siswa Kelas IX

Technology has a big role in education. Education is required to be able to keep abreast of technological developments. In this case, teachers must be able to apply technology-based learning, one of which is in learning mathematics. The findings from the observations also show that the mathematical reasoning abilities of class IX A students at SMP Negeri 1 Mertoyudan are still in the low category. Therefore, innovation is needed in learning, one of which is using technology in learning. The effort that can be done is to develop a multimedia Scraperat to improve students' mathematical reasoning abilities. This type of research is research and development with the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation). the conclusion obtained is that multimedia learning scraperat to improve students' mathematical reasoning abilities is stated to be valid, practical, and effective. Advice for readers is to always be motivated to provide products that can improve the quality of learning and education

Using a lightweight multimedia content model for semantic annotation

In this paper we discuss the use of a multimedia content model for automatic extraction of semantic metadata from multimedia content. We developed a modular and extensible framework to model the content feature of multimedia data and also describe the way it can be integrated with other existing vocabularies. The goal of this model is to generate sufficient understanding of media content, its context and its relation to domain knowledge in order to perform multimedia reasoning. We implemented a tool that analyzes and links low-level descriptions to higher-level domain specific semantic concepts by means of statistical learning and clustering analysis. Experimental result shows the approach performs well in visual concept prediction in the image which can be further augmented with other information sources such as context text and or audio source

Video semantic content analysis based on ontology

The rapid increase in the available amount of video data is creating a growing demand for efficient methods for understanding and managing it at the semantic level. New multimedia standards, such as MPEG-4 and MPEG-7, provide the basic functionalities in order to manipulate and transmit objects and metadata. But importantly, most of the content of video data at a semantic level is out of the scope of the standards. In this paper, a video semantic content analysis framework based on ontology is presented. Domain ontology is used to define high level semantic concepts and their relations in the context of the examined domain. And low-level features (e.g. visual and aural) and video content analysis algorithms are integrated into the ontology to enrich video semantic analysis. OWL is used for the ontology description. Rules in Description Logic are defined to describe how features and algorithms for video analysis should be applied according to different perception content and low-level features. Temporal Description Logic is used to describe the semantic events, and a reasoning algorithm is proposed for events detection. The proposed framework is demonstrated in a soccer video domain and shows promising results

A spatial/temporal relation computing technology for multimedia presentation designs

[[abstract]]Relations among temporal intervals can be used to assist the automatic generation of multimedia presentations. In this paper, we analyze the domains of interval temporal relations. A set of algorithms is proposed to derive reasonable relations between intervals. Possible conflicts in the user specification are firstly detected and eliminated. Our mechanism then constructs partial order relations among temporal intervals before the presentation time chart is built. The algorithm is extended for objects in an arbitrary n-dimensional space. Thus, presentation layouts in a 2-D space, or Virtual Reality object representations in a 3-D space can be constructed. We use our algorithms to design a reasoning system that generates the schedule and layout of multimedia presentations. The main contributions of this paper are in its theoretical analysis of interval relation composition and a systematic approach for automation. We hope that, with our analysis and algorithms, the knowledge underlying temporal interval relations can be used in many computer applications, especially those in multimedia computing.[[conferencetype]]國際[[conferencedate]]19980106~19980109[[booktype]]紙本[[conferencelocation]]Kohala Coast, HI, US

Post processing of multimedia information - concepts, problems, and techniques

Currently, most research work on multimedia information processing is focused on multimedia information storage and retrieval, especially indexing and content-based access of multimedia information. We consider multimedia information processing should include one more level-post-processing. Here &quot;post-processing&quot; means further processing of retrieved multimedia information, which includes fusion of multimedia information and reasoning with multimedia information to reach new conclusions. In this paper, the three levels of multimedia information processing storage, retrieval, and post-processing- are discussed. The concepts and problems of multimedia information post-processing are identified. Potential techniques that can be used in post-processing are suggested, By highlighting the problems in multimedia information post-processing, hopefully this paper will stimulate further research on this important but ignored topic.<br /

Differences in Statistical Reasoning Abilities through Behavioral-Cognitive Combinations of Videos and Formative Assessments in Undergraduate Statistics Courses

This study evaluated whether significant differences in statistical reasoning abilities exist for completers of short online instructional videos and formative quizzes for students in undergraduate introductory statistics courses. Data for the study were gathered during the Fall 2013 semester at a community college in Northeast Tennessee. Computer-based pedagogical tools can promote improved conceptual reasoning ability (Trumpower & Sarwar, 2010; Van der Merwe, 2012). Additionally, prior research demonstrated a significant relationship between formative quiz access and student achievement (Stull, Majerich, Bernacki, Varnum, & Ducette, 2011; Wilson, Boyd, Chen, & Jamal, 2011), as well as multimedia object access and student achievement (Bliwise, 2005; Miller, 2013). Four research questions were used to guide the study. A series of analysis of variance (ANOVA) statistical procedures was used to analyze the data. Findings indicated no significant differences in statistical reasoning abilities between students who were provided access to supplemental online instructional videos and formative quizzes and students who were not provided access. Moreover, statistical reasoning abilities did not differ significantly based upon number of quizzes successfully completed, average number of quiz attempts, or number of videos accessed