Symbolic quality control for multimedia applications

We present a fine grain quality control method for multimedia applications. The method takes as input an application software composed of actions. The execution times of actions are unknown increasing functions of quality level parameters. The method allows the construction of a Controller which computes adequate action schedules and corresponding quality levels, so as to meet QoS requirements for a given platform. These include requirements for safety (action deadlines are met) as well optimality (maximization and smoothness of quality levels). The Controller consists of a Quality Manager and a Scheduler. For each action, the Controller uses a quality management policy for choosing a schedule and quality levels meeting the QoS requirements. The schedule is selected amongst a set of optimal schedules computed by the Scheduler. We extend and improve results of previous papers providing a solid theoretical basis for designing and implementing the Controller. We propose a symbolic quality management method using speed diagrams, a representation of the controlled system’s dynamics. Instead of numerically computing a quality level for each action, the Quality Manager changes action quality levels based on the knowledge of constraints characterizing control relaxation regions. These are sets of states in which quality management for a given number of computation steps can be relaxed without degrading quality. We study techniques for efficient computation of optimal schedules. We present experimental results including the implementation of the method and benchmarks for an MPEG4 video encoder. The benchmarks show drastic performance improvement for controlled quality with respect to constant quality. They also show that symbolic quality management allows significant reduction of the overhead with respect to numeric quality management. Finally, using optimal schedules can lead to considerable performance gains.     

An intelligent cloud-based data processing broker for mobile e-health multimedia applications

Mobile e-health applications provide users and healthcare practitioners with an insightful way to check users/patients’ status and monitor their daily calorie intake. Mobile e-health applications provide users and healthcare practitioners with an insightful way to check users/patients’ status and monitor their daily activities. This paper proposes a cloud-based mobile e-health calorie system that can classify food objects in the plate and further compute the overall calorie of each food object with high accuracy. The novelty in our system is that we are not only offloading heavy computational functions of the system to the cloud, but also employing an intelligent cloud-broker mechanism to strategically and efficiently utilize cloud instances to provide accurate and improved time response results. The broker system uses a dynamic cloud allocation mechanism that takes decisions on allocating and de-allocating cloud instances in real-time for ensuring the average response time stays within a predefined threshold. In this paper, we further demonstrate various scenarios to explain the workflow of the cloud components including: segmentation, deep learning, indexing food images, decision making algorithms, calorie computation, scheduling management as part of the proposed cloud broker model. The implementation results of our system showed that the proposed cloud broker results in a 45% gain in the overall time taken to process the images in the cloud. With the use of dynamic cloud allocation mechanism, we were able to reduce the average time consumption by 77.21% when 60 images were processed in parallel.     

Ontology and rule-based natural language processing approach for interpreting textual regulations on underground utility infrastructure

The nation’s massive underground utility infrastructure must comply with a multitude of regulations. The regulatory compliance checking of underground utilities requires an objective and consistent interpretation of the regulations. However, utility regulations contain a variety of domain-specific terms and numerous spatial constraints regarding the location and clearance of underground utilities. It is challenging for the interpreters to understand both the domain and spatial semantics in utility regulations. To address the challenge, this paper adopts an ontology and rule-based Natural Language Processing (NLP) framework to automate the interpretation of utility regulations – the extraction of regulatory information and the subsequent transformation into logic clauses. Two new ontologies have been developed. The urban product ontology (UPO) is domain-specific to model domain concepts and capture domain semantics on top of heterogeneous terminologies in utility regulations. The spatial ontology (SO) consists of two layers of semantics – linguistic spatial expressions and formal spatial relations – for better understanding the spatial language in utility regulations. Pattern-matching rules defined on syntactic features (captured using common NLP techniques) and semantic features (captured using ontologies) were encoded for information extraction. The extracted information elements were then mapped to their semantic correspondences via ontologies and finally transformed into deontic logic (DL) clauses to achieve the semantic and logical formalization. The approach was tested on the spatial configuration-related requirements in utility accommodation policies. Results show it achieves a 98.2% precision and a 94.7% recall in information extraction, a 94.4% precision and a 90.1% recall in semantic formalization, and an 83% accuracy in logical formalization.     

KAIST image computing system (KICS): A parallel architecture for real-time multimedia data processing

An efficient parallel architecture is proposed for high-performance multimedia data processing using multiple multimedia video processors (MVP; TMS320C80), which are fully programmable general digital signal processors (DSP). This paper describes several requirements for a multimedia data processing system and the system architecture of an image computing system called the KAIST Image Computing System (KICS). The performance of the KICS is evaluated in terms of its I/O bandwidth and the execution time for some image processing functions. An application of the KICS to the real-time Moving Picture Expert Group 2 (MPEG-2) encoder is introduced. The programmability and the high-speed data-access capability of the KICS are its most important features as a high-performance system for real-time multimedia data processing.     

AutoBrief: a multimedia presentation system for assisting data analysis

We present an approach to generating multimedia presentations that integrates hierarchical planning to achieve communicative goals, and task-based graphic design. A planning process decomposes domain-specific goals to domain-independent goals, which in turn are realized by media-specific techniques such as task-based graphic design. We apply our approach to developing AutoBrief, a system that summarizes large data sets using natural language and information graphics. Finally, we analyze AutoBrief in terms of the standard reference model (SRM).     

Optimizing data stream processing for large‐scale applications

Stream processing systems are designed to analyze data arriving in real time and using continuous queries and respond when a specific event or sequence of events are detected. An important aspect of these systems is Streaming Analytics, which facilitates statistical calculations on continuous data within the stream. These systems must be designed to handle high volumes of data, be scalable, and accommodate a multitude of long‐lived concurrently running analytics. The challenges involved in the development of stream processing include on‐the‐fly transformation of data streams to match the query needs of users and the ability to model stream transformations to detect overlaps and possibilities for optimizations and to specify a methodology to deliver optimizations. In particular, this work focuses on exposing data stream application internals in order to detect reusable parts and then consolidate applications to optimize computational resource usage. The Streaming Data Analytics Model presented in this paper adopts a declarative approach that enables processing and manipulation of data streams in a simple manner while facilitating powerful optimizations necessary for managing high volumes of streaming data in real time. An evaluation is provided to demonstrate in both theoretical and quantitative aspects the high performance offered by our approach.     

Evaluating techniques for learning non-taxonomic relationships of ontologies from text

Learning non-taxonomic relationships is a sub-field of Ontology Learning that aims at automating the extraction of these relationships from text. Several techniques have been proposed based on Natural Language Processing and Machine Learning. However just like for other techniques for Ontology Learning, evaluating techniques for learning non-taxonomic relationships is an open problem. Three general proposals suggest that the learned ontologies can be evaluated in an executable application or by domain experts or even by a comparison with a predefined reference ontology. This article proposes two procedures to evaluate techniques for learning non-taxonomic relationships based on the comparison of the relationships obtained with those of a reference ontology. Also, these procedures are used in the evaluation of two state of the art techniques performing the extraction of relationships from two corpora in the domains of biology and Family Law.     

Nondeterministic Temporal Relations in Multimedia Data

Synchronization is an important issue in multimedia systems which integrate a variety of temporally related media objects.One part of synchronization is the representation of temporal information.Wish the emerging interactive multimedia,deterministic temporal models are replaced by nondeterministic ones with more expressiveness.This paper classifies temporal models by their expressiveness,and evealuates relevant nondeterministic temporal relations in multimedia data.Additionally,an interval-based nondeterministic model based on a complete temporal operator set is proposed providing high-leval abstractions and a high degree of expressiveness for interactive multimedia systems.     

Enhancing information systems management with natural language processing techniques

Natural language and databases are core components of information systems. They are related to each other because they share the same purpose: the conceptualization aspects of the real world in order to deal with them in some way. Natural language processing (NLP) techniques may substantially enhance most phases of the information system lifecycle, starting with requirements analysis, specification and validation, and going up to conflict resolution, result processing and presentation. Furthermore, natural language based query languages and user interfaces facilitate the access to information for anyone and allow for new paradigms in the usage of computerized services. This paper investigates the use of NLP techniques in the design phase of information systems. Then, it reports on data base querying and information retrieval enhanced with NLP.     

Analysis of utility and use of a web-based tool for digital signal processing teaching by means of a technological acceptance model

This paper presents an exploratory study about the development of a structural and measurement model for the technological acceptance (TAM) of a web-based educational tool. The aim consists of measuring not only the use of this tool, but also the external variables with a significant influence in its use for planning future improvements. The tool, designed with Shockwave™ and Macromedia Director™, is a web-based educational environment that has been applied as a teaching methodology in an undergraduate course involved with modern microprocessors architectures and applications. The proposed methodology may be extensible to similar tools and tries to fill the lack of scientific studies in the validation and acceptance of computer-based educational tools.     

Supporting interactive presentation for distributed multimedia applications

In this paper we present IMP, a platform to support the authoring and run-time management of interactive multimedia applications in distributed environments. The multi-level platform model encourages slot-in extensibility to cater for the evolving environment. We illustrate with our prototype platform built above the Cambridge multimedia environment. The authoring model emphasises reusability, tailorability and use of environment features in a uniform way. We have shown the validity of our approach by constructing a variety of application types.     

A Semantic Web data retrieval implementation with an adaptive model for supporting agent decision structures

This paper proposes an adaptive learning approach that yields decision models that can be applied by a transactions agent. This model can learn effectively with a variety of data distributions. This research uses the Semantic Web as a data access approach. The Semantic Web is a method that sellers can use to publish semantically meaningful information on Websites so automated applications can reliably access that information. We implemented a Semantic Web composed of 30 vendors’ Web pages and a spider to search those pages to obtain product and vendor information. This information was used to train a learning agent, which then provided a decision model to a transaction agent. James Hansen is J. Owen Cherrington Professor in the Information Systems Department of the Marriott School of Management at Brigham Young University. He is an associate editor for IEEE Intelligent Systems and Information Systems Frontiers. His research is in machine learning and planning as model checking. James B. McDonald is Professor of Economics at Brigham Young University. His research interests are in econometrics and quantitative methods. He has recently published in Econometrica, Journal of the American Statistical Association, Management Science, and Journal of Business Conan C. Albrecht is a professor of Information Systems at Brigham Young University. He teaches classes in enterprise development, middleware, and business programming. Conan researches computer-based fraud detection techniques, ecommerce platforms, and online group dynamics. He has published articles on fraud detection and information theory in The Journal of Forensic Accounting, The Journal of Accounting, The Communications of the ACM, Decision Support Systems, Information and Management, and other academic and professional outlets. Conan is currently working on an open source framework for computer-based fraud detection. The core of this research is detectlets, which encode background and detection information for specific fraud schemes. He is researching with the United Nations and the World Bank to use detectlets to prevent and detect fraud in third world countries. In the next few years, he hopes the system will serve as the foundation of a large, online repository of detectlets about all types of fraud. Douglas L. Dean is an Associate Professor at the Marriott School of Management at Brigham Young University. He is also research coordinator for the Rollins Center for E-business. He received his Ph.D. in MIS from the University of Arizona in 1995. Dr. Dean’s research interests include electronic commerce technology and strategy, online communities, requirements analysis, and collaborative tools and methods. His work has been published in Management Science, Journal of Management Information Systems, Information and Management, The DATA BASE for Advances in Information Systems, Communications of the AIS, Expert Systems with Applications, Group Decision and Negotiation, and IEEE Transactions on Systems, Man, and Cybernetics. Bonnie Brinton Anderson is the LeAnn Albrecht Fellow and an Assistant Professor in the Information Systems Department of the Marriott School at Brigham Young University (Provo, UT). She received her Ph.D. from Carnegie Mellon University. Dr. Anderson has published in Decision Support Systems; IEEE Transactions on Systems, Man, and Cybernetics; Communications of the ACM; Journal of Accountancy, among others. She researches in the areas of knowledge management, information systems security, and intelligent agents.     

Nine methods to extend SMIL for multimedia applications

The SMIL 2.0 multimedia standard has been designed for use on the Web. It supports keyboard and mouse as input devices. Typically, SMIL players can play out text, images, audio, and video. SMIL also has a strong support for declarative synchronization and timing. Being an open standard, SMIL could well be utilized in custom applications, in environments such as info kiosks and multimedia consoles. However, these environments usually require better input and output capabilities not available in SMIL. This paper presents nine methods to extend SMIL for custom multimedia applications. The methods include ways to attach new input sources, output capabilities, and extended internal logic. Also, an implementation of an extensible SMIL player is given. As a conclusion, SMIL can be extended in several ways for custom multimedia applications. These extensions will provide new ideas for the future multimedia languages. Recently, W3C has published SMIL 2.1, which is an updated version of the SMIL 2.0 specification. Kari Pihkala graduated from the Helsinki University of Technology, Finland, where he received his M.Sc. degree in 1999 and Ph.D. degree in 2004. His research interests include XML, multimedia, and mobile devices. Petri Vuorimaa is head of the Telecommunications Software and Multimedia Laboratory, Helsinki University of Technology, Finland. He has both M.Sc. (1990) and Dr.Tech. (1995) degrees from Tampere University of Technology, Finland. His current research interests include digital television, mobile multimedia, and XML.     

Intelligent multimedia data: data + indices + inference

In this paper, we present a novel approach for multimedia data indexing and retrieval that is machine independent and highly flexible for sharing multimedia data across applications. Traditional multimedia data indexing and retrieval problems have been attacked using the central data server as the main focus, and most of the indexing and query-processing for retrieval are highly application dependent. This precludes the use of created indices and query processing mechanisms for multimedia data which, in general, have a wide variety of uses across applications. The approach proposed in this paper addresses three issues: 1. multimedia data indexing; 2. inference or query processing; and 3. combining indices and inference or query mechanism with the data to facilitate machine independence in retrieval and query processing. We emphasize the third issue, as typically multimedia data are huge in size and requires intra-data indexing. We describe how the proposed approach addresses various problems faced by the application developers in indexing and retrieval of multimedia data. Finally, we present two applications developed based on the proposed approach: video indexing; and video content authorization for presentation.     

The IP multimedia domain: service architecture for the delivery of voice, data, and next generation multimedia applications

The IP Multimedia Subsystem (IMS) is defined by the 3 ^rd Generation Partnership Project (3GPP) as a new core network domain. IMS provides a service control platform that allows creation of new multimedia and multi-session applications utilizing wireless and wireline transport capabilities. In this paper we will cover the concepts and standards defining IMS and review the network architecture from a mobile perspective. We will see how IMS interacts with the Packet Switched Domain (e.g. Wireless LAN, GPRS, and UMTS networks), the Internet, and application services. Then we will examine the key IMS capabilities and show how they can be combined to create new mobile IP services. Finally, we present a software architecture, which is enabled by IMS and allows development of unique applications (with multimedia/multi-session functionality, single/multi-user, service to user). The software architecture is illustrated by an example of a prototype application. This work was done when the author was with Siemens Communications Inc., Boca Raton, FL     

The IP multimedia domain: service architecture for the delivery of voice, data, and next generation multimedia applications

The IP Multimedia Subsystem (IMS) is defined by the 3^rd Generation Partnership Project (3GPP) as a new core network domain. IMS provides a service control platform that allows creation of new multimedia and multi-session applications utilizing wireless and wireline transport capabilities. In this paper we will cover the concepts and standards defining IMS and review the network architecture from a mobile perspective. We will see how IMS interacts with the Packet Switched Domain (e.g. Wireless LAN, GPRS, and UMTS networks), the Internet, and application services. Then we will examine the key IMS capabilities and show how they can be combined to create new mobile IP services. Finally, we present a software architecture, which is enabled by IMS and allows development of unique applications (with multimedia/multi-session functionality, single/multi-user, service to user). The software architecture is illustrated by an example of a prototype application. This work was done when the author was with Siemens Communications Inc., Boca Raton, FL     

现代语义技术及其应用

语义技术成为当前语义Web与知识管理的重要支撑技术。就现代语义技术的基本原理、语义模型、知识表达语言及其关键技术给出了具体的论述,介绍了语义技术的典型应用架构及当前主要应用领域。