Context Modeling

This thesis describes a research work aimed at improving the interfaces of online discussion forums (ODFs) in relation to their functional support to enhance learning. These ODFs form part of almost all Learning Management Systems (LMSs) such as WebCT, Moodle and Blackboard, which are widely used in education nowadays. Although ODFs are identified as valuable sources to learning, their interfaces are limited in terms of providing support to students, such as in the areas of managing their postings as well as in facilitating them to quickly locate and obtain specified information. In addition, these systems lack features to support inter-institutional cooperation that could potentially increase knowledge sharing between students and educators of different institutions. The interface design objective of this study therefore was to explore and overcome the limitations identified as above, and enhance the effectiveness and efficiency of ODFs' support to learning. Using a task centered design approach; the required features were developed, and implemented in a working prototype called eQuake (electronic Question answer knowledge environment). eQuake is a shared online discussion forum system developed as an add-on to a well-known open source e-learning platform (Moodle). This system was intended for use among interinstitutional students in New Zealand tertiary institutions that teach similar courses. The improved interface functionalities of eQuake are expected to enhance learning support in terms of widening communication among users, increasing knowledge base, providing existing matching answer(s) quickly to students, and exposing students to multiple perspectives. This study considers such improvements to ODF interfaces as vital to enable users to enjoy the benefits of technology-mediated environment. The perceived usefulness and ease-of-use of improved features in eQuake were evaluated using a quantitative experimental research method. The evaluation was conducted at three tertiary institutions in New Zealand, and the overall results indicated positive response, although some suggestions for improvement have been made in the evaluation. This thesis presents a review of the related literature, describes the design and development of a user interface, followed by its implementation in eQuake, and a description of the evaluation. The thesis concludes with recommendations for better interface design of ODFs and provides suggestions for future research in this area.

This thesis describes a research work aimed at improving the interfaces of online discussion forums (ODFs) in relation to their functional support to enhance learning. These ODFs form part of almost all Learning Management Systems (LMSs) such as WebCT, Moodle and Blackboard, which are widely used in education nowadays. Although ODFs are identified as valuable sources to learning, their interfaces are limited in terms of providing support to students, such as in the areas of managing their postings as well as in facilitating them to quickly locate and obtain specified information. In addition, these systems lack features to support inter-institutional cooperation that could potentially increase knowledge sharing between students and educators of different institutions. The interface design objective of this study therefore was to explore and overcome the limitations identified as above, and enhance the effectiveness and efficiency of ODFs' support to learning. Using a task centered design approach; the required features were developed, and implemented in a working prototype called eQuake (electronic Question answer knowledge environment). eQuake is a shared online discussion forum system developed as an add-on to a well-known open source e-learning platform (Moodle). This system was intended for use among interinstitutional students in New Zealand tertiary institutions that teach similar courses. The improved interface functionalities of eQuake are expected to enhance learning support in terms of widening communication among users, increasing knowledge base, providing existing matching answer(s) quickly to students, and exposing students to multiple perspectives. This study considers such improvements to ODF interfaces as vital to enable users to enjoy the benefits of technology-mediated environment. The perceived usefulness and ease-of-use of improved features in eQuake were evaluated using a quantitative experimental research method. The evaluation was conducted at three tertiary institutions in New Zealand, and the overall results indicated positive response, although some suggestions for improvement have been made in the evaluation. This thesis presents a review of the related literature, describes the design and development of a user interface, followed by its implementation in eQuake, and a description of the evaluation. The thesis concludes with recommendations for better interface design of ODFs and provides suggestions for future research in this area.

Context-aware Web services are emerging as a promising technology for the electronic businesses in mobile and pervasive environments. Unfortunately, complex contextaware services are still hard to build. In this paper, we present a modeling language for the model-driven development of context-aware Web services based on the Unified Modeling Language (UML). Specifically, we show how UML can be used to specify information related to the design of context-aware services. We present the abstract syntax and notation of the language and illustrate its usage using an example service. Our language offers significant design flexibility that considerably simplifies the development of context-aware Web services.

As modern information systems become increasingly business-and safety-critical, it is extremely important to improve both the trust that a user places in a system and their understanding of the risks associated with making a decision. This paper presents the STRAPP framework, a generic framework that supports both of these goals through the use of personalised provenance reasoning engines and state-of-art risk assessment techniques. We present the high-level architecture of the framework, and describe the process of systematically modelling system provenance with the W3C PROV provenance data model. We discuss the business drivers behind the concept of personalizing provenance information, and describe the STRAPP approach to enabling this through a user-adaptive system style. We discuss using data provenance for risk management and treatment in order to evaluate risk levels, and discuss the use of CORAS to develop a risk reasoning engine representing core classes and relationships. Finally, we demonstrate the initial implementation of our personalised provenance system in the context of the Rolls-Royce Equipment Health Management, and discuss its operation, the lessons we have learnt through our research and implementation (both technical and in business), and our future plans for this project.

Middleware support for pervasive context-aware systems relieves context-aware applications from dealing with the complexity of context-specific operations such as context acquisition, aggregation, reasoning and distribution. The middleware decouples applications from the underlying heterogeneous context sensors, and offers advantages such as rapid development of context-aware applications and efficient usage of the context sensors. Context sensors have inherent limitations with respect to the quality of the context information they produce. Without breaking the decoupling, the middleware needs to explicitly model and quantify this Quality of Context in order to allow application to adapt their behavior based on the Quality of Context, for efficiency reasons and to enable Quality-of-Context-aware privacy policies. In this paper we identify and define five Quality-of-Context indicators for context-aware middleware, and discuss different alternatives for their quantification. These Quality-of-Context indicators are: precision, freshness, spatial resolution, temporal resolution and probability of correctness.

In the last years, authoring based on e-learning standards has been consolidated as a core factor of industry and development of interoperable and effective virtual learning environments. However, there is a need for further research on abstraction to provide a more instructional view in the context of authoring tools in a variety of ways, in order to avoid being driven by Learning Technology (LT) specifications, facilitate instructional knowledge aggregation, and to provide an appropriate level of clarity and semantics in the design of collaborative activities. We propose a combination of techniques to provide this instructional abstraction in the context of the new European educational model, combining instructional layers and collaborative scripts in authoring tools, and semantic web techniques for extending e-learning material in order to harness the wealth of existing web content and semantically labeled repositories.

Distributed simulation has proved to be a cost effecti ve technique for studying and understanding complex realworld systems. Many distributed simulations need to incorporate hundreds or even thousands of processors, across both local and wide area networks. As the size and complexity of distributed simulations increase so do the demands on the hardware and software resources that provide simulation services, causing performance bottlenecks that limit the effectiveness of distributed simulation. Different abstractions of performance are needed depending on who is analyzing the distributed simulation and for what reason. This paper proposes a framework for identifying the factors affecting performance and provides a technique for associating the performance factors with high-level system metrics that describe the behavior of the physical and logical resources and services used in the design and implementation of distributed simulations. Dynamic and static analysis of the performance information provides feedback on the execution of the simulation and can provide meaningful information as a guide in making decisions about the configuration and control of the available hardware and software resources for distributed simulation exercises.

The Schoolsenses@Internet project explores the creation of georeferenced multisensory information in elementary schools collaborative contexts to promote educational success with a deeper learning at this educational level. Workshops with teachers and children in real contexts play an important role in the design and validation of project tools. This paper reports on the design process of one of these tools to support the use and the creation of georeferenced multisensory simulations and models in elementary education contexts.

We present an extension of Astree to concurrent C software. Astree is a sound static analyzer for run-time errors previously limited to sequential C software. Our extension employs a scalable abstraction which covers all possible thread interleavings, and soundly reports all run-time errors and data races: when the analyzer does not report any alarm, the program is proven free from those classes of errors. We show how this extension is able to support a variety of operating systems (such as POSIX threads, ARINC 653, OSEK/AUTOSAR) and report on experimental results obtained on concurrent software from different domains, including large industrial software.

As the range of communication services and consumer expectations continue to grow, the demands on telecommunication organizations to find ways to make their systems more adaptable and flexible, while remaining dependable, becomes critical. Autonomic communications is an emerging strategic approach for addressing these needs. This paper discusses the unified fault management architecture and the proposal to add autonomicity through a 'reflex-healing' dual strategy. The autonomic reflex unified fault management architecture has goals in common with the perceived new construct of autonomic communications, the knowledge plane.

Multiagent Systems (MAS) are applicable to engineering evolvable systems. It is essential to be able to capture and communicate the varied experiences of researchers in developing MAS. Software patterns are potentially useful for sharing experiences of what is and is not successful for MAS development. This paper presents our Two Way Classification Scheme and pattern template structure that is based on the classification scheme, for the effective classification and description of agent oriented software patterns.

We show computer simulations of a neural network comprising two sensory cortical areas and a hippocampal area which demonstrates how the hippocampal input to the cortex can facilitate the formation of polymodal assemblies and the learning of contexts.

Database system performance problems have a cascading effect into all aspects of an enterprise application. Database vendors and application developers provide guidelines, best practices and even initial database settings for good performance. But database performance tuning is not a one-off task. Database administrators have to keep a constant eye on the database performance as the tuning work carried out earlier could be invalidated due to multitude of reasons. Before engaging in a performance tuning endeavor, a database administrator must prioritize which tuning tasks to carry out first. This prioritization is done based on which tuning action would yield highest performance benefit. However, this prediction may not always be accurate. Experiment-based performance tuning methodologies have been introduced as an alternative to prediction-based performance tuning approaches. Experimenting on a representative system similar to the production one allows a database administrator to accurately gauge the performance gain for a particular tuning task. In this paper we propose a novel approach to experiment-based performance tuning with the use of a context-aware application model. Using a proof-of-concept implementation we show how it could be used to automate the detection of performance changes, experiment creation and evaluate the performance tuning outcomes for mixed workload types through database configuration parameter changes.

Reactivity is one of the key features of hardware description languages. We present an efficient implementation of reactivity in the Scenic framework that allows the system designer to model hardware blocks. Scenic allows the designer to use C++ to model mixed hardware-software systems with a C++ compiler and a small library and without the need of a complex event-driven run-time kernel often found embedded in hardware description languages (HDL) such as VHDL and Verilog. Moreover, Scenic hardware descriptions can be easily mapped to HDL and synthesized into hardware implementations using commercially available tools. In this paper we present Scenic's implementation of concurrency (signals and processes) and reactivity (waiting and watching). When C++ is used as an HDL, context-switching overhead can become a significant performance issue during simulation. We introduce the notion of delayed expression objects, or lambdas, to reduce context-switching. Examples and experimental results are presented to show the utility and simulation efficiency using the Scenic framework.

this article presents a set of conventional approaches to the modelling of dysfunctioning of a process in the context of maintenance activities. It recalls advantages and disadvantages in their use. The normative framework of maintenance, the priorities of the long-term maintenance and the content of the maintenance function will also be presented. This paper presents a state of the research of usual maintenance methods. We give hints about the use of these methods for new products for emaintenance.

this article presents a set of conventional approaches to the modelling of dysfunctioning of a process in the context of maintenance activities. It recalls advantages and disadvantages in their use. The normative framework of maintenance, the priorities of the long-term maintenance and the content of the maintenance function will also be presented. This paper presents a state of the research of usual maintenance methods. We give hints about the use of these methods for new products for emaintenance.

Gameplay research about experiential phenomena is a challenging undertaking, given the variety of experiences that gamers encounter when playing and which currently do not have a formal taxonomy, such as flow, immersion, boredom, and fun. These informal terms require a scientific explanation. Ludologists also acknowledge the need to understand cognition, emotion, and goaloriented behavior of players from a psychological perspective by establishing rigorous methodologies. This paper builds upon and extends prior work in an area for which we would like to coin the term "affective ludology." The area is concerned with the affective measurement of player-game interaction. The experimental study reported here investigated different traits of gameplay experience using subjective (i.e., questionnaires) and objective (i.e., psychophysiological) measures. Participants played three Half-Life 2 game level design modifications while measures such as electromyography (EMG), electrodermal activity (EDA) were taken and questionnaire responses were collected. A level designed for combat-oriented flow experience demonstrated significant high-arousal positive affect emotions. This method shows that emotional patterns emerge from different level designs, which has great potential for providing real-time emotional profiles of gameplay that may be generated together with selfreported subjective player experience descriptions.

i* is a framework for goal-oriented requirements modeling. This framework contains an organizational modeling language defined by actors and dependencies among them. Although i* has been widely used, it is still hard to work with it due to its diversity of variants. In this paper, we propose a unified metamodel that presents elements of two well-known variants of i*. We show how OCL restrictions can be applied this metamodel in order to generate a specific variant. In doing so, we intend to unify the efforts of i* community. This metamodel can be useful when developing generic tools for i*, as well as to reason about i* language. Moreover, it makes it easier to extend the i* language. Thus, our proposal is an attempt towards the unification on i* variants by considering similarities and differences among them.

Automatic generation of models from a set of positive and negative samples and a-priori knowledge {if available) is a crucial issue for pattern recognition applications. Grammatical inference can play an important role in this issue since it can be used to generate the set of model classes, where each class consists on the rules to generate the models. In this paper we present the process of learning context dependent bidimensional objects from outdoors images as context sensitive languages. We show how the process is conceived to overcome the problem of generalizing rules based on a set of samples which have small diflerences due to noisy pixels. The learned models can be used to identify objects in outdoors i m g e s irrespectively of their size and partial occlusions. Some results of the inference procedure are

Dimensionality reduction aims at representing high-dimensional data into low-dimensionality space. In order to make sense, the low-dimensional representation, or embedding, has to preserve some well-defined structural properties of data. The general idea is that similar data items should be displayed close to each other, whereas longer distances should separate dissimilar ones. This principle applies to data that can

Following a toxic contaminant release, either accidental or intentional, predicting the transport and dispersion of the contaminant becomes a critical problem for Homeland Defense and DoD agencies. To produce accurate predictions requires characterizing both the source of hazardous material and the local meteorological conditions. Decision makers use information on contaminant source location and transport prediction to decide on the best methods to mitigate and prevent effects. The problem has both observational and computational aspects. Field monitors are likely to be used to detect the release and measure concentrations of the toxic material. Algorithms are then required to invert the problem in order to infer the characteristics of the source and the local meteorology. Here, a genetic algorithm is coupled with transport and dispersion models to assimilate sensor data in order to characterize emission sources and the wind vector. The parameters computed include two dimensional source location, amount of the release, and wind direction. This methodology is demonstrated for a basic Gaussian plume dispersion model and verified in the context of an identical twin numerical experiment, in which synthetic dispersion data is created with the same dispersion model. Error bounds are set using Monte Carlo techniques and robustness assessed by adding white noise. Algorithm speed is tuned by optimizing the parameters of the genetic algorithm. Specific GA configurations and cost function formulations are discussed.

The VIMS workshop encompasses both virtual instruments and "virtual sensors", but historically the latter are hardly represented in the program. This paper is not so much a research report as a friendly challenge to the VIMS community to begin thinking actively and creatively about this complementary aspect of our theme. In particular, it raises the question of what and how much we need to build into a virtual sensor agent for it to pass something analogous to the Turing Test, i.e., for a virtual sensor, as a "black-box", to be indistinguishable from a real sensor.

We present computational methods to extract and model different joints of a generic subject, in an automatic way. The input for our methods is simply a set of trajectories of 3D points, obtained from a motion capture system (MOCAP). Due to the piecewise rigidity of the skeleton, these trajectories belong to different subspaces (of the space of all possible trajectories). We use this knowledge to derive computationally simple algorithms that are able to infer joint properties from the trajectories of the 3D points. This model-free approach enables analyzing directly specific subjects, rather than requiring user-defined a priori models of the skeleton. Such data-driven models are useful in the analysis of human walk and the evaluation of joint stress for pathology detection. Also, the customized skeleton models, together with physiological muscular information, allow to accurately analyze high level performances in sports.

The Technology Acceptance Model (TAM) predicts the user acceptance of end-user applications by specifying causal relationships among belief and attitudinal constructs that subsequently influence usage behavior. Although the perceived usefulness and perceived ease of use constructs have received a great deal of attention in MIS literature, very few follow-up studies have used the original TAM constructs. Moreover, the various studies investigating TAM use different measurement items, or factors, to assess the belief constructs. In this TAM study, the impact of external variables affecting usage behavior is examined. The results suggest that the impact of the external variables on usage behavior is not fully mediated by the belief constructs.

In this paper, a novel spatial error concealment algorithm, which adaptively switches between directional interpolation and region matching, is proposed. Different from the previous spatial error concealment methods, which just utilize smooth property, the algorithm exploits both smooth property and texture information to recover the lost blocks. Based on the DCT coefficients in the available neighboring MBs, the algorithm automatically analyzes whether the MB is "smooth-like" or "texture-like" and adaptively select directional interpolation or region matching to recover the lost MB. The proposed algorithm has been evaluated on H.264 reference software JM 9.0. The experimental results demonstrate that the proposed method can achieve better PSNR performance and visual quality, compared with weighted pixel average (WPA) which is adopted in H.264, directional interpolation-only and region matching-only.

This paper presents a design methodology and an accompanying platform for the design and fast development of Context-Aware Mobile mashUpS (CAMUS). The approach is characterized by the role given to context as a first-class modeling dimension used to support i) the identification of the most adequate resources that can satisfy the users' situational needs and ii) the consequent tailoring at runtime of the provided data and functions. Context-based abstractions are exploited to generate models specifying how data returned by the selected services have to be merged and visualized by means of integrated views. Thanks to the adoption of Model-Driven Engineering (MDE) techniques, these models drive the flexible execution of the final mobile app on target mobile devices. A prototype of the platform, making use of novel and advanced Web and mobile technologies, is also illustrated.

Mobility Models try to represent the movement behavior of devices in Mobile Ad hoc Networks. These models are used in performance evaluation of applications and communication systems, allowing the analysis of the mobility's impact. In this context, two individual mobility models for Mobile Ad hoc Networks are proposed in this paper. These models were based in [1] and intend to represent a wider movement capability. Using the proposed models it is possible to move in the same direction, in adjacent directions, to accelerate and to stop, avoiding sharp turns and sudden stops. This way, it is tried a closer representation to the real movement of the users in urban enviroments and roads. In this paper, the models are described analytically by Markov Chains. Besides that, some comparisions between the proposed models, the Waypoint model and the Random Walk model are presented through the use of simulations. *

Context is an essential capability for robots that are to be as adaptive as possible in challenging environments. Although there are many context modeling efforts, they assume a fixed structure and number of contexts. In this paper, we propose an incremental deep model that extends Restricted Boltzmann Machines. Our model gets one scene at a time, and gradually extends the contextual model when necessary, either by adding a new context or a new context layer to form a hierarchy. We show on a scene classification benchmark that our method converges to a good estimate of the contexts of the scenes, and performs better or on-par on several tasks compared to other incremental models or non-incremental models.

In this paper we discuss key requirements for collecting behavioral data concerning technology supported learning activities. It is argued that the common practice of collecting machine generated logfiles of user actions is not enough for building a thorough view of the activity. Instead more contextual information is needed to be captured in heterogeneous media like video, audio files, snapshots, etc in order to reconstruct the learning process. A software environment (Collaborative Analysis Tool ColAT) that supports interrelation of such resources in order to analyse the collected evidence and produce interpretative views of the activity is described.

When modelling complex computer systems, four primary modelling techniques are available to the software engineer, i.e. abstraction, composition, functional decomposition and multiple client interfaces (views). Abstraction and composition can directly be expressed in traditional object oriented models and functional decomposition can often be expressed using subsystems. However, traditional object models provide no expressiveness for multiple client interfaces. This forces designers to either model all different views on a conceptual object as a large, complex implementation object or to model each view as an object and relate the different view objects. Both approaches suffer from problems of complexity, maintainability and reusability. As a solution, we propose the layered object model that allows for the expressive, flexible and extensible definition of multiple client interfaces. To illustrate the problems associated with views and our proposed solution, the domain of industrial...

Event management and response generation are two essential aspects of systems for Ambient Intelligence. This work proposes handling these issues through the use of a semantic model for Ambient Intelligence which, under the umbrella of a philosophical and common sense optic, describes what actions and events are, how they are connected, and how computational systems should think about their meaning. This model entails an approach with which to both reason about and model context events and generate behavioral responses to those events, when necessary. The model supports this ad-hoc response generation by automatically composing services when those which are available do not meet the expected functionality (without requesting user intervention). An evaluation methodology is presented and illustrated with a case scenario, in which synthetic data has been generated to emulate events in which system response is expected. The evaluation of the system response is carried out on the basis of a vector of attributes.

The development of applications that target dynamic networks often adresses the same difficulties. Since the underlying network topology is unstable, the application has to handle it in a contextreactive manner, and, when possible, with algorithms that are localized and decentralized. The present paper shows how the DA-GRS model, that is a high level abstract model for localized graph algorithms, can be used to drastically improve the development time of self-organized systems. The canonical method that is proposed makes use of generic pieces of code, along with some additional code generation. All the concepts presented here are illustrated by means of a spanning forest algorithm, that can then be used by the application at a high abstraction level.

The main objective of our research is to gain a comprehensive understanding of the relationship between language usage within different communities and delineating the ideological narratives. We focus specifically on utilizing Natural Language Processing techniques to identify underlying narratives in the coded or suggestive language employed by non-normative communities associated with targeted violence. Earlier studies addressed the detection of ideological affiliation through surveys, user studies, and a limited number based on the content of text articles, which still require label curation. Previous work addressed label curation by using ideological subreddits (r/Liberal and r/Conservative for Liberal and Conservative classes) to label the articles shared on those subreddits according to their prescribed ideologies, albeit with a limited dataset. Building upon previous work, we use subreddit ideologies to categorize shared articles. In addition to the conservative and liberal classes, we introduce a new category called ''Restricted'' which encompasses text articles shared in subreddits that are restricted, privatized, or banned, such as r/TheDonald. The ''Restricted'' class encompasses posts tied to violence, regardless of conservative or liberal affiliations. Additionally, we augment our dataset with text articles from self-identified subreddits like r/progressive and r/askaconservative for the liberal and conservative classes, respectively. This results in an expanded dataset of 377,144 text articles, consisting of 72,488 liberal, 79,573 conservative, and 225,083 restricted class articles. Our goal is to analyze language variances in different ideological communities, investigate keyword relevance in labeling article orientations, especially in unseen cases (922,522 text articles), and delve into radicalized communities, conducting thorough analysis and interpretation of the results.

Context-aware mobile computing aims at designing applications that automatically adapt their behaviors to the available location information and the available nearby sensors and devices. This is done in order to fulfill tasks in a way that suits users' current context best. To achieve this, context representation and manipulation are important issues, so as to establish formal context models. In this paper, basic elements of context-aware systems are described with an emphasis on location information representations. Space models for location-based applications are presented. Considered realistic applications concern intelligent vehicles and pervasive road networks.

A noiseless-channel coding system in which the source probabilities change continuously in time is introduced. Using a geometry defined by the second derivative matrix of the information-theoretic entropy function, a bound on the number of excess channel symbols (the redundancy) sent by a retrospective ncoder during a fixed time interval is derived. Results on minimal-cost coding and their implications for a real-time coding algorithm are then explored. Underlying most of the discussion here is the assumption made on the separability of the time scales corresponding to the (high) character ate as compared with the rate of change of the source probabilities. I.

Proximity aware mobile service is a wireless application for mobile users to interact with services offered by service providers present in the user's environment. Many issues are still prevalent in this area which includes context modeling, sensors integration, proximity resolution and service management. One important aspect in service management is congestion control which plays a key part in providing proximity aware services in a wireless networking environment. This paper investigates the feasibility of neural network based connection admission controllers by comparing simulated Back-Propagation and Learning Vector Quantization Neural Networks. It is found that the Backpropagation Neural Network has higher performance.

In this paper, we e v aluate multi-Gaussian HMM systems and hybrid HMM/ANN systems in the framework of task independent training for small size (75 words) and medium size (600 words) vocabularies. To d o t h i s , we use the Phonebook database 6] which is particularly well suited to this kind of experiments since (1) it is a very large telephone database and (2) the size and content of the test vocabulary is very exible. For each system, di erent HMM topologies are compared to test the in uence of state tying (with a number of parameters approximately kept constant) on the recognition performance. Two lexica (Phonebook and CMU) are also compared and it is shown that the CMU lexicon is leading to signi cantly better performance. Finally, it is shown that with a quite simple system and a few adaptations to the basic HMM/ANN scheme, recognition performance of 98.5% and 94.7% can easily be achieved, respectively on a lexicon of 75 and 600 words (isolated words, telephone speech and lexicon words not present in the training data).

Given a plant M A and a specification M C , the largest solution of the FSM equation M X • M A M C contains all possible discrete controllers M X. Often we are interested in computing the complete solutions whose composition with the plant is exactly equivalent to the specification. Not every solution contained in the largest one satisfies such property, that holds instead for the complete solutions of the series topology. We study the relation between the solvability of an equation for the series topology and of the corresponding equation for the controller's topology. We establish that, if M A is a deterministic FSM, then the FSM equation M X • M A M C is solvable for the series topology with an unknown head component iff it is solvable for the controller's topology. Our proof is constructive, i.e., for a given solution M B of the series topology it shows how to build a solution M D of the controller's topology and viceversa.

A time-domain, non-Monte Carlo method for computer simulation of electn'cal noise in nonlinear dynamic circuits with arbitrary excitations and arbitrary large-signal waveforms is presented. This time-domain noise simulation method is based on results from the theory of stochastic differential equations. The noise simulation method is general in the following sense. Any nonlinear dynamic circuit with any kind of excitation, which can be simulated by the transient analysis routine in a circuit simulator, can be simulated by our noise simulator in timedomain to produce the noise variances and covariances of circuit variables as a function of time, provided that noise models for the devices in the circuit are available. Noise correlations between circuit variables at different time points can also be calculated. Previous work on computer simulation of noise in electronic circuits is reviewed with comparisons to our method. Shot, thermal, and flicker noise models for integrated-circuit devices, in the context of our time-domain noise simulation method, are discussed. The implementation of this noise simulation method in a circuit simulator (SPICE) is described. Two examples of noise simulation (a CMOS inverter and a BJT active mixer) are given.

Radio Frequency Identification (RFID) technology is used in numerous applications and offers a plethora of interesting potential new applications. However, this potential raises issues that require addressing to achieve its widespread acceptance by consumers. This paper investigates the factors that affect consumer acceptance of RFID technology. The purpose of this effort is to develop and test a theoretical model that contextualizes the Technology Acceptance Model (TAM) within the context of RFID technology. The research model proposes that convenience, culture, privacy, regulation, and security are the principal factors influencing the consumers' acceptance of RFID. However, the results show that convenience, culture, and security are significant predictors. This study is the first in the RFID literature to use the technology acceptance model for explaining consumer acceptance of RFID technology. The findings suggest that (1) higher perceived convenience of RFID technology leads to greater acceptance of this technology, (2) societal beliefs, value systems, norms, and/or behaviors influence the extent of consumer acceptance of RFID technology, and (3) higher perceived importance of and less willingness to sacrifice personal information security lead to lower intention to use RFID technology. Contextualization of TAM to RFID technology makes this study relevant to practitioners because the results can provide insight to organizations using or exploring the use of RFID technology.

This Model management is an important issue in many different contexts and domains. Due to the specific characteristics of models, specific systems are required for model management, in order to model, store, retrieve, and manipulate models in an efficient and effective way. Thus, there need to create model repositories where the knowledge artifacts known as models will reside, as primitive data reside in data base management systems (DBMSs). This paper introduces model characteristics and its importance in present scenario. The problems and challenges with model management are also presented here. The research direction towards a model base management system (PBMSs) by abstracting common features of models and management requirements is discussed.