A City Metaphor to Support Navigation in Complex Information Spaces

A major problem in modern information systems is to locate information and to re-find information one has seen before. Systems like the World Wide Web are heavily interlinked, but do not show structures that help users to navigate the information it contains. The use of appropriate navigation metaphors can help to make the structure of modern information systems easier to understand and therefore easier to use.We propose a conceptual user interface metaphor based on the structure of a city. Cities are very complex spatial environments and people know how to get information, how to reach certain locations in a city, and how to make use of the available infrastructure, etc. Cities provide a rich set of navigational infrastructure that lends itself to creating sub-metaphors for navigational tools. A city metaphor makes this existing knowledge about a structured environment available to the user of a computerized information system.We first focus on several properties necessary for future user interfaces (or user interface metaphors) that will distinguish them from current systems, like the richness of information or the use of visualizations to show the structure of information spaces. We also describe the strengths and problems of spatial user-interface metaphors. Then we present the structure of the information city metaphor, its structuring and navigation metaphors and what we see as its main advantages and problems. We further outline a few scenarios of how an Information City might work. Finally, we compare implementing this metaphor using either a textual or a graphical virtual environment or a combination.     

IT managers’ vs. IT auditors’ perceptions of risks: An actor–observer asymmetry perspective

With the growing role of information technology (IT), many organizations struggle with IT-related risks. Both IT managers and IT auditors are involved in assessing, monitoring, and reporting IT risks, but this does not necessarily mean that they share the same views. In this study, we draw upon the actor–observer asymmetry perspective to understand differences in IT managers’ vs. IT auditors’ perceptions of risks. Through a quasi-experiment with 76 employees of a financial institution, we found that IT managers and IT auditors showed the expected actor–observer differences. Implications for both research and practice are discussed.     

Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming

In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.     

Preconceptions and Individual Differences in Understanding Visual Metaphors

Understanding information visualization is more than a matter of reading a series of data values; it is also a matter of incorporating a visual structure into one's own thinking about a problem. We have proposed visual metaphors as a framework for understanding high-level visual structure and its effect on visualization use. Although there is some evidence that visual metaphors can affect visualization use, the nature of this effect is still ambiguous. We propose that a user's preconceived metaphors for data and other individual differences play an important role in her ability to think in a variety of visual metaphors, and subsequently in her ability to use a visualization. We test this hypothesis by conducting a study in which a participant's preconceptions and thinking style were compared with the degree to which she is affected by conflicting metaphors in a visualization and its task questions. The results show that metaphor compatibility has a significant effect on accuracy, but that factors such as spatial ability and personality can lessen this effect. We also find a complex influence of self-reported metaphor preference on performance. These findings shed light on how people use visual metaphors to understand a visualization.     

“Telling tales”: Stories,metaphors and tacit knowledge at the fuzzy front‐end of NPD

Breakthrough ideas depend upon the generation of new knowledge, which emerge from the conversion of tacit knowledge at the fuzzy front‐end (FFE) of new product development (NPD). The occurrence of stories and metaphors has been strongly linked to tacit knowledge, however, empirical studies that examine how stories and metaphors harness tacit knowledge in the FFE are lacking. In addition, how managers can use stories and metaphors to develop breakthrough product ideas is unclear. To address these gaps, an ‘in‐situ’ empirical case study was conducted in the European subsidiary of a B2C multinational. This study investigated the role, characteristics and interplay of stories and metaphors emerging in discussions between managers and customers in a collaborative design workshop (CDW). Taking a knowledge management theoretical perspective, the data were analyzed using the well‐known SECI framework. The results clearly show that significant new knowledge was created based on the discussions in a CDW and stories and metaphors were important mechanisms for this. Importantly, it was stories related to product usage that triggered breakthrough ideas. The study extends the understanding of how breakthrough ideas emerge; it proposes a tentative conceptual framework; and it provides managers with recommendations of how to use stories and metaphors effectively during the FFE.     

Interface Adaptation to Novice Older Adults’ Mental Models through Concrete Metaphors

Metaphors could be used to help older adults build mental models of Information and Communication Technology (ICT) applications and services and thereby reduce the number of interaction problems, but currently, metaphors generally are not tailored to these older adults, and their construction remains vague. Inspired by concrete icons, this study proposes concrete metaphors to help novice older adults understand ICT applications and services and retains their knowledge over time. A new communication application based on concrete metaphors was designed, developed, and comparatively tested against another application based on abstract metaphors over a period of 9 days. The results indicated that the application with concrete metaphors contributed to fewer perception and cognition errors, higher usage intention, higher satisfaction, and higher preference compared to the application with abstract metaphors. In addition, the advantage of concrete metaphors was lasting, unlike concrete icons. These findings indicated that concrete metaphors could be an icebreaker or stepping stone to draw novice older adults into the information society. To further increase the impact of concrete metaphors, representing real-world operations is an essential challenge.     

An online fault tolerant actor-critic neuro-control for a class of nonlinear systems using neural network HJB approach

In this paper, we propose an actor-critic neuro-control for a class of continuous-time nonlinear systems under nonlinear abrupt faults, which is combined with an adaptive fault diagnosis observer (AFDO). Together with its estimation laws, an AFDO scheme, which estimates the faults in real time, is designed based on Lyapunov analysis. Then, based on the designed AFDO, a fault tolerant actor- critic control scheme is proposed where the critic neural network (NN) is used to approximate the value function and the actor NN updates the fault tolerant policy based on the approximated value function in the critic NN. The weight update laws for critic NN and actor NN are designed using the gradient descent method. By Lyapunov analysis, we prove the uniform ultimately boundedness (UUB) of all the states, their estimation errors, and NN weights of the fault tolerant system under the unpredictable faults. Finally, we verify the effectiveness of the proposed method through numerical simulations.     

Lightweight techniques for structural evaluation of animated metaphors

Visual metaphors in the form of still or animated pictures have been used in user interfaces with the hope of enhancing learning and use of computer applications. This paper studies animated metaphors with the intent to understand how they relate to human cognition and how their quality can be measured. We present a model of the relationships within metaphors, suggest lightweight evaluation techniques based on this model, and test these techniques in an empirical investigation. The results indicate that a lightweight analysis based on still images and made by domain-aware but metaphor-unaware judges can be used as a first step in deciding which metaphors are worthy of further study, and to direct animation efforts to overcome the most crucial problems. Furthermore, the results show that animation may increase or decrease the quality of a metaphor by considerable amounts; hence the final evaluation must be based on actual use of fully implemented metaphors. The results also confirm earlier suggestions to use rich metaphors and provides evidence that richness of the still image is important for the effectiveness of animation.     

Non-linear observer based on the Euler discretization

In this paper we present an estimator based on the Euler discretization. First, we show that the proposed estimator is an exponential observer in the case when the error between trajectories of continuous time and its associated Euler discretized system can be neglected. Otherwise, we show that the error of estimation is exponentially attracted by some neighbourhood of the origin which depends only on the sampling time. Finally, numerical simulations using a bioreactor model are given in order to highlight the performance of the proposed estimator.     

Representing UNIX Domain Metaphors

The language used to describe technical domains like UNIX is filled with metaphor. An approach to metaphor, based on theexplicit representation of knowledge about metaphors, has beendeveloped. MIDAS (Metaphor Interpretation, Denotation, andAcquisition System) is a computer program that that has been developedbased upon this approach. MIDAS can be used to representknowledge about conventional metaphors, interpret metaphoric languageby applying this knowledge, and dynamically learn new metaphors asthey are encountered during normal processing.     

INCONSISTENCY OF KNOWLEDGE AND COLLECTIVE INTELLIGENCE

Is the intelligence of a collective larger than the intelligence of its members? How does one determine the knowledge of a collective on the basis of the knowledge of its members? In this paper, we try to answer these questions. Many examples show that the knowledge of a collective is not a usual union of the knowledge of its members. If we assume that the members of a collective possess their knowledge states about some common real world, and these states reflect to some degree the proper (real) state of the knowledge about the real world, then a question arises: How does one determine the knowledge of the collective, and what is its quality? In this paper, we use Consensus Theory to solve this problem and to show that, in many cases, the collective knowledge state is more proper than the knowledge states of the collective intelligence.     

Observer-based state-feedback control of timed Petri nets with deadlock recovery

This paper discusses the problem of controlling a timed Petri net whose marking cannot be measured but is estimated using an observer. The control objective is that of enforcing a set of generalized mutual exclusion constraints (GMEC) and all transitions are assumed to be controllable. We show that the use of marking estimates may significantly reduce the performance of the closed-loop system and in particular may lead to a deadlock. First, we present a linear algebraic characterization of deadlock markings based on siphon analysis. Second, we show how this characterization may be used to derive a procedure that may be invoked to recover from a controller induced deadlock. Finally, we assume that the timing delays associated to transitions are known and show how this knowledge can be used to improve the marking estimate and to recover the net from partial deadlocks. This procedure is similar to the one used for deadlock recovery and may be invoked whenever a transition has not fired for a time longer than its expected delay.     

“遥感数字影像处理”实验课教学改革初探

实验教学是遥感数字影像处理课程的重要组成部分,针对其特点,从实验环境、教学内容、教学方法以及实验考核等方面进行一系列改革。实践结果表明,这些改革措施可以有效帮助学生对知识的理解和掌握,达到了预期的教学效果。     

Cognitive agents — a procedural perspective relying on the predictability of Object-Action-Complexes (OACs)

Embodied cognition suggests that complex cognitive traits can only arise when agents have a body situated in the world. The aspects of embodiment and situatedness are being discussed here from the perspective of linear systems theory. This perspective treats bodies as dynamic, temporally variable entities, which can be extended (or curtailed) at their boundaries. We show how acting agents can, for example, actively extend their body for some time by incorporating predictably behaving parts of the world and how this affects the transfer functions. We suggest that primates have mastered this to a large degree increasingly splitting their world into predictable and unpredictable entities. We argue that temporary body extension may have been instrumental in paving the way for the development of higher cognitive complexity as it is reliably widening the cause-effect horizon about the actions of the agent. A first robot experiment is sketched to support these ideas.We continue discussing the concept of Object-Action Complexes (OACs) introduced by the European PACO-PLUS consortium to emphasize the notion that, for a cognitive agent, objects and actions are inseparably intertwined. In another robot experiment we devise a semi-supervised procedure using the OAC-concept to demonstrate how an agent can acquire knowledge about its world. Here the notion of predicting changes fundamentally underlies the implemented procedure and we try to show how this concept can be used to improve the robot’s inner model and behaviour. Hence, in this article we have tried to show how predictability can be used to augment the agent’s body and to acquire knowledge about the external world, possibly leading to more advanced cognitive traits.     

The shaping of information by visual metaphors

The nature of an information visualization can be considered to lie in the visual metaphors it uses to structure information. The process of understanding a visualization therefore involves an interaction between these external visual metaphors and the user's internal knowledge representations. To investigate this claim, we conducted an experiment to test the effects of visual metaphor and verbal metaphor on the understanding of tree visualizations. Participants answered simple data comprehension questions while viewing either a treemap or a node-link diagram. Questions were worded to reflect a verbal metaphor that was either compatible or incompatible with the visualization a participant was using. The results suggest that the visual metaphor indeed affects how a user derives information from a visualization. Additionally, we found that the degree to which a user is affected by the metaphor is strongly correlated with the user's ability to answer task questions correctly. These findings are a first step towards illuminating how visual metaphors shape user understanding, and have significant implications for the evaluation, application, and theory of visualization.     

COMMUNICATING KNOWLEDGE SYSTEMS: Part II—Big Talk among Small Systems

Abstract

A new model is introduced for describing societies of communicating knowledge systems from an artificial intelligence perspective. The society is defined as a time-evolving concept con-taining both static and dynamic knowledge. The static knowledge includes the components of the society, namely the actor types, the communication issue types, and the behavior pattern types. The dynamic knowledge specifies how the society can evolve. Each component is a metatheory described using a specific representation system. The metatheories representing the actor types include self-knowledge, knowledge about other actors, and knowledge about the real world. The communication issues always reflect the knowledge of the sending actor and produce some effect on the receiving actor, for instance, enrichments of its knowledge or conflicts with its own knowledge. Conflicts are of two forms: routine conflicts to be solved by mediating actors with no intelligent knowledge, or conflicts that require intelligent knowledge. and that must be solved by special actors called managers. Behavior pattern rules are theories that express cause-effect rules regarding the society behavior as a whole. Technically, modifications of the society are defined as parameterized theories whose argument specifies the requirements to be satisfied before a change of the situation and whose body introduces the modifications to be performed.     

Adaptive actor-critic learning for the control of mobile robots by applying predictive models

In this paper, we propose two methods of adaptive actor-critic architectures to solve control problems of nonlinear systems. One method uses two actual states at time k and time k+1 to update the learning algorithm. The basic idea of this method is that the agent can directly take some knowledge from the environment to improve its knowledge. The other method only uses the state at time k to update the algorithm. This method is called, learning from prediction (or simulated experience). Both methods include one or two predictive models, which are assumed to be applied to construct predictive states and a model-based actor (MBA). Here, the MBA as an actor can be viewed as a network where the connection weights are the elements of the feedback gain matrix. In the critic part, two value-functions are realized as a pure static mapping, which can be reduced to a nonlinear current estimator by using the radial basis function neural networks (RBFNNs). Simulation results obtained for a dynamical model of nonholonomic mobile robots with two independent driving wheels are presented. They show the effectiveness of the proposed approaches for the trajectory tracking control problem.     

Systems Thinkers: Check Your Scotomas and Watch Your Language!

Abstract

Various belief constructs such as framing, dualisms, worldviews, paradigms, and values are discussed to understand how these might result in scotomas. The observer is not independent and objective but has purpose and values within a group with a language. This leads to an exploration of conceptual metaphor within language as a way of accessing what is largely unconscious thinking and a potential source of scotomas. Examples of the use of conceptual metaphors in systems and complexity thinking are provided illustrating that conceptual metaphors can do work that is not easily performed by other methods in systems thinking currently. Without awareness of beliefs, certain types of interactions or perspectives are neglected; patterns of behavior survive when they no longer serve; meaning is lost and marginalization continues unchecked.     

Adaptive output control of a class of uncertain chaotic systems

In this paper, a new observer-based backstepping output control scheme is proposed for stabilizing and controlling a class of uncertain chaotic systems. The controller is designed through the use of a robust observer and backstepping technique. We firstly show that many chaotic systems as paradigms in the research of chaos can be transformed into a class of nonlinear systems in the feedback form. Secondly, the synchronization problem is converted to the tracking problem from control theory, thereby leading to the use of state observer design techniques. A new observer is utilized to estimate the unmeasured states. Unlike some existing methods for chaos control, no priori knowledge on the system parameters is required and only the output signal is available for control purpose. The Lyapunov functions are quadratic in the state estimates, the observer errors and the parameter estimation error based on the backstepping technique. It is shown that not only global stability is guaranteed by the proposed controller, but also both transient and asymptotic tracking performances are quantified as explicit functions of the design parameters so that designers can tune the design parameters in an explicit way to obtain the desired closed-loop behavior.