Using perceptual syntax to enhance semantic content in diagrams

Diagrams are essential in documenting large information systems. They capture, communicate, and leverage knowledge indispensable for solving problems and act as cognitive externalizations (intertwining internal and external processes to extract information from the external world to enhance thought). A diagram provides a mapping from the problem domain to the visual representation by supporting cognitive processes that involve perceptual pattern finding and cognitive symbolic operations. However, not all mappings are equal, and for effectiveness we must embed a diagram's representation with characteristics, which lets users easily perceive meaningful patterns. Consequently, a diagram's effectiveness depends to some extent on how well we construct it as an input to our visual system. In our research, we focus on a class of diagrams commonly referred to as graphs or node-link diagrams. Nodes representing entities, objects, or processes, and links or edges representing relationships between the nodes characterize them. Their most common form is outline circles or boxes denoting nodes and lines of different types representing links between the nodes. Entity-relationship diagrams, software structure diagrams, and data-flow models are examples of node-link diagrams used to model the structure of processes, software, or data     

Creating cognitive tutors for collaborative learning: steps toward realization

Our long-term research goal is to provide cognitive tutoring of collaboration within a collaborative software environment. This is a challenging goal, as intelligent tutors have traditionally focused on cognitive skills, rather than on the skills necessary to collaborate successfully. In this paper, we describe progress we have made toward this goal. Our first step was to devise a process known as bootstrapping novice data (BND), in which student problem-solving actions are collected and used to begin the development of a tutor. Next, we implemented BND by integrating a collaborative software tool, Cool Modes, with software designed to develop cognitive tutors (i.e., the cognitive tutor authoring tools). Our initial implementation of BND provides a means to directly capture data as a foundation for a collaboration tutor but does not yet fully support tutoring. Our next step was to perform two exploratory studies in which dyads of students used our integrated BND software to collaborate in solving modeling tasks. The data collected from these studies led us to identify five dimensions of collaborative and problem-solving behavior that point to the need for abstraction of student actions to better recognize, analyze, and provide feedback on collaboration. We also interviewed a domain expert who provided evidence for the advantage of bootstrapping over manual creation of a collaboration tutor. We discuss plans to use these analyses to inform and extend our tools so that we can eventually reach our goal of tutoring collaboration.     

Reuse-Conducive Development Environments

Despite its well-recognized benefits, software reuse has not met its expected success due to technical, cognitive, and social difficulties. We have systematically analyzed the reuse problem (especially the cognitive and social difficulties faced by software developers who reuse) from a multidimensional perspective, drawing on our long-term research on information retrieval, human-computer interaction, and knowledge-based systems. Based on this analysis, we propose the concept of reuse-conducive development environments, which encourage and enable software developers to reuse through the smooth integration of reuse repository systems and development environments. We have designed, implemented, and evaluated CodeBroker—a reuse-conducive development environment—that autonomously locates and delivers task-relevant and personalized components into the current software development environment. Empirical evaluations of CodeBroker have shown that the system is effective in promoting reuse by enabling software developers to reuse components unknown to them, reducing the difficulties in locating components, and augmenting the programming capability of software developers.     

Allocation and analysis of reliability: multiple levels: system, subsystem, and module

We model the reliability allocation and prediction process across a hierarchical software system comprised of modules, subsystems, and system. We experiment in modeling complex reliability software systems using several software reliability models to test the feasibility of the process and to evaluate the accuracy of the models for this application. This is a subject deserving research and experimentation because this type of system is implemented in safety-critical projects, such as National Aeronautics and Space Administration (NASA) flight software modules, that we use in our experiments. Given the reliability requirement of a software system in the software planning or design stage, we predict each module’s reliability and their relationships (e.g., reliability interactions among modules, subsystems, and system), Our critical interfaces and components are failure-mode sequences and the modules that comprise these sequences, respectively. In addition, we evaluate how sensitive the achievement of reliability goals is to predicted component reliabilities that do not meet expectations.     

Abstract,link, publish,exploit: An end to end framework for workflow sharing

Scientific workflows are increasingly used to manage and share scientific computations and methods to analyze data. A variety of systems have been developed that store the workflows executed and make them part of public repositories However, workflows are published in the idiosyncratic format of the workflow system used for the creation and execution of the workflows. Browsing, linking and using the stored workflows and their results often becomes a challenge for scientists who may only be familiar with one system. In this paper we present an approach for addressing this issue by publishing and exploiting workflows as data on the Web with a representation that is independent from the workflow system used to create them. In order to achieve our goal, we follow the Linked Data Principles to publish workflow inputs, intermediate results, outputs and codes; and we reuse and extend well established standards like W3C PROV. We illustrate our approach by publishing workflows and consuming them with different tools designed to address common scenarios for workflow exploitation.     

Skeleton-Sectional Structural Analysis for 3D Printing

3D printing has become popular and has been widely used in various applications in recent years. More and more home users have motivation to design their own models and then fabricate them using 3D printers. However, the printed objects may have some structural or stress defects as the users may be lack of knowledge on stress analysis on 3D models. In this paper, we present an approach to help users analyze a model’s structural strength while designing its shape. We adopt sectional structural analysis instead of conventional FEM (Finite Element Method) analysis which is computationally expensive. Based on sectional structural analysis, our approach imports skeletons to assist in integrating mesh designing, strength computing and mesh correction well. Skeletons can also guide sections building and load calculation for analysis. For weak regions with high stress over a threshold value in the model from analysis result, our system corrects them by scaling the corresponding bones of skeleton so as to make these regions stiff enough. A number of experiments have demonstrated the applicability and practicability of our approach.     

Design of robotic arm’s action to imitate the mechanism of an animal’s consciousness

We are attempting to develop a system so that a user is able to let robots perform an intellectual action that has a healing and friendly feeling. Based on the development process of the actions and consciousness of animals, we constructed a structure model which connects consciousness and action hierarchically, built a valuation function for action selection, and developed software to control the action of a robot. This software is called Consciousness-Based Architecture (CBA). With it, our aim is to connect a user and robot as closely as possible and to allow smooth communications between them by developing an emotional system that takes notice of consciousness. In our system, the robotic arm’s finger is outfitted with a small Web camera, which allows the arm to recognize external information so that the robot can select various actions that comply with certain factors in the outside environment. Furthermore, by using the actuator of the robotic arm, the system we have built provides a correspondence between the robot’s internal states, such as the degree of rotation angle, and the outside temperature. In the present study, a motivation model which considers the outside environment and the internal states has been built into the CBA, and the behavior of the robotic arm has been verified.     

Virtual worlds,augmented reality,blended reality

The internet could be considered just another collection of digital tools, but it has had a more profound impact on how we live, work and play than most people would have predicted. The internet makes everywhere local. For the exchange of ideas and data, it does not matter where you are on the planet. For an industrialized society, or for one forming to emulate existing ones, this creates an interesting situation. If people can work and communicate at any distance the current organization of corporations, cities and even countries changes. However, the legacy set of tools available to communicate at distance miss many of the vital elements we use as humans. Telephones, email and web pages filter out much of who we are. It is very easy, therefore, to dismiss any new technology as more of the same and to stick with the legacy tools. We lose the notion of space, location, size, physical presence and nonverbal communication in the “traditional” internet tools. This tension between what we currently do and what we need to do acts as a flashpoint that causes new inventions to emerge.The internet helps spread these inventions, and acts as the expression of them. These evolutionary steps in our communication are not restricted to the keyboard and screen, so not only challenge our organizations they also challenge our ideas of what the norm is for computing. Looking at the components of some already exciting technologies and combining them with a changing society becoming well versed in freedom the internet can offer, leads us to a very close future, etc. One with a blended reality where digital and physical mixes in combinations that suit us as people. The future is already here we just have to join it.     

The distribution of rewards in sensorimotor maps acquired by cognitive robots through exploration

To exhibit intelligent behavior, cognitive robots must have some knowledge about the consequences of their actions and their value in the context of the goal being realized. We present a neural framework using which explorative sensorimotor experiences of cognitive robots can be efficiently ‘internalized’ using growing sensorimotor maps and planning realized using goal induced quasi-stationary value fields. Further, if there are no predefined reward functions (or the case when they are not good enough in a slightly modified world), the robot will have to try and realize its goal by exploration after which reward/penalty is given at the end. This paper proposes three simple rules for distribution of the received end reward among the contributing neurons in a high dimensional sensorimotor map. Importantly, reward/penalty distribution over hundreds of neurons in the sensorimotor map is computed one shot. This resulting reward distribution can be visualized as an additional value field, representing the new learnt experience and can be combined with other such fields in a context dependent fashion to plan/compose novel emergent behavior. The simplicity and efficiency of the approach is illustrated through the resulting behaviors of the GNOSYS robot in two different scenarios (a) learning ‘when’ to optimize ‘what constraint’ while realizing spatial goals and (b) learning to push a ball intelligently to the corners of a table, while avoiding traps randomly placed by the teacher (this scenario replicates the famous trap tube paradigm from animal reasoning carried out on chimpanzees, capuchins and infants).     

Trends in the development of communication networks: Cognitive networks

One of the main challenges already faced by communication networks is the efficient management of increasing complexity. The recently proposed concept of cognitive network appears as a candidate that can address this issue. In this paper, we survey the existing research work on cognitive networks, as well as related and enabling techniques and technologies. We start with identifying the most recent research trends in communication networks and classifying them according to the approach taken towards the traditional layered architecture. In the analysis we focus on two related trends: cross-layer design and cognitive networks. We classify the cognitive networks related work in that mainly concerned with knowledge representation and that predominantly dealing with the cognition loop. We discuss the existing definitions of cognitive networks and, with respect to those, position our understanding of the concept. Next, we provide a summary of artificial intelligence techniques that are potentially suitable for the development of cognitive networks, and map them to the corresponding states of the cognition loop. We summarize and compare seven architectural proposals that comply with the requirements for a cognitive network. We discuss their relative merits and identify some future research challenges before we conclude with an overview of standardization efforts.     

A model of software design

In this article we present a preliminary cognitive model of the process of software design. Our goal was to develop a model of expert problem-solving skills for a task in which domain knowledge played an extensive role. In our model the process of design is captured via goals-and-operators interacting with a knowledge base. We have defined the goals and operators as ones which are general to design, rather than specific to the current task. In addition, we have structured the atomic level operators so that they are able to access domain specific knowledge acquired through experience. This structure enables both general processes and domain specific knowledge to play critical roles in producing any particular design artifact. From our protocol analysis we have built a model which unites several recurring behaviors into an interpretable whole. the behaviors we account for include the building of mental models, mental simulation, and balanced development.     

Experience with model-based performance, reliability, and adaptability assessment of a complex industrial architecture

In this paper, we report on our experience with the application of validated models to assess performance, reliability, and adaptability of a complex mission critical system that is being developed to dynamically monitor and control the position of an oil-drilling platform. We present real-time modeling results that show that all tasks are schedulable. We performed stochastic analysis of the distribution of task execution time as a function of the number of system interfaces. We report on the variability of task execution times for the expected system configurations. In addition, we have executed a system library for an important task inside the performance model simulator. We report on the measured algorithm convergence as a function of the number of vessel thrusters. We have also studied the system architecture adaptability by comparing the documented system architecture and the implemented source code. We report on the adaptability findings and the recommendations we were able to provide to the system’s architect. Finally, we have developed models of hardware and software reliability. We report on hardware and software reliability results based on the evaluation of the system architecture.     

A model of cognitive loads in massively multiplayer online role playing games

Being one of the most commercially successful entertainment software applications, massively multiplayer online role playing games (MMORPGs) continue to expand in term of the revenue they generate as well as the involvement of users who congregate in their virtual space and form communities around them to support each other. Unlike conventional offline computer games, or networked games with limited numbers of players, MMORPGs are not merely software applications as they are usually seen as a space with complicated dynamics of social interactions. Hence, it is believed that playing these games might cause cognitive overload problems among the players as they have to constantly interact with the game world as well as with other users. We conducted an exploratory study using qualitative methods to explore cognitive overloads in Maple Story, a typical MMORPG. Our results reveal that several types of cognitive overloads emerge during the game playing. While some of these overloads pose serious problems even to expert players, players seem to develop strategies to overcome them. It is found that some forms of cognitive load are actually desirable in order to make the game challenging. We have also created a set of recommendations that can help game developers handle cognitive load problems in MMORPGs.     

A Hybrid Theory of Event Memory

Amongst philosophers, there is ongoing debate about what successful event remembering requires. Causal theorists argue that it requires a causal connection to the past event. Simulation theorists argue, in contrast, that successful remembering requires only production by a reliable memory system. Both views must contend with the fact that people can remember past events they have experienced with varying degrees of accuracy. The debate between them thus concerns not only the account of successful remembering, but how each account explains the various forms of memory error as well. Advancing the debate therefore must include exploration of the cognitive architecture implicated by each view and whether that architecture is capable of producing the range of event representations seen in human remembering. Our paper begins by exploring these architectures, framing casual theories as best suited to the storage of event instances and simulation theories as best suited to store schemas. While each approach has its advantages, neither can account for the full range of our event remembering abilities. We then propose a novel hybrid theory that combines both instance and schematic elements in the event memory. In addition, we provide an implementation of our theory in the context of a cognitive architecture. We also discuss an agent we developed using this system and its ability to remember events in the blocks world domain.

     

A software metric system for module coupling

Low module coupling is considered to be a desirable quality for modular programs to have. Previously, coupling has been defined subjectively and not quantified, making it difficult to use in practice. In this article, we extend previous work to reflect newer programming languages and quantify coupling by developing a general software metric system that allows us to automatically measure coupling. We have precisely defined the levels of coupling so that they can be determined algorithmically, incorporated the notion of direction into the coupling levels, and accounted for different types of nonlocal variables present in modern programming languages. With our system, we can measure the coupling between all pairs of modules in a system, measure the coupling of a particular module with all other modules in a system, and measure the coupling of an entire system. We have implemented our metric system so that it measures the coupling between pairs of procedures in arbitrary C programs and have analyzed several well-used systems of various sizes.     

Software reliability from the customer view

We augment our defect-prevention activities with analyses of what our customers experience - software failures. NED provides a suite of hardware and software products for business-critical applications, including the operating system, database management system, communication protocols, transaction monitors, and application-development tools. We update this suite quarterly in a release version update. An RVU is an aggregation of all product changes plus the previous version of all unchanged products, which totals several million lines of code. We track the failure rates for each RVU so that we can measure the customer experience with that update. We have been analyzing software failure data for the past several years to provide feedback on the effectiveness of our defect-prevention activities.     

Diagnosis using a first-order stochastic language that learns

We have created a diagnostic/prognostic software tool for the analysis of complex systems, such as monitoring the “running health” of helicopter rotor systems. Although our software is not yet deployed for real-time in-flight diagnosis, we have successfully analyzed the data sets of actual helicopter rotor failures supplied to us by the US Navy. In this paper, we discuss both critical techniques supporting the design of our stochastic diagnostic system as well as issues related to its full deployment. We also present four examples of its use.Our diagnostic system, called DBAYES, is composed of a logic-based, first-order, and Turing-complete set of software tools for stochastic modeling. We use this language for modeling time-series data supplied by sensors on mechanical systems. The inference scheme for these software tools is based on a variant of Pearl’s loopy belief propagation algorithm [Pearl, P. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Francisco, CA: Morgan Kaufmann]. Our language contains variables that can capture general classes of situations, events, and relationships. A Turing-complete language is able to reason about potentially infinite classes and situations, similar to the analysis of dynamic Bayesian networks. Since the inference algorithm is based on a variant of loopy belief propagation, the language includes expectation maximization type learning of parameters in the modeled domain. In this paper we briefly present the theoretical foundations for our first-order stochastic language and then demonstrate time-series modeling and learning in the context of fault diagnosis.     

Performance and usability tradeoff in a cluster display wall

Cluster-based display walls provide cost-effective and scalable display infrastructures with high resolution and large display area, making them suitable for a wide range of high-resolution applications. As a consequence, a wide offer of new cluster display-wall platforms together with their software frameworks have been proposed. Their performance and the satisfaction of their users have aroused the interest of some researchers. This work is focused on the Liquid Galaxy cluster display wall originally built to run Google Earth to create an immersive experience for the users. In this paper, the Liquid Galaxy is benchmarked running Google Earth, as a representative interactive application with high performance requirements, in different configurations and environments, to test the satisfaction, effectiveness and efficiency. Thus, we wish to know how users react to the system performance. In order to do so, we use a performance metric defined in previous research to relate the performance of the system with the user’s perception. Taking into account the trend of this metric in the experimentation, we model the behavior of the system in a way that the performance for any given visualization cluster running Google Earth could be predicted by using a reference system.