Task Muddiness, Intelligence Metrics, and the Necessity of Autonomous Mental Development

This paper introduces a concept called task muddiness as a metric for higher intelligence. Task muddiness is meant to be inclusive and expendable in nature. The intelligence required to execute a task is measured by the composite muddiness of the task described by multiple muddiness factors. The composite muddiness explains why many challenging tasks are muddy and why autonomous mental development is necessary for muddy tasks. It facilitates better understanding of intelligence, what the human adult mind can do, and how to build a machine to acquire higher intelligence. The task-muddiness indicates a major reason why a higher biological mind is autonomously developed from autonomous, simple-to-complex experience. The paper also discusses some key concepts that are necessary for understanding the mind and intelligence, such as intelligence metrics, the mode a task is conveyed to the task executor, a human and a machine being a joint task performer in the traditional artificial intelligence (AI), a developmental agent (human or machine) being a sole task performer, and the need for autonomy in task-nonexplicit learning.

On improving resource utilization and system throughput of master slave job scheduling in heterogeneous systems

The rapid advances of network technologies shed light on many aspects of the practicability of large scale ubiquitous computing. Grid technology has been recognized as an efficient solution to coordinate large-scale shared resources and execute complex applications in heterogeneous network environments. The problem of resource management and task allocation has always been one of the main challenges. In this paper, we present an efficient task allocation strategy for distributing tasks onto computing nodes in the underlying heterogeneous networks. The contribution of the proposed technique is to minimize average turnaround time by dispatching tasks to processors with smallest communication ratio. System throughput could be also enhanced by dispersing processor idle time. The proposed technique can be applied to heterogeneous cluster systems as well as computational grid environments, in which the communication costs vary in different clusters. Experimental results show that the proposed scheme outperforms other previous algorithms in terms of throughput and turnaround time.

Handheld Augmented Reality for underground infrastructure visualization

In this paper, we present an Augmented Reality (AR) system for aiding field workers of utility companies in outdoor tasks such as maintenance, planning or surveying of underground infrastructure. Our work addresses these issues using spatial interaction and visualization techniques for mobile AR applications and as well as for a new mobile device design. We also present results from evaluations of the prototype application for underground infrastructure spanning various user groups. Our application has been driven by feedback from industrial collaborators in the utility sector, and includes a translation tool for automatically importing data from utility company databases of underground assets.

Specifying active databases as non-Markovian theories of actions

Over the last 15 years, database management systems (DBMSs) have been enhanced by the addition of rule-based programming to obtain active DBMSs. One of the greatest challenges in this area is to formally account for all the aspects of active behavior using a uniform formalism. In this paper, we formalize active relational databases within the framework of the situation calculus by uniformly accounting for them using theories embodying non-Markovian control in the situation calculus. We call these theories active relational theories and use them to capture the dynamics of active databases. Transaction processing and rule execution is modelled as a theorem proving task using active relational theories as background axioms. We show that the major components of an ADBMS, namely the rule sets and the execution models, may be given a clear semantics using active relational theories. More precisely: we represent the rule set as a program written in a suitable version of the situation calculus based language ConGolog; then we extend an existing situation calculus based framework for modelling advanced transaction models to one for modelling the execution models of active behaviors.

IPBGA: a hybrid P2P based grid architecture by using information pool protocol

The current representatives of Grid systems are Globus and Web services, however, they have poor scalability and single point failure. It is these two factors which make the building of an improved P2P and grid hybrid framework for resource management and task schedule such a popular research topic. This paper differs from current research because it puts forward an Information Pool Based Grid Architecture (IPBGA), which is a real sense hybrid of P2P and grid instead of only introducing P2P methods into grid systems for resource management. Based on virtualizations, abstract physical resources and tasks to be, the information requests from resources for tasks and appeals from tasks for resources are upgraded as information services by using an information pool protocol (IPP). Thus, grid resource management and task scheduling are regarded as information matching by IPP which is adaptive to the heterogeneous, dynamic, and distributed characteristics of a grid system. Tri-Information Center (Tri-IC) and source ranking mechanisms are presented in IPP to improve robustness, prevent sybil attack, and to discourage free riding. Experiments and theory analysis show that the IPP of the IPBGA is more efficient and robust in dealing with information while both the bandwidth and process costs are less.

A vector gravitational force model for classification

The paper presents a decision algorithmic model called vector gravitational force model in the feature space. The algorithmic model, inspired by and similar to the Law of Universal Gravitation, is derived from the vector geometric analysis of the linear classifier and established in the feature space. Based on this algorithmic model, we propose a classification method called vector gravitational recognition. The proposed method is applied to the benchmark Glass Identification task in the UCI Database available from USA Forensic Science Service, and other two UCI benchmark tasks. The experimental and comparative results show that the proposed approach yields quite good results and outperforms some well known and recent approaches on the tasks, and other applications may benefit from ours.

How relative absolute can be: SUMI and the impact of the nature of the task in measuring perceived software usability

This paper addresses the possibility of measuring perceived usability in an absolute way. It studies the impact of the nature of the tasks performed in perceived software usability evaluation, using for this purpose the subjective evaluation of an application’s performance via the Software Usability Measurement Inventory (SUMI). The paper reports on the post-hoc analysis of data from a productivity study for testing the effect of changes in the graphical user interface (GUI) of a market leading drafting application. Even though one would expect similar evaluations of an application’s usability for same releases, the analysis reveals that the output of this subjective appreciation is context sensitive and therefore mediated by the research design. Our study unmasked a significant interaction between the nature of the tasks used for the usability evaluation and how users evaluate the performance of this application. This interaction challenges the concept of absolute benchmarking in subjective usability evaluation, as some software evaluation methods aspire to provide, since subjective measurement of software quality will be affected most likely by the nature of the testing materials used for the evaluation.

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A web services framework for distributed model management

Distributed model management aims to support the wide-spread sharing and usage of decision support models. Web services is a promising technology for supporting distributed model management activities such as model creation and delivery, model composition, model execution and model maintenance to fulfill dynamic decision-support and problem solving requests. We propose a web services based framework for model management (called MM-WS) to support various activities of the model management life cycle. The framework is based on the recently proposed Integrated Service Planning and Execution (ISP & E) approach for web services integration. We discuss encoding of domain knowledge (as individual models) and utilize the MM-WS framework to interleave synthesis of composite models with their execution. A prototypical implementation with an example is used to illustrate the utility of the framework to enable distributed model management and knowledge integration. Benefits and issues of using the framework to support model-based decision-making in organizational contexts are outlined.

High performance motion detection: some trends toward new embedded architectures for vision systems

The goal of this article is to compare some optimised implementations on current high performance platforms in order to highlight architectural trends in the field of embedded architectures and to get an estimation of what should be the components of a next generation vision system. We present some implementations of robust motion detection algorithms on three architectures: a general purpose RISC processor—the PowerPC G4—a parallel artificial retina dedicated to low level image processing—Pvlsar34—and the Associative Mesh, a specialized architecture based on associative net. To handle the different aspects and constraints of embedded systems, execution time and power consumption of these architectures are compared.

Static priority scheduling of event-triggered real-time embedded systems

Real-time embedded systems are often specified as a collection of independent tasks, each generating a sequence of event-triggered code blocks. The goal of scheduling tasks in this domain is to find an execution order which satisfies all real-time constraints. Within the context of recurring real-time tasks, all previous work either allowed preemptions, or only considered dynamic scheduling, and generally had exponential complexity. However, for many embedded systems running on limited resources, preemptive scheduling may be very costly due to high context switching and memory overheads, and dynamic scheduling can be less desirable due to high CPU overhead. In this paper, we study static priority scheduling of recurring real-time tasks. We focus on and obtain schedule-theoretic results for the non-preemptive uniprocessor case. To achieve this, we derive a sufficient (albeit not necessary) condition for schedulability under static priority scheduling and show that this condition can be efficiently tested in practice. The latter technique is demonstrated with examples, where in each case, an optimal solution for a given problem specification is obtained within reasonable time, by first detecting good candidates using meta-heuristics, and then by testing them for schedulability.

Image-based tree modeling from a few images with very narrow viewing range

Creating 3D tree models from actual trees is a task receiving increasing attention. Some approaches have been developed to reconstruct a tree based on a number of photographs around the tree, typically spanning a wide viewing range. However, due to the environmental restrictions, sometimes it is quite difficult to capture so many acceptable images from so many different viewpoints. In this paper, we propose a tree modeling system which is capable of reconstructing the 3D model of a tree from a few images with very narrow viewing ranges. Because only a few images are required to generate the model, our system has the distinct advantage of fewer environmental restrictions, resulting in the extended usability and flexibility for real applications.

Schedulability analysis of global edf

The multiprocessor edf scheduling of sporadic task systems is studied. A new sufficient schedulability test is presented and proved correct. It is shown that this test generalizes the previously-known exact uniprocessor edf-schedulability test, and that it offers non-trivial quantitative guarantees (including a resource augmentation bound) on multiprocessors.

Inverse multi-objective robust evolutionary design

In this paper, we present an Inverse Multi-Objective Robust Evolutionary (IMORE) design methodology that handles the presence of uncertainty without making assumptions about the uncertainty structure. We model the clustering of uncertain events in families of nested sets using a multi-level optimization search. To reduce the high computational costs of the proposed methodology we proposed schemes for (1) adapting the step-size in estimating the uncertainty, and (2) trimming down the number of calls to the objective function in the nested search. Both offline and online adaptation strategies are considered in conjunction with the IMORE design algorithm. Design of Experiments (DOE) approaches further reduce the number of objective function calls in the online adaptive IMORE algorithm. Empirical studies conducted on a series of test functions having diverse complexities show that the proposed algorithms converge to a set of Pareto-optimal design solutions with non-dominated nominal and robustness performances efficiently.

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Optimal virtual cluster-based multiprocessor scheduling

Scheduling of constrained deadline sporadic task systems on multiprocessor platforms is an area which has received much attention in the recent past. It is widely believed that finding an optimal scheduler is hard, and therefore most studies have focused on developing algorithms with good processor utilization bounds. These algorithms can be broadly classified into two categories: partitioned scheduling in which tasks are statically assigned to individual processors, and global scheduling in which each task is allowed to execute on any processor in the platform. In this paper we consider a third, more general, approach called cluster-based scheduling. In this approach each task is statically assigned to a processor cluster, tasks in each cluster are globally scheduled among themselves, and clusters in turn are scheduled on the multiprocessor platform. We develop techniques to support such cluster-based scheduling algorithms, and also consider properties that minimize total processor utilization of individual clusters. In the last part of this paper, we develop new virtual cluster-based scheduling algorithms. For implicit deadline sporadic task systems, we develop an optimal scheduling algorithm that is neither Pfair nor ERfair. We also show that the processor utilization bound of us-edf{m/(2m−1)} can be improved by using virtual clustering. Since neither partitioned nor global strategies dominate over the other, cluster-based scheduling is a natural direction for research towards achieving improved processor utilization bounds.

Robust Surface Fitting from Two Views using Restricted Correspondence

The restricted correspondence problem is the task of solving the classical stereo correspondence problem when the surface being observed is known to belong to a family of surfaces that vary in a known way with one or more parameters. Under this constraint the surface can be extracted far more robustly than by classical stereo applied to an arbitrary surface, since the problem is solved semi-globally, rather than locally for each epipolar line. Here, the restricted correspondence problem is solved for two examples, the first being the extraction of the parameters of an ellipsoid from a calibrated stereo pair. The second example is the estimation of the osculating paraboloid at the frontier points of a convex object.

Using trust assumptions with security requirements

Assumptions are frequently made during requirements analysis of a system about the trustworthiness of its various components (including human components). These trust assumptions, whether implicit or explicit, affect the scope of the analysis, derivation of security requirements, and in some cases how functionality is realized. This paper presents trust assumptions in the context of analysis of security requirements. A running example shows how trust assumptions can be used by a requirements engineer to help define and limit the scope of analysis and to document the decisions made during the process. The paper concludes with a case study examining the impact of trust assumptions on software that uses the secure electronic transaction specification.

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Spatial Query Estimation without the Local Uniformity Assumption

Existing estimation approaches for spatial databases often rely on the assumption that data distribution in a small region is uniform, which seldom holds in practice. Moreover, their applicability is limited to specific estimation tasks under certain distance metric. This paper develops the Power-method, a comprehensive technique applicable to a wide range of query optimization problems under both L_∞ and L₂ metrics. The Power-method eliminates the local uniformity assumption and is, therefore, accurate even for datasets where existing approaches fail. Furthermore, it performs estimation by evaluating only one simple formula with minimal computational overhead. Extensive experiments confirm that the Power-method outperforms previous techniques in terms of accuracy and applicability to various optimization scenarios.

Computing the Discrete Fourier Transform on a Hexagonal Lattice

The computation of the Discrete Fourier Transform for a general lattice in ℝ ^d can be reduced to the computation of the standard 1-dimensional Discrete Fourier Transform. We provide a mathematically rigorous but simple treatment of this procedure and apply it to the DFT on the hexagonal lattice.