Enhancing self-scheduling algorithms via synchronization and weighting

Existing dynamic self-scheduling algorithms, used to schedule independent tasks on heterogeneous clusters, cannot handle tasks with dependencies because they lack the support for internode communication. To compensate for this deficiency we introduce a synchronization mechanism that provides inter-processor communication, thus, enabling self-scheduling algorithms to handle efficiently nested loops with dependencies. We also present a weighting mechanism that significantly improves the performance of dynamic self-scheduling algorithms. These algorithms divide the total number of tasks into chunks and assign them to processors. The weighting mechanism adapts the chunk sizes to the computing power and current run-queue state of the processors. The synchronization and weighting mechanisms are orthogonal, in the sense that they can simultaneously be applied to loops with dependencies. Thus, they broaden the application spectrum of dynamic self-scheduling algorithms and improve their performance. Extensive testing confirms the efficiency of the synchronization and weighting mechanisms and the significant improvement of the synchronized–weighted versions of the algorithms over the synchronized-only versions.     

Prognostics of machine condition using soft computing

This paper presents a system for monitoring and prognostics of machine conditions using soft computing (SC) techniques. The machine condition is assessed through a suitable ‘monitoring index’ extracted from the vibration signals. The progression of the monitoring index is predicted using an SC technique, namely adaptive neuro-fuzzy inference system (ANFIS). Comparison with a machine learning method, namely support vector regression (SVR), is also presented. The proposed prediction procedures have been evaluated through benchmark data sets. The prognostic effectiveness of the techniques has been illustrated through previously published data on several types of faults in machines. The performance of SVR was found to be better than ANFIS for the data sets used. The results are helpful in understanding the relationship of machine conditions, the corresponding indicating features, the level of damage/degradation and their progression.     

“How do you know that I don’t understand?” A look at the future of intelligent tutoring systems

Many software systems would significantly improve performance if they could adapt to the emotional state of the user, for example if Intelligent Tutoring Systems (ITSs), ATM’s, ticketing machines could recognise when users were confused, frustrated or angry they could guide the user back to remedial help systems so improving the service. Many researchers now feel strongly that ITSs would be significantly enhanced if computers could adapt to the emotions of students. This idea has spawned the developing field of affective tutoring systems (ATSs): ATSs are ITSs that are able to adapt to the affective state of students. The term “affective tutoring system” can be traced back as far as Rosalind Picard’s book Affective Computing in 1997.This paper presents research leading to the development of Easy with Eve, an ATS for primary school mathematics. The system utilises a network of computer systems, mainly embedded devices to detect student emotion and other significant bio-signals. It will then adapt to students and displays emotion via a lifelike agent called Eve. Eve’s tutoring adaptations are guided by a case-based method for adapting to student states; this method uses data that was generated by an observational study of human tutors. This paper presents the observational study, the case-based method, the ATS itself and its implementation on a distributed computer systems for real-time performance, and finally the implications of the findings for Human Computer Interaction in general and e-learning in particular. Web-based applications of the technology developed in this research are discussed throughout the paper.     

User simulation in a stochastic dialog system

We present a new methodology of user simulation applied to the evaluation and refinement of stochastic dialog systems. Common weaknesses of these systems are the scarceness of the training corpus and the cost of an evaluation made by real users. We have considered the user simulation technique as an alternative way of testing and improving our dialog system. We have developed a new dialog manager that plays the role of the user. This user dialog manager incorporates several knowledge sources, combining statistical and heuristic information in order to define its dialog strategy. Once the user simulator is integrated into the dialog system, it is possible to enhance the dialog models by an automatic strategy learning. We have performed an extensive evaluation, achieving a slight but clear improvement of the dialog system.     

Design and Implementation of an Agent Home Scheme Strategy for Prefetch-Based DSM Systems

In recent years, cluster computing has been widely investigated and there is no doubt that it can provide a cost-effective computing infrastructure by aggregating computational power, communication, and storage resources. Moreover, it is also considered to be a very attractive platform for low-cost supercomputing. Distributed shared memory (DSM) systems utilize the physical memory of each computing node interconnected in a private network to form a global virtual shared memory. Since this global shared memory is distributed among the computing nodes, accessing the data located in remote computing nodes is an absolute necessity. However, this action will result in significant remote memory access latencies which are major sources of overhead in DSM systems. For these reasons, in order to increase overall system performance and decrease this overhead, a number of strategies have been devised. Prefetching is one such approach which can reduce latencies, although it always increases the workload in the home nodes. In this paper, we propose a scheme named Agent Home Scheme. Its most noticeable feature, when compared to other schemes, is that the agent home distributes the workloads of each computing nodes when sending data. By doing this, we can reduce not only the workload of the home nodes by balancing the workload for each node, but also the waiting time. Experimental results show that the proposed method can obtain about 20% higher performance than the original JIAJIA, about 18% more than History Prefetching Strategy (HPS), and about 10% higher than Effective Prefetch Strategy (EPS).     

Extended stochastic gradient identification algorithms for Hammerstein–Wiener ARMAX systems

An extended stochastic gradient algorithm is developed to estimate the parameters of Hammerstein–Wiener ARMAX models. The basic idea is to replace the unmeasurable noise terms in the information vector of the pseudo-linear regression identification model with the corresponding noise estimates which are computed by the obtained parameter estimates. The obtained parameter estimates of the identification model include the product terms of the parameters of the original systems. Two methods of separating the parameter estimates of the original parameters from the product terms are discussed: the average method and the singular value decomposition method. To improve the identification accuracy, an extended stochastic gradient algorithm with a forgetting factor is presented. The simulation results indicate that the parameter estimation errors become small by introducing the forgetting factor.     

Lose Fat, Not Muscle: An Examination of Supervisor Reduction in Discrete-Event Systems

This paper is concerned with the elimination of unnecessary states in discrete-event system control agents. Several approaches to supervisor reduction are studied and a new relation between agents, comparability, is defined to encapsulate most of the concepts found in the aforementioned methods. This relation is also proven to be preserved under conjunction, which is commonly employed to determine the centralized representation of two decentralized DES supervisors.

Delay composition in preemptive and non-preemptive real-time pipelines

Uniprocessor schedulability theory made great strides, in part, due to the simplicity of composing the delay of a job from the execution times of higher-priority jobs that preempt it. In this paper, we bound the end-to-end delay of a job in a multistage pipeline as a function of job execution times on different stages under preemptive as well as non-preemptive scheduling. We show that the end-to-end delay is bounded by that of a single virtual “bottleneck” stage plus a small additive component. This contribution effectively transforms the pipeline into a single stage system. The wealth of schedulability analysis techniques derived for uniprocessors can then be applied to decide the schedulability of the pipeline. The transformation does not require imposing artificial per-stage deadlines, but rather models the pipeline as a whole and uses the end-to-end deadlines directly in the single-stage analysis. It also does not make assumptions on job arrival patterns or periodicity and thus can be applied to periodic and aperiodic tasks alike. We show through simulations that this approach outperforms previous pipeline schedulability tests except for very short pipelines or when deadlines are sufficiently large. The reason lies in the way we account for execution overlap among stages. We discuss how previous approaches account for overlap and point out interesting differences that lead to different performance advantages in different cases. Further, we also show that in certain cases non-preemptive scheduling can result in higher system utilization than preemptive scheduling in pipelined systems. We hope that the pipeline delay composition rule, derived in this paper, may be a step towards a general schedulability analysis foundation for large distributed systems.

Diagnosability Analysis of a Class of Hierarchical State Machines

This paper addresses the problem of fault detection and isolation for a particular class of discrete event dynamical systems called hierarchical finite state machines (HFSMs). A new version of the property of diagnosability for discrete event systems tailored to HFSMs is introduced. This notion, called L₁-diagnosability, captures the possibility of detecting an unobservable fault event using only high level observations of the behavior of an HFSM. Algorithms for testing L₁-diagnosability are presented. In addition, new methodologies are presented for studying the diagnosability properties of HFSMs that are not L₁-diagnosable. These methodologies avoid the complete expansion of an HFSM into its corresponding flat automaton by focusing the expansion on problematic indeterminate cycles only in the associated extended diagnoser.

A multi-agent legal recommender system

Infonorma is a multi-agent system that provides its users with recommendations of legal normative instruments they might be interested in. The Filter agent of Infonorma classifies normative instruments represented as Semantic Web documents into legal branches and performs content-based similarity analysis. This agent, as well as the entire Infonorma system, was modeled under the guidelines of MAAEM, a software development methodology for multi-agent application engineering. This article describes the Infonorma requirements specification, the architectural design solution for those requirements, the detailed design of the Filter agent and the implementation model of Infonorma, according to the guidelines of the MAAEM methodology.