Information sharing between heterogeneous uncertain reasoning models in a multi-agent environment : a case study

The issue of information sharing and exchanging is one of the most important issues in the areas of artificial intelligence and knowledge-based systems (KBSs), or even in the broader areas of computer and information technology. This paper deals with a special case of this issue by carrying out a case study of information sharing between two well-known heterogeneous uncertain reasoning models: the certainty factor model and the subjective Bayesian method. More precisely, this paper discovers a family of exactly isomorphic transformations between these two uncertain reasoning models. More interestingly, among isomorphic transformation functions in this family, different ones can handle different degrees to which a domain expert is positive or negative when performing such a transformation task. The direct motivation of the investigation lies in a realistic consideration. In the past, expert systems exploited mainly these two models to deal with uncertainties. In other words, a lot of stand-alone expert systems which use the two uncertain reasoning models are available. If there is a reasonable transformation mechanism between these two uncertain reasoning models, we can use the Internet to couple these pre-existing expert systems together so that the integrated systems are able to exchange and share useful information with each other, thereby improving their performance through cooperation. Also, the issue of transformation between heterogeneous uncertain reasoning models is significant in the research area of multi-agent systems because different agents in a multi-agent system could employ different expert systems with heterogeneous uncertain reasonings for their action selections and the information sharing and exchanging is unavoidable between different agents. In addition, we make clear the relationship between the certainty factor model and probability theory.

An agent-based framework for petroleum information services from distributed heterogeneous data resources

For making good decisions in the area of petroleum production, it is becoming a big problem how to timely gather sufficient and correct information, which may be stored in databases, data files, or on the World Wide Web. In this paper, Gaia methodology and Open Agent Architecture were employed to contribute a framework to solve above problem. The framework consists of three levels, namely, role mode, agent type, and agent instance. The model with five roles is analyzed. Four agent types are designed Six agent instances are developed for constructing the system of petroleum information services. The experimental results show that all agents in the system can work cooperatively to organize and retrieve relevant petroleum information. The successful implementation of the framework shows that agent-based technology can significantly facilitate the construction of complex systems in distributed heterogeneous data resource environment.

Spamcooling : a parallel heterogeneous ensemble spam filtering system based on active learning techniques

Anti-spam technology is developing rapidly in recent years. With the emerging applications of machine learning in diverse fields, researchers as well as manufacturers around the world have attempted a large number of related algorithms to prevent spam. In this paper, we designed an effective anti-spam protection system, SpamCooling, based on the mechanism of active learning and parallel heterogeneous ensemble learning techniques. The system adopts a batch method to filter spam and can be easily incorporated with existing mail clients (MUA). It can actively obtain user feedbacks for providing users with personalized spam filtering experiences. The parallel heterogeneous ensemble method can help system achieve high spam detection rate as well as low ham misclassification rate.

RGO/ZnO nanocomposite: an efficient, sustainable, heterogeneous, amphiphilic catalyst for synthesis of 3-substituted indoles in water

A nanocomposite consisting of reduced graphene oxide and zinc oxide nanoparticles (RGO/ZnO) with unique structural features was developed as an efficient, sustainable, amphiphilic, heterogeneous catalyst for the synthesis of various 3-substituted indoles in water. The catalyst was recycled six times without significant loss in catalytic activity. The higher environmental compatibility and sustainability factors such as smaller E-factor and higher atom economy make the present methodology a true green and sustainable process for the synthesis of various biologically important 3-substituted indoles.

Atom probe tomography investigation of heterogeneous short-range ordering in the 'komplex' phase state (K-state) of Fe-18Al (at.%)

We study an Fe-18Al (at.%) alloy after various thermal treatments at different times (24-336 h) and temperatures (250-1100 °C) to determine the nature of the so-called 'komplex' phase state (or "K-state"), which is common to other alloy systems having compositions at the boundaries of known order-disorder transitions and is characterised by heterogeneous short-range-ordering (SRO). This has been done by direct observation using atom probe tomography (APT), which reveals that nano-sized, ordered regions/particles do not exist. Also, by employing shell-based analysis of the three-dimensional atomic positions, we have determined chemically sensitive, generalised multicomponent short-range order (GM-SRO) parameters, which are compared with published pairwise SRO parameters derived from bulk, volume-averaged measurement techniques (e.g. X-ray and neutron scattering, Mössbauer spectroscopy) and combined ab-initio and Monte Carlo simulations. This analysis procedure has general relevance for other alloy systems where quantitative chemical-structure evaluation of local atomic environments is required to understand ordering and partial ordering phenomena that affect physical and mechanical properties.

Heterogeneous atrophy occurs within individual lower limb muscles during 60 days of bed rest

To better understand disuse muscle atrophy, via magnetic resonance imaging, we sequentially measured muscle cross-sectional area along the entire length of all individual muscles from the hip to ankle in nine male subjects participating in 60-day head-down tilt bed rest (2nd Berlin BedRest Study; BBR2-2). We hypothesized that individual muscles would not atrophy uniformly along their length such that different regions of an individual muscle would atrophy to different extents. This hypothesis was confirmed for the adductor magnus, vasti, lateral hamstrings, medial hamstrings, rectus femoris, medial gastrocnemius, lateral gastrocnemius, tibialis posterior, flexor hallucis longus, flexor digitorum longus, peroneals, and tibialis anterior muscles (P ≤ 0.004). In contrast, the hypothesis was not confirmed in the soleus, adductor brevis, gracilis, pectineus, and extensor digitorum longus muscles (P ≥ 0.20). The extent of atrophy only weakly correlated (r = -0.30, P < 0.001) with the location of greatest cross-sectional area. The rate of atrophy during bed rest also differed between muscles (P < 0.0001) and between some synergists. Most muscles recovered to their baseline size between 14 and 90 days after bed rest, but flexor hallucis longus, flexor digitorum longus, and lateral gastrocnemius required longer than 90 days before recovery occurred. On the basis of findings of differential atrophy between muscles and evidence in the literature, we interpret our findings of intramuscular atrophy to reflect differential disuse of functionally different muscle regions. The current work represents the first lower-limb wide survey of intramuscular differences in disuse atrophy. We conclude that intramuscular differential atrophy occurs in most, but not all, of the muscles of the lower limb during prolonged bed rest.

Optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers

For multiple heterogeneous multicore server processors across clouds and data centers, the aggregated performance of the cloud of clouds can be optimized by load distribution and balancing. Energy efficiency is one of the most important issues for large-scale server systems in current and future data centers. The multicore processor technology provides new levels of performance and energy efficiency. The present paper aims to develop power and performance constrained load distribution methods for cloud computing in current and future large-scale data centers. In particular, we address the problem of optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers. Our strategy is to formulate optimal power allocation and load distribution for multiple servers in a cloud of clouds as optimization problems, i.e., power constrained performance optimization and performance constrained power optimization. Our research problems in large-scale data centers are well-defined multivariable optimization problems, which explore the power-performance tradeoff by fixing one factor and minimizing the other, from the perspective of optimal load distribution. It is clear that such power and performance optimization is important for a cloud computing provider to efficiently utilize all the available resources. We model a multicore server processor as a queuing system with multiple servers. Our optimization problems are solved for two different models of core speed, where one model assumes that a core runs at zero speed when it is idle, and the other model assumes that a core runs at a constant speed. Our results in this paper provide new theoretical insights into power management and performance optimization in data centers.

Exponential stability of non-autonomous neural networks with heterogeneous time-varying delays and destabilizing impulses

In this paper, the problem of global exponential stability analysis of a class of non-autonomous neural networks with heterogeneous delays and time-varying impulses is considered. Based on the comparison principle, explicit conditions are derived in terms of testable matrix inequalities ensuring that the system is globally exponentially stableunder destabilizing impulsive effects. Numerical examples are given to demonstrate the effectiveness of the obtained results.