Hybrid Rule Research Articles

An intelligent rule management scheme for Software Defined Networking

Software Defined Networking (SDN) enables network innovation and brings flexibility by separation of the control and data planes and logically centralized control. However, this network paradigm complicates flow rule management. Current approaches generally install rules reactively after table misses or pre-installs them by flow prediction. Such approaches consume nontrivial network resources during interactions between the controller and switches (especially for maintaining consistency). In this paper, we explore an intelligent rule management scheme (IRMS), which extends the one-big-switch model and employs a hybrid rule management approach. To achieve this, we first transform all rules into path-based and node-based rules. Path-based rules are pre-installed whilst the paths for flows are selected at the edge switches of the network. To maintain consistency of forwarding paths, we update path-based rules as a whole and employ a lazy update policy. Node-based rules are optimally partitioned into disjoint chunks by an intelligent partition algorithm and organized hierarchically in the flow table. In this way, we significantly reduce the interaction cost between the control and data planes. This scheme enforces an efficient sliding window policy to enhance the hit rate for the installed chunks. We evaluate our scheme by comprehensive experiments. The results show that IRMS reduces the total flow entries by more than 59.9% on average and the update time by over 56%. IRMS also reduces the flow setup requests by more than one order of magnitude.

Balancing Lexicographic Fairness and a Utilitarian Objective With Application to Kidney Exchange

In this work, we close an open theoretical problem regarding the price of fairness in modern kidney exchanges. We then propose a hybrid fairness rule that balances a lexicographic preference ordering over agents, with a utilitarian objective. This rule has one parameter which controls a bound on the price of fairness. We apply this rule to real data from a large kidney exchange and show that our hybrid rule produces more reliable outcomes than other fairness rules.

Balancing Lexicographic Fairness and a Utilitarian Objective With Application to Kidney Exchange

Balancing fairness and efficiency in resource allocation is a classical economic and computational problem. The price of fairness measures the worst-case loss of economic efficiency when using an inefficient but fair allocation rule; for indivisible goods in many settings, this price is unacceptably high. One such setting is kidney exchange, where needy patients swap willing but incompatible kidney donors. In this work, we close an open problem regarding the theoretical price of fairness in modern kidney exchanges. We then propose a general hybrid fairness rule that balances a strict lexicographic preference ordering over classes of agents, and a utilitarian objective that maximizes economic efficiency. We develop a utility function for this rule that favors disadvantaged groups lexicographically; but if cost to overall efficiency becomes too high, it switches to a utilitarian objective. This rule has only one parameter which is proportional to a bound on the price of fairness, and can be adjusted by policymakers. We apply this rule to real data from a large kidney exchange and show that our hybrid rule produces more reliable outcomes than other fairness rules.

Instrument-Based vs. Target-Based Rules

We develop a simple delegation model to study rules based on instruments vs. targets. A principal faces a better informed but biased agent and relies on joint punishments as incentives. Instrument-based rules condition incentives on the agent's observable action; target-based rules condition incentives on outcomes that depend on the agent's action and private information. In each class, an optimal rule takes a threshold form and imposes the worst punishment upon violation. Target-based rules dominate instrument-based rules if and only if the agent's information is sufficiently precise. An optimal hybrid rule relaxes the instrument threshold whenever the target threshold is satisfied.

Analysis of Rules for Islamic Inheritance Law in Indonesia Using Hybrid Rule Based Learning

Along with the development of human civilization in Indonesia, the changes and reform of Islamic inheritance law so as to conform to the conditions and culture cannot be denied. The distribution of inheritance in Indonesia can be done automatically by storing the rule of Islamic inheritance law in the expert system. In this study, we analyze the knowledge of experts in Islamic inheritance in Indonesia and represent it in the form of rules using rule-based Forward Chaining (FC) and Davis-Putman-Logemann-Loveland (DPLL) algorithms. By hybridizing FC and DPLL algorithms, the rules of Islamic inheritance law in Indonesia are clearly defined and measured. The rules were conceptually validated by some experts in Islamic laws and informatics. The results revealed that generally all rules were ready for use in an expert system.

Estudo da influência da estimativa inicial na qualidade da solução no algoritmo genético binário em problemas de sequenciamento da produção em ambiente job shop

A proposta deste trabalho é avaliar a influência da estimativa inicial, usada na geração de populações de soluções pelo Algoritmo Genético em problemas de sequenciamento, em relação à qualidade e à viabilidade das soluções. O problema de sequenciamento da produção consiste em encontrar a sequência da execução das tarefas no parque de máquinas que otimize, por exemplo, a utilização de recursos e o tempo total de processamento (makespan). É comum tratar problemas desta natureza com o uso de técnicas metaheuristicas de otimização como Algoritmo Genético principalmente em função da sua complexidade computacional. Neste trabalho foram realizados experimentos com os um conjunto de instâncias da literatura, variando a regra de sequenciamento utilizada na geração das soluções iniciais. Foram testadas regras usuais da literatura e identificada uma regra híbrida que gera um menor número de soluções não factíveis e um número maior de instâncias que atingiram o makespan ótimo.

Three-dimensional thermoelastic properties of general composite laminates

This paper presents a hybrid rule of mixtures for calculating the complete set of effective three-dimensional thermoelastic properties of a composite laminate when it is approximated as an equivalent, homogeneous, anisotropic solid. The laminate can be made of generally anisotropic layers with arbitrary layup sequence. This hybrid rule of mixtures is based on the exact solution obtained using the recently discovered mechanics of structure genome. Since mechanics of structure genome minimizes the loss of accuracy for homogenization, the three-dimensional thermoelastic properties obtained using the mechanics of structure genome-based hybrid rule of mixtures will be the most accurate one could obtain for composite laminates. The results of the hybrid rule of mixtures are compared with several other representative methods for predicting thermoelastic properties of composite laminates.

STRUCTURAL STABILITY OF THE GENERALIZED TAYLOR RULE

This paper analyzes the dynamical properties of monetary models with regime switching. We start with the analysis of the evolution of inflation when policy is guided by a simple monetary rule where coefficients switch with the policy regime. We rule out the possibility of a Hopf bifurcation and demonstrate the possibility of a period-doubling bifurcation. As a result, a small change in the parameters (e.g., a more active policy response) can lead to a drastic change in the path of inflation. We show that the New Keynesian model with a current-looking Taylor rule is not prone to bifurcations. A New Keynesian model with a hybrid rule, however, exhibits the same pattern of period-doubling bifurcations as the analysis with a simple monetary rule.

Cognitive Networks Intelligent Decision-Making Approach Based on Hybrid Rule Reasoning

Cognitive Networks Intelligent Decision-Making Approach Based on Hybrid Rule Reasoning

A hybrid rule and machine learning based generic alerting platform for smart environments.

Existing smart environment based alert solutions have adopted a relatively complex and tailored approach to supporting individuals. These solutions have involved sensor based monitoring, activity recognition and assistance provisioning. Traditionally they have suffered from a number of issues, rooted in scalability and performance, associated with complex activity recognition processes. This paper introduces a generic approach to realizing an alerting platform within a smart environment. The core concept of this approach is presented and placed within the context of related work. A description of the approach is provided, followed by an evaluation. This evaluation shows the approach offers reasonable accuracy, future work will increase accuracy.

THE HYBRID APPROACH OF INFLATION TARGETING: WHAT OPPORTUNITIES FOR AN EMERGING ECONOMY LIKE TUNISIA?

After economic crises happening in many emerging countries, flexible exchange rates became a required theoretical condition helping to target inflation. Many countries stopped using exchange rate as an anchor for monetary policy and started using inflation targeting framework. In emerging countries, monetary authorities work to stabilize the exchange rate because of their “fear of floating”. They are against high volatility of interest rate allowing speculative attacks and causing free fluctuations of their national currency. To avoid uncontrolled market movements, they have to choose between active and public exchange rate management and tight inflation targeting. In the same vein, Central bank of Tunisia follows financial measures linked closely to inflation without focusing especially on monetary aggregates in order to study a possible transition to targeting inflation strategy. It uses a simple Taylor rule where interest rates adjustment are guided by the anticipated inflation deviation from its original target and also by the gap between observed and potential GDP.As an emerging economy with a high degree of financial vulnerability, and facing different shocks, Tunisia should adopt a hybrid rule of inflation targeting in an open economy. This hybrid rule explicitly takes into account the evolution of the exchange rate in the reaction function of the central bank.

NOVEL HYBRID RULE NETWORK BASED ON TS FUZZY RULES

A novel hybrid rule network based on TS fuzzy rules is proposed to resolve the problems of fuzzy classication and prediction. The proposed model learns by using genetic algorithm and is able to cover the whole distribution regions of the samples. In the learning process: (1) fuzzy intervals of each dimension of the samples are partitioned evenly; (2) computing intervals (CIs) are established based on the even intervals; (3) linear weighted model of several normal probability distributions is used to describe the sample probability distribution on CIs; (4) membership degree of each CI is learnt to evaluate the importance of each CI, avoiding the problem that the optimal intervals are difficult to cover the original sample spaces; (5) dynamic rule selection mechanism is used to dynamically com- bine a small number of optimal rules linearly to achieve nonlinear approximation, reducing the computation load. Three experiments are performed: the experiments on Iris and Mackey-Glass chaotic time series show that HRN can achieve satisfactory results and is more effective in terms of generalization ability, whereas the experiment on exhaust gas temperature demonstrates that HRN can predict the EGT of aero engine effectively.

Combining hybrid rule ordering strategies based on netconf and a novel satisfaction mechanism for CAR-based classifiers

In Associative Classification, building a classifier based on Class Association Rules (CARs) consists in finding an ordered CAR list by applying a rule ordering strategy, and selecting a satisfaction mechanism to determine the class of unseen transactions. In this paper, we introduce four novel hybrid rule ordering strategies; the first three combine the Netconf measure with different Support-Confidence based rule ordering strategies. The fourth strategy combines the Netconf measure with a rule ordering strategy based on the CAR's size. Additionally, we combine the proposed strategies with a novel Dynamic K satisfaction mechanism. Experiments over several datasets show that the proposed rule ordering strategies jointly with the Dynamic K satisfaction mechanism allow improving the performance of CAR-based classifiers.

A Simplified Life Cycle Analysis of an Automotive Parking Brake Lever Using Polymer Composites

This paper presents a simplified life cycle analysis case study of a new developed automotive parking brake lever using polymer composites. Two composite materials were analysed for the component construction, namely glass fiber/polypropylene composites and hybrid kenaf/glass fiber/polypropylene composites. The rule of mixture and hybrid rule of mixture composite’s micromechanical models were utilized to determine the functional unit in the life cycle analysis. Later, the life cycle inventory data were defined for the production, use and end-of-life stages for the component. The Eco-indicator 99 method was selected for the overall LCA process. The final life cycle analysis results show that parking brake lever using the hybrid kenaf/glass fiber/polypropylene composites scores better environmental impact when compared to the similar component using glass fiber composites. This proved that the introduction of kenaf natural fiber, as the alternative reinforcement material in the polymer composites construction, is able to reduce the environmental impact throughout the product life cycle towards achieving better sustainable performance of the product.

Hybrid support vector machine rule extraction method for discovering the preferences of stock market investors: Evidence from Montenegro

In this study we developed a support vector machine (SVM) rule extraction method for discovering the effects of the features of investors and stock and corporate performance on stock trading preferences. We used this system to combine strengths of two approaches: SVM as an accurate classifier and a decision tree (DT) as a generator of interpretable models. The method is applied to Montenegro data in order to generate interpretable rules for stock market decision-makers. The results showed that this method, in terms of accuracy and interdependency of factors, outperformed the methods for detecting stock trading preferences from previous studies.

Novel adaptive hybrid rule network based on TS fuzzy rules using an improved quantum-behaved particle swarm optimization

A novel adaptive hybrid rule network (AHRN) based on Takagi-Sugeno (TS) fuzzy rules is proposed to resolve chaotic system prediction problems. This model automatically adjusts its structure and dynamically establishes rule sets (apart from statically) to adapt in learning new samples. For the learning process, the opinion leader-based quantum-behaved particle swarm optimization (OLB-QPSO) algorithm is proposed. This algorithm uses composed particles generated according to AHRN and emphasizes the importance of the composed particle with the highest fitness based on a social communication law. To improve the chance of finding the best global solution, the movement of the composed particle is affected by the subparticles as inner factors and by the swarm as outer factor.Three chaotic time series experiments are performed to validate the proposed method. Results show that AHRN that uses the OLB-QPSO with composed particles can effectively provide the appropriate rules to search for solutions in a wide space and significantly improve the probability of obtaining the optimal global solution. A novel adaptive model is proposed to deal with the chaotic time series prediction.Dynamic rule selection mechanism is proposed for the model to establish the rule set.Opinion leader-based QPSO (OLB-QPSO) algorithm is proposed to learn this model.The composed particle with a multiple structure is used in OLB-QPSO.

Weighing Outcomes by Time or Against Time? Evaluation Rules in Intertemporal Choice

Models of intertemporal choice draw on three evaluation rules, which we compare in the restricted domain of choices between smaller sooner and larger later monetary outcomes. The hyperbolic discounting model proposes an alternative-based rule, in which options are evaluated separately. The interval discounting model proposes a hybrid rule, in which the outcomes are evaluated separately, but the delays to those outcomes are evaluated in comparison with one another. The tradeoff model proposes an attribute-based rule, in which both outcomes and delays are evaluated in comparison with one another: People consider both the intervals between the outcomes and the compensations received or paid over those intervals. We compare highly general parametric functional forms of these models by means of a Bayesian analysis, a method of analysis not previously used in intertemporal choice. We find that the hyperbolic discounting model is outperformed by the interval discounting model, which, in turn, is outperformed by the tradeoff model. Our cognitive modeling is among the first to offer quantitative evidence against the conventional view that people make intertemporal choices by discounting the value of future outcomes, and in favor of the view that they directly compare options along the time and outcome attributes.

Operating Rules for an Off-Stream Blending Reservoir to Control Nitrate in a Municipal Water System

AbstractMunicipal water supply systems dependent on surface water sources that are susceptible to both low flows and contamination can use off-stream blending reservoirs (OSBR) both to increase their yield and avoid investments in expensive treatment technology. Three water quality operating rules (WQORs) are simulated from a retrospective planning perspective using the OSBR system that controls nitrate contamination for Pontiac, Illinois as an example case. These rules regulate pumped reservoir inflow based on (1) a maximum river concentration (Riv-CR), (2) a maximum reservoir concentration (Res-CR), and (3) a hybrid rule incorporating both maximum reservoir and river concentrations (Hyb-CR). The Hyb-CR rule typically registers the highest yield when nitrate is modeled as a conservative contaminant, while Res-CR tends to produce the highest yields when nitrate losses are considered. In general, reservoir concentration-based WQORs are effective for increasing the yield of OSBR systems that control the con...

A novel artificial immune clonal selection classification and rule mining with swarm learning model

Metaheuristic optimisation algorithms have become popular choice for solving complex problems. By integrating Artificial Immune clonal selection algorithm (CSA) and particle swarm optimisation (PSO) algorithm, a novel hybrid Clonal Selection Classification and Rule Mining with Swarm Learning Algorithm (CS2) is proposed. The main goal of the approach is to exploit and explore the parallel computation merit of Clonal Selection and the speed and self-organisation merits of Particle Swarm by sharing information between clonal selection population and particle swarm. Hence, we employed the advantages of PSO to improve the mutation mechanism of the artificial immune CSA and to mine classification rules within datasets. Consequently, our proposed algorithm required less training time and memory cells in comparison to other AIS algorithms. In this paper, classification rule mining has been modelled as a miltiobjective optimisation problem with predictive accuracy. The multiobjective approach is intended to allow the PSO algorithm to return an approximation to the accuracy and comprehensibility border, containing solutions that are spread across the border. We compared our proposed algorithm classification accuracy CS2 with five commonly used CSAs, namely: AIRS1, AIRS2, AIRS-Parallel, CLONALG, and CSCA using eight benchmark datasets. We also compared our proposed algorithm classification accuracy CS2 with other five methods, namely: Naïve Bayes, SVM, MLP, CART, and RFB. The results show that the proposed algorithm is comparable to the 10 studied algorithms. As a result, the hybridisation, built of CSA and PSO, can develop respective merit, compensate opponent defect, and make search-optimal effect and speed better.

STDP and STDP variations with memristors for spiking neuromorphic learning systems.

In this paper we review several ways of realizing asynchronous Spike-Timing-Dependent-Plasticity (STDP) using memristors as synapses. Our focus is on how to use individual memristors to implement synaptic weight multiplications, in a way such that it is not necessary to (a) introduce global synchronization and (b) to separate memristor learning phases from memristor performing phases. In the approaches described, neurons fire spikes asynchronously when they wish and memristive synapses perform computation and learn at their own pace, as it happens in biological neural systems. We distinguish between two different memristor physics, depending on whether they respond to the original “moving wall” or to the “filament creation and annihilation” models. Independent of the memristor physics, we discuss two different types of STDP rules that can be implemented with memristors: either the pure timing-based rule that takes into account the arrival time of the spikes from the pre- and the post-synaptic neurons, or a hybrid rule that takes into account only the timing of pre-synaptic spikes and the membrane potential and other state variables of the post-synaptic neuron. We show how to implement these rules in cross-bar architectures that comprise massive arrays of memristors, and we discuss applications for artificial vision.