Performance and population state metrics for rule-based learning systems

Achieving at least some level of explainability requires complex analyses for many machine learning systems, such as common black-box models. We recently proposed a new rule-based learning system, SupRB, to construct compact, interpretable and transparent models by utilizing separate optimizers for the model selection tasks concerning rule discovery and rule set composition. This allows users to specifically tailor their model structure to fulfil use-case specific explainability requirements. From an optimization perspective, this allows us to define clearer goals and we find that—in contrast to many state of the art systems—this allows us to keep rule fitnesses independent. In this paper we investigate this system’s performance thoroughly on a set of regression problems and compare it against XCSF, a prominent rule-based learning system. We find the overall results of SupRB’s evaluation comparable to XCSF’s while allowing easier control of model structure and showing a substantially smaller sensitivity to random seeds and data splits. This increased control can aid in subsequently providing explanations for both training and final structure of the model.KeywordsRule-based learningLearning classifier systemsEvolutionary machine learningInterpretable modelsExplainable AI

Rule-based evolutionary systems like learning classifier system are widely used in industry and automation. In some types of problems we have additional information about problem solution. This information can be used in the process of problem solutions. A rule-based system can be augmented by additional information concerning the given problem to enrich the process of system adaptation to solve the problem. In many pattern matching tasks we know the patterns and we are looking for a pattern identification in environment. We provide representative of a rule-based learning classifier system augmented with information about a property of solution. The augmented system solves an example of pattern matching problem of simple Battleship game. This modified learning classifier system provides better convergence results by using the probability model of the Battleship game problem space.KeywordsSoft ComputingCovering ProbabilityReward PredictionEnemy BattleshipPattern Match ProblemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Summary of United States Human Proteome Organisation (HUPO) Symposium Entitled “Standardized Clinical Proteomics Platforms”

Since Internet of Things (IoT) is suggested as the fundamental platform to adapt massive connections and secure transmission, we study physical-layer authentication in the point-to-point wireless systems relying on reconfigurable intelligent surfaces (RIS) technique. Due to lack of direct link from IoT devices (both legal and illegal devices) to the access point, we benefit from RIS by considering two main secure performance metrics. As main goal, we examine the secrecy performance of a RIS-aided wireless communication systems which show secure performance in the presence of an eavesdropping IoT devices. In this circumstance, RIS is placed between the access point and the legitimate devices and is designed to enhance the link security. To specify secure system performance metrics, we firstly present analytical results for the secrecy outage probability. Then, secrecy rate is further examined. Interestingly, we are to control both the average signal-to-noise ratio at the source and the number of metasurface elements of the RIS to achieve improved system performance. We verify derived expressions by matching Monte-Carlo simulation and analytical results.

Adaptive Body Biasing (ABB) is a popularly used technique to mitigate the increasing impact of manufacturing process variations on leakage power dissipation. The efficacy of the ABB technique can be improved by partitioning a design into a number of islands, each with its individual body-bias voltage. In this paper, we propose a system-level leakage variability mitigation framework to partition a multiprocessor system into body-bias islands at the processing element (PE) granularity at design time, and to optimally assign body-bias voltages to each island post-fabrication. As opposed to prior gate- and circuit-level partitioning techniques that constrain the global clock frequency of the system, we allow each island to run at a different speed and constrain only the relevant system performance metrics - in our case the execution deadlines. Experimental results show the efficacy of the proposed framework in reducing the mean and standard deviation of leakage power dissipation compared to a baseline system without ABB. At the same time, the proposed techniques provide significant runtime improvements over a previously proposed Monte-Carlo based technique while providing similar reductions in leakage power dissipation.

Optimizing directly the system performance metric is an important method for correcting wave-front distortions in adaptive optics (AO) systems. In extended object imaging, some classical image quality metrics are often used as system performance evaluation function, which is the optimized object of the control algorithm. But those metrics do not consider the existence of imaging noise. Practically, the observed object images are degraded not only by the atmospheric turbulence but also imaging system noise. The noise in image will affect the value of image quality criteria and further affect the correction capability of AO system. An AO system with Stochastic Parallel Gradient Descent (SPGD) algorithm and a 61-element deformable mirror is simulated to restore the image of a turbulence-degraded extended object and the gray level variance function acted the optimized object by control algorithm. The analysis results show that the correction capability is affected severely when the turbulence strength is relatively big (D/r0<10). This paper presents a method that the observed image is processed by the bilateral filter before the system performance metric is calculated, so that the effect of noise on the system performance metric can be mitigated. Numerical simulation results verify the method is effective and the image quality based on AO technique and the bilateral filter is much better than that of only using AO technique.

Model-based analysis of production systems is one of the main areas in manufacturing research. The foundation of the successful application of these theoretical studies is the availability of valid and high-fidelity mathematical models that are capable of capturing the behavior of job flow in production systems. The modeling process of a production system, however, may require a significant amount of nonstandardized work that can only be done properly by someone with solid training in the area and extensive experience through real case studies. This poses a critical challenge in the effective implementation of these valuable theoretical results in the Industry 4.0 era. To overcome this, we propose a new production systems modeling paradigm inspired by system identification: calculate production system model parameters that best match the standard system performance metrics measured on the factory floor. Specifically, in this article, we consider production lines characterized by the Bernoulli serial line model and develop algorithms that identify model parameters to fit the system throughput and work-in-process. Analytical algorithms are derived to solve this problem in a two-machine line case and then extended to multi-machine lines. The accuracy and computational efficiency of the algorithms are demonstrated through extensive numerical experiments. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —A high-fidelity mathematical model is of critical importance to the implementation of any model-based production system analysis method. Currently, the construction of such models is carried out in an ad hoc manner. The quality of the resulting models may heavily depend on the training, experience, intuition, and personal preference of the modeler. The proposed model parameter identification method focuses on standard key performance indices commonly measured on the factory floor. The advantage is twofold. First, these standard performance metrics are consistently defined regardless of industry, thus avoiding any data-ambiguity issue that may occur when using complex machine/equipment status data. Second, measuring these performance metrics in real time is typically convenient and cost effective, even for manufacturing plants without high-end IT infrastructure, thus making the technology accessible to not only large but also small- and mid-sized manufacturers. Using the algorithms developed in this article, a practitioner can quickly construct a serial production line model and then utilize it to access the rich library of production analysis, design, and control methods available in the literature.

Text-based Question Answering (QA) is an essential task in natural language processing that aims to provide relevant and accurate answers to users’ queries. Traditional approaches to QA relied on rule-based systems and hand-crafted features. However, recent advancements in information retrieval and deep learning have shown promising results in improving the performance of QA systems. The combination of IR and deep learning techniques has led to significant improvements in QA performance. Hybrid models, such as the bi-encoder and tri-encoder architectures, have been proposed to leverage the strengths of both IR and deep learning models. Additionally, pre-training techniques, such as BERT and RoBERTa, have been shown to improve the performance of QA systems by providing pre-trained contextualized word embeddings. This study provides a brief overview of the QA system and its different domains and subdomains. This study also provides review of different proposed models and different datasets available for the task and evaluate their performance using some performance metrics. A comparison between various techniques are employed using the obtained result.

Many storage systems are undergoing a significant shift from dedicated appliance-based model to software-defined storage (SDS) because the latter is flexible, scalable and cost-effective for modern workloads. However, it is challenging to provide a reliable guarantee of end-to-end performance in SDS due to complex software stack, time-varying workload and performance interference among tenants. Therefore, modeling and monitoring the performance of storage systems is critical for ensuring reliable QoS guarantees. Existing approaches such as performance benchmarking and analytical modeling are inadequate because they are not efficient in exploring large configuration space, and cannot support elastic operations and diverse storage services in SDS. This paper presents Inside-Out, an automatic model building tool that creates accurate performance models for distributed storage services. Inside-Out is a black-box approach. It builds high-level performance models by applying machine learning techniques to low-level system performance metrics collected from individual components of the distributed SDS system. Inside-Out uses a two-level learning method that combines two machine learning models to automatically filter irrelevant features, boost prediction accuracy and yield consistent prediction. Our in-depth evaluation shows that Inside-Out is a robust solution that enables SDS to predict end-to-end performance even in challenging conditions, e.g., changes in workload, storage configuration, available cloud resources, size of the distributed storage service, and amount of interference due to multi-tenants. Our experiments show that Inside-Out can predict end-to-end performance with 91.1% accuracy on average. Its prediction accuracy is consistent across diverse storage environments.

High level performance metrics for FPGA-based multiprocessor systems

An M/G/c/c state dependent network is a quantitative model for replicating and analyzing the behavior of occupants travelling through a network. The model, however, assumes that its inter-arrival times are independent and exponentially distributed, which inadequately reflects the dynamic and high-stakes nature in emergency situations. This paper simulates the M/G/c/c model for emergency evacuations using Erlang-k distributions—representing the real-world arrival patterns of evacuees in a more controlled and less random arrivals—and correlated inter-arrival times—common during evacuations as occupants moving in clusters due to panic or structural flow patterns. To achieve this, an M/G/c/c simulation model was first developed using Arena simulation software. The model was used to analyze the impact of various arrival rates on system performance metrics. Consequently, the performance metrics were compared with those obtained by replacing exponential inter-arrival times with Erlang-k distributions and independent arrivals with correlated cluster arrivals. The results show that Erlang-k distributions lead to better performance and smoother flow since the arrival is more controlled, while correlated arrivals increase congestion.

Mobile data traffic is currently growing exponentially and these rapid increases have caused the backhaul data rate requirements to become the major bottleneck to reducing costs and raising revenue for operators. To address this problem, caching techniques have attracted significant attention since they can effectively reduce the backhaul traffic by eliminating duplicate data transmission that carries popular content. In addition, other system performance metrics can also be improved through caching techniques, e.g., spectrum efficiency, energy efficiency, and transmission delay. In this paper, we provide a systematical survey of the state-of-the-art caching techniques that were recently developed in cellular networks, including macro-cellular networks, heterogeneous networks, device-to-device networks, cloud-radio access networks, and fog-radio access networks. In particular, we give a tutorial on the fundamental caching techniques and introduce caching algorithms from three aspects, i.e., content placement, content delivery, and joint placement and delivery. We provide comprehensive comparisons among different algorithms in terms of different performance metrics, including throughput, backhaul cost, power consumption, and network delay. Finally, we summarize the main research achievements in different networks, and highlight main challenges and potential research directions.

Uncertainty propagation is an important step in technology forecasting. Uncertainty in technology impacts and other system variables must be propagated into the system performance metrics that will be used to judge competing technology portfolios. Monte Carlo simulation and Evidence Theory are two uncertainty propagation techniques commonly used for this purpose. This paper explores the differences between these two techniques when applied to a representative aircraft engine technology forecasting problem. As an example problem, uncertainties in compressor, turbine, and combustor efficiencies are propagated into seven aircraft engine performance metrics. A surrogate model built from NPSS and WATE++ data provides modeling and simulation of the system. A representative set of expert opinion derived uncertainty intervals drives this process. The MATLAB IPP Toolbox is used to conduct Evidence Theory uncertainty propagation on the example problem. The results presented show the impact of sample size selection (number of cases), method selection (Monte Carlo or Evidence Theory), and mixing rule (Dempster’s rule or weighted mixing) on the resulting CDFs. The increase in runtime with sample size for both methods is shown, as well.

An increasing concern when dealing with critical systems or services design is their preparation for a wide range of potentially uncertain operating conditions. In this chapter a four-step simulation-driven decision making methodology (SDDM) is presented to address sensitivity analysis of candidate designs to uncertainty and extreme operating conditions. The approach accounts for, not only the conventional average system performance metrics, but also (i) upper-tail or extreme values of these metrics, and (ii) performance sensitivity measures to uncertainties in the simulation model. The example used to illustrate the application of this technique is a hospital emergency department design case study wherein different design alternatives are compared using patient time-in-system performance metric under multiple uncertain operating conditions.

A novel approach for low mutual coupling and ultra-compact Two Port MIMO antenna development for UWB wireless application