Candidate Rules Research Articles

KGRED: Knowledge-graph-based rule discovery for weakly supervised data labeling

In weakly supervised learning, labeling rules can automatically label data to train models. However, due to insufficient prior knowledge, rule discovery often suffers from semantic drift. Since misclassified rules are generated from wrongly matched sentences, the sentences matched by rules shift from the target labels to other labels. It is worth noting that rules do not exist in isolation. The multi-dimensional semantic associations among rules can impose semantic constraints for rule generation, as well as enrich the semantic information of rules for rule matching. Therefore, we propose a Knowledge-Graph-based RulE Discovery method (KGRED), which can leverage the multi-dimensional semantic associations among rules to alleviate semantic drift in rule discovery. Specifically, to decrease misclassified rules, we design a label-aware rule generation approach to attentively propagate prior knowledge from seed rules to candidate rules based on rule KG. To reduce wrongly-matched sentences, we present a cross-attention-based semantic matching mechanism to refine the semantic information of sentences while enriching that of rules. Moreover, we propose an inconsistency-directed active learning strategy to verify rules that perform inconsistently in rule generation and matching. Experiments on two public datasets prove that KGRED can achieve at least 5.1 % gain in F1 score compared to state-of-the-art methods.

Training neural networks with classification rules for incorporating domain knowledge

Neural networks are capable of learning complex concepts and tasks, given abundant training data. In real-world applications where data collection can be difficult, integrating domain knowledge into the model can reduce the burden on data requirements and allow human experts greater control over model decisions. This paper focuses on incorporating conditional statements for tabular data as classification rules, which have a simple structure and are easy to construct. We introduce a general rule loss constraint to guide neural network training in a model agnostic manner, and propose confidence learning to automatically weigh the contribution of multiple candidate rules. Experimental evaluation with three real-world datasets shows that the rule loss can substantially increase model performance, particularly when training data is limited.

Completing Priceable Committees: Utilitarian and Representation Guarantees for Proportional Multiwinner Voting

When selecting committees based on preferences of voters, a variety of different criteria can be considered. Two natural objectives are maximizing the utilitarian welfare (the sum of voters' utilities) and coverage (the number of represented voters) of the selected committee. Previous work has studied the impact on utilitarian welfare and coverage when requiring the committee to satisfy minimal requirements such as justified representation or weak proportionality. In this paper, we consider the impact of imposing much more demanding proportionality axioms. We identify a class of voting rules that achieve strong guarantees on utilitarian welfare and coverage when combined with appropriate completions. This class is defined via a weakening of priceability and contains prominent rules such as the Method of Equal Shares. We show that committees selected by these rules (i) can be completed to achieve optimal coverage and (ii) can be completed to achieve an asymptotically optimal approximation to the utilitarian welfare if they additionally satisfy EJR+. Answering an open question of Elkind et al. (2022), we use the Greedy Justified Candidate Rule to obtain the best possible utilitarian guarantee subject to proportionality. We also consider completion methods suggested in the participatory budgeting literature and other objectives besides welfare and coverage.

ReLiCADA: Reservoir Computing Using Linear Cellular Automata design algorithm

In this paper, we present a novel algorithm to optimize the design of Reservoir Computing using Cellular Automata models for time series applications. Besides selecting the models’ hyperparameters, the proposed algorithm particularly solves the open problem of Linear Cellular Automaton rule selection. The selection method pre-selects only a few promising candidate rules out of an exponentially growing rule space. When applied to relevant benchmark datasets, the selected rules achieve low errors, with the best rules being among the top 5% of the overall rule space. The algorithm was developed based on mathematical analysis of Linear Cellular Automaton properties and is backed by almost one million experiments, adding up to a computational runtime of nearly one year. Comparisons to other state-of-the-art time series models show that the proposed Reservoir Computing using Cellular Automata models have lower computational complexity and, at the same time, achieve lower errors. Hence, our approach reduces the time needed for training and hyperparameter optimization by up to several orders of magnitude.

Development of a new control rule for managing anthropogenic removals of protected, endangered or threatened species in marine ecosystems.

Human activities in the oceans are increasing and can result in additional mortality on many marine Protected, Endangered or Threatened Species (PETS). It is necessary to implement ambitious measures that aim to restore biodiversity at all nodes of marine food webs and to manage removals resulting from anthropogenic activities. We developed a stochastic surplus production model (SPM) linking abundance and removal processes under the assumption that variations in removals reflect variations in abundance. We then consider several 'harvest' control rules, included two candidate ones derived from this SPM (which we called 'Anthropogenic Removals Threshold', or ART), to manage removals of PETS. The two candidate rules hinge on the estimation of a stationary removal rate. We compared these candidate rules to other existing control rules (e.g. potential biological removal or a fixed percentage rule) in three scenarios: (i) a base scenario whereby unbiased but noisy data are available, (ii) scenario whereby abundance estimates are overestimated and (iii) scenario whereby abundance estimates are underestimated. The different rules were tested on a simulated set of data with life-history parameters close to a small-sized cetacean species of conservation interest in the North-East Atlantic, the harbour porpoise (Phocoena phocoena), and in a management strategy evaluation framework. The effectiveness of the rules were assessed by looking at performance metrics, such as time to reach the conservation objectives, the removal limits obtained with the rules or temporal autocorrelation in removal limits. Most control rules were robust against biases in data and allowed to reach conservation objectives with removal limits of similar magnitude when averaged over time. However, one of the candidate rule (ART) displayed greater alignment with policy requirements for PETS such as minimizing removals over time.

Analysis of Line Balancing using Ranked Positional Weight (RPW), Largest Candidate Rule (LCR), and J-Wagon Methods in Crane Girder Production at PT MHE Demag Surabaya, Indonesia

Every company is required to enhance its quality by implementing improvement actions within the company's production system. PT MHE Demag Surabaya is a company engaged in crane girder manufacturing. This research aims to determine the optimal line balancing using the Helgesson-Birnie/Ranked Positional Weight (RPW) method, the Largest Candidate Rule method, and the J-Wagon method. Line balancing is necessary due to excess operating time in the crane girder manufacturing workstations, which increases the risk of accidents for the workers. The results of data processing which employed these three heuristic methods demonstrate that the RPW method is the most efficient. The Helgesson-Birnie/Ranked Positional Weight (RPW) method yields the most optimal output, with a line efficiency of 96.67%, balanced delay of 3.33%, and total idle time of 4936.8 seconds using 15 workstations. By utilizing the heuristic methods for line balancing, the efficiency decreases to 144.76%, the balanced delay increases to 14.76%, and the total idle time increases to 86,400 seconds.

Statistical rules for safety monitoring in clinical trials.

Protecting patient safety is an essential component of the conduct of clinical trials. Rigorous safety monitoring schemes are implemented for these studies to guard against excess toxicity risk from study therapies. They often include protocol-specified stopping rules dictating that an excessive number of safety events will trigger a halt of the study. Statistical methods are useful for constructing rules that protect patients from exposure to excessive toxicity while also maintaining the chance of a false safety signal at a low level. Several statistical techniques have been proposed for this purpose, but the current literature lacks a rigorous comparison to determine which method may be best suitable for a given trial design. The aims of this article are (1) to describe a general framework for repeated monitoring of safety events in clinical trials; (2) to survey common statistical techniques for creating safety stopping criteria; and (3) to provide investigators with a software tool for constructing and assessing these stopping rules. The properties and operating characteristics of stopping rules produced by Pocock and O'Brien-Fleming tests, Bayesian Beta-Binomial models, and sequential probability ratio tests (SPRTs) are studied and compared for common scenarios that may arise in phase II and III trials. We developed the R package "stoppingrule" for constructing and evaluating stopping rules from these methods. Its usage is demonstrated through a redesign of a stopping rule for BMT CTN 0601 (registered at Clinicaltrials.gov as NCT00745420), a phase II, single-arm clinical trial that evaluated outcomes in pediatric sickle cell disease patients treated by bone marrow transplant. Methods with aggressive stopping criteria early in the trial, such as the Pocock test and Bayesian Beta-Binomial models with weak priors, have permissive stopping criteria at late stages. This results in a trade-off where rules with aggressive early monitoring generally will have a smaller number of expected toxicities but also lower power than rules with more conservative early stopping, such as the O-Brien-Fleming test and Beta-Binomial models with strong priors. The modified SPRT method is sensitive to the choice of alternative toxicity rate. The maximized SPRT generally has a higher number of expected toxicities and/or worse power than other methods. Because the goal is to minimize the number of patients exposed to and experiencing toxicities from an unsafe therapy, we recommend using the Pocock or Beta-Binomial, weak prior methods for constructing safety stopping rules. At the design stage, the operating characteristics of candidate rules should be evaluated under various possible toxicity rates in order to guide the choice of rule(s) for a given trial; our R package facilitates this evaluation.

Incorporating logic rules with textual representations for interpretable knowledge graph reasoning

Reasoning on knowledge graphs (KGs) is significant for downstream applications, such as question answering and information extraction. On the basis of using factual triples in KGs, learning logic rules has been explored to handle KG reasoning in an interpretable pattern. Some rule learning methods simply apply thresholds to reduce the exponential complexity caused by the vast candidate rules derived from KGs. Others are designed to prune candidates on the basis of embedding facts in the KG. However, these methods only consider the factual triples, which mainly contain structural information, and ignore sufficient semantic information in real-world KGs. This situation weakens the methods in searching for high-quality rules. To this end, we propose a joint method by incorporating logic rules with textual representations for interpretable KG reasoning, named LoTus. Firstly, LoTus obtains the structural embeddings with only factual triples, and the textual embeddings with descriptions of entities, which are later integrated into the representations of entities and relations. Secondly, top candidate rules are selected by a proposed joint pruning strategy incorporating structural and textual representations in KGs. Finally, LoTus captures high-quality rules and removes low-quality ones by a filtering process with metrics of rules. It reduces the time complexity on KG reasoning by learning rules and provides interpretability simultaneously. Extensive experiments on different datasets show that LoTus obtains competitive performance comparing to baselines on KG reasoning metrics Hits@1, Hits@10 and mean reciprocal rank (MRR), and achieves significant superiority among the recent rule learning methods.

Surrogate model for memetic genetic programming with application to the one machine scheduling problem with time-varying capacity

Surrogate evaluation is a useful, if not the unique, technique in population-based evolutionary algorithms where exact fitness calculation is too expensive. This situation arises, for example, in Genetic Programming (GP) applied to evolve scheduling priority rules, since the evaluation of a candidate rule amounts to solve a large number of problem instances acting as training set. In this paper, a simplified model is proposed that relies on finding and then exploiting a small set of small problem instances, termed filter, such that the evaluation of a rule on the filter may help to estimate the performance of the same rule in solving the training set. The problem of finding the best filter is formulated as a variant of the optimal subset problem, which is solved by means of a Genetic Algorithm (GA). The surrogate evaluation of a new candidate rule consist in solving the instances of the filter. This model is exploited in combination with a Memetic Genetic Program (MGP); the resulting algorithm is termed Surrogate Model MGP (SM-MGP). An experimental study was performed on the problem of scheduling a set of jobs on a machine with varying capacity over time, denoted (1,Cap(t)||∑Ti). The results of this study provided interesting insights into the problems of filter and rules calculation, and showcase that the priority rules evolved by SM-MGP outperform those evolved by MGP.

Heterogeneity of rules in Bayesian reasoning: A toolbox analysis

How do people infer the Bayesian posterior probability from stated base rate, hit rate, and false alarm rate? This question is not only of theoretical relevance but also of practical relevance in medical and legal settings. We test two competing theoretical views: single-process theories versus toolbox theories. Single-process theories assume that a single process explains people’s inferences and have indeed been observed to fit people’s inferences well. Examples are Bayes’s rule, the representativeness heuristic, and a weighing-and-adding model. Their assumed process homogeneity implies unimodal response distributions. Toolbox theories, in contrast, assume process heterogeneity, implying multimodal response distributions. After analyzing response distributions in studies with laypeople and professionals, we find little support for the single-process theories tested. Using simulations, we find that a single process, the weighing-and-adding model, nevertheless can best fit the aggregate data and, surprisingly, also achieve the best out-of-sample prediction even though it fails to predict any single respondent’s inferences. To identify the potential toolbox of rules, we test how well candidate rules predict a set of over 10,000 inferences (culled from the literature) from 4,188 participants and 106 different Bayesian tasks. A toolbox of five non-Bayesian rules plus Bayes’s rule captures 64% of inferences. Finally, we validate the Five-Plus toolbox in three experiments that measure response times, self-reports, and strategy use. The most important conclusion from these analyses is that the fitting of single-process theories to aggregate data risks misidentifying the cognitive process. Antidotes to that risk are careful analyses of process and rule heterogeneity across people.

Line Balancing Analysis to Optimize Production line of Bushing Rubber Using Theory of Constraints and Heuristics Method with Promodel Simulation at PT. Madya Putera Tehnik

The development of manufacturing industry is currently experiencing very rapid development, although the Covid-19 pandemic is still ongoing, this development cannot be inhibited. PT. Madya Putera Tehnik is an industry-engaged automotive part manufacturer that produces bushings. The problem experienced is that the high demand for bushing rubber products makes companies have to optimize the performance of employees and their machines so that targets can be met on time. This study aims to analyze the effectiveness of the production line in the process of making rubber bushings as well as to provide suggestions regarding the balance of the track to increase productivity by using the Theory of Constraints and Heuristic methods. The Theory of Constraints method aims to identify bottlenecks at the outer pipe cutting, rubber roll, rubber clean, and packing work stations that are experiencing problems, and the Heuristic method aims to calculate the balance of the production line. The results obtained from data processing, namely the ranked positional weight and largest candidate rules method have the same results with the number of work stations as many as 14 work stations, track the efficiency of 70%, balance delay of 30%, smoothing index of 66.57, and total exits as many as 1145 units of rubber bushings. It can be interpreted that the two methods have proposed the most optimal trajectory conditions. The company should review the balance of the track and the current production capacity of the company.

Joint Learning of Network Topology and Opinion Dynamics Based on Bandit Algorithms

We study joint learning of network topology and a mixed opinion dynamics, in which agents may have different update rules. Such a model captures the diversity of real individual interactions. We propose a learning algorithm based on multi-armed bandit algorithms to address the problem. The goal of the algorithm is to find each agent's update rule from several candidate rules and to learn the underlying network. At each iteration, the algorithm assumes that each agent has one of the updated rules and then modifies network estimates to reduce validation error. Numerical experiments show that the proposed algorithm improves initial estimates of the network and update rules, decreases prediction error, and performs better than other methods such as sparse linear regression and Gaussian process regression.

Framework From a Multidisciplinary Approach for Transitioning Variants of Unknown Significance From Clinical Genetic Testing in Kidney Disease to a Definitive Classification

IntroductionMonogenic causes in over 300 kidney-associated genes account for approximately 12% of end stage kidney disease (ESKD) cases. Advances in sequencing and large customized panels enable the noninvasive diagnosis of monogenic kidney disease at relatively low cost, thereby allowing for more precise management for patients and their families. A major challenge is interpreting rare variants, many of which are classified as variants of unknown significance (VUS). We present a framework in which we thoroughly evaluated and provided evidence of pathogenicity for HNF1B-p.Arg303His, a VUS returned from clinical diagnostic testing for a kidney transplant candidate.MethodsA blueprint was designed by a multidisciplinary team of clinicians, molecular biologists, and diagnostic geneticists. The blueprint included using a health system-based cohort with genetic and clinical information to perform deep phenotyping of VUS heterozygotes to identify the candidate VUS and rule out other VUS, examination of existing genetic databases, as well as functional testing.ResultsOur approach demonstrated evidence for pathogenicity for HNF1B-p.Arg303His by showing similar burden of kidney manifestations in this variant to known HNF1B pathogenic variants, and greater burden compared to noncarriers.ConclusionDetermination of a molecular diagnosis for the example family allows for proper surveillance and management of HNF1B-related manifestations such as kidney disease, diabetes, and hypomagnesemia with important implications for safe living-related kidney donation. The candidate gene-variant pair also allows for clinical biomarker testing for aberrations of linked pathways. This working model may be applicable to other diseases of genetic etiology.

Keseimbangan Lintasan Produksi Bis Cap yang Efisien Menggunakan Metode Largest Candidate Rules dan Ranked Positional Weight di PT. P

P Ltd., is a company engaged in the manufacturing industry which produces various technical plastic supporting components with plastic ore as the main raw material. This company produces aircon boxes, bis caps, lamp holders, front grilles, port rings, and bolt-hole masking cups. Some of these items become essential or supplementary components of consumer orders. It employs the Make to Order (MTO) system by making products according to customized products. Of various products produced by P Ltd., a bis cap product was chosen to be the research object for the analysis of its track balance because the bis cap becomes a product that is often ordered by customers and the number of orders is also very large. The high demand of consumers has caused the bis cap production process to get stuck in the quality checking process due to the buildup of finished goods from the production machine. After processing data through the LCR (Largest Candidate Rules) and RPW (Rangked Positional Weight) methods, the optimal results occurred in the LCR method, in which 1 workstation had a line efficiency value of 100.03% and a balance delay value of 0.03%.

Diaphyseal and Metaphyseal Modeling Defects-Clinical Findings and Identification of WRAP53 Deficiency in Craniometadiaphyseal Dysplasia.

Background: Diaphyseal and metaphyseal modeling defects lead to severe changes in bone mass and shape, which are common features in osteoporosis that linked to non-vertebral fractures. Original mechanism of diaphyseal and metaphyseal modeling defects has proved elusive. Studying rare syndromes can elucidate mechanisms of common disorders and identify potential therapeutic targets.Methods: We evaluated a family pedigree with craniometadiaphyseal dysplasia (CRMDD, OMIM 269300), a genetic disorder that is characterized by cortical-bone thinning, limb deformity, and absent of normal metaphyseal flaring and diaphyseal constriction. Systemic radiographic examination and serum hormone test were made for this rare disease. One patient and her two normal parents were examined by means of whole-exome sequencing (WES) to identify the candidate pathogenic gene and rule out mucopolysaccharidosis and Prader–Willi Syndrome by means of Sanger sequencing.Results: There are several conspicuous radiographic characteristics: (1) bullet-shaped phalanges, (2) long and narrow pelvic inlet, absent of supra-acetabular constriction, (3) round rod-shaped long tubular bones, (4) prominent aiploic mastoid, (5) bending-shaped limb, genua varus and genu varum, and (6) congenital dislocation of elbow. Here, we did not find any wormian bones, and there are several typical clinical characteristics: (1) macrocephaly and wide jaw, (2) Avatar elf-shaped ears, pointed and protruding ears, (3) hypertrophy of limbs, (4) flat feet and giant hand phenomenon, (5) nail dystrophy, (6) limb deformity, (7) high-arched palate, (8) superficial hemangiomas, (9) tall stature, and intellectual disability. In this patient, we found biallelic frameshift deletion mutations in WRAP53, and those two mutations were transmitted from her parents respectively.Conclusions: We describe her clinical and radiological findings and presented a new subtype without wormian bones and with a tall stature. Our study showed that craniometadiaphyseal dysplasia was caused by a deficiency of WRAP53 with autosomal recessive inheritance.

Water sharing policies conditioned on hydrologic variability to inform reservoir operations

Abstract. Water resources infrastructure is critical for energy and food security; however, the development of large-scale infrastructure, such as hydropower dams, may significantly alter downstream flows, potentially leading to water resources management conflicts and disputes. Mutually agreed upon water sharing policies for the operation of existing or new reservoirs is one of the most effective strategies for mitigating conflict, yet this is a complex task involving the estimation of available water, identification of users and demands, procedures for water sharing, etc. A water sharing policy framework that incorporates reservoir operating rules optimization based on conflicting uses and natural hydrologic variability, specifically tailored to drought conditions, is proposed. First, the trade-off between downstream and upstream water availability utilizing multi-objective optimization of reservoir operating rules is established. Next, reservoir operation with the candidate (optimal) rules is simulated, followed by their performance evaluations, and the rule selections for balancing water uses. Subsequently, a relationship between the reservoir operations simulated from the selected rules and drought-specific conditions is built to derive water sharing policies. Finally, the reservoir operating rules are re-optimized to evaluate the effectiveness of the drought-specific water sharing policies. With a case study of the Grand Ethiopian Renaissance Dam (GERD) on the Blue Nile river, it is demonstrated that the derived water sharing policy can balance GERD power generation and downstream releases, especially in dry conditions, effectively sharing the hydrologic risk in inflow variability among riparian countries. The proposed framework offers a robust approach to inform water sharing policies for sustainable management of water resources.

Towards an efficient rule-based framework for legal reasoning

A rule based knowledge system consists of three main components: a set of rules, facts to be fed to the reasoning corresponding to the data of a case, and an inference engine. In general, facts are stored in (relational) databases that represent knowledge in a first-order based formalism. However, legal knowledge uses defeasible deontic logic for knowledge representation due to its particular features that cannot be supported by first-order logic. In this work, we present a unified framework that supports efficient legal reasoning. In the framework, a novel inference engine is proposed in which the Semantic Rule Index can identify candidate rules with their corresponding semantic rules if any, and an inference controller is able to guide the executions of queries and reasoning. It can eliminate rules that cannot be fired to avoid unnecessary computations in early stages. The experiments demonstrated the effectiveness and efficiency of the proposed framework.

Constructing ANFIS With Sparse Data Through Group-Based Rule Interpolation: An Evolutionary Approach

An adaptive-network-based fuzzy inference system (ANFIS) offers a popular and powerful fuzzy inference mechanism. As with many other advanced data-driven techniques, developing an effective ANFIS typically requires sufficient training data. However, in many real-world applications, it is not always straightforward to obtain a large amount of representative data that cover the entire problem space to accomplish the required training, seriously restricting the performance of a learned ANFIS. This article introduces a new ANFIS learning approach through an evolutionary process, which is able to generate an ANFIS with only a small amount of training data in a certain problem region, by interpolating well-trained ANFISs in the neighboring regions. Such a process works by first producing an initial population of candidate fuzzy rules in the region of data shortage, through interpolating a rule dictionary constructed from trained ANFISs in the neighborhood regions. The crossover and mutation operations over these candidate rules are then executed in an effort to attain candidates of improved performance. When this genetic learning process terminates, the chromosomes in the final population either collectively form or each individually represents a learned ANFIS, depending on whether a single fuzzy rule or a set of fuzzy rules representing an entire ANFIS is implemented with a chromosome within the evolving population. Comparative experimental evaluations on both synthetic and real-world datasets are carried out, demonstrating that in spite of data shortage, the proposed interpolation approach is able to produce ANFIS models that significantly outperform those trained using existing learning mechanisms.

Multi-Target Regression Rules With Random Output Selections

In this article, we address the task of multi-target regression (MTR), where the goal is to predict multiple continuous variables. We approach MTR by learning global models that simultaneously predict all of the target variables, as opposed to learning a separate model for predicting each of the target variables. Specifically, we learn rule ensembles by generating many candidate rules and assigning them weights that are then optimized in order to select the best performing subset of rules. Candidate rules are generated by transforming ensembles of generalized decision trees, called predictive clustering trees (PCTs), into rules. We propose to extend an existing multi-target regression rule learning method named FIRE by learning tree ensembles that use random output selections (ROS). Such ensembles force individual PCTs to focus only on randomly selected subsets of target variables. The rules obtained from the tree ensemble also focus on various subsets of the target variables (FIRE-ROS). We use three different ensemble methods to generate candidate rules: bagging and random forests of PCTs, and ensembles of extremely randomized PCTs. An experimental evaluation on a range of benchmark datasets has been conducted, where FIRE-ROS is compared to three interpretable methods, namely predictive clustering rules, MTR trees and the original FIRE method, as well as state-of-the-art MTR methods, in particular ensembles of extremely randomized PCTs with ROS, random linear combinations and extremely randomized MTR trees with random projections of the target space. The results show that FIRE-ROS can improve the predictive performance of the FIRE method and that it performs on par with state-of-the-art (non-interpretable) MTR methods.

Improvement in production line efficiency of hemming unit using line balancing techniques

The present work aims at improving the production line efficiency of a hemming unit by implementing three different line balancing techniques. For real time study, a hemming unit which involves 12 different work stations with manual execution was considered. The average actual cycle time at each station obtained through past data and the takt time for each station were compared using Yamazumi chart, which pertains to a line efficiency of 44% initially. It is found out that implementation of the line balancing techniques such as Kilbridge and Wester Column, Ranked Positional Weight, and Largest Candidate Rule significantly improved the line efficiency to 69%. The methods also established the possibility of reducing the number of work stations from 12 to 8. Out of the three methods, the Ranked Positional Weight method is found to be more effective for the present case, through Arena® simulation.