Multiple Rule Sets Research Articles

Navigating plural legal constellations at the coal mining frontier in Mozambique

In this article, I will focus on the emergence and dynamics of different laws, standards, and norms in the context of an extractive frontier. The extractive frontier is presented as a place where multiple jurisdictions overlap and in which new governance constellations and practices emerge. This article focuses on the laws and standards related to resettlement processes and compensation for loss of residence, land, and livelihood in the surroundings of two coal mines in Tete province, Mozambique. Resettlement processes are one of the most direct ways in which populations are affected by extractive projects and often associated with human rights violations. The paper focuses on resettlement officers of the mining companies who are at the forefront of planning and implementing such processes. Based on ethnographic fieldwork, including interviews with resettlement officers and participation in their daily lives, the article details how these individuals position themselves in relation to multiple sets of laws and rules (e.g. Standards of the International Finance Corporation, state mining law and resettlement regulation, land law, human rights law) and a variety of actors (e.g. mining companies, international finance institutes, government agencies, local populations, NGOs). Resettlement officers often work with standards that surpass national law but are in their implementation of regulations also curtailed by a seemingly absent state, divergent company policies, critical civil society organisations, and global commodity markets. A focus on the everyday intricacies of the work of resettlement officers, shows their power and constraints, and the dynamics by which hierarchies of rules and laws become unsettled at the extractive frontier.

Efficient multi-category packet classification using TCAM

Packet classification is the base of various network functions such as firewall filtering, network intrusion detection and quality of services, etc. Ternary content addressable memory (TCAM) is widely employed in performing efficient packet classification. However, TCAM has some drawbacks, including limited capacity, high energy consumption, and incapability to store arbitrary ranges. Moreover, TCAM is only suitable for single-match packet classification natively, which is associated with one rule-set and reports one rule, for it only reports the first matching entry. However, except for single-match packet classification, another type of packet classification, multi-category packet classification, which is associated with multiple rule-sets and reports one matching rule for each rule-set, is also required in some scenarios, such as in the consolidation of multiple single-match network functions. The naive scheme performing multi-category packet classification with TCAM is to search a packet in multiple rule-sets one by one. Its performance decreases linearly as the number of rule-sets increases. To efficiently perform multi-category packet classification using TCAM, a novel scheme named REM is proposed in this paper. REM is based on the idea of reducing TCAM accesses per classification by merging rule-entry sets converted from rule-sets. The experiments show that compared with the naive scheme, REM can achieve 3x to 5x improvement on packet classification throughput, and reduce the energy consumption by 50% to 75%.

Effects of the Color Wheel System on Students With Disabilities in Inclusion Classrooms.

The Color Wheel System (CWS) is a class-wide, rule-based, behavior management strategy that incorporates multiple sets of classroom rules to provide specific behavioral expectations for different classroom activities. This study used the CWS within three inclusion classrooms to evaluate improvements of classroom behavior for students with disabilities. A multiple-baseline design across classrooms evaluated the effects of the CWS on on-task behavior for three students with identified disabilities in each classroom. Momentary time-sampling was used to record on-task behavior, which was operationally defined as eyes on teacher or work or following teacher instructions. Visual analysis of time-series graphs and nonoverlap of all pairs (NAP) measures suggested the CWS caused immediate, large, and sustained increases in on-task behavior for students with disabilities when data are aggregated by classroom. However, individual changes in on-task behavior were variable across students. Implications for using the CWS as part of a class-wide behavioral prevention program and directions for future research are discussed.

A Robust Rule-Based Ensemble Framework Using Mean-Shift Segmentation for Hyperspectral Image Classification

This paper assesses the performance of DoTRules—a dictionary of trusted rules—as a supervised rule-based ensemble framework based on the mean-shift segmentation for hyperspectral image classification. The proposed ensemble framework consists of multiple rule sets with rules constructed based on different class frequencies and sequences of occurrences. Shannon entropy was derived for assessing the uncertainty of every rule and the subsequent filtering of unreliable rules. DoTRules is not only a transparent approach for image classification but also a tool to map rule uncertainty, where rule uncertainty assessment can be applied as an estimate of classification accuracy prior to image classification. In this research, the proposed image classification framework is implemented using three world reference hyperspectral image datasets. We found that the overall accuracy of classification using the proposed ensemble framework was superior to state-of-the-art ensemble algorithms, as well as two non-ensemble algorithms, at multiple training sample sizes. We believe DoTRules can be applied more generally to the classification of discrete data such as hyperspectral satellite imagery products.

Youth-Driven Tactics of Public Space Appropriation in Hanoi: The Case of Skateboarding and Parkour

Starting in the 2000s, there has been a rise in youth-led appropriation of public spaces in Hanoi, Vietnam. Through case studies of skateboarders and traceurs (practitioners of parkour) in two of the city's formal public spaces, we explore and analyze the tactics deployed by these young urbanites to claim a part of the characteristically overcrowded and socio-politically restrictive public spaces of the Vietnamese capital. These case studies show that, by seeking to access public spaces for their new activities, skaters and traceurs have had to confront multiple sets of rules, imposed by not only the state, but also corporate actors and resident-driven surveillance. We find that skateboarders and traceurs deal with these forms of control largely through small-scale, non-ideological, and non-confrontational tactics. As a result, these youth practices have become normalized in Hanoi's public spaces. These findings broaden the discourses on everyday urbanism and social-political transformations in post-socialist urban contexts, and shed light on the ways in which contemporary youths engage with the city.

Collaborative rule generation: An ensemble learning approach

Due to the vast and rapid increase in data, data mining has become an increasingly important tool for the purpose of knowledge discovery in order to prevent the presence of rich data but poor knowledge. Data mining tasks can be undertaken in two ways, namely, manual walkthrough of data and use of machine learning approaches. Due to the presence of big data, machine learning has thus become a powerful tool to do data mining in intelligent ways. A popular approach of machine learning is inductive learning, which can be used to generate a rule set (a set of rules) using a particular algorithm. Inductive learning can involve a single base algorithm learning from a single data set following a standard learning approach. In this approach, the learning algorithm can generate a single rule set such as decision trees. On the other hand, the inductive learning can also involve a single base algorithm learning from multiple data sets following an ensemble learning approach. In this approach, the learning algorithm can generate multiple rule sets such as random forests. The latter approach is usually designed to reduce overfitting of models that usually arises when the former approach is adopted. In this context, the ensemble learning approach usually enables the improvement of the overall accuracy in prediction. The aim of this paper is to introduce a new approach of ensemble learning called Collaborative Rule Generation. In the new approach, the inductive learning involves multiple base algorithms learning from a single data set to generate a single rule set, which aims to enable each rule to have a higher quality. This paper also includes an experimental study validating the Collaborative Rule Generation approach and discusses the results in both quantitative and qualitative ways.

A novel multiple rule sets data classification algorithm based on ant colony algorithm

Ant colony optimization (ACO) algorithms have been successfully applied in data classification, which aim at discovering a list of classification rules. However, due to the essentially random search in ACO algorithms, the lists of classification rules constructed by ACO-based classification algorithms are not fixed and may be distinctly different even using the same training set. Those differences are generally ignored and some beneficial information cannot be dug from the different data sets, which may lower the predictive accuracy. To overcome this shortcoming, this paper proposes a novel classification rule discovery algorithm based on ACO, named AntMinermbc, in which a new model of multiple rule sets is presented to produce multiple lists of rules. Multiple base classifiers are built in AntMinermbc, and each base classifier is expected to remedy the weakness of other base classifiers, which can improve the predictive accuracy by exploiting the useful information from various base classifiers. A new heuristic function for ACO is also designed in our algorithm, which considers both of the correlation and coverage for the purpose to avoid deceptive high accuracy. The performance of our algorithm is studied experimentally on 19 publicly available data sets and further compared to several state-of-the-art classification approaches. The experimental results show that the predictive accuracy obtained by our algorithm is statistically higher than that of the compared targets.

Tax Exemptions in Status of Forces Agreements

When a country stations military and associated personnel, including contractors, in another country, the question arises whether these activities and related income attract tax in the so-called host country. Typically, the sending and host countries will conclude a so-called status of forces agreement (SOFA) to address a number of practical and legal issues associated with the presence of foreign personnel in the host country, including entry and departure rules, postal services, and taxation. SOFAs may relate to military or law enforcement purposes, or serve in the context of foreign aid programmes. After a stage-setting section, explaining the background, purpose, and scope of SOFAs in general, this article focuses on the tax provisions typically found in SOFAs. A small number of SOFAs has been selected to show similarities and differences in tax provisions and explain the associated legal issues: the multilateral NATO-SOFA and EU-SOFA as well as a number of bilateral SOFAs concluded by the United States in the areas of security, foreign assistance and counter-narcotics operations. The tax provisions vary significantly in scope, the categories of persons covered and the extent to which they are protected from taxation in the host country. Determining the correct allocation of taxing rights is further complicated due to the concurrence of multiple sets of tax rules in SOFA, bilateral tax treaties and domestic law of the host country.

Developing a GP-based Framework for Knowledge Integration

Knowledge integration is one of the important tasks for applying knowledge management in an organization to improve organizational performance and competitive competence. In this paper, we have proposed a GP-based knowledge-integration framework that automatically combines multiple rule sets into one integrated knowledge base. The proposed framework consists of three phases: knowledge collection and translation, knowledge integration, and knowledge output. In the collection and translation phase, each knowledge source is obtained and expressed as a rule set and then translated as a classification tree. In the integration phase, the genetic programming technique is used to generate a nearly optimal classification tree. In the output phase, the final derived classification tree is transferred as a rule set, then output to the knowledge base to facilitate the inference of new data. Two new genetic operators, abridgement and compromise, are designed in the proposed approach to remove redundancy, subsumption and contradiction, thus producing more accurate and concise classification rules than that without using them. Experimental results from diagnosis of breast cancer also show the feasibility of the proposed algorithm.

TH-C-224-01: Reimbursement: Resources and Updates

Healthcare reimbursement is a complex, changing environment with multiple payers and multiple sets of rules. The practicing medical physicist should be familiar with reimbursement and coding so that he/she can: 1) assure that fair reimbursement is received for the physics work performed, 2) assure that physics billing is done correctly and in compliance with applicable rules and procedures, and 3) justify the physics staffing levels necessary in the clinic. This presentation will build upon previous general reviews of the mechanisms of Medicare reimbursement and will cover practical aspects for the practicing physicist including where to find information on reimbursement and coding. The National Correct Coding Initiative (NCCI) and Medically Unlikely Edits (MUE) and their effect on daily billing will be reviewed. In addition, proposed changes in the Hospital Outpatient Prospective Payment System (HOPPS) and Medicare Physician Fee Schedule (MPFS) for calendar year 2012 will be discussed. Learning Objectives: 1. Become familiar with available resources for reimbursement and coding 2. Understand the National Correct Coding Initiative and Medically Unlikely Edits 3. Be aware of proposed changes in Medicare reimbursement for 2012

The State, the Rebel and the Chief: Public Authority and Land Disputes in Assam, India

ABSTRACTBased upon an ethnographic study of two land disputes in the rural Assamese district of Karbi Anglong (India), this article challenges the idea that the entry of new institutional players, with their multiple sets of rules, inevitably leads to open institutional conflict. Although a wide range of political actors are involved in the regulation of land tenure in Karbi Anglong, they cannot be regarded as institutional structures ready to undercut one another. As in other parts of Northeast India, none of the claimants of public power involved —‘the state’, ‘the rebel’ or ‘the chief’— attain full sovereignty, which forces them to exercise authority predominantly through practices of negotiation and accommodation, and only selective contestation. If open institutional conflict does occur, as in the Dhansiri forest and the Singhason plateau cases studied here, this is due to the fact that one of the institutional players has overstretched and attempted to exercise authority beyond its realm of power. This article thus argues for a more agency‐oriented method of analysis in the study of land relations. The focus on everyday interactions between ‘the state’, ‘the rebel’ and ‘the chief’ in Karbi Anglong is a first attempt in that regard.

Combining models from neural networks and inductive learning algorithms

The knowledge-based artificial neural network (KBANN) is composed of phases involving the expression of domain knowledge, the abstraction of domain knowledge at neural networks, the training of neural networks, and finally, the extraction of rules from trained neural networks. The KBANN attempts to open up the neural network black box and generates symbolic rules with (approximately) the same predictive power as the neural network itself. An advantage of using KBANN is that the neural network considers the contribution of the inputs towards classification as a group, while rule-based algorithms like C5.0 measure the individual contribution of the inputs one at a time as the tree is grown. The knowledge consolidation model (KCM) combines the rules extracted using KBANN (NeuroRule), frequency matrix (which is similar to the Naïve Bayesian technique), and C5.0 algorithm. The KCM can effectively integrate multiple rule sets into one centralized knowledge base. The cumulative rules from other single models can improve overall performance as it can reduce error-term and increase R-square. The key idea in the KCM is to combine a number of classifiers such that the resulting combined system achieves higher classification accuracy and efficiency than the original single classifiers. The aim of KCM is to design a composite system that outperforms any individual classifier by pooling together the decisions of all classifiers. Another advantage of KCM is that it does not need the memory space to store the dataset as only extracted knowledge is necessary in build this integrated model. It can also reduce the costs from storage allocation, memory, and time schedule. In order to verify the feasibility and effectiveness of KCM, personal credit rating dataset provided by a local bank in Seoul, Republic of Korea is used in this study. The results from the tests show that the performance of KCM is superior to that of the other single models such as multiple discriminant analysis, logistic regression, frequency matrix, neural networks, decision trees, and NeuroRule. Moreover, our model is superior to a previous algorithm for the extraction of rules from general neural networks.

Ensembles of jittered association rule classifiers

The ensembling of classifiers tends to improve predictive accuracy. To obtain an ensemble with N classifiers, one typically needs to run N learning processes. In this paper we introduce and explore Model Jittering Ensembling, where one single model is perturbed in order to obtain variants that can be used as an ensemble. We use as base classifiers sets of classification association rules. The two methods of jittering ensembling we propose are Iterative Reordering Ensembling (IRE) and Post Bagging (PB). Both methods start by learning one rule set over a single run, and then produce multiple rule sets without relearning. Empirical results on 36 data sets are positive and show that both strategies tend to reduce error with respect to the single model association rule classifier. A bias–variance analysis reveals that while both IRE and PB are able to reduce the variance component of the error, IRE is particularly effective in reducing the bias component. We show that Model Jittering Ensembling can represent a very good speed-up w.r.t. multiple model learning ensembling. We also compare Model Jittering with various state of the art classifiers in terms of predictive accuracy and computational efficiency.

The Brandenburg Paradigm and Other First Amendments

This article addresses fracturing of modern First Amendment law into multiple different sets of rules and rationales for protection of depending on what kind of speech is an issue. It is no longer accurate to say that there is one universal First Amendment jurisprudence; indeed, it is no longer accurate to say that there is one First Amendment. Today there are many different - often very different - First Amendments for different types of speech. On a practical level, this fracturing of First Amendment law creates difficulties only in that it requires litigators and judges addressing First Amendment issues to identify category or categories into which a particular example of speech fits. But on a theoretical level, fracturing of First Amendment jurisprudence is much more problematic. These theoretical problems arise because First Amendment jurisprudence is not just a collection of narrow rules and doctrines. These rules and doctrines are based on series of presuppositions about nature of individuals, proper relationship between government and its citizens, extent to which society should accept risks posed by dangerous or antisocial ideas, and liability of speakers for consequences of their speech. The problem is that courts make one set of assumptions when dealing with one area of expression and very different (and often contradictory) assumptions when dealing with other areas of expression. The article starts by describing baseline for all free speech jurisprudence - jurisprudence that applies to political advocacy. It then distills from Court's major political speech cases a set of principles that will be called the Brandenburg paradigm. The remainder of article discusses whether theoretical assumptions made in Brandenburg paradigm should also be applied to areas of speech other than political advocacy. Several areas of expression are addressed specifically, including threats, obscenity, teaching speech, and student speech in public schools. An assessment of these different speech categories indicates that there is no good reason to ignore Brandenburg paradigm outside political advocacy category. Indeed, there is one very good reason to apply Brandenburg paradigm to entire range of First Amendment issues: The assumptions that underlie Brandenburg - for example, that citizens control government rather than vice versa, that citizens should develop their own value systems free of government coercion, and that government should suppress ideas it dislikes only in face of serious, concrete harms stemming from that expression - should not be regarded solely as artifacts of First Amendment, but rather as indispensable elements of constitutional democracy itself.

Comparing extended classifier system and genetic programming for financial forecasting: an empirical study

As a broad subfield of artificial intelligence, machine learning is concerned with the development of algorithms and techniques that allow computers to learn. These methods such as fuzzy logic, neural networks, support vector machines, decision trees and Bayesian learning have been applied to learn meaningful rules; however, the only drawback of these methods is that it often gets trapped into a local optimal. In contrast with machine learning methods, a genetic algorithm (GA) is guaranteeing for acquiring better results based on its natural evolution and global searching. GA has given rise to two new fields of research where global optimization is of crucial importance: genetic based machine learning (GBML) and genetic programming (GP). This article adopts the GBML technique to provide a three-phase knowledge extraction methodology, which makes continues and instant learning while integrates multiple rule sets into a centralized knowledge base. Moreover, the proposed system and GP are both applied to the theoretical and empirical experiments. Results for both approaches are presented and compared. This paper makes two important contributions: (1) it uses three criteria (accuracy, coverage, and fitness) to apply the knowledge extraction process which is very effective in selecting an optimal set of rules from a large population; (2) the experiments prove that the rule sets derived by the proposed approach are more accurate than GP.

The Relationship Between Executive Attention and Dissociation in Children

SUMMARY Dissociation involves disruption in the usually integrated functions of consciousness, memory, identity, and perception. Recent research with adults suggests that dissociation is associated with alterations in attention. Little work, however, has examined the attentional correlates of dissociation in childhood. This study is the first to investigate the specificity of cognitive functions related to dissociation in children. Twenty-four 5- to 8-year-old foster children completed several subtests of the NEPSY: A Developmental Neuropsychological Assessment (Korkman, Kirk, & Kemp, 1998) in the Executive Functioning/ Attention domain. Foster caregivers completed the Child Dissociative Checklist (Bernstein & Putnam, 1986). Consistent with the adult literature, higher levels of childhood dissociation were associated with deficits in tasks requiring inhibition, but not with tasks requiring primarily planning, strategy, or multiple rule sets.

Integrating extended classifier system and knowledge extraction model for financial investment prediction: An empirical study

Abstract Machine learning methods such as fuzzy logic, neural networks and decision tree induction have been applied to learn rules, however they can get trapped into a local optimal. Based on the principle of natural evolution and global searching, a genetic algorithm is promising for obtaining better results. This article adopts the learning classifier systems (LCS) technique to provide a three-phase knowledge extraction methodology, which makes continues and instant learning while integrates multiple rule sets into a centralized knowledge base. This paper makes three important contributions: (1) it represents various rule sets that are derived from different sources and encoded as a fixed-length bit string in the knowledge encoding phase; (2) it uses three criteria (accuracy, coverage, and fitness) to select an optimal set of rules from a large population in the knowledge extraction phase; (3) it applies genetic operations to generate optimal rule sets in the knowledge integration phase. The experiments prove that the rule sets derived by the proposed approach is more accurate than other machine learning algorithm.

A Genetics-Based Approach to Knowledge Integration and Refinement

In this paper, we propose a genetics-based knowledge integration approach to integrate multiple rule sets into a central rule set. The proposed approach consists of two phases: know ledge encoding and know ledge integrating. In the encoding phase, each knowledge input is translated and expressed as a rule set, and then encoded as a bit string. The combined bit strings form an initial knowledge population, which is then ready for integrating. In the knowledge integration phase, a genetic algorithm generates an optimal or nearly optimal rule set from these initial knowledge inputs. Furthermore, a rule-refinement scheme is proposed to refine inference rules via interaction with the environment. Experiments on diagnosing brain tumors were carried out to compare the accuracy of a rule set generated by the proposed approach with that of initial rule sets derived from different groups of experts or induced by means of various machine learning techniques. Results show that the rule set derived using the proposed approach is much more accurate than each initial rule set on its own.

A coverage-based genetic knowledge-integration strategy

In this paper, we propose a coverage-based genetic knowledge-integration approach to effectively integrate multiple rule sets into a centralized knowledge base. The proposed approach consists of two phases: knowledge encoding and knowledge integration. In the knowledge-encoding phase, each rule in the various rule sets that are derived from different sources (such as expert knowledge or existing knowledge bases) is first translated and encoded as a fixed-length bit string. The bit strings combined together thus form an initial knowledge population. In the knowledge-integration phase, a genetic algorithm applies genetic operations and credit assignment at each rule-string to generate an optimal or nearly optimal rule set. Experiments on diagnosing brain tumors were made to compare the accuracy of a rule set generated by the proposed approach with that of the initial rule sets derived from different groups of experts or induced by various machine learning techniques. Results show that the rule set derived by the proposed approach is more accurate than each initial rule set on its own.

Integrating membership functions and fuzzy rule sets from multiple knowledge sources

In this paper, we propose a GA-based fuzzy knowledge-integration framework that can simultaneously integrate multiple fuzzy rule sets and their membership function sets. The proposed two-phase approach includes fuzzy knowledge encoding and fuzzy knowledge integration. In the encoding phase, each fuzzy rule set with its associated membership functions is first transformed into an intermediary representation, and further encoded as a string. The combined strings form an initial knowledge population, which is then ready for integration. In the knowledge-integration phase, a genetic algorithm is used to generate an optimal or nearly optimal set of fuzzy rules and membership functions from the initial knowledge population. The hepatitis diagnostic problem was used to show the performance of the proposed knowledge-integration approach. Results show that the fuzzy knowledge-base resulting from using our approach performs better than every individual knowledge base.