United States Tax Rules for Nonresident Authors, Artists, Musicians, and Other Creative Professionals

Although the United States rules for taxation of non-resident creative professionals may seem straight forward, the Internal Revenue Code contains significant traps for the unwary nonresident. However, it also offers significant opportunities for nonresidents to protect themselves from United States taxation. Discovering these traps and loopholes requires close attention to the Code and to income tax treaties which further complicate the system. This Note examines the Code\u27s rules on taxation of nonresidents and discusses the effect that tax treaties have on those rules. The Note, intended to be a practical guide for nonresident authors, artists, musicians, and other creative professionals, then offers suggestions to nonresident creative professionals for structuring their affairs to avoid United States taxation

Learning a Static Analyzer from Data

To be practically useful, modern static analyzers must precisely model the effect of both, statements in the programming language as well as frameworks used by the program under analysis. While important, manually addressing these challenges is difficult for at least two reasons: (i) the effects on the overall analysis can be non-trivial, and (ii) as the size and complexity of modern libraries increase, so is the number of cases the analysis must handle. In this paper we present a new, automated approach for creating static analyzers: instead of manually providing the various inference rules of the analyzer, the key idea is to learn these rules from a dataset of programs. Our method consists of two ingredients: (i) a synthesis algorithm capable of learning a candidate analyzer from a given dataset, and (ii) a counter-example guided learning procedure which generates new programs beyond those in the initial dataset, critical for discovering corner cases and ensuring the learned analysis generalizes to unseen programs. We implemented and instantiated our approach to the task of learning JavaScript static analysis rules for a subset of points-to analysis and for allocation sites analysis. These are challenging yet important problems that have received significant research attention. We show that our approach is effective: our system automatically discovered practical and useful inference rules for many cases that are tricky to manually identify and are missed by state-of-the-art, manually tuned analyzers

Contrast set mining in temporal databases

Understanding the underlying differences between groups or classes in certain contexts can be of the utmost importance. Contrast set mining relies on discovering significant patterns by contrasting two or more groups. A contrast set is a conjunction of attribute–value pairs that differ meaningfully in its distribution across groups. A previously proposed technique is rules for contrast sets, which seeks to express each contrast set found in terms of rules. This work extends rules for contrast sets to a temporal data mining task. We define a set of temporal patterns in order to capture the significant changes in the contrasts discovered along the considered time line. To evaluate the proposal accuracy and ability to discover relevant information, two different real-life data sets were studied using this approach.(undefined

Temporal fuzzy association rule mining with 2-tuple linguistic representation

This paper reports on an approach that contributes towards the problem of discovering fuzzy association rules that exhibit a temporal pattern. The novel application of the 2-tuple linguistic representation identifies fuzzy association rules in a temporal context, whilst maintaining the interpretability of linguistic terms. Iterative Rule Learning (IRL) with a Genetic Algorithm (GA) simultaneously induces rules and tunes the membership functions. The discovered rules were compared with those from a traditional method of discovering fuzzy association rules and results demonstrate how the traditional method can loose information because rules occur at the intersection of membership function boundaries. New information can be mined from the proposed approach by improving upon rules discovered with the traditional method and by discovering new rules

Procesos de explotación de información basados en sistemas inteligentes

En esta tesis se caracterizan los procesos de explotación de información asociados a los problemas de inteligencia de negocio: descubrimiento de reglas de comportamiento, descubrimiento de grupos, descubrimiento de atributos significativos (atributos importantes para el entorno de negocio que se aplica), descubrimiento de reglas de pertenencia a grupos y ponderación de reglas de comportamiento o de pertenencia a grupos. Se identifican las tecnologías de sistemas inteligentes que pueden utilizarse para los procesos caracterizados, validando estos procesos a través de casos aceptados por la comunidad internacional. Se proponen las funcionalidades de un ambiente de explotación de información que integra las tecnologías identificadas. Este ambiente administra en forma unificada los distintos procesos explotación de información que requieren las tecnologías referenciadas.This PhD thesis proposes a characterization of data mining processes associated to the following business intelligence problems: behavior rules discovering, group discovering, significant attributes discovering, group belonging rules discovering, behavior rules and group belonging rules weighting. The intelligent systems technologies that may be used in the characterized data mining processes are identified. Processes are validated through cases of study accepted by international community. Data mining environment functionalities which integrates the intelligent systems technology identified is proposed. This environment is able to manage in unified way the different data mining process that uses the technologies previously referenced.Facultad de Informátic

Evolving temporal association rules with genetic algorithms

A novel framework for mining temporal association rules by discovering itemsets with a genetic algorithm is introduced. Metaheuristics have been applied to association rule mining, we show the efficacy of extending this to another variant - temporal association rule mining. Our framework is an enhancement to existing temporal association rule mining methods as it employs a genetic algorithm to simultaneously search the rule space and temporal space. A methodology for validating the ability of the proposed framework isolates target temporal itemsets in synthetic datasets. The Iterative Rule Learning method successfully discovers these targets in datasets with varying levels of difficulty

An artificial immune system for fuzzy-rule induction in data mining

This work proposes a classification-rule discovery algorithm integrating artificial immune systems and fuzzy systems. The algorithm consists of two parts: a sequential covering procedure and a rule evolution procedure. Each antibody (candidate solution) corresponds to a classification rule. The classification of new examples (antigens) considers not only the fitness of a fuzzy rule based on the entire training set, but also the affinity between the rule and the new example. This affinity must be greater than a threshold in order for the fuzzy rule to be activated, and it is proposed an adaptive procedure for computing this threshold for each rule. This paper reports results for the proposed algorithm in several data sets. Results are analyzed with respect to both predictive accuracy and rule set simplicity, and are compared with C4.5rules, a very popular data mining algorithm