Numerical Rules Research Articles

Cooperative ant colony optimization for a hierarchical scheduling problem

An important part of modern integrated decision support systems in any area is scheduling. At the same time, domain knowledge can be extremely complex and includes numerous restrictions and process rules, which makes the corresponding optimization problem highly complicated. This paper is based on a hierarchical problem structure where the top-level problem is the travelling salesman problem, and the nested resource-constrained project scheduling problem is replaced by the simulation model. Cooperative ant colony optimization as well as other biology-inspired algorithms were evaluated on a set of generated problems, and the obtained experimental results are presented here.

Impact of Transfer Pricing for Special Tax Adjustment Measures on Corporate Effective Tax Rate in China

Corporate tax avoidance activities are prevalent in many countries, some of which have formulated anti-tax avoidance policies to curb such activities. China amended the chapter on special tax adjustment in the Enterprise Income Tax Law in 2008 and implemented Measures for Special Tax Adjustment in 2009. This study applies the Shanghai and Shenzhen A-share securities markets in 2005-2015 to investigate whether the implementation of Measures for Special Tax Adjustment has effectively curbed corporate tax avoidance activities and increased income tax costs. The empirical results indicate that company effective tax rates (ETRs) are increased significantly after the implementation of Measures for Special Tax Adjustment. And, companies with greater amounts of goods (or service) related party transactions significantly increased ETRs after the implementation of Measures for Special Tax Adjustment. The Chinese government continues to issue numerous rules focused on transfer pricing to amend Measures for Special Tax Adjustment. Therefore, we recommend that corporations carefully assess relevant transactions to avoid being subject to Measures for Special Tax Adjustment legislation.

Wpływ reguły wydatkowej na typ polityki fiskalnej w Polsce

<p><strong>Uzasadnienie teoretyczne:</strong> Przedstawienie polskich doświadczeń z wprowadzaniem reguły wydatkowej do krajowego prawa pod wpływem regulacji unijnych.</p><p><strong>Cel artykułu:</strong> Wykazanie, że modyfikacja reguły wydatkowej z 2015 r. skutkowała utratą jej stabilizującego charakteru, który miał stanowić jej główną właściwość.</p><p><strong>Metody badawcze:</strong> Identyfikacji zmian w nastawieniu w polityce fiskalnej w latach 2013–2018 dokonano na podstawie zmian w ujęciu rocznym salda pierwotnego dostosowanego cyklicznie względem luki produkcyjnej.</p><p><strong>Główne wnioski:</strong> Przeprowadzone w pracy analizy wykazały, że reguła wydatkowa funkcjonująca w pierwotnym kształcie sprzyjała łagodzeniu wahań koniunkturalnych. Wraz ze zmniejszeniem restrykcyjności reguły wydatkowej w 2015 r., polegającej na zastąpieniu w formule wskaźnika prognozowanej inflacji wskaźnikiem średniookresowego celu inflacyjnego, jej funkcja stabilizująca została zatracona. Stwierdzono, że dobrze skonstruowane numeryczne reguły fiskalne mogą być ważnym instrumentem zachowania kontroli nad stanem finansów publicznych, lecz doświadczenia z regułą wydatkową w Polsce dostarczają wielu argumentów przeciwnikom stosowania reguł numerycznych w polityce fiskalnej.</p>

Transforming a sole proprietorship into a limited liability company – selected tax aspects

This article deals with the transformation of a sole proprietorship into a sole-shareholder limited liability company, with particular emphasis on tax aspects. Considerations are supported by numerous rulings of courts of general and administrative jurisdiction, as well as interpretations of the tax authorities. At the beginning, the author presents the binding normative acts regulating this subject and the main reasons for transformations. The methods of transforming a sole proprietorship into a sole-shareholder limited liability company binding until the end of 2011 have been described, as well as provisions regulating the issue of formal transformation. In the further part of the article the legal consequences of formal transformation were discussed, with particular emphasis on the succession of rights and obligations under tax law. The next part of the article is devoted to the effects of transformation on the basis of the tax on goods and services, personal income tax and income tax from juridical persons, as well as tax on civil law transactions.

Načelo traženja materijalne istine i upravni postupak

Nakon konstatacije da su procesna načela kao opća pravna pravila činitelj jedinstva općih i posebnih upravnoprocesnih propisa te djelomičnog jedinstva sudskih i upravnog postupka, autor u radu zastupa stajalište da je načelo traženja materijalne istine znanstveno opravdano i prihvatljivo, ali ne u smislu klasične filozofske teorije istine, tzv. teorije korespondencije kao „potpune suglasnosti subjektivne spoznaje s objektivnom stvarnošću“, nego kao konstrukcija pravne teorije u značenju metode ili načina prikupljanja procesne građe i utvrđivanja činjeničnog stanja koje se shvaća kao činjenično stanje na kojem je legitimno utemeljiti odluku procesnog tijela. Rad sadržava definiciju načela traženja materijalne istine, obradu najvažnijih pravila našega upravnoga procesnog prava kojima je to načelo utemeljeno te pravila kojima je ono ograničeno. Slijedi razmatranje načela traženja materijalne istine u parničnom postupku te usporedba toga koliko to načelo vrijedi u upravnom postupku i parničnom postupku.

General Six-Step Discrete-Time Zhang Neural Network for Time-Varying Tensor Absolute Value Equations

This article presents a general six-step discrete-time Zhang neural network (ZNN) for time-varying tensor absolute value equations. Firstly, based on the Taylor expansion theory, we derive a general Zhang et al. discretization (ZeaD) formula, i.e., a general Taylor-type 1-step-ahead numerical differentiation rule for the first-order derivative approximation, which contains two free parameters. Based on the bilinear transform and the Routh–Hurwitz stability criterion, the effective domain of the two free parameters is analyzed, which can ensure the convergence of the general ZeaD formula. Secondly, based on the general ZeaD formula, we design a general six-step discrete-time ZNN (DTZNN) for time-varying tensor absolute value equations (TVTAVEs), whose steady-state residual error changes in a higher order manner than those presented in the literature. Meanwhile, the feasible region of its step size, which determines its convergence, is also studied. Finally, experiment results corroborate that the general six-step DTZNN model is quite efficient for TVTAVE solving.

New application method of the zonal model for simulations of pulverized coal-fired furnaces based on correction of total exchange areas

New application method of the zonal model in numerical simulations of pulverized coal-fired furnaces, continual corrections of the total exchange areas (CCTEA), is presented. The method is based on the corrections of the surface–surface, surface–volume, and volume–volume total exchange areas according to the changes of the surface zone emissivities during the iterative calculation of the numerical simulation and summation rules of the total exchange areas, so that it provides zero radiative exchange in an isothermal system. The method was developed for the homogeneous and uniform flame radiative properties. The results obtained by the CCTEA method were compared with the results obtained by the previously developed repeated run of numerical simulation (RRNS) method, based on multiple succesive run of the numerical simulation. The differences of the result obtained by the methods were found for various conditions of numerical investigations: initial wall emissivity, total extinction coefficient, and thickness of the ash deposit layer. The gas-phase phase radiative properties were determined by the simple gray gas model and weighted sum of gray gases model. Investigation showed small differences of the results obtained by the methods and their sensitivity to conditions of numerical investigation. The CCTEA method is an improvement of the RRNS method as it provides almost the same results after the single run of the numerical simulation and reduces the computation time.

Do fiscal rules reduce political polarization?

In recent years, several countries have enacted numerical fiscal rules, which set limits for government expenditure, revenue, deficits and debt. According to both proponents and critics of these rules, they are a tool to depoliticize fiscal policy and perhaps the political system more broadly, by forcing political parties to adopt increasingly similar political positions. However, little empirical research has been done on whether the introduction of fiscal rules actually affects countries’ internal political life. This article explores whether fiscal rules do, in fact, cause legislative parties to adopt similar political positions, by testing the effects of fiscal rules on the level of political polarization. Using party manifestos data from 185 elections in 32 OECD countries, this article finds little robust evidence that fiscal rules independently reduce the level of political polarization within national legislatures. Contrary to popular arguments about fiscal rules and depoliticization, there is little evidence for an independent effect of fiscal rules on the variance of political parties’ positions.

Do fiscal rules reduce government borrowing costs in developing countries?

AbstractWe examine whether adopting a numerical fiscal rule framework to guide fiscal policy helped reduce the cost of borrowing by governments in a sample of 61 low‐ and middle‐income countries for 1985–2017, 24 of which adopted such rules. We address the self‐selection problem of policy adoption by applying a variety of propensity score matching methods and show that the average treatment effect of fiscal rules on government borrowing costs is quantitatively quite large and statistically significant in rule adopting countries. We also find that the presence of institutional arrangements to strengthen fiscal rules results in a larger reduction in borrowing costs than is the case without these arrangements, which is consistent with strong rules adding to the credibility of the fiscal policy framework.

Abusive Language Detection: A Comprehensive Review

Objectives: To provide an organised literature on the detection of Abusive language on Twitter using natural language processing (NLP). Methods: In this study, the survey has been conducted on different methods and research conducted on the types of Abusive language used in social media, why it is important? How it has been detected in real time social media platforms and the performance metrics that are used by researchers in evaluating the performance of the detection of abusive language on Twitter by the users. Results: Giving an organised review of past methodologies, including methods, important features and core algorithms, this study arranges and depicts the present condition about this area. The study also talks about the intricacy of hate speech idea which is characterised in numerous stages ad settings. This area of study has an obvious potential for societal effect, especially in digital media and online networks. A crucial step in propelling automatic hate speech detection is the advancement and systemisation of common assets, for example, clarified data sets in numerous dialects, rules, and calculations. Conclusion: This survey study contains all the relevant references related to detection of abusive language on social media using NLP and machine learning methods. Ultimately, it can be as source of references to the other researchers in finding the literatures that are relevant to their research area in the detection of Abusive language on Twitter. Keywords: Abusive Language, Natural Language Processing, Social media analysis, Text Classification and Analysis

Numerical modelling and shear design rules of stainless steel lipped channel sections

The demand for highly structurally efficient stainless steel is limited to a certain extent by its high initial cost. Therefore, the utilisation of material to the optimum possible level is important. In achieving this, further consideration should be given to enhance the design rules where beneficial effects such as pronounced strain hardening in stainless steel should be taken into account in the design process. In addition to that, a thorough understanding of the structural behaviour of stainless steel sections is also required. However, the shear behaviour and capacity of cold-formed stainless steel lipped channel beams (LCBs) have not been thoroughly investigated previously. Therefore, experimental and detailed finite element (FE) modelling were undertaken to investigate the shear behaviour and strength of stainless steel LCBs. A comprehensive parametric study was also conducted by developing 100 FE models. From the results, the available post-buckling strength in slender stainless steel LCBs was highlighted. Furthermore, the beneficial strength increment due to the strain hardening effect of stainless steel, particularly for compact LCBs in shear, was investigated. Comparisons indicated that current EN1993-1-4 and direct strength method (DSM) shear design rules are too conservative in particularly for compact sections. Thus, existing shear design rules were modified to enhance the overall prediction accuracy for stainless steel LCBs while attention was given to capture the available inelastic reserve capacity.

ÖZERK MALİ OTORİTELER VE ÇEŞİTLİ ÜLKE UYGULAMALARI: TÜRKİYE İÇİN OLASI BİR ÇIKARSAMA

In the study, information is provided about the general characteristics of the formations named “financial council”, “financial court” and “parliamentary budget offices”, which has been active since the past in some of the developed countries, especially in the European countries after the 2008 - 2009 Global Contraction process, and becoming common also in developing countries. United States, Sweden and Slovenia being examined under separate headings and for Turkey evaluations are made such a possible formation should have in the context of the innovations brought by Law No. 5018 on Public Financial Management and Control. In parallel with the changes in the fiscal policy, autonomous structures are becoming widespread along with numerical rules and some general expectations are brought up, although they do not show full homogeneity. In particular, their budgets, personnel structures, procedures for appointment of senior management and term of office are considered as key elements for these structures. Although their organization and functions are different, the legitimacy of their existence, especially in terms of democratic culture, is closely related to these characteristics. Even though they are named by different names, it is seen that these dominant features are taken into consideration especially in developed countries during the establishment stages. Turkey also made innovations in the area of fiscal policy and continue to try, though in terms of financial rules and structures must take into account some basic features

Performance analysis of multi-objective artificial intelligence optimization algorithms in numerical association rule mining

Association rules mining (ARM) is one of the most popular tasks of data mining. Although there are many effective algorithms run on binary or discrete-valued data for the problem of ARM, these algorithms cannot run efficiently on data that have numeric-valued attributes. However, in many real-world applications, the data usually consist of numerical values. It is a difficult problem to determine which attributes will be included in the discovered rules; automatically adjust the ranges of the attributes in the most appropriate way; rapidly discover the reduced high-quality rules directly without generating the frequent itemsets ensuring the rules to be comprehensible, surprising, interesting, accurate, and confidential. Furthermore, adjusting all these processes without the need for metrics to be determined a priori for each data set is of great importance in terms of automating this problem. Recently, numerical ARM has been dealt with as a multi-objective problem that best meets different criteria at the same time. In this study, algorithms which consider numerical ARM as a multi-objective optimization problem were examined and the performance analysis of these algorithms was performed for the first time to the best of our knowledge. A comparative analysis of MOPNAR, QAR-CIP-NSGA II, NICGAR, MODENAR, MOEA_Ghosh, and ARMMGA methods in terms of the number of rules, average support, average confidence, average lift, average conviction, average certain factor, average netconf, average yulesQ, and coverage percentage metrics in the real-world data consisting of numerical attributes was performed. The performances these algorithms were tested with single-objective optimization methods for ARM in this study. It is found that MOEA-Ghosh is the most effective multi-objective method in terms of average support and average confidence measures in data sets containing high number records and attributes. The best results in terms of average support value were obtained by MOEA-Ghosh algorithm and the average confidence values were obtained by multi-objective QAR-CIP-NSGAII in data sets containing relatively few records and attributes. Furthermore, it can be concluded that multi-objective algorithms outperformed the single-objective algorithms with respect to average support, lift, certain factor, netconf, and yulesQ metrics.

Intelligent optimization algorithms for the problem of mining numerical association rules

There are many effective approaches that have been proposed for association rules mining (ARM) on binary or discrete-valued data. However, in many real-world applications, the data usually consist of numerical values and the standard algorithms cannot work or give promising results on these datasets. In numerical ARM (NARM), it is a difficult problem to determine which attributes will be included in the rules to be discovered and which ones will be on the left of the rule and which ones on the right. It is also difficult to automatically adjusting of most relevant ranges for numerical attributes. Directly discovering the rules without generating the frequent itemsets as used in the literature as the first step of ARM accelerates the whole process without determining the metrics needed for this step. In classical ARM algorithms, generally one or two metrics are considered. However, mined rules are needed to be comprehensible, surprising, interesting, accurate, confidential, and etc. in many real-world applications. Adjusting all of these processes without the need for the metrics to be pre-determined for each dataset seems another problem. For these purposes, evolutionary intelligent optimization algorithms seem potential solution method for this complex problem. In this paper, the performance analysis of seven evolutionary algorithms and fuzzy evolutionary algorithms; namely Alatasetal, Alcalaetal, EARMGA, GAR, GENAR, Genetic Fuzzy Apriori, and Genetic Fuzzy AprioriDC for NARM problem has been performed within eleven real datasets for the first time. The obtained results have also been compared with the classical Apriori algorithm to show the efficiencies of the intelligent algorithms on NARM problem. Performances of eight algorithms in terms of support, confidence, number of mined rules, number of covered records, and time metrics have been comparatively performed with eleven real-world datasets. One of the best-mined rules obtained by each algorithm has been given and analyzed with respect to confidence, support, and lift metrics.

Международно-правовые вопросы установления морских охраняемых районов

Международно-правовые вопросы установления морских охраняемых районов

Multitask TSK Fuzzy System Modeling by Jointly Reducing Rules and Consequent Parameters

Existing multitask Takagi-Sugeno-Kang (TSK) fuzzy modeling methods always produce high complex fuzzy models with numerous redundant rules and consequent parameters. To this end, we propose a novel multitask TSK fuzzy modeling method called mtSparseTSK, which learns a compact set of fuzzy rules and shared consequent parameters across tasks in a unified procedure. Specifically, we consider the fuzzy rule reduction and consequent parameter selection across tasks by devising novel group sparsity regularizations in the learning criterion of the model. We also integrate the intertask relations in the proposed TSK model for multitask learning. We fully utilize the block structure in the TSK fuzzy models in formulating a joint block sparse optimization problem and develop a procedure for alternating direction method of multipliers (ADMMs) to find the optimal solution of the problem. Experiments on the synthetic and real-world datasets demonstrate the distinctive performance of the proposed methods over the existing ones on multitask fuzzy system modeling.

A Study on the Extraterritorial Application of the General Data Protection Regulation with a Focus on Computing

A Study on the Extraterritorial Application of the General Data Protection Regulation with a Focus on Computing

Continual learning of context-dependent processing in neural networks

Deep neural networks (DNNs) are powerful tools in learning sophisticated but fixed mapping rules between inputs and outputs, thereby limiting their application in more complex and dynamic situations in which the mapping rules are not kept the same but changing according to different contexts. To lift such limits, we developed a novel approach involving a learning algorithm, called orthogonal weights modification (OWM), with the addition of a context-dependent processing (CDP) module. We demonstrated that with OWM to overcome the problem of catastrophic forgetting, and the CDP module to learn how to reuse a feature representation and a classifier for different contexts, a single network can acquire numerous context-dependent mapping rules in an online and continual manner, with as few as $\sim$10 samples to learn each. This should enable highly compact systems to gradually learn myriad regularities of the real world and eventually behave appropriately within it.

A MODEL FOR EVALUATING THE RISK EFFECTS ON CONSTRUCTION PROJECT ACTIVITIES

Cost overruns and time delays are considered to be very important challenges for the majority of construction projects. These challenges are typically attributed to their associated risks. Due to the risky and uncertain nature of construction projects, an increasing amount of attention is given to estimating and overcoming cost overruns and time delays. New techniques are being developed to help project managers to contractually complete projects within cost and time constraints. The objective of this study was to develop a new qualitative and quantitative risk analysis model that can be employed for construction projects. The proposed model, which is based on a fuzzy logic tool, consists of two modules for assessing risk factors that affect the main construction activities and computing the expected cost overruns and time delays that are associated with these risks. Using numerous logical rules, the model applies the probability of occurrences and impacts of the risks on the cost and time of the main activities. The Spearman and Kendall correlation coefficient tests are applied to verify and select a suitable membership function. Using four proposed membership functions, the results of these tests confirmed that the triangle membership function is suitable for the model. The model is verified by application to HVAC system activities in two actual construction projects, which serve as case studies. Two different methods are proposed and applied to quantify the cost overruns and time delays. The first method is based on determining the cost overruns and time delay values for each activity according to their weight in the system. Triple premise rules are proposed and applied in the second method, which is established to relate all activities. The results from the second method are more accurate compared with the first method based on actual data from the case study projects. In addition, the results demonstrated that the proposed model can be used to quantify the expected cost overrun and time delays in construction project activities and can be generalized and implemented in different construction activities.

Data-Driven Enhanced Nonlinear Gaussian Filter

Nonlinear Gaussian filters are usually developed from numerical quadrature rules to approximate the mean and covariance. However, the Gaussian assumption may not be viable after propagation of the estimate through nonlinear dynamics, which may lead to degraded performance. In this brief, we propose an enhanced nonlinear Gaussian quadrature filter in which the quadrature points/weights are generated by a data-driven arbitrary polynomial chaos (aPC) method without relying on the Gaussian assumption. A set of Monte Carlo samples are first propagated through nonlinear dynamics. The statistic moments can be calculated directly from these Monte Carlo samples. The enhanced quadrature points/weights are generated from these moments using the aPC method. Since such quadrature points contain higher order statistic information of the propagated distribution, they can better represent the state distribution and provide a more accurate estimate. Numerical examples show the effectiveness of the proposed filter.