Interpretation Of Rules Research Articles

Wybrane ograniczenia egzekucji z ruchomości na podstawie art. 829 k.p.c. – część 2

This article discusses selected limitations to debt collection from real properties based on the Civil Procedure Code. It presents the structured description of the scope of limitations (which real properties are excluded from those enforcement proceedings) by referring to acceptable legal interpretation rules, as well as the achievements of the doctrine and judicature, including in particular definitions. In addition, the article examines practical problems that can appear in the context of the definition of the scope of limitations to debt collection from particular categories of objects specified in Art. 829 of the Civil Procedure Code. It refers to the question of values presented by the legislator, which seem to constitute the basis for the regulation.

Learning dispatching rules via novel genetic programming with feature selection in energy-aware dynamic job-shop scheduling

The incorporation of energy conservation measures into production efficiency is widely recognized as a crucial aspect of contemporary industry. This study aims to develop interpretable and high-quality dispatching rules for energy-aware dynamic job shop scheduling (EDJSS). In comparison to the traditional modeling methods, this paper proposes a novel genetic programming with online feature selection mechanism to learn dispatching rules automatically. The idea of the novel GP method is to achieve a progressive transition from exploration to exploitation by relating the level of population diversity to the stopping criteria and elapsed duration. We hypothesize that diverse and promising individuals obtained from the novel GP method can guide the feature selection to design competitive rules. The proposed approach is compared with three GP-based algorithms and 20 benchmark rules in the different job shop conditions and scheduling objectives considered energy consumption. Experiments show that the proposed approach greatly outperforms the compared methods in generating more interpretable and effective rules. Overall, the average improvement over the best-evolved rules by the other three GP-based algorithms is 12.67%, 15.38%, and 11.59% in the meakspan with energy consumption (EMS), mean weighted tardiness with energy consumption (EMWT), and mean flow time with energy consumption (EMFT) scenarios, respectively.

The Inspector-General of the Australian Defence Force Afghanistan Inquiry Report and the applicability of Additional Protocol II to intervening foreign forces

AbstractThis article critiques the articulation of the legal framework applicable to Australian Defence Force operations in Afghanistan found in the Inspector-General of the Australian Defence Force Afghanistan Inquiry Report (Brereton Report). In particular, using the Australian experience in Afghanistan as a case study, the article argues, on the basis of the rules of treaty interpretation, that where a foreign State party to Additional Protocol II (AP II) intervenes in a non-international armed conflict (NIAC) to which AP II applies, that foreign State is bound by AP II, in addition to the host State and non-State armed actors that are parties to the NIAC. The article concludes by outlining the reasons why the Brereton Report's silence in relation to AP II matters.

Mutual Supervised Fusion & Transfer Learning with Interpretable Linguistic Meaning for Social Data Analytics

Social data analytics is often taken as the most commonly used method for community discovery, product recommendations, knowledge graph, and so on. In this study, social data are firstly represented in different feature spaces by using various feature extraction algorithms. Then we build a transfer learning model to leverage knowledge from multiple feature spaces. During modeling, since the assumption that the training and the testing data have the same distribution is always true, we give a theorem and its proof which asserts the necessary and sufficient condition for achieving a minimum testing error. We also theoretically demonstrate that maximizing the classification error consistency across different feature spaces can improve the classification performance. Additionally, the cluster assumption derived from semi-supervised learning is introduced to enhance knowledge transfer. Finally, aTagaki-Sugeno-Kang (TSK)fuzzy system-based learning algorithm is proposed, which can generate interpretable fuzzy rules. Experimental results not only demonstrate the promising social data classification performance of our proposed approach but also show its interpretability which is missing in many other models.

Metastable States of a Fluid Inside a Binodal in the Context of Cluster Variation

The behavior of the isotherm and molecular distributions inside the binodal is analyzed to solving an Ising model obtained on the basis of cluster variation for planar lattices with coordination numbers 3, 4, 6. It is found that the microscopic approach gives a probabilistic interpretation of the Maxwell macroscopic rule and explains how the isotherm a secant appears between the regions of coexistence of two phases. A region of no solutions (the region of degeneracy) is found inside the binodal, and the critical temperatures of degeneracy at which the nontrivial solution to the equations disappears are calculated for this region. The region of degeneracy inside the binodal expands and approaches the binodal curve as the temperature falls, so the degeneracy curve and the binodal become indistinguishable. Numerical iterative calculations are used to study the dependence of the region of no solution inside the binodal as a cluster grows. The critical temperature of degeneracy asymptotically approaches that of the binodal as the cluster grows. Existing ways of interpreting metastable states are discussed, along with as the correspondence between the new results and previously known mean-field (ignoring correlations) and quasi-chemical (considering only direct correlations) approximations, and an exact result of the Yang–Lee condensation theory.

Integrating medical rules to assist attention for sleep apnea detection

AbstractSleep apnea is one of the most common sleep disorders. The consequences of undiagnosed sleep apnea can be very serious, increasing the risk of high blood pressure, heart disease, stroke, and Alzheimer’s disease over a long period of time. However, many people are often unaware of their condition. The gold standard for diagnosing sleep apnea is nighttime polysomnography monitoring in a specialized sleep laboratory. However, these diagnoses are expensive and the number of beds is limited, and there is insufficient monitoring in terms of time dimension. Existing methods for automated detection use no more than three physiological signals, but all other signals are also associated with the patient’s sleep. In addition, the limited amount of medical real annotation data, especially abnormal samples, lead to weak model generalization capability. The gap between model generalization capability and medical field needs still exists. In this paper, we propose a method for integrating medical interpretation rules into a long short-term memory neural network based on self-attention with multichannel respiratory signals as input. We obtain attention weights through a token-level attention mechanism and then extract key rules of medical interpretation to assist the weights, improving model generalization and reducing the dependence on data volume. Compared with the best prediction performance of existing methods, the average improvements of our method in accuracy, precision, and f1-score are 3.26%, 7.03%, and 1.78%, respectively. The algorithm tested the performance of our model on the Sleep Heart Health Study data set and found that the model outperformed existing methods and could help physicians make decisions in their practices.

Nick Brennan. Divine Christology in the Epistle to the Hebrews: The Son as God

Nick Brennan. <i>Divine Christology in the Epistle to the Hebrews: The Son as God</i>

Wybrane ograniczenia egzekucji z ruchomości na podstawie art. 829 k.p.c. – część 1

This article discusses selected limitations to debt collection from real properties based on the Civil Procedure Code. It presents the structured description of the scope of limitations (which real properties are excluded from those enforcement proceedings) by referring to acceptable legal interpretation rules, as well as the achievements of the doctrine and judicature, including in particular definitions. In addition, the article examines practical problems that can appear in the context of the definition of the scope of limitations to debt collection from particular categories of objects specified in Art. 829 of the Civil Procedure Code. It refers to the question of values presented by the legislator, which seem to constitute the basis for the regulation.

The Global Anti-Base Erosion (GloBE) Rules and Tax Certainty: A Proposed Architecture to Prevent and Resolve GloBE Disputes

In the framework of the implementation of the global minimum tax – the Global Anti-Base Erosion (GloBE) rules – the development of effective dispute prevention and resolution mechanisms continues to be a key concern for both businesses and tax administrations, as also evidenced by recent public consultations promoted by the OECD. In the present study, the authors further develop as a standalone model the reciprocal domestic dispute resolution proposal already outlined in an earlier article of theirs published in the World Tax Journal. The authors also enhance this rule with a dispute prevention component and an adjustment provision taking into account the outcome of the dispute resolution. In particular, following the introduction, section 2. is devoted to a short classification of GloBE disputes and the presentation of the distinction drawn in this study between conflicts of interpretation (section 2.1.) and conflicts of transposition (section 2.2.) Section 3. displays the authors’ proposal to achieve tax certainty for the GloBE rules. They begin by setting out the fundamental principles of the proposal (section 3.1.) and providing a general overview of its architecture (section 3.2.). Turning to the concerns raised by the 2023 Public Consultation Document, the authors explain why their proposal would not lead to any override of domestic law and, therefore, should remain compatible with the principle of legality (section 3.3.). A detailed discussion of the key components of their proposal, most notably – as a complement to the domestic reciprocal dispute resolution mechanism – a new interpretative model rule and a new lex specialis model rule then follows in section 3.4. In section 4., a comparison is finally developed between the proposal by the authors and the option of a multilateral convention.

Self-Constructed Deep Fuzzy Neural Network for Traffic Flow Prediction

Traffic flow prediction is a critical component of intelligent transportation systems, especially in the prevention of traffic congestion in urban areas. While significant efforts have been devoted to enhancing the accuracy of traffic prediction, the interpretability of traffic prediction also needs to be considered to enhance persuasiveness, particularly in the era of deep-learning-based traffic cognition. Although some studies have explored interpretable neural networks from the feature and result levels, model-level explanation, which explains the reasoning process of traffic prediction through transparent models, remains underexplored and requires more attention. In this paper, we propose a novel self-constructed deep fuzzy neural network, SCDFNN, for traffic flow prediction with model interpretability. By leveraging recent advances in neuro-symbolic computation for automatic rule learning, SCDFNN learns interpretable human traffic cognitive rules based on deep learning, incorporating two innovations: (1) a new fuzzy neural network hierarchical architecture constructed for spatial-temporal dependences in the traffic feature domain; (2) a modified Wang–Mendel method used to fuse regional differences in traffic data, resulting in adaptive fuzzy-rule weights without sacrificing interpretability. Comprehensive experiments on well-known traffic datasets demonstrate that the proposed approach is comparable to state-of-the-art deep models, and the SCDFNN’s unique hierarchical architecture allows for transparency.

A framework for inherently interpretable optimization models

With dramatic improvements in optimization software, the solution of large-scale problems that seemed intractable decades ago are now a routine task. This puts even more real-world applications into the reach of optimizers. At the same time, solving optimization problems often turns out to be one of the smaller difficulties when putting solutions into practice. One major barrier is that the optimization software can be perceived as a black box, which may produce solutions of high quality, but can create completely different solutions when circumstances change leading to low acceptance of optimized solutions. Such issues of interpretability and explainability have seen significant attention in other areas, such as machine learning, but less so in optimization. In this paper we propose an optimization framework that inherently comes with an easily interpretable optimization rule, that explains under which circumstances certain solutions are chosen. Focusing on univariate decision trees to represent interpretable optimization rules, we propose integer programming formulations as well as a heuristic method that ensure applicability of our approach even for large-scale problems. By presenting several extensions to the univariate decision tree approach, we showcase the generality of the proposed framework. Computational experiments using random and real-world data of a road network indicate that the costs of inherent interpretability can be very small.

A deep neural network framework to derive interpretable decision rules for accurate traumatic brain injury identification of infants

ObjectiveWe aimed to develop a robust framework to model the complex association between clinical features and traumatic brain injury (TBI) risk in children under age two, and identify significant features to derive clinical decision rules for triage decisions.MethodsIn this retrospective study, four frequently used machine learning models, i.e., support vector machine (SVM), random forest (RF), deep neural network (DNN), and XGBoost (XGB), were compared to identify significant clinical features from 24 input features associated with the TBI risk in children under age two under the permutation feature importance test (PermFIT) framework by using the publicly available data set from the Pediatric Emergency Care Applied Research Network (PECARN) study. The prediction accuracy was determined by comparing the predicted TBI status with the computed tomography (CT) scan results since CT scan is the gold standard for diagnosing TBI.ResultsAt a significance level of p = 0.05, DNN, RF, XGB, and SVM identified 9, 1, 2, and 4 significant features, respectively. In a comparison of accuracy (Accuracy), the area under the curve (AUC), and the precision-recall area under the curve (PR-AUC), the permutation feature importance test for DNN model was the most powerful framework for identifying significant features and outperformed other methods, i.e., RF, XGB, and SVM, with Accuracy, AUC, and PR-AUC as 0.915, 0.794, and 0.974, respectively.ConclusionThese results indicate that the PermFIT-DNN framework robustly identifies significant clinical features associated with TBI status and improves prediction performance. The findings could be used to inform the development of clinical decision tools designed to inform triage decisions.

Moral appraisals guide intuitive legal determinations.

We sought to understand how basic competencies in moral reasoning influence the application of private, institutional, and legal rules. We predicted that moral appraisals, implicating both outcome-based and mental state reasoning, would shape participants' interpretation of rules and statutes-and asked whether these effects arise differentially under intuitive and reflective reasoning conditions. In six vignette-based experiments (total N = 2,473; 293 university law students [67% women; age bracket mode: 18-22 years] and 2,180 online workers [60% women; mean age = 31.9 years]), participants considered a wide range of written rules and laws and determined whether a protagonist had violated the rule in question. We manipulated morally relevant aspects of each incident-including the valence of the rule's purpose (Study 1) and of the outcomes that ensued (Studies 2 and 3), as well as the protagonist's accompanying mental state (Studies 5 and 6). In two studies, we simultaneously varied whether participants decided under time pressure or following a forced delay (Studies 4 and 6). Moral appraisals of the rule's purpose, the agent's extraneous blameworthiness, and the agent's epistemic state impacted legal determinations and helped to explain participants' departure from rules' literal interpretation. Counter-literal verdicts were stronger under time pressure and were weakened by the opportunity to reflect. Under intuitive reasoning conditions, legal determinations draw on core competencies in moral cognition, such as outcome-based and mental state reasoning. In turn, cognitive reflection dampens these effects on statutory interpretation, allowing text to play a more influential role. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Meta-learning biologically plausible plasticity rules with random feedback pathways

Backpropagation is widely used to train artificial neural networks, but its relationship to synaptic plasticity in the brain is unknown. Some biological models of backpropagation rely on feedback projections that are symmetric with feedforward connections, but experiments do not corroborate the existence of such symmetric backward connectivity. Random feedback alignment offers an alternative model in which errors are propagated backward through fixed, random backward connections. This approach successfully trains shallow models, but learns slowly and does not perform well with deeper models or online learning. In this study, we develop a meta-learning approach to discover interpretable, biologically plausible plasticity rules that improve online learning performance with fixed random feedback connections. The resulting plasticity rules show improved online training of deep models in the low data regime. Our results highlight the potential of meta-learning to discover effective, interpretable learning rules satisfying biological constraints.

Cooperative and conformist behavioural preferences predict the dual dimensions of political ideology

Decades of research suggest that our political differences are best captured by two dimensions of political ideology. The dual evolutionary framework of political ideology predicts that these dimensions should be related to variation in social preferences for cooperation and group conformity. Here, we combine data from a New Zealand survey and a suite of incentivised behavioural tasks (n = 991) to test whether cooperative and conformist preferences covary with a pair of widely used measures of the two dimensions of political ideology—Social Dominance Orientation (SDO) and Right Wing Authoritarianism (RWA)—and related policy views. As predicted, we find that cooperative behaviour is negatively related to SDO and economically conservative policy views, while conformist behaviour in the form of social information use is positively related to RWA and socially conservative policy views. However, we did not find the predicted relationships between punitive and rule following behaviours and RWA or socially conservative views, raising questions about the interpretation of punishment and rule following tasks and the nature of authoritarian conformist preferences. These findings reveal how cooperative and conformist preferences that evolved to help us navigate social challenges in our ancestral past continue to track our political differences even today.

К вопросу импортозамещения Европейского суда по правам человека

In this article, the authors consider the possibility of creating an alternative judicial body of the European Court of Human Rights through the prism of the analysis of the basic rules of interpretation of legal norms in the Anglo-Saxon system of law.

Modelling and assessing how small hydropower facilities affect sediment transport by using fuzzy inference systems

Small dams for power generation are increasingly being built around the world, and the reported impact caused by these structures on sediment transport varies widely, ranging from substantial to minimal or non-existent alterations among different dams. Such variability is still poorly understood. In this study, we present an original application of fuzzy inference systems (FIS) to extract knowledge concerning the effect of small dams on sediment transport, providing understandings of how different variables related to watershed characteristics, dam characteristics and hydrological variations can affect sedimentological responses of these structures. Numerous small dams have been built, and many more are in development or proposed, in rivers that drain into the Pantanal, a world-renowned tropical wetland, where we apply the methodology. Data on 23 hydropower facilities from the uplands of the Pantanal region were used, with a total of 535 records. The models presented very good results (NS equal to 0.92 for the most detailed FIS model) and provided a better understanding of the phenomenon through their explicit linguistic rules. The upstream suspended sediment concentration (SSC) is the most determinant variable for the alteration caused by dams. Higher SSC entering the reservoir is related to higher sediment retention. Vegetation cover and soil type of the drainage area of dams also influence the alterations on SSC caused by these structures. Dams located in basins with greater vegetation cover tend to affect less sediment transport, and dams located in basins with high percentages of agriculture and pasture tend to alter more. FIS models also confirmed that the size of dams is directly proportional to their capacity to alter sediment transport. The methodologies developed for the interpretation of fuzzy rules proved to be useful for assessing how small dams affect sediment transport along river systems.

Rule Ambiguity, Institutional Clashes, and Population Loss: How Wikipedia Became the Last Good Place on the Internet

Scholars usually portray institutions as stable, inviting a status quo bias in their theories. Change, when it is theorized, is frequently attributed to exogenous factors. This paper, by contrast, proposes that institutional change can occur endogenously through population loss, as institutional losers become demotivated and leave, whereas institutional winners remain. This paper provides a detailed demonstration of how this form of endogenous change occurred on the English Wikipedia. A qualitative content analysis shows that Wikipedia transformed from a dubious source of information in its early years to an increasingly reliable one over time. Process tracing shows that early outcomes of disputes over rule interpretations in different corners of the encyclopedia demobilized certain types of editors (while mobilizing others) and strengthened certain understandings of Wikipedia’s ambiguous rules (while weakening others). Over time, Wikipedians who supported fringe content departed or were ousted. Thus, population loss led to highly consequential institutional change.

On the performance and interpretability of Mamdani and Takagi-Sugeno-Kang based neuro-fuzzy systems for medical diagnosis

PurposeNeuro-fuzzy systems aim to combine the benefits of artificial neural networks and fuzzy inference systems: a neural network can learn patterns from data and achieves high performance, whereas a fuzzy system matches inputs and outputs using linguistic and interpretable rules. The combination of these two techniques yields models that can both perform well and provide interpretability in a fuzzy linguistic manner. DesignIn this paper, the performance and interpretability of five neuro-fuzzy classifiers were evaluated (three Takagi-Sugeno-Kang (TSK) classifiers: adaptive neuro-fuzzy inference system (ANFIS), dynamic evolving neuro-fuzzy system (DENFIS), self-organizing fuzzy neural network (SOFNN), and two Mamdani classifiers: hybrid fuzzy inference system (HyFIS) and neuro-fuzzy classifier (NEFCLASS)). All the empirical evaluations were over four benchmark medical datasets (Wisconsin breast cancer dataset, SPECTF heart dataset, Parkinsons dataset, and diabetic retinopathy Debrecen dataset), and used five performance criteria (accuracy, precision, recall, f score, and training time) and two interpretability criteria (number of rules and number of membership functions). FindingsResults showed that the TSK-based self-organizing fuzzy neural network classifier, in general, outperformed the others. In terms of interpretability, DENFIS and NEFCLASS were the best Takagi-Sugeno-Kang and Mamdani classifiers respectively. The findings also suggested that three classifiers: DENFIS, SOFNN, and NEFCLASS achieved a good performance-interpretability tradeoff. OriginalityTo the best of our knowledge, no study has compared the neuro-fuzzy techniques presented in this paper in terms of performance and interpretability in the medical domain.

Assessing water resources systems’ dynamic resilience under hazardous events via a genetic fuzzy rule-based system

AbstractIn this paper, the use of a novel genetic fuzzy rule-based system (FRBS) is proposed for assessing the resilience of a water resources system to hazards. The proposed software framework generates a set of highly interpretable rules that transparently represent the causal relationships of hazardous events, their timings, and intensities that can lead to the system's failure. This is achieved automatically through an evolutionary learning procedure that is applied to the data acquired from system dynamics (SD) and hazard simulations. The proposed framework for generating an explainable predictive model of water resources system resilience is applied to the Pirot water resources system in the Republic of Serbia. The results indicate that our approach extracted high-level knowledge from the large datasets derived from multi-model simulations. The rule-based knowledge structure facilitates its common-sense interpretation. The presented approach is suitable for identifying scenario components that lead to increased system vulnerability, which are very hard to detect from massive raw data. The fuzzy model also proves to be a satisfying fuzzy classifier, exhibiting precisions of 0.97 and 0.96 in the prediction of low resilience and high rapidity, respectively.