Inductive Reasoning Research Articles

Applying inductive logic programming to automate the function of an intelligent natural language interfaces for databases

Applying inductive logic programming to automate the function of an intelligent natural language interfaces for databases

Implementing community policing practices to manage conflict resolution in Galeshewe, South Africa

Community policing has emerged as a vital instrument for combatting crime and enhancing public safety in South Africa. As a result, it has the capacity to go beyond traditional law enforcement functions as a mediator in disputes, fostering improved relationships between the police and the communities where they work. This paper analyses the implementation of community policing strategies by the South African police with the purpose of resolving conflicts. This study aims to address social crime prevention-related concerns through community policing methods in the Galeshewe police area within the Francis Baard policing regions of the Sol Plaatje Municipality, South Africa. The paper examines the tactics that community police employ to enforce the law, avoid social issues, and manage conflict resolution in the communities. A qualitative method and descriptive design were employed. Comprehensive document analysis, semi-structured interviews, and observations were employed as data collection strategies. An inductive reasoning model was used to analysis data. The findings of the study demonstrated that community policing plays an important role in optimizing problem mapping and it increases public knowledge of the importance of upholding security and order in the different police operations that support the community policing program.

Enhanced predictive PDF control of stochastic distribution systems with neural network compensation and its application

Compared to the conventional control algorithms that use mean and variance as indicators, predictive probability density function (PDF) control can effectively handle the output PDF control problem of non-Gaussian stochastic distribution systems. However, the existing predictive PDF control method does not consider the construction error between output PDF and weight, thus the control performance is still unsatisfactory. Therefore, this paper proposes a new Enhanced Predictive PDF control method (En-PDF) to improve the output PDF control performance of stochastic distribution systems. The proposed method mainly consists of two parts: the predictive PDF control part and the neural network compensation control part aiming to reduce the bias of output PDF. First, the Radical Basis Functions (RBFs) are used to approximate the PDF of the stochastic systems output, and then a prediction model representing the relationship between input and weight is established using the subspace identification algorithm to design the predictive PDF control for the stochastic systems. Next, the Kullback-Leibler (KL) divergence is used to measure the similarity between the output PDF and the set PDF, combined with the weight error and compensation to design a new performance index. Based on this, the parameters of the neural network are adjusted using the gradient descent algorithm to obtain the optimal compensation, and the stability and tracking performance of the proposed algorithm are analyzed using inductive reasoning method. Finally, the predictive PDF control input with the compensation work together on the controlled plant to achieve high-performance control of the output PDF of non-Gaussian stochastic distribution systems. Both simulation experiments and physical control experiments validate the effectiveness and superiority of the proposed method.

A comparison of imprecise Bayesianism and Dempster–Shafer theory for automated decisions under ambiguity

Abstract Ambiguity occurs insofar as a reasoner lacks information about the relevant physical probabilities. There are objections to the application of standard Bayesian inductive logic and decision theory in contexts of significant ambiguity. A variety of alternative frameworks for reasoning under ambiguity have been proposed. Two of the most prominent are Imprecise Bayesianism and Dempster–Shafer theory. We compare these inductive logics with respect to the Ambiguity Dilemma, which is a problem that has been raised for Imprecise Bayesianism. We develop an agent-based model comparison that isolates the difference between the two inductive logics in their updating methods. We find that Dempster–Shafer theory does not avoid the Ambiguity Dilemma. We discuss the implications of this result.

Do young American children essentialize ethnicity? Examining inductive inferences about Hispanic/Latinx individuals in an ethnically diverse sample

AbstractHispanic and Latinx individuals represent one of the largest and fastest growing ethnic groups in the United States. Yet research has not investigated whether young children hold essentialist beliefs about this prevalent social category. The present study addressed this issue by examining whether children used this ethnic category to make inductive inferences about novel individuals, one dimension of essentialism. A total of 108 children, 5 to 7 years of age (54 female; 56 Hispanic, 46 non‐Hispanic), completed a forced‐choice inference task. Children did not expect members of the same ethnic group to share properties, and this did not vary with their own ethnic group membership. This suggests that in the US, the belief that ethnicity is causally informative undergoes a protracted developmental trajectory, as has been observed for essentialist beliefs about race.

ADA-UDA: A transferable transformer framework for rumor detection using Adversarial Domain Alignment within Unsupervised Domain Adaptation

The rapid spread of misinformation on social media has posed significant challenges, particularly in the early detection of rumors, which is critical to mitigating their negative impact. However, the limited data available during the initial stages of trending topics poses significant challenges, including analysis constraints and model overfitting. Therefore, the adoption of specific methods are needed to identify patterns from historical sample data and enable effective knowledge transfer, facilitating inductive reasoning with limited data on new topics. To address these challenges, we propose an Adversarial Domain Alignment Unsupervised Domain Adaptation (ADA-UDA) method based on the Transformer architecture. Our approach leverages labeled historical data from the source domain alongside a limited quantity of unlabeled data from new trending topics in the target domain. At the heart of our method is the Parameter Transferable Module (PTM), which guides the Transformer to focus on transferable and distinguishable features, thereby enhancing the model’s ability to perform effective inductive reasoning with limited data. We conducted extensive experiments to evaluate our method, benchmarking it against current mainstream rumor detection techniques. The findings indicate that our ADA-UDA method outperforms existing approaches, underscoring its potential for early and accurate rumor detection in emerging topics.

Comparative analysis of critical thinking skills between ChatGPT and college students

This study employed a questionnaire survey using the California Critical Thinking Skills Test (CCTST) on a sample of 240 Chinese university students. The results obtained were then compared with 240 questionnaire results created by ChatGPT in order to investigate variations in critical thinking skills across different dimensions. The study uncovered that, although ChatGPT had exceptional performance in inference and deductive reasoning, it fell behind university students in the areas of evaluation and inductive reasoning. Meanwhile, with respect to analysis, the capabilities of both college students and ChatGPT were not significantly different. These findings provide essential insights for the advancement of artificial intelligence and human progress.

The Curve Fitting Problem, Data Validation, and Inductive Generalization in Machine Learning

AbstractAris Spanos and Deborah Mayo’s error-statistical approach to statistical modeling and inference adopts the reliability of inductive inference as a primary criterion for statistical model and estimator selection (e.g., curve fitting). In this paper, we expand the error-statistical approach’s adoption of reliable inductive inference by scrutinizing the epistemic legitimacy of contemporary techniques leveraged in data science. We argue that data validation and testing potentially provides a direct, measurable method of evaluating evidence for reliable inductive inferences in cases where the error-statistical approach is not easily applied, and conclude with an exploration of core methodological foils to the reliability of inductive inference revealed by this argument.

Impact of a PD-L1 Learning Collaborative: outcomes from a mixed-methods evaluation

Abstract Introduction/Objective PD-L1 Immunohistochemistry testing is often required to determine eligibility for immune checkpoint inhibitor therapy. An ASCP PD-L1 Learning Collaborative (LC) was formed aiming to: 1) identify ways to streamline PD-L1 testing; 2) encourage members to locally implement changes; and 3) develop a resource guide for the community. Methods/Case Report The PD-L1 LC (n=38 pathologists and laboratory professionals) participated in 3 activities: 1) 4 meetings in which LC members discussed current literature and practice; 2) 3 30-minute on-demand, credit-bearing panel videos, in which selected LC members summarized the LC outputs and shared their experiences; and 3) a guide summarizing resources relevant to streamlining PD-L1 testing. The mixed-methods evaluation included: 1) five- minute surveys before (n=24), immediately after (n=11) and 7-months post-LC (n=17); 2) polling questions (2-4 per meeting); 3) semi-structured interviews (n=5). Quantitative data was analysed using descriptive and inferential analysis, qualitative data using a thematic analysis / inductive reasoning approach. Results (if a Case Study enter NA) Baseline data confirmed delays in testing caused by unstandardized PD-L1 testing processes and suboptimal confidence in PD-L1 validation and methodologies. Post-LC, members self-reported perceived increased knowledge and higher confidence levels regarding discussion of PD-L1 scientific evidence and best practices. At the 7-month follow-up, 59% of respondents reported at least one PD-L1-related practice change, with 29% of participants selecting:1) Improving protocols for specimen acquisition, handling, or processing; 2) Improving communication with multidisciplinary care team; 3) Optimizing biomarker testing workflows. Remaining suboptimal knowledge post-LC suggests need for further educational efforts. Participants identified “Tumor-specific considerations” as the main resource missing for PD-L1 testing. Conclusion A learning collaborative has shown impact in improving PD-L1 testing processes and related practices among a group of pathology professionals. The group successfully made available three panel videos and a resource guide, and PD-L1-related practice changes were reported. Future initiatives should address remaining gaps and develop tumor-specific PD-L1 testing considerations.

Konsep Tata Ruang Goa Gajah dan Kolam Patirthan Berdasarkan Arsitektur Tradisional Bali

The aim is to find out the application of the spatial layout of Goa Gajah and the Patirthan pond based on traditional Balinese architecture and to find out the relationship between Goa Gajah and the Patirthan pond reflected in the function of the building. The research method is qualitative with inductive reasoning. Data collection was carried out using literature studies, observations, interviews and documentation studies. The collected data was processed using spatial character analysis, contextual analysis and qualitative analysis supported by spatial theory of traditional Balinese architecture and architectural function theory. The results of the research concluded that Goa Gajah and the Patirthan pond were identified as being based on traditional Balinese architectural spatial concepts, namely; Tri Hita Karana; Tri Angga and Tri Loka; and orientations. Analysis of the relationship between Goa Gajah and the Patirthan pond also implies a philosophical relationship between mountains and the sea. The activities at Goa Gajah and the Patirthan pond clarify the relationship between buildings, and represent the building as a social environment for ascetics.

O AVANÇO DAS INOVAÇÕES DIGITAIS NA EDUCAÇÃO CORPORATIVA

This study aims to present the advancement of digital innovations in corporate education, observing how new technologies and business models influence teaching and learning at work. Digitalization has generated new opportunities for professional development, allowing a more effective integration between educational practices and the work environment, and promoting continuous learning in a competitive market. Corporate education must be a fundamental pillar for change in future organizations, transforming into digital learning ecosystems. These ecosystems will face challenges to improve usability and the educational experience, promoting technological, social and cultural changes within companies. The proposal is to conduct exploratory research with a qualitative approach and inductive logic, which focuses on the quality and depth of the information collected, rather than being based on pre-existing theoretical concepts. Inductive logic, which is based on observation of facts, allows the validation or refutation of existing concepts. The chosen method is a qualitative literature review, ideal for exploring the complexity of innovative corporate education and its role in organizational transformation. This method is suitable for investigating ideas and models about educational practices and technologies in a context of digital transformation, as it allows a practical analysis of the phenomenon in its real environment.

A Review of Common Acceleration Models in Accelerated Life Testing

The core of accelerated life testing (ALT) is the development of acceleration models. These models can be classified into three types based on their underlying methodologies: physics-based, empirical inductive, and statistical inference models. This paper primarily examines the acceleration models that are commonly applied in engineering practice and presents the relevant computational formulas to highlight the fundamental research methodologies of ALT. Additionally, the paper underscores that ALT is increasingly recognized as a vital and emerging trend, driven by its role in shortening design cycles, enhancing product reliability, and managing costs.

Learning to explain is a good biomedical few-shot learner.

Significant progress has been achieved in biomedical text mining using deep learning methods, which rely heavily on large amounts of high-quality data annotated by human experts. However, the reality is that obtaining high-quality annotated data is extremely challenging due to data scarcity (e.g. rare or new diseases), data privacy and security concerns, and the high cost of data annotation. Additionally, nearly all researches focus on predicting labels without providing corresponding explanations. Therefore, in this paper, we investigate a more realistic scenario, biomedical few-shot learning, and explore the impact of interpretability on biomedical few-shot learning. We present LetEx-Learning to explain-a novel multi-task generative approach that leverages reasoning explanations from large language models (LLMs) to enhance the inductive reasoning ability of few-shot learning. Our approach includes (1) collecting high-quality explanations by devising a suite of complete workflow based on LLMs through CoT prompting and self-training strategies, (2) converting various biomedical NLP tasks into a text-to-text generation task in a unified manner, where collected explanations serve as additional supervision between text-label pairs by multi-task training. Experiments are conducted on three few-shot settings across six biomedical benchmark datasets. The results show that learning to explain improves the performances of diverse biomedical NLP tasks in low-resource scenario, outperforming strong baseline models significantly by up to 6.41%. Notably, the proposed method makes the 220M LetEx perform superior reasoning explanation ability against LLMs. Our source code and data are available at https://github.com/cpmss521/LetEx.

Structural equations modelling applied to the study of Communities Supported Agriculture (CSAs) located in the Southeastern region of Brazil

Food production is an increasingly relevant issue in the current framework associated with food and water security and future years projections. In addition, the production of organic food has gained significance in recent years, revealing a growing change in the population's consumption habits. The Communities Supported Agriculture (CSAs) play an important role in enabling productive areas for planting crops in agroecological and biodynamic standards. Given this, this study aimed to investigate the relationship between different CSAs variables located in the Southeastern region of Brazil to validate or not the hypotheses created. Thus, the study was designed as a descriptive research and quantitative approach, using an inductive logic survey. In addition, the methodology was set up with the support of the Structural Equation Modeling technique. The study confirmed the hypothesis (H1) that socioeconomic characteristics positively influence CSAs’ performance. The limitations were associated with the method of data collection and the difficulties imposed by the Covid-19 pandemic.

The practice and innovation of compatible marine utilization models in China

Marine Spatial Planning (MSP) is essential for optimizing marine resources allocation and resolving conflicts in sea use, but it is challenged by the complex and multi-functional nature of maritime resources. This study utilizes participatory methods, inductive reasoning, and overlay analysis to explore both theoretical and practical aspects of compatible marine utilization models. The paper begins by clarifying the logic of compatible sea use through marine functional zoning and identifying its core characteristics: quantity, spatial conflict, and impacts on natural attributes. Building on China’s national territorial spatial planning reforms and practical experiences, the paper introduces three major categories of compatible marine use models: development sequencing compatibility, spatial coexistence compatibility, and functional synergy compatibility. A compatibility discrimination matrix is developed to assess these models across different marine functional areas. Combined with the compatibility discrimination results, taking the marine and coastal spatial planning of Yantai City in China as a case study, the paper analyzes the compatibility demands and planning strategies within various marine functional areas. Finally, it evaluates the risks associated with each compatibility model and proposes targeted control strategies tailored to the specific features of each model and characteristics of sea use activities. The research findings highlight the positive role of compatible sea use models in promoting marine economic development, enhancing spatial efficiency, and mitigating sea use conflicts. However, these models also present varying potential risks, necessitating differentiated control strategies aligned with legal frameworks and specific sea use activities. The study offers valuable insights for MSP and contributes to the efficient utilization of marine resources and the advancement of marine spatial governance.

Exploring multi-granularity contextual semantics for fully inductive knowledge graph completion

Fully inductive knowledge graph completion (KGC) aims to predict triplets involving both unseen entities and relations. Recent several approaches transform paths between entities into descriptions and modeling semantic correlations between paths using pre-trained language models (PLMs), have emerged as a promising solution for fully inductive reasoning. However, these methods often adopt a simplistic concatenation strategy for path-to-sentence transformation, which impedes PLMs’ ability to capture subtle nuances in context, resulting in sub-optimal path context embeddings. Furthermore, they ignore the high-order semantics underlying the complete context, which can provide richer information for inductive reasoning. To address these issues, we propose a Multi-Granularity Contextual Semantic (MGCS) modeling framework, utilizing a Path Modeling Network (PMN) and a Subgraph Modeling Network (SMN) to extract two granularity levels of contextual semantics from single paths and complete subgraphs, for fully inductive KGC. The PMN extracts paths between head and tail entities and employs reasoning patterns from similar cases to filter out unreliable paths. Then two innovative path conversion strategies are designed to significantly enhance the pre-trained language model’s understanding of specific path contexts. The SMN employs a neighbor interactive graph neural network to extract high-order semantics from the complete subgraph context with a concept-enhanced relation encoding, and optimizes it through a contrastive learning method. Finally, the confidence of the triples is evaluated from the perspective of global complete context by comparing the semantics between the subgraphs surrounding the target triplet and the subgraphs surrounding similar cases. Experimental results on benchmark datasets demonstrate the effectiveness of MGCS.

Inference and reading comprehension in university students

Purpose: The objective of this research was to determine the relationship between inference and reading comprehension. We sought to verify the relationship between inductive and deductive inferences in the comprehensive reading of Daniel Alcides Carrión National University Peru students majoring in communication and literature. Design/Methodology/Approach: The correlational-explanatory research design was used since the correlation of the study variables was sought through scientific and specifically analytical, deductive and interpretive methodology on a population of 104 and the probabilistic sample of 83 students. Similarly, a 20-item questionnaire was used through survey, observation and recording techniques to collect the information. Therefore, the research was developed using a basic quantitative approach. Findings: The result of the study allowed us to obtain a correlation coefficient of 0.215 which was positive but moderate according to Spearman's Rho statistical statistician. Conclusion: The process of inductive and deductive inferences is related to the reading comprehension of university students. Therefore, reading strategies must be implemented in university training institutions to develop inferences in a relevant way. Practical Implications: The result of the study will allow university training centers to develop the inference process in the academic training of students. Contribution to the Literature: Information is contributed about the theoretical underpinnings of inference and reading comprehension in this study.

Inductive reasoning: Problems, methods of justification and interaction between mathematics and computer science.

Our paper reports on the the fondations of inductive reasoning, more precisely, the reasons for the conflicts betwen several scientists around the place, the notion of induction and its role in differents sciences, in particular mathematics. We start from an epistemological and didactic reflection of this reasoning to understand this concept. First, we present the epistemological problem of induction in relation to its meaning and validity. These problems highlight the complexity of induction.This leads us,in a second step, to set up a typology of the notion of induction developed by philosophers,linguists,empiricists and mathematicians. Finally ,we present a didactic analysis of mathematical induction which highlights the specific comprehension difficulties of learners and emphasizes the interaction between mathematics and computer science , in particular the relationship between mathematical induction and recursion.

Approximations of system W for inference from strongly and weakly consistent belief bases

In this article, we investigate approximations of the inductive inference operator system W that has been shown to exhibit desirable inference properties and to extend both system Z, and thus rational closure, and c-inference. For versions of these inference operators that are extended to also cover inference from belief bases that are only weakly consistent, we first show that extended system Z and extended c-inference are captured by extended system W. Then we introduce general functions for generating inductive inference operators: the combination of two inductive inference operators by union, and the completion of an inductive inference operator by an arbitrary set of axioms. We construct the least inductive inference operator extending system Z and c-inference that is closed under system P and show that it is still strictly extended by extended system W. Furthermore, we introduce an inductive inference operator that strictly extends extended system W and that is strictly extended by lexicographic inference. This leads to a comprehensive map of inference relations between rational closure and extended c-inference on the one side and lexicographic inference on the other side with extended system W and its approximations at its centre, where all relationships also hold for the unextended versions.

Thermally Robust Aramid Triboelectric Materials Enabled by Heat‐Induced Cross‐Linking

AbstractThe rapid development of the Internet of Things has led to numerous functional electronic devices, making it a significant challenge to provide power for these distributed devices. Triboelectric self‐power technology is ideal for smart devices due to its material diversity and high energy efficiency. However, traditional triboelectric materials have weak electrostatic induction due to their low dielectric constant. Additionally, the thermionic emission effect reduces their electric output in high temperatures, limiting their applications. This study designes a thermally robust aramid triboelectric material with a high dielectric constant through heat‐induced cross‐linking heterogeneous interface engineering. This novel material not only achieves high triboelectric output but also maintains high performance across a wide temperature range. The heat‐induced cross‐linking process enhances the interaction between aramid nanofibers and Al2O3 nanosheets, significantly improving the electrical performance, and flame retardance of the material, and the relative dielectric constant increases 16‐fold. The triboelectric nanogenerator constructed with this material achieves a power density of 3.97 W m−2, and maintains high triboelectric output at 260 °C. Finally, a self‐powered detection system collects high‐temperature gas energy and drives sensors is designed. This research presents a novel strategy for high‐performance triboelectric materials, showing significant potential for self‐powered devices.