Reasoning Framework Research Articles

PHƯƠNG PHÁP DẠY HỌC “BẮC GIÀN GIÁO” VÀ VIỆC ỨNG DỤNG VÀO GIẢNG DẠY MÔN KHẨU NGỮ TIẾNG TRUNG QUỐC Ở TRƯỜNG ĐẠI HỌC SƯ PHẠM HÀ NỘI

The “scaffolding” teaching method is a teaching model originating from Constructivist Theory and the Zone of Proximal Development (ZPD) developed by Vygotsky. It offers new insights into dealing with the relationship between “teaching” and “learning”. The changing roles of teachers and students, promoting students' collaborative learning and exploration abilities, and cultivating students' independent problem-solving abilities of this teaching model have helped teachers improve their skills and enhance their educational and teaching abilities. In the process of implementing “scaffolding” teaching, we need to grasp the following key points: teachers need to clarify their roles, arrange a reasonable conceptual framework according to students’ levels, and check control interactive teaching in the student's “ZPD” and gradually reduce the role of the “scaffolder”. In this article, we would like to analyze the birth of the concept of “scaffolding”, its basic characteristics, and teaching steps. Finally, we applied it to teaching Chinese oral courses for students majoring in Chinese language at HNUE.

Efficient Differential Diagnosis using Cost-aware Active Testing

The diagnosis of complex systems benefits greatly from a differential, multistep approach that narrows down the list of possible conditions or failures that share the same observable effects to a single root cause. We provide a suitable and practically applicable methodology for this. In extension to existing work, it covers all types of diagnostic actions, i.e., the observation of system properties, active testing, and system interventions like providing a dedicated diagnostic input or forcing the system into discriminating states, but also the replacement of components. Combining all these possible steps into one probabilistic and causal reasoning framework, we I) stepwise generate the diagnostic model systematically to correctly cover the interplay of observations and diagnostic interventions, and II) provide decision support based on counterfactuals for the selection of the next diagnostic step, countering the vast number of possible actions that arise in machine diagnostic processes, considering costs of actions to minimize the expected overall cost of the diagnosis.We developed and successfully tried our methodology for diagnosing cyber-physical systems in the high-tech industry, but we found that it supports more processes, such as computing intervention actions for autonomous robots.

Iris-MSWasm: Elucidating and Mechanising the Security Invariants of Memory-Safe WebAssembly

WebAssembly offers coarse-grained encapsulation guarantees via its module system, but does not support fine-grained sharing of its linear memory. MSWasm is a recent proposal which extends WebAssembly with fine-grained memory sharing via handles, a type of capability that guarantees spatial and temporal safety, and thus enables an expressive yet safe style of programming with flexible sharing. In this paper, we formally validate the pen-and-paper design of MSWasm. To do so, we first define MSWasmCert, a mechanisation of MSWasm that makes it a fully-defined, conservative extension of WebAssembly 1.0, including the module system. We then develop Iris-MSWasm, a foundational reasoning framework for MSWasm composed of a separation logic to reason about known code, and a logical relation to reason about unknown, potentially adversarial code. Iris-MSWasm thereby makes explicit a key aspect of the implicit universal contract of MSWasm: robust capability safety. We apply Iris-MSWasm to reason about key use cases of handles, in which the effect of calling an unknown function is bounded by robust capability safety. Iris-MSWasm thus works as a framework to prove complex security properties of MSWasm programs, and provides a foundation to evaluate the language-level guarantees of MSWasm.

Navigating the Complexities of Inter-Religious Peacebuilding: Implications for Theory and Practice

As conflict dynamics become complex and escalate globally, especially identity-based conflicts, we are witnessing an unprecedented shift in the Conflict Analysis and Resolution and Peacebuilding field toward contextually innovative and effective community-led approaches. The inadequacies of liberal and neoliberal paradigms and the increase in identity-based conflicts, religious pluralism, and differences in communities have motivated evidence-based inter-religious community-level engagements over the past two decades. These interventions rely on the theoretical frameworks of emancipatory peacebuilding and compassionate reasoning, and reflect an in-depth sense of spirituality, longing, and the essence of human relationship building and practice. This study gathers data from primary sources (which include findings from hybrid interviews) through a semi-participatory and empirical qualitative explorative research process in order to critique the underlying philosophies of traditional paradigms and explore emerging alternatives. It also posits that inter-religious community-led interventions are founded on the emancipatory elicitive religious peacebuilding (EERPb) framework. They are adaptive to non-linear (and sometimes non-scientific) approaches and are less focused on international standards. The framework fundamentally embraces phenomenological, metaphysical, and ethical realities in peacebuilding, operationalizes the concept of just peace, and acknowledges a global approach to peace that offers the opportunity to resolve the difficulties encountered by the various CAR and peacebuilding theoretical schools.

IoT as Audit Evidence: A Reasonable Assurance Framework

ABSTRACT This study examines the continuing expansion of the Internet of Things (IoT) and its possible development as a source of audit evidence for both internal and external audits. In other words, the auditor checks whether a measure (metric) related to classifying and prioritizing requests and/or incidents is available in the enterprise’s current incident reporting system. This approach uses Design Science Research and is based on the use of auditing standards and frameworks to drive the design of audit plans using metrics available on or derived from the enterprise IoT. This study extends the research on enterprise IoT and its potential application to the enterprise audit process. It provides a framework that illustrates how both internal and external auditors can reasonably assure the provenance and validity of enterprise IoT data while also evaluating the security and controls surrounding its production.

TabMoE: A General Framework for Diverse Table-Based Reasoning with Mixture-of-Experts

Tables serve as a widely adopted data format, attracting considerable academic interest concerning semantic understanding and logical inference of tables. In recent years, the prevailing paradigm of pre-training and fine-tuning on tabular data has become increasingly prominent in research on table understanding. However, existing table-based pre-training methods frequently exhibit constraints, supporting only single tasks while requiring substantial computational resources, which hinders their efficiency and applicability. In this paper, we introduce the TabMoE, a novel framework based on mixture-of-experts, designed to handle a wide range of tasks involving logical reasoning over tabular data. Each expert within the model specializes in a distinct logical function and is trained through the utilization of a hard Expectation–Maximization algorithm. Remarkably, this framework eliminates the necessity of dependency on tabular pre-training, instead exclusively employing limited task-specific data to significantly enhance models’ inferential capabilities. We conduct empirical experiments across three typical tasks related to tabular data: table-based question answering, table-based fact verification, and table-to-text generation. The experimental results underscore the innovation and feasibility of our framework.

Causes of moral distress among midwives: A scoping review.

Numerous studies have evidenced moral distress among midwives; however, to date no research synthesis on causes of moral distress among midwives has been conducted. A scoping review was carried out to identify, comprehensively map, and categorize possible causes of moral distress among midwives, and to identify knowledge gaps. Six data bases were searched using Boolean logic. To be included, studies had to (a) present empirical findings on (b) causes of moral distress (c) among midwives (d) in English, German, French, or Italian. We included a final set of 43 studies. The vast majority of studies came from high-income countries (83.7%) and used a qualitative approach (69.8%); 48.8% of the studies were published in the past 5years. Identified single reasons of moral distress were grouped into eight broader clusters, forming a coherent framework of reasons of moral distress: societal disregard, contemporary birth culture, resources, institutional characteristics, interprofessional relationships, interpersonal mistreatment of service users, defensive practice, and challenging care situations. These clusters mostly capture moral distress resulting from a conflict between external constraints and personal moral standards, with a smaller proportion also from an intraindividual conflict between multiple personal moral standards. Despite projected increases in demand for midwives, the midwifery workforce globally faces a crisis and is experiencing substantial strain. Moral distress further exacerbates the shortage of midwives, which negatively affects birth experiences and birth outcomes, ultimately rendering it a public health issue. Our findings offer points of leverage to better monitor and alleviate moral distress among midwives, contributing to reducing attrition rates and improving birth experiences and birth outcomes. Further research is essential to explore the issue of ecological moral distress, develop evidence-based interventions aimed at alleviating moral distress among midwives, and evaluate the effects of both individual and system-level interventions on midwives, intrapartum care, and service users' outcomes.

Creative Minds in a Polarized World

ABSTRACTIn today's digital milieu, characterized by pervasive media exposure, the intricate interplay between individual differences and cognitive processes has garnered significant scholarly interest. A notable facet of this interrelation pertains to the nexus between cognitive flexibility and individuals' engagement with online information. Recognized as essential for creative cognition, cognitive flexibility encompasses various facets of human reasoning vital for adapting to new information and challenging entrenched perspectives. This review expands the traditional “out‐of‐the‐box” thinking paradigm beyond creative cognition to encompass a comprehensive reasoning framework that questions entrenched viewpoints and fixed cognitive schemas. By analyzing recent scholarly discussions on fake news and polarization, we underscore the pivotal role of cognitive flexibility in guiding individuals' critical navigation of the digital information landscape, promoting nuanced and less polarized perspectives. Introducing the concept of Socio‐Cognitive Polarization, we elucidate how individuals with differing ideological backgrounds can exhibit escalating cognitive divisions and absolutist tendencies. Leveraging insights from existing literature, we present methodological strategies for combating misinformation and nurturing critical thinking to counteract the detrimental impact of deceptive narratives on societal discourse. This scholarly exposition advances the understanding of the challenges associated with misinformation dissemination and offers empirically grounded approaches for enhancing critical cognition in the digital era.

Integrating the plate tectonic and rock genesis systems for secondary school students

This paper describes how plate tectonics and rock genesis, two topics that are typically addressed separately in secondary Earth science classes, can be taught together as an integrated system. We define the TecRocks Reasoning Framework, developed to support student reasoning about rock formation situated in the context of plate tectonics. We also explain how we leveraged the framework in the designs of a new curriculum, interactive computer simulation, and assessment instrument. We show how the instrument was used to evaluate the curriculum, which included the computer simulation. We analyzed pretest and post-test data of 319 students taught by 10 teachers in diverse middle and high schools in the United States. Our analysis allowed us to discern different levels of reasoning in student explanations. On average, students made a significant pretest to post-test gain (p < .001) with a large effect size (Cohen’s d = 1.03) after using the curriculum. All student groups made significant gains regardless of their gender, English language status, race, or school level. However, the amount of gain significantly differed by school level, p < .001. The middle school students as a group made a larger pretest to post-test gain (d = 1.42) than the high school students (d = 0.66).

Random walk in random permutation set theory.

Random walk is an explainable approach for modeling natural processes at the molecular level. The random permutation set theory (RPST) serves as a framework for uncertainty reasoning, extending the applicability of Dempster-Shafer theory. Recent explorations indicate a promising link between RPST and random walk. In this study, we conduct an analysis and construct a random walk model based on the properties of RPST, with Monte Carlo simulations of such random walk. Our findings reveal that the random walk generated through RPST exhibits characteristics similar to those of a Gaussian random walk and can be transformed into a Wiener process through a specific limiting scaling procedure. This investigation establishes a novel connection between RPST and random walk theory, thereby not only expanding the applicability of RPST but also demonstrating the potential for combining the strengths of both approaches to improve problem-solving abilities.

Second-order covariation: enlarging the theoretical framework of covariational reasoning

ABSTRACT Research in mathematics education has shown the need to explore new forms of covariational reasoning to conceptualize dynamic situations. Indeed, describing a dynamic phenomenon requires conceptualizing which quantities are varying, how they are varying and co-varying in relation to the other quantities, and also how the quantities may affect the behavior of the phenomenon itself. This paper introduces an enlarged theoretical framework for covariational reasoning. Among the three orders of covariation it includes, second-order covariation is then discussed more deeply by analyzing data from a 10th-grade class experiment on the conceptualization of the motion of a ball on an inclined plane with the support of digital tools. This study theoretically broadens the perspective on covariational reasoning by reviewing and framing coherently constructs already introduced in the literature; empirically, it elaborates on the characterization of students’ second-order covariational reasoning and on the features of the adopted digital tools that supported it.

Decision-making based on Markov decision process in integrated artificial reasoning framework—Part I: Theory

This paper presents a decision-making framework based on an integrated artificial reasoning framework and Markov decision process (MDP). The integrated artificial reasoning framework provides a physics-based approach that converts system information into state transition models, and the analysis result will be represented by the transition probabilities that can be used with an MDP to find a traceable and explainable optimal pathway. A dynamic Bayesian network (DBN) is well suited for representing the structure of an MDP. The causality information among process variables (or among subsystems) is mathematically represented in a DBN by the conditional probabilities of the node’s states provided different probabilities of the parent node’s states. To define node states in a physically understandable manner, we used multilevel flow modeling (MFM). An MFM follows the fundamental energy and mass conservation laws and supports the selection of process variables that represent the system of interest so that causal relations among process variables are properly captured. An MFM-based DBN supports developing state transition models in an MDP to capture the effect of process variables of system having physical relations. The operators of the target system can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from component degradation or random failures. We analyzed a simplified exemplary system to illustrate an optimal operational policy using the suggested approach.

Benchmark comparison study on metal single atom versus metal nanoparticle in photoredox catalysis: Which is better?

Reducing the size of metal nanoparticle (NP) cocatalyst down to single-atom level to improve photocatalytic efficiency is inevitably accompanied by the changes of its coordination environment, geometric configuration, electronic structure and active site. Thus, the construction of single metal atom (SA) photocatalyst is not necessarily a panacea for activity improvement toward target catalytic reactions. Herein, we report a critical and benchmark comparison in a reasonable framework of ZnIn2S4/Pt NP (ZIS/Pt NP) and ZnIn2S4/Pt SA (ZIS/Pt SA) towards photocatalytic hydrogen (H2) evolution, aiming to demonstrate which is better between Pt NP and Pt SA as cocatalyst in boosting photoredox catalysis. Mechanism study proves that the higher charge separation/transfer and weaker H* adsorption strength over ZIS/Pt NP than ZIS/Pt SA promote the more effective reduction of protons to H2, leading to the higher activity of ZIS/Pt NP than ZIS/Pt SA. Our work is expected to timely inspire the critical and rational thinking on the function and intrinsic mechanism of SA and NP cocatalysts in enhancing the photoredox catalysis performance.

Evaluation of the Ei SMART training programme in the UK

IntroductionEarly intervention (Ei) is recommended for infants at high risk of neurodevelopmental challenges. Ei SMART is an evidence-based clinical reasoning framework supporting infant development by integrating Sensory, Motor, Attention and regulation, and Relational development through healthcare professionals (HCPs) and parents working Together. Aim: To evaluate learning outcomes following Ei SMART training. Methods34 staff members from one UK neonatal operational delivery network participated in face-to-face training, co-produced and co-presented by parents and HCPs via online modules/tutorials. Participants completed pre/post-training surveys. Questions quantified self-perceived understanding of Infant Development & Well-Being, Parent/Family Support and Engagement, and Staff Engagement & Well-Being. Total and Domain scores were compared pre/post-training. Results33/34(97%) respondents completed both pre-and post-training surveys; one respondent was excluded (incomplete data). Median (IQR) post-training score was significantly increased (152(22) v 108(26) (p < 0.001), as were domain scores. ConclusionEi SMART training provided measurable improvements in perceived learning for HCPs.

A Comparative Study of Causality Detection Methods in Root Cause Diagnosis: From Industrial Processes to Brain Networks.

Abstracting causal knowledge from process measurements has become an appealing topic for decades, especially for fault root cause analysis (RCA) based on signals recorded by multiple sensors in a complex system. Although many causality detection methods have been developed and applied in different fields, some research communities may have an idiosyncratic implementation of their preferred methods, with limited accessibility to the wider community. Targeting interested experimental researchers and engineers, this paper provides a comprehensive comparison of data-based causality detection methods in root cause diagnosis across two distinct domains. We provide a possible taxonomy of those methods followed by descriptions of the main motivations of those concepts. Of the two cases we investigated, one is a root cause diagnosis of plant-wide oscillations in an industrial process, while the other is the localization of the epileptogenic focus in a human brain network where the connectivity pattern is transient and even more complex. Considering the differences in various causality detection methods, we designed several sets of experiments so that for each case, a total of 11 methods could be appropriately compared under a unified and reasonable evaluation framework. In each case, these methods were implemented separately and in a standard way to infer causal interactions among multiple variables to thus establish the causal network for RCA. From the cross-domain investigation, several findings are presented along with insights into them, including an interpretative pitfall that warrants caution.

PLEASING: Exploring the historical and potential events for temporal knowledge graph reasoning

Temporal Knowledge Graphs (TKGs) enable effective modeling of knowledge dynamics and event evolution, facilitating deeper insights and analysis into temporal information. Recently, extrapolation of TKG reasoning has attracted great significance due to its remarkable ability to capture historical correlations and predict future events. Existing studies of extrapolation aim mainly at encoding the structural and temporal semantics based on snapshot sequences, which contain graph aggregators for the association within snapshots and recurrent units for the evolution. However, these methods are limited to modeling long-distance history, as they primarily focus on capturing temporal correlations over shorter periods. Besides, a few approaches rely on compiling historical repetitive statistics of TKGs for predicting future facts. But they often overlook explicit interactions in the graph structure among concurrent events. To address these issues, we propose a PotentiaL concurrEnt Aggregation and contraStive learnING (PLEASING) method for TKG extrapolation. PLEASING is a two-step reasoning framework that effectively leverages the historical and potential features of TKGs. It includes two encoders for historical and global events with an adaptive gated mechanism, acquiring predictions with appropriate weight of the two aspects. Specifically, PLEASING constructs two auxiliary graphs to capture temporal interaction among timestamps and correlations among potential concurrent events, respectively, enabling a holistic investigation of temporal characteristics and future potential possibilities in TKGs. Furthermore, PLEASING incorporates contrastive learning to strengthen its capacity to identify whether queries are related to history. Extensive experiments on seven benchmark datasets demonstrate the state-of-the-art performances of PLEASING and its comprehensive ability to model TKG semantics.

RuMER-RL: A hybrid framework for sparse knowledge graph explainable reasoning

Knowledge Graph (KG) reasoning is a crucial technology for ensuring the accuracy and utility of KGs. However, robust and explainable reasoning on sparse KGs is challenging due to the lack of information and truncated paths. To address this issue, we introduce RuMER-RL, a hybrid reasoning framework comprising three modules: Rule Mining (RM), Embedding Representation (ER), and Reinforcement Learning (RL). The ER and RM modules collaborate to enhance the embedding models and rule quality, generating additional triples to mitigate the sparsity of the KG. The RL module models multi-hop KG reasoning as a Markov Decision Process (MDP), employing dynamic anticipation, action space expansion, and curiosity-driven strategies to enrich the reasoning process and mitigate sparsity. Additionally, we reshape the reward function by incorporating embedding representation, rule matching, and curiosity rewards to guide the training and optimization of the policy network. Extensive experiments on six sparse KG datasets demonstrate that RuMER-RL outperforms state-of-the-art models in terms of link prediction accuracy and interpretability.

Robust Commonsense Reasoning Against Noisy Labels Using Adaptive Correction.

Commonsense reasoning based on knowledge graphs (KGs) is a challenging task that requires predicting complex questions over the described textual contexts and relevant knowledge about the world. However, current methods typically assume clean training scenarios with accurately labeled samples, which are often unrealistic. The training set can include mislabeled samples, and the robustness to label noises is essential for commonsense reasoning methods to be practical, but this problem remains largely unexplored. This work focuses on commonsense reasoning with mislabeled training samples and makes several technical contributions: 1) we first construct diverse augmentations from knowledge and model, and offer a simple yet effective multiple-choice alignment method to divide the training samples into clean, semi-clean, and unclean parts; 2) we design adaptive label correction methods for the semi-clean and unclean samples to exploit the supervised potential of noisy information; and 3) finally, we extensively test these methods on noisy versions of commonsense reasoning benchmarks (CommonsenseQA and OpenbookQA). Experimental results show that the proposed method can significantly enhance robustness and improve overall performance. Furthermore, the proposed method is generally applicable to multiple existing commonsense reasoning frameworks to boost their robustness. The code is available at https://github.com/xdxuyang/CR_Noisy_Labels.

Conceptual Properties of Islamic Economic Modeling: Complexity, Nonlinearity, and Endogeneity in Epistemological Perspectives

This paper introduces a new perspective of economic reasoning and analysis pertaining to situations of complexity combined with nonlinearity and endogenous relations among interacting, integrating and evolutionary entities. The entities comprise multi-variables representing different embedded systems that encompass economy, society, science, and ethics. In the emergent model of endogenous inter-causal relationships between such entities the ontological perspective assumes the nature of evaluating inter-causal intra- and inters- systemic relations by means of formalism belonging to functional ontology of mathematical forms (Gruber, 1993), as opposed to invoking metaphysical ontological speculation. The functional ontological approach to structure and formalize leads to a new framework of economic reasoning in the presence of embedded systems that continuously participate and complement, as opposed to being subjected to the central postulate of marginal rate of substitution and exogenous treatment of knowledge in mainstream economics. The continuous phenomenon of interaction, integration, and evolutionary (IIE) learning in such inter-causal systems establish ethical endogeneity. Thus, philosophical background of economic reasoning is invoked in the functional epistemological formalism of the interactive, integrative, and evolutionary systems. That is sush systems are characterized by IIE-learning processes induced by the episteme of unity of knowledge.

Model-agnostic counterfactual reasoning for identifying and mitigating answer bias in knowledge tracing

Knowledge tracing (KT) aims to monitor students’ evolving knowledge states through their learning interactions with concept-related questions, and can be indirectly evaluated by predicting how students will perform on future questions. In this paper, we observe that there is a common phenomenon of answer bias, i.e., a highly unbalanced distribution of correct and incorrect answers for each question. Existing models tend to memorize the answer bias as a shortcut for achieving high prediction performance in KT, thereby failing to fully understand students’ knowledge states. To address this issue, we approach the KT task from a causality perspective. A causal graph of KT is first established, from which we identify that the impact of answer bias lies in the direct causal effect of questions on students’ responses. A novel COunterfactual REasoning (CORE) framework for KT is further proposed, which separately captures the total causal effect and direct causal effect during training, and mitigates answer bias by subtracting the latter from the former in testing. The CORE framework is applicable to various existing KT models, and we implement it based on the prevailing DKT, DKVMN, and AKT models, respectively. Extensive experiments on three benchmark datasets demonstrate the effectiveness of CORE in making the debiased inference for KT. We have released our code at https://github.com/lucky7-code/CORE.