OraPlan–SQL: A Planning-Centric Framework for Complex Bilingual NL2SQL Reasoning

This study analyzes the ethical architecture of the teaching profession in Indonesia by examining how teacher ethics is governed and evaluated within a context of institutional and religious pluralism. It seeks to explain why teacher ethics has largely functioned as an instrument of professional governance rather than as a framework of public moral reasoning that supports teachers’ moral agency. This study adopts a qualitative normative–philosophical approach, analyzing Indonesian education policy documents—particularly the Regulation of the Minister of Education, Culture, Research, and Technology No. 67 of 2024—and codes of ethics issued by major teacher professional organizations (PGRI, IGI, PERGUNU, and Muhammadiyah). The analysis employs conceptual and argumentative methods, using Kantian ethics as an evaluative framework, while media-reported cases are referenced illustratively to contextualize normative tensions. The study identifies three central findings. First, teacher ethics in Indonesia is primarily framed in the language of compliance, discipline, and procedure, positioning ethics as a mechanism of professional governance. Second, the pluralism of organizational codes of ethics produces ethical fragmentation, whereby similar professional actions may be evaluated differently depending on institutional affiliation and adjudicative authority. Third, this configuration constrains teachers’ moral agency by prioritizing administrative conformity over rational moral justification that is public and universal in character. The findings suggest that addressing ethical fragmentation in the teaching profession requires more than regulatory harmonization or procedural standardization. Instead, there is a need for a shared framework of public moral reasoning that enables plural religious and institutional ethics to be evaluated through consistent and publicly justifiable criteria. Such a framework has implications for education policy, professional ethical governance, and the cultivation of teachers as autonomous moral agents in plural societies. This study contributes to religious studies and professional ethics scholarship by reframing ethical fragmentation not as a technical governance problem but as a problem of public moral justification within plural moral traditions. By employing Kantian ethics as an evaluative lens rather than a prescriptive doctrine, the study offers an original conceptual contribution to debates on religious pluralism, professional ethics, and moral agency in highly regulated educational contexts such as Indonesia.

This paper presents a formal approach for the analysis of privacy properties of complex electronic services. A flexible framework for logic reasoning allows for formally modeling these services in a typed first-order logic and for inferring privacy properties that can be interpreted by all the stakeholders including consumers. The inference strategy consists of compiling user profiles according to the expectations of the consumer about the data practices of the service providers involved. The data in these profiles originates from information that has been disclosed by the consumer during the service interactions or that may have been exchanged between organizations thereafter. The framework can infer relevant privacy properties from these profiles. To validate our work, the approach is applied to the modeling of a web shop.Keywordsprivacytrustelectronic servicesformal model

Political defeat led many in the seventeenth and early eighteenth centuries to quit the public sphere. The ideal and practice of retirement was shaped by Epicureanism, Stoicism and the Christian vita contemplativa. John Milton finds value in the quiet life through Christian patience. A Royalist, Abraham Cowley withdrew to rural Surrey when political events went against him. His poetry is influenced by Stoicism and discovers in retirement a freedom that nurtures creativity. With a scientific bent, he recommends Christian virtue within a framework of reason. Andrew Marvell explores otium in his country-house poems, conjuring up a radical vision of mental tranquillity in greenness. In the work of Anne Finch, Countess of Winchilsea, the idea crystallises that the tranquillity afforded by retirement creates a duty to celebrate it. Alexander Pope enjoyed the contradiction of becoming famous for his worldly retreat; his creativity also flourished in the shade.

In this article, a new model for living systems is advanced. Organisms are described in terms of an electromagnetic vector field, Π derived from the distributed charge and current densities, ρ and J, within the system, with the electric polarization vector P playing a key role. Those biochemical processes traditionally thought of as the basis for living things become secondary to a more fundamental underlying electromagnetic description. This overarching approach is suggested by many observations during the past half century, showing a remarkable relationship between electrical properties of organisms and biological function. Further, it recently has been discovered that resonance applications of magnetic fields lead to changes in a wide variety of organisms, indicating that biological responses at the system level can be directly field mediated. This unusual systemic response is in sharp contrast to the often weakly system-connected interaction of microscopic processes, for example, enzyme activated pathways. This approach also provides a vehicle for expressing the genomic operation in terms that are more readily mathematical than possible using the so-called visible characteristics. The Π -field concept is immediately holistic unlike the present biological paradigm. Its intrinsic long-range nature makes it a reasonable framework in which to study morphogenesis. Despite its radically different approach, this concept does not appear to violate any known biological facts.

This chapter attempts to give an answer to the following question: Given an obligation and a set of potentially-inconsistent, ethically-charged beliefs, how can an artificially-intelligent agent ensure that its actions maximize the likelihood that the obligation is satisfied? Our approach to answering this question is in the intersection of several areas of research, including automated planning, reasoning with uncertainty, and argumentation. We exemplify our reasoning framework in a case study based on the famous, heroic ditching of US Airways Flight 1549, an event colloquially known as the “Miracle on the Hudson.”

One of the key requirements for a cognitive vision system to support reasoning is the possession of an effective mechanism to exploit context both for scene interpretation and for action planning. Context can be used effectively provided the system is endowed with a conducive memory architecture that supports contextual reasoning at all levels of processing, as well as a contextual reasoning framework. In this paper we describe a unified apparatus for reasoning using context, cast in a Bayesian reasoning framework. We also describe a modular memory architecture developed as part of the VAMPIRE* vision system which allows the system to store raw video data at the lowest level and its semantic annotation of monotonically increasing abstraction at the higher levels. By way of illustration, we use as an application for the memory system the automatic annotation of a tennis match.KeywordsMemory ArchitectureTennis BallReasoning EngineContextual ReasoningForeground ImageThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Scene graph generation denotes the process of parsing a visual input into a graph representation for downstream reasoning tasks. Most existing research models input scenes as flat scene graphs, capturing solely on horizontal relationships between two objects, while disregarding vertical dependencies. Such flat scene graphs merely describe direct relationships between objects, and cannot represent higher-level semantics. The lack of hierarchical relationships also limits the performance of scene graphs in downstream reasoning tasks. In this work, we propose hierarchical scene graph generation, a new problem task that requires the model to generate scene graphs with clear hierarchy. To achieve this goal, a hierarchical scene graph generation with coarse-to-fine reasoning framework is proposed. It contains three stages: first, summarize the main caption of the scene; second, describe the main visual elements related to the theme; and finally, add secondary elements with background. To facilitate training, a high-quality hierarchical scene graph dataset that comprises 40 K image-scene description pairs is constructed. Utilize rich knowledge and powerful multi-turn conversation capabilities of multi-modal large language models, the proposed framework can not only achieve better semantic understanding and relational modeling, but also seamlessly integrating its relational modeling capabilities to enhance visual-language tasks. Extensive experimental results demonstrate that our method constructs an effective scene representation, outperforming the state-of-the-art by 3.35% in recall while generating significantly fewer average triplets 8.7vs. 9.2. The results on downstream tasks also indicate that hierarchical scene graph significantly contributes to visual-language interactive reasoning.

Varied context reasoning approaches are used across a variety of applications in the pervasive computing domain including: health monitoring, intrusion detection, airport security and military target tracking. The same types of context are being inferred in diverse ways across a number of platforms. For example, human activity has been inferred using a number of statistical, ontological, and logical approaches. What we do not find in sufficient measure is context aware reasoning framework and knowledge reuse. Lack of reuse limits rapid context aware application development. This further restrains innovation. Among the factors contributing to this situation are: i) insufficient modeling formalisms; ii) limited generalized APIs for reasoning; and iii) inefficient and inflexible reasoning choices that fail to meet application needs. Utilizing reusable components for application development is a fundamental challenge in generalizing context reasoning. In this paper, we attempt to design and develop a foundation for implementing a generalized hierarchical hybrid context reasoning engine for pervasive applications. With such a framework we can achieve greater context reasoning framework and knowledge reuse, support for context modularization and improved context aware application performance.

Introduction: Not many writers have made suggestions about Saul's mental state as reported in the Judaic-Christian bible. He became king under the tutelage of Samuel, a highly-respected prophet of the Israelite God, Yahweh. At some points during his reign, the biblical narrative depicted him as, at least, mentally unstable, if not decidedly insane. Modern-day writers, in some cases have provided lists of conditions, which purport to represent Saul's psychological malady. None, however proves adequate or appropriate to encompass the complexity of his behavior. The Safety-Oriented Personality Style (SOPS) or Phobicentric Psychopathology (PCP) appears to offer the best explanation of Saul's psychological condition. Purpose: The main purpose of this paper is to offer a modern-day analysis/explanation of Saul's apparently disturbed behavior. Evidence for the facts about Saul's fascinating presentation is adduced both from the biblical passages and from present-day psychological theory. The previous explanations of the psychological disorder that plagued Saul, the first king of Israel, have focused on depression, post-traumatic stress disorder, hysteria, and bipolar disorder. The associated symptoms, however, have been vague and, in some cases, apparently unrepresentative of psychological nosology or, in general, they fail to conform to the prevailing nomenclature. Method: The present study introduces the Safety-Oriented Personality Style (SOPS) or Phobicentric Psychopathology (PCP), a relatively newly-unveiled chronic, anxiety-based disorder, to provide the "best" explanation of the ancient king's perplexing conduct. Biblical passages pertaining to the topic provide the historical context of the events, which depict Saul's provable psychopathology. Result: The evidence seems to clearly indicate that the explanation that Saul's actions were mainly controlled by SOPS/PCP, a chronic anxiety disorder, is valid, applying most aptly to his reaction to David's military and religious successes, and even to the recorded circumstances of his How to cite this paper: Bickersteth, P.

Collaborative Framework for Dynamic Knowledge Updating and Transparent Reasoning with Large Language Models

This study delves into the realm of multi-modality (i.e., video and motion modalities) human behavior understanding by leveraging the powerful capabilities of Large Language Models (LLMs). Diverging from recent LLMs designed for video-only or motion-only understanding, we argue that understanding human behavior necessitates joint modeling from both videos and motion sequences (e.g., SMPL sequences) to capture nuanced body part dynamics and semantics effectively. In light of this, we present MotionLLM, a straightforward yet effective framework for human motion understanding, captioning, and reasoning. Specifically, MotionLLM adopts a unified video-motion training strategy that leverages the complementary advantages of existing coarse video-text data and fine-grained motion-text data to glean rich spatial-temporal insights. Furthermore, we collect a substantial dataset, MoVid, comprising diverse videos, motions, captions, and instructions. Additionally, we propose the MoVid-Bench, with carefully manual annotations, for better evaluation of human behavior understanding on video and motion. Extensive experiments show the superiority of MotionLLM in the caption, spatial-temporal comprehension, and reasoning ability. Project page: https://lhchen.top/MotionLLM.

Studies suggest that both affective and cognitive processes are involved in the perception of vulnerability to cancer and that affect has an early influence in this assessment of risk. We constructed a path model based on a conceptual framework of heuristic reasoning (affect, resemblance, and availability) coupled with cognitive processes involved in developing personal models of cancer causation. From an eligible cohort of 16 700 women in a managed care organization, we randomly selected 2524 women at high, elevated, and average risk of ovarian cancer and administered a questionnaire to test our model (response rate 76.3%). Path analysis delineated the relationships between personal and cognitive characteristics (number of relatives with cancer, age, ideas about cancer causation, perceived resemblance to an affected friend or relative, and ovarian cancer knowledge) and emotional constructs (closeness to an affected relative or friend, time spent processing the cancer experience, and cancer worry) on perceived risk of ovarian cancer. Our final model fit the data well (root mean square error of approximation (RMSEA) = 0.028, comparative fit index (CFI) = 0.99, normed fit index (NFI) = 0.98). This final model (1) demonstrated the nature and direction of relationships between cognitive characteristics and perceived risk; (2) showed that time spent processing the cancer experience was associated with cancer worry; and (3) showed that cancer worry moderately influenced perceived risk. Our results highlight the important role that family cancer experience has on cancer worry and shows how cancer experience translates into personal risk perceptions. This understanding informs the discordance between medical or objective risk assessment and personal risk assessment. Published in 2014. This article is a U.S. Government work and is in the public domain in the USA.

Within the framework of constraint reasoning we introduce a newer distributed particle swarm approach. The latter is a new multi-agent approach which addresses additive Constraint Satisfaction problems ((CSPs). It is inspired by the dynamic distributed double guided genetic algorithm (D3G2A) for Constraint reasoning. It consists of agents dynamically created and cooperating in order to solve problems. Each agent performs locally its own particle swarm optimization algorithm (PSO). This algorithm is slightly different from other PSO algorithms. As well, not only do the new approach parameters allow diversification but also permit escaping from local optima. Second,. Experimentations are held to show effectiveness of our approach.

Our goal is the presentation of a uniform framework for compositional reasoning about the development of distributed processes and data structures. This framework should be a synthesis because, depending on the structure of the processes involved and the verification steps required, different formalisms are most suitable for carrying out one's reasoning. We illustrate this uniform framework by presenting a methodology for reasoning about refinement of distributed data structures, i.e., data structures implemented by means of distributed networks. Our synthesis is compositional, state-based, history-based, and contains sat style, Hoare style, trace-invariant reasoning and assumption/commitment style specifications as dialects. The resulting formalism can be unfolded as if it were a portable telescope, yielding the style required according to its degree of unfolding.KeywordsProof SystemPriority QueueAdaptation ProblemAdaptation RuleProof RuleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

BayesIntuit: A Neural Framework for Intuition-Based Reasoning