Context Reasoning Research Articles

Compound Knowledge Graph-Enabled AI Assistant for Accelerated Materials Discovery

Materials scientists are facing increasingly challenging multi-objective performance requirements to meet the needs of modern systems such as lighter-weight and more fuel-efficient aircraft engines, and higher heat and oxidation-resistant steam turbines. While so-called second wave statistical machine learning techniques are beginning to accelerate the materials development cycle, most materials science applications are data-deprived when compared to the vastness and complexity of the search space of possible solutions. In line with DARPA’s vision of third wave AI approaches, we believe a combination of data-driven statistical machine learning and domain knowledge will be required to achieve a true revolution in materials discovery. To that end, we envision and have begun reducing to practice a system that fuses three forms of knowledge—factual scientific knowledge, physics-based and/or data-driven analytical models, and domain expert knowledge—into a single ‘Compound Knowledge Graph’ in which contextual reasoning and adaptation can be performed to answer increasingly complex questions. We believe this Compound Knowledge Graph-based system can be the nucleus of a collaborative AI assistant that supports stateful natural language back-and-forth dialogs between materials scientists and the AI to accelerate the development and discovery of new materials. This paper details our vision, summarizes our progress to date on a steam turbine blade coating use case, and outlines our thoughts on the key challenges in making this vision a reality.

A comparison of misinformation feature effectiveness across issues and time on Chinese social media

Misinformation on social media is a nonnegligible phenomenon that causes successive adverse impacts. Numerous scholarly efforts have been devoted to automatic misinformation detection to address this problem. The effective feature is the key to achieving high identification performance. However, the effectiveness of the feature may change in different issues and time considering the manifold social contextual reasons. Most extant literature on misinformation detection does not differentiate between topics, issues or domains. Although some research compares detection across domains, they concentrate on the model's overall performance, neglecting the effectiveness of individual features. Furthermore, the comparison studies mainly incorporate single-domain issues rather than issues that cover multiple domains. It is still difficult to determine which domain's misinformation characteristics will match those of multi-dimensional issues. Since the misinformation nowadays covers multiple domains, finding robust features in misinformation detection over issues and time is an urgent research agenda. In this study, we collected datasets of two issues, climate change and genetically modified organisms (GMOs), between January 1st, 2010 and December 31st, 2020 on Weibo, manually annotated the veracity status of the posts, and compared the performance of the proposed features in identifying misinformation by applying logistic regression. The results demonstrate that (1) the predicting power of content-based features, including topic and sentiment, is relatively robust compared to user-based and propagation-based features across issues and time. (2) The feature effectiveness varied at different time points. Our findings imply that future research could consider focusing more on content-based features, especially implicit features from the content in misinformation detection. Moreover, researchers should evaluate the feature effectiveness at different time stages to improve the efficiency of misinformation detection.

Fast deliberation is related to unconditional behaviour in iterated Prisoners’ Dilemma experiments

People have different preferences for what they allocate for themselves and what they allocate to others in social dilemmas. These differences result from contextual reasons, intrinsic values, and social expectations. What is still an area of debate is whether these differences can be estimated from differences in each individual’s deliberation process. In this work, we analyse the participants’ reaction times in three different experiments of the Iterated Prisoner’s Dilemma with the Drift Diffusion Model, which links response times to the perceived difficulty of the decision task, the rate of accumulation of information (deliberation), and the intuitive attitudes towards the choices. The correlation between these results and the attitude of the participants towards the allocation of resources is then determined. We observe that individuals who allocated resources equally are correlated with more deliberation than highly cooperative or highly defective participants, who accumulate evidence more quickly to reach a decision. Also, the evidence collection is faster in fixed neighbour settings than in shuffled ones. Consequently, fast decisions do not distinguish cooperators from defectors in these experiments, but appear to separate those that are more reactive to the behaviour of others from those that act categorically.

Engineering process safety research instrument: Assessing students’ moral reasoning in process safety contexts

Process safety decision making is a key component of undergraduate chemical engineering education. Despite this, there are no existing survey instruments designed to measure students’ moral reasoning in the context of process safety decision making. The Engineering Process Safety Research Instrument (EPSRI) was developed to address this deficit in process safety assessment. The EPSRI was modeled after existing moral reasoning instruments including the DIT2, EERI, and ESIT. The process safety scenarios included were drawn from personal experience and reports from the Chemical Safety Board. Each scenario in the instrument was followed by a decision prompt and 12–15 considerations. The EPSRI went through content validation with chemical engineering industry practitioners and chemical engineering faculty members. Subsequently, three rounds of exploratory factor analysis were conducted to finalize the instrument design before a final confirmatory factor analysis was completed to ensure validity and reliability of the instrument. Completion of the exploratory factor analysis resulted in five dilemmas with 9–12 considerations each that loaded onto pre-conventional, conventional, and post-conventional reasoning constructs according to Kohlberg’s moral development theory. Confirmatory factor analysis reaffirmed the validity and reliability of the instrument and its ability to measure chemical engineering students’ moral reasoning within process safety contexts.

Phenotyping in clinical text with unsupervised numerical reasoning for patient stratification.

Phenotypic information of patients, as expressed in clinical text, is important in many clinical applications such as identifying patients at risk of hard-to-diagnose conditions. Extracting and inferring some phenotypes from clinical text requires numerical reasoning, for example, a temperature of 102°F suggests the phenotype Fever. However, while current state-of-the-art phenotyping models using natural language processing (NLP) are in general very efficient in extracting phenotypes, they struggle to extract phenotypes that require numerical reasoning. In this article, we propose a novel unsupervised method that leverages external clinical knowledge and contextualized word embeddings by ClinicalBERT for numerical reasoning in different phenotypic contexts. Experiments show that the proposed method achieves significant improvement against unsupervised baseline methods with absolute increase in generalized Recall and F1 scores of up to 79% and 71%, respectively. Also, the proposed method outperforms supervised baseline methods with absolute increase in generalized Recall and F1 scores of up to 70% and 44%, respectively. In addition, we validate the methodology on clinical use cases where the detected phenotypes significantly contribute to patient stratification systems for a set of diseases, namely, HIV and myocardial infarction (heart attack). Moreover, we find that these phenotypes from clinical text can be used to impute the missing values in structured data, which enrich and improve data quality.

Free and Informed Elections? Disinformation and Democratic Elections Under Article 3 of Protocol 1 of the ECHR

Abstract This article examines European Court of Human Rights (ECtHR) jurisprudence concerning free elections and identifies relevant approaches that can be applied to electoral disinformation. The relationship between disinformation and freedom of expression has attracted considerable academic scrutiny in recent years. However, surprisingly little attention has been given to the right to free elections. This article addresses this gap by identifying key ECtHR approaches to free elections under Article 3 of Protocol 1 of the ECHR and evaluating the Court’s interpretive reasoning in the disinformation context. Focus is given to cases where the Court has addressed falsified information in the electoral process. Considering the special relationship between freedom of expression and free elections in Strasbourg jurisprudence, focus is also given to the Court’s contemplation of acceptable limitations to freedom of expression under Article 10 of the ECHR in response to deceptive political expression. Mapping the Court’s reasoning in key decisions, this article identifies informed democratic engagement as a crucial requirement that permeates the Court’s approach to elections. Considering the importance of democracy in the Court’s reasoning, this article argues that the Court should be more proactive in elucidating key standards for Contracting Parties to make democracies more resilient to electoral disinformation.

Referring expression grounding by multi-context reasoning

• MCRN is a unified architecture for referring expression grounding. • The appearance details information can be highlighted by Local Node Attention. • Interpretability of reasoning can be enhanced by multi-step reason. • Multi-step Graph Reasoning can explore relationship context. Referring expression grounding plays a fundamental role in vision-language understanding, which aims at locating a certain target region in an image described by a natural language expression. It needs to understand high-level semantic correlations between objects in the image according to the referred expression for the task. Thus, it inherently requires reasoning the context information, i.e ., appearance context and relationship context. While most existing approaches either ignore to explore the appearance details of the target region or rely on a manually designed reasoning structure and treat the context information of each neighboring object equivalently, inflexible to the scenario where referring expressions are complicated. In this paper, we put forward Multi-context Reasoning Network (MCRN) for referring expression grounding task, which can apply appearance context reasoning and relationship context reasoning simultaneously. Methodologically, for appearance context reasoning, we propose a local node attention to obtain local representation of the target object, which gives a more focus on its appearance details. For relationship context reasoning, we approach it as a language-guided multi-step reasoning problem and design a multi-step graph reasoning module to capture intra-context and inter-context between the target region of its intra-class and inter-class neighboring objects in an iterative way, which makes the reasoning process more reliable and interpretable. Our method demonstrates superiority based on extensive experimental outputs on three popular benchmark datasets.

An Empirical Study of Unethical Behavioral Conducts to Understand Why Managers Fail to Do the "Right" Thing

The primary goal of this report is to investigate the relationship between management decision-making behavior and ethics. This report will delve into two aspects: one is to examine how managers are aware of ethical consequences when making choices; the other is to analyze why good managers may also be ethically transgressive due to personal and contextual reasons.

As Though “Wit” Never Were: The Dual Pronoun as Interpretive Crux in The Wife's Lament

As Though “Wit” Never Were: The Dual Pronoun as Interpretive Crux in <i>The Wife's Lament</i>

Müsnedün İleyhin Tevâbi‘ Unsurlarla İfade Ettiği Anlamsal Yorumlar -Sekkâkî’nin Miftâḥu’l-ʿulûm’u Bağlamında-

Belâgat ilminde cümlenin temel rükünleri olan müsnedün ileyh ve müsnede ilişkin hâller meânî bölümünde incelenmektedir. Belâgatçılar, müsnedün ileyhin (özne) hazfi-zikri, ma‘rife-nekre olması, takdim-tehiri, tevâbi‘ ögeler ile fasıl zamiri alması vb. konularla isnadın önemli bir kısmını farklı belâgī yönleriyle ele almaktadır. Aynı şey cümlenin diğer rüknü olan müsned (yüklem) için de yapılmaktadır. Ma‘rife bir müsnedün ileyhin sıfat, tekit, bedel, atf-ı beyân, atf-ı nesak denilen tevâbi‘ unsurlar ile fasıl/ayırma zamiri alması cümleye pek çok belâgī nükte kazandırmaktadır. Bir cümlede müsnedün ileyhin tâbi/bağlı kelimeler ya da fasıl zamiri alması ile almaması sözdizimsel açıdan aynı şey değildir. Her ne kadar tevâbi‘ ögeler ile fasıl zamiri cümlenin esasına taalluk eden sözcükler olmasa da ma‘rife müsnedün ileyhin bunları alması hem kastı açık ve anlaşılır kılmakta hem de farklı semantik yorumlar ile cümleye anlam zenginliği katmaktadır. Ebû Ya‘kūb es-Sekkâkî, ma‘rife bir müsnedün ileyhin sıfat alması durumunda cümlenin beyân, medih-zem, tahsis ve tekit gibi vurgulu anlamlarla etkilendiğini belirtmektedir. Tekit edilmesi durumunda yanlış anlama önlenmekte veya umumi anlam işaret edilmektedir. Atf-ı beyân ile daha fazla izah edilmekte atf-ı nesak ile de detaylandırılmakta, hükmü değiştirmekte veya açıklamaktadır. Müsnedün ileyh bedel aldığında daha fazla açıklığa kavuşturma ve pekiştirme kastı güdülmektedir. Fasıl zamiri alması hâlinde de müsned, müsnedün ileyhe tahsis edilmektedir. Bu çalışmada, ma‘rife bir müsnedün ileyhin aldığı tevâbi‘ unsurlar ile fasıl zamirinin sözdizimdeki farklı belâgī ve anlamsal yorumları Sekkâkî’nin Miftâḥu’l-ʿulûm’u bağlamında ele alınmıştır. Ma‘rife müsnedün ileyhin kasta bağlı olarak tâbi sözcükler ve fasıl zamiri alması cümlenin semantik boyutunu değiştirmektedir. Sekkâkî’ye göre müsnedün ileyhin tevâbi‘ ögeleri almasının bağlamsal gerekçeleri de ayrıca vurgulanmıştır.

COVID-19 Pandemisinde Aşı Karşıtı Hareketlerin Sosyolojik Bağlamı ve Siyasallaşma Dinamiği

Bu çalışma siyasal, dinsel, ideolojik ve tıbbi nedenlerle gerekçelendirilen aşı karşıtlığını sosyolojik bir bağlamda incelemektedir. Aşı karşıtlarının bir toplumsal hareket olma potansiyelini dikkate alarak aşı karşıtlarının örgütsel ve eylem yapısını Manuel Castells’in ağ toplumu kavramıyla açıklamaktadır. Ayrıca aşı karşıtı hareketlerin COVID-19 pandemisi ile birlikte sosyal medyada ve çeşitli ülkelerde sokak eylemleriyle kamusal alanda örgütlü bir şekilde görünür olmaya başlamaları politik bir hareket olma potansiyelini güçlendirmektedir. Dolayısıyla çalışma COVID-19 pandemisiylebirlikte yükselişe geçen aşı karşıtlarının hem sosyolojik bağlamına hem de politikleşme dinamiğine odaklanarak görece yeni bir olgu olan aşı karşıtı hareketleri açıklamaya çalışan teorik bir çerçeve sunmayı hedeflemektedir. Makale teorik bir değerlendirmedir ancak aşı karşıtlığıyla ilgili çeşitli saha çalışmalarından da veriler kullanılmıştır.

Measuring evolution learning: impacts of student participation incentives and test timing

BackgroundPolicy documents like Vision and Change and the Next Generation Science Standards emphasize the importance of using constructed-response assessments to measure student learning, but little work has examined the extent to which administration conditions (e.g., participation incentives, end-of-course timing) bias inferences about learning using such instruments. This study investigates potential biases in the measurement of evolution understanding (one time point) and learning (pre-post) using a constructed-response instrument.MethodsThe constructed-response ACORNS instrument (Assessment of COntextual Reasoning about Natural Selection) was administered at the beginning of the semester, during the final exam, and at end of the semester to large samples of North American undergraduates (N = 488–1379, 68–96% participation rate). Three ACORNS scores were studied: number of evolutionary core concepts (CC), presence of evolutionary misconceptions (MIS), and presence of normative scientific reasoning across contexts (MODC). Hierarchical logistic and linear models (HLMs) were used to study the impact of participation incentives (regular credit vs. extra credit) and end-of-course timing (final exam vs. post-test) on inferences about evolution understanding (single time point) and learning (pre-post) derived from the three ACORNS scores. The analyses also explored whether results were generalizable across race/ethnicity and gender.ResultsVariation in participation incentives and end-of-course ACORNS administration timing did not meaningfully impact inferences about evolution understanding (i.e., interpretations of CC, MIS, and MODC magnitudes at a single time point); all comparisons were either insignificant or, if significant, considered to be small effect sizes. Furthermore, participation incentives and end-of-course timing did not meaningfully impact inferences about evolution learning (i.e., interpretations of CC, MIS, and MODC changes through time). These findings were consistent across race/ethnicity and gender groups.ConclusionInferences about evolution understanding and learning derived from ACORNS scores were in most cases robust to variations in participation incentives and end-of-course timing, suggesting that educators may have some flexibility in terms of when and how they deploy the ACORNS instrument.

Occluded Video Instance Segmentation: A Benchmark

Can our video understanding systems perceive objects when a heavy occlusion exists in a scene? To answer this question, we collect a large-scale dataset called OVIS for occluded video instance segmentation, that is, to simultaneously detect, segment, and track instances in occluded scenes. OVIS consists of 296k high-quality instance masks from 25 semantic categories, where object occlusions usually occur. While our human vision systems can understand those occluded instances by contextual reasoning and association, our experiments suggest that current video understanding systems cannot. On the OVIS dataset, the highest AP achieved by state-of-the-art algorithms is only 16.3, which reveals that we are still at a nascent stage for understanding objects, instances, and videos in a real-world scenario. We also present a simple plug-and-play module that performs temporal feature calibration to complement missing object cues caused by occlusion. Built upon MaskTrack R-CNN and SipMask, we obtain a remarkable AP improvement on the OVIS dataset. The OVIS dataset and project code are available at http://songbai.site/ovis.

Indonesian Religious Court Decisions on Child Custody Cases: Between Positivism and Progressive Legal Thought

Indonesian judges of Religious Court in deciding child custody cases have different legal reasoning. Some preferred to use juridical reasoning and others use progressive and sociological reasoning. This different legal reasoning causes various insights in the meaning of justice for child custody. This study aims to analyze the positivistic and progressive Islamic legal thought in judges' decisions of child custody cases. This is normative legal research with statutory and case approaches. The legal material is six judges' decisions and is supported by books, scientific article, statutes, and interview. Then, it is analyzed by content analysis. The result states that judges who use textual reasoning tend to decide that child custody is the mothers right, referring textually to an article 105 of the Islamic Law Compilation. Meanwhile, the other judges who prefer to contextual reasoning, decide that child custody is the fathers right. The textual reasoning is steered by legal-positivism: logical coherence of the text is the main method in concluding decision. The contextual reasoning is driven by sociological as well as critical-progressive thought: deconstruction of legal text, contra legem approach by qiyâs (analogy) and istiḥsân (legal teleology), to produce the justice values based on child interests and parents conditions.

Inquisitive logic as an epistemic logic of knowing how

In this paper, we present an alternative interpretation of propositional inquisitive logic as an epistemic logic of knowing how. In our setting, an inquisitive logic formula α being supported by a state is formalized as knowing how to resolve α (more colloquially, knowing how α is true) holds on the S5 epistemic model corresponding to the state. Based on this epistemic interpretation, we use a dynamic epistemic logic with both know-how and know-that operators to capture the epistemic information behind the innocent-looking connectives in inquisitive logic. We show that the set of valid know-how formulas corresponds precisely to the inquisitive logic. The main result is a complete axiomatization with intuitive axioms using the full dynamic epistemic language. Moreover, we show that the know-how operator and the dynamic operator can both be eliminated without changing the expressivity over models, which is consistent with the modal translation of inquisitive logic existing in the literature. We hope our framework can give an intuitive alternative interpretation to various concepts and technical results in inquisitive logic, and also provide a powerful and flexible tool to handle both the inquisitive reasoning and declarative reasoning in an epistemic context.

Learning Global Proliferation Expertise Evolution Using AI-Driven Analytics and Public Information

Detecting and anticipating global proliferation expertise and capability evolution from unstructured, noisy, and incomplete public data streams is a highly desired, but extremely challenging task. In this article, we present our pioneering data-driven approach to support the non-proliferation mission to detect and explain the evolution of proliferation expertise and capability development globally from terabytes of publicly available information (PAI), focusing on our knowledge extraction pipeline and descriptive analytics. We first discuss how we fuse nine open-source data streams, including multilingual data, to convert 4 TB of unstructured data to structured knowledge and encode dynamically evolving proliferation expertise representations—content and context graphs. For this, we rely on natural language processing (NLP) and deep learning (DL) models to perform information extraction, topic modeling, and distributed text representation (aka embedding) learning. We then present interactive, usable, and explainable descriptive analytics to refine domain knowledge and present it in a human-understandable form. Finally, we introduce future work avenues that will leverage our dynamic knowledge representations and descriptive analytics to enable predictive and prescriptive inferences to achieve real-time domain understanding and contextual reasoning about global proliferation expertise and capability evolution.

Spherical Linguistic Petri Nets for Knowledge Representation and Reasoning Under Large Group Environment

Fuzzy Petri nets (FPNs) are a promising modeling tool for knowledge representation and reasoning of rule-based expert systems. However, there exist limitations in representing ambiguous knowledge and performing approximate inference in traditional FPNs. Additionally, knowledge parameters are usually provided by some experts in existing FPN methods. In response to these issues, a new version of FPNs, named spherical linguistic Petri nets (SLPNs), is introduced in this article for knowledge representation and reasoning in the large group context. To this end, spherical linguistic sets are applied to capture imprecise knowledge and represent the uncertainty of experts’ judgements. Furthermore, a large group knowledge acquisition approach is developed to determine knowledge parameters. A bidirectional inference algorithm is developed for implementing the reasoning process and identifying the root causes of an appointed event. Finally, the efficacy and superiority of our developed SLPNs are illustrated by a realistic example regarding stampede risk level assessment in a high-speed railway station.

Effects of situated learning and clarification of misconceptions on contextual reasoning about natural selection

BackgroundNatural selection is a core principle of evolution. Understanding natural selection enables students to think about the evolution and the variability of life. Despite its great importance, understanding natural selection is challenging for students. This is evident in the phenomenon of contextual reasoning, showing that students can often explain natural selection in one context (e.g., trait gain) but not in another (e.g., trait loss). The study pursues the following aims: First, to examine the link between contextual reasoning and situated learning. Second, to explore whether different instructional strategies differ in their associated cognitive load. Third, to investigate whether clarifying common misconceptions about natural selection (no vs. yes) is an effective strategy to regular instructions when aiming to increase key concepts and reduce misconceptions. Fourth, to exploratively examine the effectiveness of different instructional strategies.MethodIn a 2 × 2 factorial intervention study with a total of N = 373 secondary school students, we varied the instructional material of a 90-min intervention in terms of the evolutionary context (trait gain vs. trait loss) and the availability of additional support in the form of a clarification of misconceptions (no vs. yes). We measured students’ cognitive load immediately after instruction and assessed their ability to reason about natural selection (i.e., use of key concepts and misconceptions) later.ResultsWe documented low knowledge about evolution in the pre-test and persisting misconceptions in the post-test. The results showed that the intervention context of trait loss elicited a higher intrinsic and extraneous cognitive load than trait gain. Moreover, when the clarification of misconceptions is analyzed in connection to the intervention context, it reveals a potential for reducing misconceptions in some contexts. Students who have learned in trait gain contexts with a clarification used significantly fewer misconceptions in later reasoning than students who learned in trait gain contexts without a clarification of misconceptions.ConclusionOur study creates new insights into learning about natural selection by outlining the complex interplay between situated learning, cognitive load, clarification of misconceptions, and contextual reasoning. Additionally, it advises researchers and educators on potential instructional strategies.

Varanje na ispitima: uloga motivacije za učenje i kontekstualnih čimbenika

The aim of this study was to examine the relationships between motivational beliefs (achievement goals, self-efficacy, self-efficacy in self-regulated learning), as well as contextual factors, and academic cheating in midterm and final exams at technical faculties of the University of Zagreb. The participants were 398 students enrolled in all five years of the study. Data were collected by the Achievement Goals Scale, the Self-Efficacy Scale, the Self-Efficacy for Self- -Regulated Learning Scale, the Contextual Reasons for Cheating on Exams Scale, and the Academic Cheating Scale. The results demonstrated that, compared to lower- -year students, higher-year students had higher self-efficacy and self-efficacy in self-regulated learning, but lower performance-avoidance and work-avoidance goals. There were no differences in other motivational beliefs, frequency of academic cheating and reliance on contextual factors. Frequency of academic cheating was positively related to work-avoidance goals and contextual factors, and it was negatively related to average grade in previous academic year, self-efficacy in self-regulated learning, and mastery- -approach goals. The analysis of incremental validity of contextual factors revealed that they explained 28.6% of the variance of academic cheating above and beyond motivational beliefs. Moreover, the results showed a greater independent contribution of contextual factors in explaining academic cheating compared to the contribution of motivational beliefs.

Inductive and combinatorial reasoning in international educational context: assessment, measurement invariance, and latent mean differences

Indonesian 15-year-old students’ educational achievement has proved to be significantly lower than the international average based on one of the most prominent international large-scale assessments, OECD PISA. This prompted our interest in Indonesian students’ cognitive development during secondary schooling in comparison with that of students who are more successful on the PISA scale. This study aims to describe the development of Indonesian students’ thinking skills, especially inductive and combinatorial reasoning, to map the influencing factors in the development of reasoning skills and make a cross-national comparison study with their Hungarian peers. The sample was drawn from 250 Indonesian and 864 Hungarian 8th and 11th graders. The reliabilities of the tests were acceptable, with Cronbach’s alpha varying between .70 and .86. Results mainly indicated that: (a) the measures of these two constructs were partially invariant across nationalities; (b) the Indonesian students’ reasoning skills did not sufficiently develop between the 8th and 11th grades; (c) the Indonesian students’ inductive and combinatorial reasoning achievement was significantly worse than that of the Hungarian students in 11th grade, but not in 8th grade; and (d) the students’ learning strategies proved to be predictive factors in students’ reasoning skills achievement in both Indonesian and Hungarian contexts, but the level of prediction varied. Overall, the results of the current study provide support for an understanding of Indonesian students’ cognitive development compared to an international benchmark. The findings have implications for revising educational methods in Indonesia and may form the basis for further studies.