Context Reasoning Research Articles

Reform of The Law on The Division of Joint Property Divorce

The purpose of this study is to find out reform of the law on the division of joint property divorce. 50-fifty provisions regarding the sharing of joint assets are often applied rigidly in accordance with the provisions of Article 97 of the KHI. The resulting output does not provide a sense of justice to the parties. This research is normative law with a substantive analysis approach to the provisions of Article 97 of the KHI. The results showed that the sharing of joint assets was regulating (regelen) not coercive (dwingon). In certain cases, this provision can be deviated by contextualizing it through the maqashid sharia approach to the substance of the case. The amount of the contribution of either party to the acquisition of joint assets and commitment to the sacred promise of marriage is a modifying element ('illat) in the case of sharing of joint assets. Maqashid shari'ah as an instrument in law making (istinbath) plays a very large role in this case, especially if the case presented is closely related to distributive justice where the elements of benefit and the purpose of the law are the main benchmark in seeking justice. Contextual reasoning and a substantive approach to the purpose of article 97 KHI by prioritizing psychological and contributive aspects to the case of division of joint property will make the article flexible. In the event that the role of one party is greater in the acquisition of joint property and there is an attitude of inconsistency of one party towards marital commitment, it becomes a ratio legis of reduced share of joint property of the former husband and wife.

A transformer-based pyramid network for coronary calcified plaque segmentation in intravascular optical coherence tomography images

Characterizing coronary calcified plaque (CCP) provides essential insight into diagnosis and treatment of atherosclerosis. Intravascular optical coherence tomography (OCT) offers significant advantages for detecting CCP and even automated segmentation with recent advances in deep learning techniques. Most of current methods have achieved promising results by adopting existing convolution neural networks (CNNs) in computer vision domain. However, their performance can be detrimentally affected by unseen plaque patterns and artifacts due to inherent limitation of CNNs in contextual reasoning. To overcome this obstacle, we proposed a Transformer-based pyramid network called AFS-TPNet for robust, end-to-end segmentation of CCP from OCT images. Its encoder is built upon CSWin Transformer architecture, allowing for better perceptual understanding of calcified arteries at a higher semantic level. Specifically, an augmented feature split (AFS) module and residual convolutional position encoding (RCPE) mechanism are designed to effectively enhance the capability of Transformer in capturing both fine-grained features and global contexts. Extensive experiments showed that AFS-TPNet trained using Lovasz Loss achieved superior performance in segmentation CCP under various contexts, surpassing prior state-of-the-art CNN and Transformer architectures by more than 6.58% intersection over union (IoU) score. The application of this promising method to extract CCP features is expected to enhance clinical intervention and translational research using OCT.

Bayesian Inverse Contextual Reasoning for Heterogeneous Semantics- Native Communication

Bayesian Inverse Contextual Reasoning for Heterogeneous Semantics- Native Communication

Contextual Factors Promoting the use of Alternative Strategies to Corporal Punishment in Basic Schools in Mampong Municipality, Ghana

The use of alternative strategies to corporal punishment has become imperative as a result of the numerous negative consequences of the use of corporal punishment. This study, which utilized the qualitative research approach, studied 15 Basic school teachers and 15 Basic school headteachers who were purposively sampled from some Basic schools in Mampong Municipality of Ghana. The study was intended to find out the contextual reasons for their use of alternative strategies to corporal punishment. Using interview, focus group discussion and the phenomenological design, it was found that, the teachers use the alternative strategies because of policy reason, need to improve behaviour and the need to avoid the negative consequences of corporal punishment. These imply that the reasons for their use of the alternative strategies are rational. It was recommended that, these grounds upon which the teachers use these alternative strategies should be sustained by educational managers in the Municipality through continuous education of the stakeholders of education.

Why does Waqf Literacy Matter?

Many studies have highlighted a low literacy level of waqf among Muslims all over the world. In Indonesia, an effort to measure the level of waqf literacy using an index was initiated by the Indonesian Waqf Board in 2020. The result has proven that there is a low level of waqf literacy in Indonesia. This study aims to explore the reasons behind the low level of waqf literacy and understand the importance of waqf literacy. Based on a literature survey, the following aspects need to be clearly explained and elaborated to effectively improve waqf literacy among the public. First, the unique characteristics of waqf compared to other Islamic alms; second, waqf literacy is positively related to waqf collection; third, the utilization of waqf can be further enhanced when the literacy is higher; and fourth, disputes and conflicts about waqf ownership are frequently due to lack of waqf literacy. In summary, this study is expected to increase the interest and attention of all stakeholders toward enhancing waqf literacy. Waqf authorities and waqf institutions are encouraged to intensify education and socialization about waqf, while researchers are expected to study more on waqf literacy. To further develop the waqf sector, it is necessary to have a road map to increase waqf literacy. Originality/Value: The study aims to explore the reasons behind the importance of waqf literacy in Indonesia. Identifying these reasons in the Indonesian context could pave the way for tailored interventions and policies to improve waqf literacy, which may have implications for similar contexts elsewhere.

Spatial reasoning in context: bridging cognitive and educational perspectives of spatial-mathematics relations

Spatial reasoning is ingrained in daily life, such as when locating our keys or parking our car. At a broad level, spatial reasoning describes the ability to mentally represent and transform objects and their relations. Spatial reasoning is comprised of distinct, yet related, spatial skills, most of which have strong links with mathematics achievement. Subsequently, understanding the ways spatial reasoning connects with mathematics has the potential to support achievement in school. However, current research practices have failed to translate into practical outcomes for students. To date, research has often focused on decontextualized spatial skills, measured by psychometric tests, to generalize about broader models of spatial reasoning. However, spatial reasoning goes beyond test performance. In this theoretical review, I have sought to find the points of connection between the fields of cognitive psychology, often based in the lab, and mathematics education, situated within classrooms, and discussed ways to connect this currently siloed work for greater impact on classroom practice. The paper addresses the emergence of spatial research from its historical roots in intelligence testing and the influence these conceptualizations have had on contemporary methodologies. It goes on to discuss how these research traditions may be limiting our ability to understand the mechanisms linking spatial reasoning and mathematics. The paper argues for a broader view of research problems and methodologies in spatial cognition research to facilitate the translation of research to meaningful contexts in pedagogy and learning.

Excellence in Antibiotic Stewardship: A Mixed-Methods Study Comparing High-, Medium-, and Low-Performing Hospitals.

Despite antibiotic stewardship programs existing in most acute care hospitals, there continues to be variation in appropriate antibiotic use. While existing research examines individual prescriber behavior, contextual reasons for variation are poorly understood. We conducted an explanatory, sequential mixed-methods study of a purposeful sample of 7 hospitals with varying discharge antibiotic overuse. For each hospital, we conducted surveys, document analysis, and semi-structured interviews with antibiotic stewardship and clinical stakeholders. Data were analyzed separately and mixed during the interpretation phase, where each hospital was examined as a case, with findings organized across cases using a strengths, weaknesses, opportunities, and threats framework to identify factors accounting for differences in antibiotic overuse across hospitals. Surveys included 85 respondents. Interviews included 90 respondents (31 hospitalists, 33 clinical pharmacists, 14 stewardship leaders, 12 hospital leaders). On surveys, clinical pharmacists at hospitals with lower antibiotic overuse were more likely to report feeling: respected by hospitalist colleagues (P = .001), considered valuable team members (P = .001), and comfortable recommending antibiotic changes (P = .02). Based on mixed-methods analysis, hospitals with low antibiotic overuse had 4 distinguishing characteristics: (1) robust knowledge of and access to antibiotic stewardship guidance; (2) high-quality clinical pharmacist-physician relationships; (3) tools and infrastructure to support stewardship; and (4) highly engaged infectious diseases physicians who advocated stewardship principles. This mixed-methods study demonstrates the importance of organizational context for high performance in stewardship and suggests that improving antimicrobial stewardship requires attention to knowledge, interactions, and relationships between clinical teams and infrastructure that supports stewardship and team interactions.

Explore Contextual Information for 3D Scene Graph Generation.

3D scene graph generation (SGG) has been of high interest in computer vision. Although the accuracy of 3D SGG on coarse classification and single relation label has been gradually improved, the performance of existing works is still far from being perfect for fine-grained and multi-label situations. In this paper, we propose a framework fully exploring contextual information for the 3D SGG task, which attempts to satisfy the requirements of fine-grained entity class, multiple relation labels, and high accuracy simultaneously. Our proposed approach is composed of a Graph Feature Extraction module and a Graph Contextual Reasoning module, achieving appropriate information-redundancy feature extraction, structured organization, and hierarchical inferring. Our approach achieves superior or competitive performance over previous methods on the 3DSSG dataset, especially on the relationship prediction sub-task.

An Efficient and Light Transformer-Based Segmentation Network for Remote Sensing Images of Landscapes

High-resolution image segmentation for landscape applications has garnered significant attention, particularly in the context of ultra-high-resolution (UHR) imagery. Current segmentation methodologies partition UHR images into standard patches for multiscale local segmentation and hierarchical reasoning. This creates a pressing dilemma, where the trade-off between memory efficiency and segmentation quality becomes increasingly evident. This paper introduces the Multilevel Contexts Weighted Coupling Transformer (WCTNet) for UHR segmentation. This framework comprises the Mult-level Feature Weighting (MFW) module and Token-based Transformer (TT) designed to weigh and couple multilevel semantic contexts. First, we analyze the multilevel semantics within a local patch without image-level contextual reasoning. It avoids complex image-level contextual associations and eliminates the misleading information carried. Second, MFW is developed to weigh shallow and deep features for enhancing object-related attention at different grain sizes from multilevel semantics. Third, the TT module is introduced to couple multilevel semantic contexts and transform them into semantic tokens using spatial attention. Then, we can capture token interactions and obtain clearer local representations. The suggested contextual weighting and coupling of single-scale patches empower WCTNet to maintain a well-balanced relationship between accuracy and computational overhead. Experimental results show that WCTNet achieves state-of-the-art performance on two UHR datasets of DeepGlobe and Inria Aerial.

Verb-driven machine reading comprehension with dual-graph neural network

Logical reasoning of context is vital for reading comprehension, which requires to explore the logical relationship through sentence structure. However, previous methods of logical symbols and graph-based models do not make full explore the relationships among entities. In this paper, we present a verb-driven dual-graph network (VDGN) that utilizes core verbs of sentences to model the inter-sentence relationship by the ability of verbs to express linguistic context and the shortest dependency path to model the relationship between entities of intra-sentence. We construct a context graph and a query graph respectively through the above method. In order to predict the answer correctly, our framework fuses information from the context graph and the query graph applying a bi-directional attention mechanism on graph data. We evaluate our approach on two public logical reasoning machine reading comprehension(MRC) datasets: ReClor and LogiQA. Experiments on representative benchmark datasets demonstrate the effectiveness of our approach.

Large Field Surveys and Short Items Sets: Estimating Reasoning Skills among Distinct Samples in Malawi and Zambia

Raven’s Progressive Matrices measure logical reasoning and are often included in large multi-topic surveys in low and middle-income countries. The matrices are image-based items that do not require formal knowledge of language or math to complete. As such, they are attractive items to measure logical reasoning in international development contexts. Many of these large field surveys include short item Raven’s sets because space is too limited to fit a full suite. However, short sets can result in restricted variation in terms of test scores. In this paper, we use a nominal response model (NRM) form of item response theory (IRT) to uncover hidden variation in right and wrong answers using short-item Raven’s tests from two large field surveys in Malawi and Zambia. We also analyze relationships between a set of other variables, comparing performance of different versions of the logical reasoning scores as both independent and dependent variables, checking the validity of the new scores. The new NRM-estimated logical reasoning scores follow a more normal distribution in both samples. Validity checks suggest that when relationships are less strong, NRM-estimated scores can capture more nuance than summed scores or even 2 parameter logistic IRT-estimated scores. NRM can uncover differences that are not apparent when using simple summed scores.

Few-shot remaining useful life prediction based on meta-learning with deep sparse kernel network

Predicting remaining useful life (RUL) of machinery is of vital importance to prognostics and health management. Reliable and accurate RUL prediction not only can reduce maintenance costs and increase machine availability but also even prevent catastrophic consequences. In reality, RUL predictions usually require numerous certain kinds of machine degradation data. However, complex operating conditions and safety issues may often result in fragmented data records generated, with very few complete samples being usable. To overcome the challenge of RUL prediction with limited data, this paper proposes a novel MetaDESK model that is based on meta-learning with deep sparse kernel network. The general idea is to train a sparse kernel with a variational posterior in a data-driven fashion, and then transfer it to a new few-shot RUL task via meta-knowledge. Specifically, we first incorporate a Gaussian Process into the model-agnostic meta-learning (MAML) framework and use variational inference to estimate latent variables as kernel features, which allows us to sample from a non-Gaussian distribution of the posterior. Then, the KL-divergence of sparse approximation is added to the kernel features as a regularization term through inference to reduce the overfitting problem. Also, to exploit the dependencies of the tasks we integrate both their shared knowledge and task-specific information into a contextual reasoning process, which is implemented by a bidirectional long short-term memory network. To evaluate our proposed model, we conduct extensive experiments using publicly available degradation data, and the results verify the model's effectiveness.

Applying EBM epistemology and the GRADE system to address practitioners' disagreements in medical malpractice allegations during COVID-19 pandemic.

The GRADE system of clinical recommendations has deontic implications and can discriminate between mandatory, prohibited, and merely permitted medical decisions. The recommendation categories of the GRADE framework map onto deontological imperatives that can lead to a better understanding and management of allegations of imprudence and appropriateness of treatments. Allegations made during the worst phase of COVID-19 pandemic are used as a case study for exploring the deontic implications of GRADE. Conceptual theoretical analysis, case study analysis, and argumentation in defence of hypotheses. Strong GRADE recommendations for or against treatment are justified by high-quality evidence and can be construed as ethical obligations and prohibitions. In contrast, when evidence for benefit or harm is of lower quality, GRADE yields weak, discretionary recommendations. In such grey area cases, the absence of a duty to prescribe or refuse to prescribe a requested treatment is compatible with the privilege of considering unproven but possibly beneficial options in a private setting. This privilege, however, does not extend to healthcare policymakers, who have a duty to promote actions that serve the public and whose recommendations should not be guided by personal or idiosyncratic preferences or values. If there is no prima facie evidence that a proposed treatment is harmful, doctors are not negligent in considering it in shared doctor-patient decision-making. But these clinical decisions under uncertainty do not transfer obligations to health authorities, who are not part of the decision-making process in clinical settings. The clinical decision-making process concerns particulars and is guided by contextual and specific reasons that do not fall within the scope of a general policy. Thus, in the context of a serious epidemic in which patients need and demand treatments, if the body of evidence is still changing and fragile, an attitude of tolerance and connivance may ensure a smoother transition to a more stable phase of progress, both in scientific and clinical medicine.

Diet or lifestyle: Consumer purchase behavior of vegan retailing. A qualitative assessment

The noticeable shift towards veganism signifies a fundamental change in how people view what they consume. Veganism has been growing and receiving increasing scientific attention. However, current studies about veganism are in the nascent stages with several gaps. The study aims to fill these gaps and add to the existing knowledge of veganism by investigating how consumers conceptualize it and how and why individuals purchase vegan products. Using the grounded theory approach, the study aligns behavioural reasoning theory (BRT) to conceptualize vegan adoption and purchase behavior. The study mainly focuses on the reasons and challenges faced while following a vegan lifestyle. 50 US and UK vegans were interviewed using a semi-structured qualitative design. The findings reflected that veganism is much more than a dietary behavior adopted as a lifestyle philosophy. The most significant factors impacting the adoption of vegan food are personal, social, contextual, ethical, and moral reasons. The study further identified psychological, social, and marketing inhibitors as challenges in adopting vegan food. The findings also identified vegan adoption and activism as behavioral intentions toward vegan food. Vegan activism may serve as a double-edged sword as it can be good in spreading awareness, but it can also be seen as an activist who tries to force their views on others. The study findings have significant implications and future directions for academics, retailers, and policymakers interested in veganism.

A second of structured chaos: Kosmos/chaos dialectic in Meshuggah’s Catch 33

Among the most frequent features of the Swedish extreme metal band Meshuggah’s music, there is an omni-presence of minor-second intervals and distorted eight-string guitars, a remarkable amount of odd time signatures, aggressive vocals and an endless attempt to thwart the listener’s expectations. In this case study I analyse the many contradictions present in Meshuggah’s album Catch 33 (2005), which I interpret as a concept album about paradox or a long single song (constituted by thirteen individual tracks not separated by breaks) that seeks to balance the variety, complexity, ‘imperfection’ of its starting material – the chaos side of the album’s style – with elements that promote order, intelligibility and ‘perfection’ – its kosmos side. By which musical means does the band represent contradiction and paradox in this album, and their style in general? I argue that there are specific musical (but also conceptual, lyrical, structural and contextual) reasons that make the album representative of the band’s discography and style, to the point of being possibly considered their quintessential work (despite often being labelled as an experimental entry in the band’s output), and one of the most coherent incarnations of (proto-)djent stylistic features. The kosmos/chaos dialectic is the idea I choose to describe the ongoing struggle for stability at work in this music, capable of magnifying the beauty of imperfection and the paradoxical truth behind the sentiment, expressed in the lyrics, that we are ‘sentenced to a lifetime | a second of structured chaos’.

Context-guided coarse-to-fine detection model for bird nest detection on high-speed railway catenary

As a critical component of ensuring the safe and stable operation of trains, the detection of bird’s nests on the rail catenary has always been essential. Low-resolution images and the lack of labelled data, however, make it difficult to detect smaller bird’s nests (those occupying small pixels in the input image). Previous solution relies on manual online patrol or offline video playback, which severely limits the detection efficiency. Previously, this challenge was addressed by manual online patrol or offline video playback, which severely limits detection efficiency. We propose in this work a context-guided coarse-to-fine detection model (CG-CFDM) for solving the bird’s nest detection problem. This solution consists of a context reasoning module and a coarse-to-fine detection network. By detecting domains and matching templates, the context reasoning module generates new labelled context bounding boxes, thereby reducing the burden of annotation. As a result of its delicately designed architecture and powerful representation learning ability, this trained coarse-to-fine detection network further facilitates the detection of bird’s nests in an efficient and accurate manner. Extensive experiments demonstrate that the proposed approach is superior to existing methods in terms of performance and has a great deal of potential for detecting bird’s nests.

How to facilitate manufacturing industry learning from problems: a review on advanced technology problem-solving

PurposeThe purpose of this paper is twofold: to identify and map contemporary research on advanced technology implementations for problem-solving purposes in the manufacturing industry, and to further understand the organizational learning possibilities of advanced technology problem-solving in the manufacturing industry.Design/methodology/approachThis paper outlines a scoping review of contemporary research on the subject. The findings of the review are discussed in the light of theories of contradicting learning logics.FindingsThis paper shows that contemporary research on the subject is characterized by technological determinism and strong solution-focus. A discussion on the manufacturing industries’ contextual reasons for this in relation to contradicting learning logics shows that a Mode-2 problem-solving approach could facilitate further learning and expand knowledge on advanced technology problem-solving in the manufacturing industry. A research agenda with six propositions is provided.Originality/valueThe introduction of advanced technology implies complex effects on the manufacturing industry in general, while previous research shows a clear focus on technological aspects of this transformation. This paper provides value by providing novel knowledge on the relationship between advanced technology, problem-solving and organizational learning in the manufacturing industry.

Scene context is predictive of unconstrained object similarity judgments

What makes objects alike in the human mind? Computational approaches for characterizing object similarity have largely focused on the visual forms of objects or their linguistic associations. However, intuitive notions of object similarity may depend heavily on contextual reasoning—that is, objects may be grouped together in the mind if they occur in the context of similar scenes or events. Using large-scale analyses of natural scene statistics and human behavior, we found that a computational model of the associations between objects and their scene contexts is strongly predictive of how humans spontaneously group objects by similarity. Specifically, we learned contextual prototypes for a diverse set of object categories by taking the average response of a convolutional neural network (CNN) to the scene contexts in which the objects typically occurred. In behavioral experiments, we found that contextual prototypes were strongly predictive of human similarity judgments for a large set of objects and rivaled the performance of models based on CNN representations of the objects themselves or word embeddings for their names. Together, our findings reveal the remarkable degree to which the natural statistics of context predict commonsense notions of object similarity.

Addressing Exoskeleton Implementation Challenges: Case Studies of Non-Acceptance in Agriculture

ABSTRACT Objectives The objective of this study was to detect success and failure factors for the implementation of passive exoskeletons in agriculture. Exoskeletons have been shown to reduce musculoskeletal loads during lab-based manual tasks, but long-term implementation experiences in agriculture are lacking. Methods We analyzed four intervention studies in agriculture focusing on methodological and contextual reasons why the trials were successful or unsuccessful. The study context, attempted intervention, and data collection of each field trial is compared. In the absence of long-term studies investigating the implementation and effectiveness of exoskeletons in agriculture, a set of multi-week pilot trials were initiated among German market vegetable farms and French vineyards from 2019 to 2022. Participant ratings, farm characteristics (e.g. employment duration and payment scheme) and intervention implementation characteristics (e.g. participation in implementation or language barriers) were analyzed using a mixed-methods approach to identify success and failure factors. Results The comparison of the studies showed that despite the organizational issues, there were several practical issues that limit the success of exoskeleton use in agriculture. We observed that participant rejection of the intervention is a major barrier to successfully conducting long-term field trials in agriculture. Factors like pain, discomfort, heat stress, or a lack of perceived benefits have been identified as failure factors but also the implementation process itself. Conclusion In addition to careful targeting of trial sites and inclusion of participatory elements in the implementation plan, successful implementation of exoskeletons in agriculture requires fundamental human factors development of the exoskeletons themselves. This will require better matching the physical needs of the workers, the production needs of the tasks, and compatibility with the environment.

Practical ethical frameworks for civil engineering and environmental systems

ABSTRACT Ethics is receiving increased emphasis in civil and environmental engineering. However, despite the proliferation of college textbooks and courses encouraging ethical reasoning, engineers in practice often limit their understanding narrowly to their individual actions. Broader issues of global importance are usually addressed in an ad-hoc manner, if at all. Our goal is to present the topic of ethics in a way that appeals to engineers, especially those receptive to ‘systems thinking’. Our broader motivation is to encourage the development of educated, virtuous and caring professionals who engage in contextual ethical reasoning about complex systems. To these ends, we summarise relevant theoretical and applied concepts for ethical analysis, building from a recent chapter that reflects on ethics from an engineering systems perspective (but applied to the healthcare industry). We emphasise virtue ethics and the ethics of care as crucial supplements to duty ethics and utilitarianism. Analytical methods we discuss include Value Sensitive Design, Design for Care, and a multi-level classification of ethical issues into the micro (individual), meso (institutional), and macro (societal) levels. This work collects relevant considerations for designers of civil and environmental engineering systems and researchers interested in developing related design and analysis methods that include practical ethical reasoning. This work is part of a Special Issue on Systems Perspectives: Clarity through Examples (see Dias 2023).