Search Results (149)

Public bads prevention problems, such as climate change, require people to cooperate above a certain threshold, which is ambiguous and varies in many situations. In that case, people conjecture and share some information about the threshold. However, little is known about how sharing such information affects people to cooperate. We experimentally examine how people’s cooperative choices are influenced by ambiguity and sharing information about the conjectures in public bads prevention, hypothesizing that sharing the information does not necessarily contribute to cooperation. We conduct the laboratory experiments with 400 subjects under five treatments, each of which differs in ambiguity as well as in presence or absence of sharing the information. We find that (i) the percentages of cooperative choices are nonmonotonic, decreasing and then increasing over ambiguity levels and (ii) sharing the information tends to uniformly discourage cooperation, and the negative impact becomes prominent as the ambiguity levels rise. The result demonstrates an adverse effect between sharing information and threshold ambiguity on cooperation, being in sharp contrast with the literature. Overall, this study suggests that how or what information is shared among people should be carefully reconsidered for resolving any public bads problem involving threshold ambiguity, as everybody is able to easily publicize their conjectures during an era of digital democracy. Additionally, providing unified public information or fostering agreement could help improve cooperation and enhance collective efforts in public bads prevention. Full article

Smart manufacturing is an important initiative to promote the transformation and upgrading of industries and the high-quality development of the economy. However, the current situation of digitalized smart transformation in manufacturing enterprises is not optimistic, which is primarily attributed to the ambiguity surrounding the pathways. This study is based on the technology-organization-environment-individual (TOE-I) analytical framework; it selects 20 case studies of advanced manufacturing enterprises; and employs case studies and necessary condition fuzzy set qualitative comparative research methods (NCA and fsQCA) to investigate the pathways through which technology, organization, the environment, and individual microelements synergistically drive smart manufacturing from a configurational perspective. The study reveals that digital technology breakthroughs, digital infrastructure, digital talent, digital sharing, organizational resilience, organizational culture, and the entrepreneurial spirit are the core influencing factors in advancing smart manufacturing for manufacturing enterprises, and four implementation paths driven by smart manufacturing are analyzed. Among them, digital technology breakthroughs and digital infrastructure have a potential substitutive relationship in the “technology + talent” empowerment organizational model. Organizational resilience, organizational culture, and the entrepreneurial spirit are important safeguards for successful advancements in smart manufacturing. In contrast, digital infrastructure plays a more indirect, supporting role. Accordingly, this paper provides theoretical reference and practical guidance. Full article

The concept of a mission is important to system design and development, especially in system of systems (SoS) engineering. However, the diverse usage of the term ’mission’ across disciplines often results in ambiguity regarding its role in practical applications in mission-centric engineering tasks. Clearly defined and precisely represented missions improve communication among stakeholders and help bridge interdisciplinary gaps. This study aims to investigate and analyze the state of the art for mission conceptualizations and representations and proposes a unified mission ontology (UMO) that improves semantic interoperability across various domains. To achieve this goal, we conducted a systematic literature review (SLR) to examine how missions are conceptualized and represented, analyzed the findings to obtain insight about cross-domain concepts related to missions, and developed a UMO that can be adapted to domain specific applications. The UMO facilitates semantic interoperability across domains through a high-level abstraction of shared concepts. To validate the comprehensiveness and adaptability of the UMO, we conducted coverage analysis using semantic similarity estimates to assess the equivalence of ontological concepts. This evaluation quantified the extent to which concepts from various domain-specific ontologies, including the mission engineering guideline, align with those in the UMO. Full article

After first appearing in December 2019, coronavirus disease 2019 (COVID-19) spread rapidly, leading to global effects and significant risks to health systems. The virus’s high replication competence in the human lung accelerated the severity of lung pneumonia cases, resulting in a catastrophic death rate. Variable observations in the clinical testing of virus-related and patient-related cases across different populations led to ambiguous results. Medical and epidemiological studies on the virus effectively use imaging and scanning devices to help explain the virus’s behavior and its impact on the lungs. Varying equipment resources and a lack of uniformity in medical imaging acquisition led to disorganized and widely dispersed data collection worldwide, while high heterogeneity in datasets caused a poor understanding of the virus and related strains, consequently leading to unstable results that could not be generalized. Hospitals and medical institutions, therefore, urgently need to collaborate to share and extract useful knowledge from these COVID-19 datasets while preserving the privacy of medical records. Researchers are turning to an emerging technology that enhances the reliability and accessibility of information without sharing actual patient data. Federated learning (FL) is a technique that learns distributed data locally, sharing only the weights of each local model to compute a global model, and has the potential to improve the generalization of diagnosis and treatment decisions. This study investigates the applicability of FL for COVID-19 under the impact of data heterogeneity, defining the lung imaging characteristics and identifying the practical constraints of FL in medical fields. It describes the challenges of implementation from a technical perspective, with reference to valuable research directions, and highlights the research challenges that present opportunities for further efforts to overcome the pitfalls of distributed learning performance. The primary objective of this literature review is to provide valuable insights that will aid in the formulation of effective technical strategies to mitigate the impact of data heterogeneity on the generalization of FL results, particularly in light of the ongoing and evolving COVID-19 pandemic. Full article

Low transitive constructions are ubiquitous in English conversation and as such can be considered “typical” clauses. This article furthers this claim by showing that these constructions are also most frequent in a different genre: arguments between participants at organized protests. It has been argued that one reason these constructions are so frequent is that they function to display participants’ stances. Arguments are a type of interaction where stance displays abound. In fact, they are defined as sequences of utterances that display opposing stances. Thus, the study goes on to examine how the most frequent of the low transitive constructions in the data—clauses with the nonverbal predicate be—function to display opposition across utterances. Du Bois has analyzed stance as resonance across utterances, created from structural parallelism, and he argues that slight changes between linguistic forms can create differing focal points that index contrasts. This framework is used to analyze how participants use the multiple semantic functions of be clauses across interactional sequences to display and modify their stances in response to their opponent. It is suggested that the versatility and ambiguity of be clauses are especially useful in arguments where participants do not share a set of common beliefs. Full article

Intense market competition has driven small- and medium-sized enterprises (SMEs) in the manufacturing sector to collaborate and form supply chain coalitions, which can improve the information flow and resource sharing and significantly enhance supply chain management efficiency. However, the distribution of cooperative benefits poses a core challenge for the long-term stability of coalitions. This paper addresses the impact of dynamic changes in complex business environments by utilizing triangular fuzzy numbers to represent the value of coalition, effectively depicting the uncertainty and ambiguity in the cooperation process. Compared to traditional models (which do not use triangular fuzzy numbers), this model is better suited to dynamic changes, offering flexible response mechanisms that ensure adaptability and fairness in the decision-making process. In addition, considering the influence of each member’s weight in the coalition, the fuzzy comprehensive evaluation method is used to determine the weights. With the goal of minimizing the dissatisfaction of enterprises in benefit distribution, a least square contribution with triangular fuzzy numbers is constructed to replace the marginal contribution of the classical Banzhaf value, and an improved Banzhaf value based on the player’s triangular fuzzy number-weighted excess contribution is proposed to arrive at a fair and reasonable benefit allocation strategy in order to enhance the long-term stability and cooperative benefits of coalition. By analyzing an example of the supply chain coalition, the effectiveness of the proposed improved Banzhaf value is verified, which satisfies the uniqueness, the individual rationality, and the group rationality. It not only promotes the level of risk management and decision making under the uncertainty conditions of complex business, but also deepens the theoretical foundation of cooperative game theory and expands its possibilities in practical applications and future development. Full article

Religious Education (RE) can be conceived of as a specific context within which secondary RE teachers from diverse backgrounds teach. This context gives rise to distinctive subject subcultures, characterised by a unique set of beliefs, norms, and practices that are shared by teachers who teach RE. Using micronarratives as a way to initiate further discussion and to distil some key points which can be elaborated upon, we illustrate some salient aspects of RE’s subcultures. These include a subculture of ambiguity, boundary crossings and objects, and a confusion of purpose and terms. Knowledge of these may better assist schools—and Catholic Schools’ Departments—to orient and support these teachers in their classroom practice. Full article

The rapid expansion of BeiDou satellite navigation applications has led to a growing demand for real-time high-precision positioning services. Currently, high-precision positioning services face challenges such as a long initialization time and heavy reliance on reference station networks, thereby failing to fulfill the requirements for real-time, wide-area, and centimeter-level positioning. In this study, we consider the multi-frequency signals that are broadcast by a satellite to share a common reference clock and possess identical RF channels and propagation paths with strict temporal, spectral, and spatial coupling between signal components, resulting in strongly coherent propagation delays. Firstly, we accurately establish a multi-frequency signal model that fully exploits those coherent characteristics among the multi-frequency BDS signals. Subsequently, we propose a rapid high-precision ranging technique using the code and carrier phases of multi-frequency signals. The proposed method unitizes multi-frequency signals via a coherent joint processing unit consisting of a joint tracking state estimator and a coherent signal generator. The joint tracking state estimator simultaneously estimates the biased pseudorange and its change rate, ionospheric delay and its change rate, and ambiguities. The coherent signal generator updates the numerically controlled oscillator (NCO) to adjust the local reference signal’s code and carrier replicas of different frequencies, changing them according to the state estimated by the joint tracking state estimator. Finally, the simulation results indicate that the proposed method efficiently diminishes the estimated biased pseudorange and ionospheric delay errors to below 0.1 m. Furthermore, this method reduces the carrier phase errors by more than 60% compared with conventional single-frequency-independent tracking methods. Consequently, the proposed method can achieve rapid centimeter-level results ranging for up to 1 min without using precise atmosphere corrections and provide enhanced tracking sensitivity and robustness. Full article

Synthetic Aperture Radar (SAR) imaging plays a vital role in maritime surveillance, yet the detection of small vessels poses a significant challenge when employing conventional Constant False Alarm Rate (CFAR) techniques, primarily due to the limitations in resolution and the presence of clutter. Deep learning (DL) offers a promising alternative, yet it still struggles with identifying small targets in complex SAR backgrounds because of feature ambiguity and noise. To address these challenges, our team has developed the AFSC network, which combines anti-aliasing techniques with fully shared convolutional layers to improve the detection of small targets in SAR imagery. The network is composed of three key components: the Backbone Feature Extraction Module (BFEM) for initial feature extraction, the Neck Feature Fusion Module (NFFM) for consolidating features, and the Head Detection Module (HDM) for final object detection. The BFEM serves as the principal feature extraction technique, with a primary emphasis on extracting features of small targets, The NFFM integrates an anti-aliasing element and is designed to accentuate the feature details of diminutive objects throughout the fusion procedure, HDM is the detection head module and adopts a new fully shared convolution strategy to make the model more lightweight. Our approach has shown better performance in terms of speed and accuracy for detecting small targets in SAR imagery, surpassing other leading methods on the SSDD dataset. It attained a mean Average Precision ($AP$) of 69.3% and a specific $AP$ for small targets ($A{P}_S$) of 66.5%. Furthermore, the network’s robustness was confirmed using the HRSID dataset. Full article

The proliferation of Internet of Things (IoT) devices has introduced significant security challenges, including weak authentication, insufficient data protection, and firmware vulnerabilities. To address these issues, we propose a linguistic secret sharing scheme tailored for IoT applications. This scheme leverages neural networks to embed private data within texts transmitted by IoT devices, using an ambiguous token selection algorithm that maintains the textual integrity of the cover messages. Our approach eliminates the need to share additional information for accurate data extraction while also enhancing security through a secret sharing mechanism. Experimental results demonstrate that the proposed scheme achieves approximately 50% accuracy in detecting steganographic text across two steganalysis networks. Additionally, the generated steganographic text preserves the semantic information of the cover text, evidenced by a BERT score of 0.948. This indicates that the proposed scheme performs well in terms of security. Full article

During the many centuries of interaction and exchange between China and Europe, one of the most complex and ambiguous relationships was that of the Catholic Church and its missionaries in China. On one hand, they contributed to and can be seen as a part of the European imperial project of world colonisation, but on the other hand, they were instrumental in sharing and exchanging knowledge, as well as creating schools and other institutions in the places they created missions. At the same time, attempts were being made within the Catholic Church to promote the development of a Chinese clergy, although this issue remained divisive. This article examines these complex relationships through the eyes of a French Jesuit, André Yverneau, who was in China between 1948 and 1951 and who left a collection of letters back to his family describing these years. His experiences, observations, reactions and attitudes towards China and the mission are presented and analysed in order to re-evaluate some of the main debates surrounding the mission in China in the mid-twentieth century: education, language, indigenisation, and politics, both internal to the Catholic Church and with its relations in China. Full article

Traditional book publishing has a pronounced and unjust deficiency of Black and African American voices. White culture, thoughts, and rules are the standard in traditional publishing. Black and African American authors are not typically picked up by white-dominated publishing companies. In traditional publishing, mostly white literary agents are gatekeepers and acquisition editors shut the doors too frequently to non-white authors. Aspiring Black authors then resort to hybrid or vanity press companies that often use unscrupulous practices, charge exorbitant fees, accomplish little or low-quality work, and deny authors’ agency and full rights in the final disposition of a book. Because a majority of traditionally published children’s books featuring Black or African American stories or characters are written by or illustrated by non-Black people, the wide possibilities of adventure, celebrations, discovery, and friendship stories are not published for Black children. Instead, publishers favor stories about slavery, the civil rights movement, famous Black people, and hair tales as well as racially ambiguous characters. Regrettably, Black and African American culturally relevant stories written and illustrated by Black or African Americans are not readily available to children in school and library settings consistent with schools’ community or student demographics. This article shares research findings and viewpoints of Kathy Anderson and Karen Bowlding, two Black children’s book writers and publishing consultants who are also parents. Black and African American students encounter education and cultural injustice because of the practices of traditional publishing companies, educators, and librarians. Parents and guardians can ameliorate these issues with discernment and action. Foremost, publishing company decision-makers must acknowledge their own racial biases that deny representation and authenticity to all children in our US classrooms. Full article

As the organizational environment becomes more volatile, uncertain, complex, and ambiguous, and the economy becomes increasingly knowledge-based, organizational knowledge management is key for companies’ success. This is especially important as organizational ties are weaker and job-hopping becomes a more prevalent phenomenon. As human resource mobility increases, companies must ensure that knowledge remains within the company despite employee exit. In this context, the current study sought to understand how leaders’ actions can facilitate employee knowledge sharing, focusing on paradoxical leadership. Besides examining the impact of paradoxical leadership on employees’ propensity to adopt knowledge-sharing behaviors, this study also explored the effects of one potential intervening variable (i.e., promotive voice behavior) and one potential boundary condition (i.e., trust in the leader) on this relationship. A two-wave time-lagged correlational study was conducted with a sample of 154 workers from various sectors. The results of moderated mediation analysis suggest that paradoxical leaders indirectly promote greater knowledge-sharing among subordinates by fostering their promotive-voice behaviors, but only for those with high levels of trust in the leader. The implications of these findings for current organizational challenges regarding knowledge management are discussed. Full article

To optimize the utilization and analysis of tables, it is essential to recognize and understand their semantics comprehensively. This requirement is especially critical given that many tables lack explicit annotations, necessitating the identification of column types and inter-column relationships. Such identification can significantly augment data quality, streamline data integration, and support data analysis and mining. Current table annotation models often address each subtask independently, which may result in the neglect of constraints and contextual information, causing relational ambiguities and inference errors. To address this issue, we propose a unified multi-task learning framework capable of concurrently handling multiple tasks within a single model, including column named entity recognition, column type identification, and inter-column relationship detection. By integrating these tasks, the framework exploits their interrelations, facilitating the exchange of shallow features and the sharing of representations. Their cooperation enables each task to leverage insights from the others, thereby improving the performance of individual subtasks and enhancing the model’s overall generalization capabilities. Notably, our model is designed to employ only the internal information of tabular data, avoiding reliance on external context or knowledge graphs. This design ensures robust performance even with limited input information. Extensive experiments demonstrate the superior performance of our model across various tasks, validating the effectiveness of unified multi-task learning framework in the recognition and comprehension of table semantics. Full article

Information that is complicated and ambiguous entails high cognitive load. Trying to understand such information can involve a lot of cognitive effort. An alternative to expending a lot of cognitive effort is to engage in motivated cognition, which can involve selective attention to new information that matches existing beliefs. In accordance with principles of least action related to management of cognitive effort, another alternative is to give up trying to understand new information with high cognitive load. In either case, high cognitive load can limit potential for understanding of new information and learning from new information. Cognitive Load Theory (CLT) provides a framework for relating the characteristics of information to human cognitive load. Although CLT has been developed through more than three decades of scientific research, it has not been applied comprehensively to improve the explainability, transparency, interpretability, and shared interpretability (ETISI) of machine learning models and their outputs. Here, in order to illustrate the broad relevance of CLT to ETISI, it is applied to analyze a type of hybrid machine learning called Algebraic Machine Learning (AML). This is the example because AML has characteristics that offer high potential for ETISI. However, application of CLT reveals potential for high cognitive load that can limit ETISI even when AML is used in conjunction with decision trees. Following the AML example, the general relevance of CLT to machine learning ETISI is discussed with the examples of SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and the Contextual Importance and Utility (CIU) method. Overall, it is argued in this Perspective paper that CLT can provide science-based design principles that can contribute to improving the ETISI of all types of machine learning. Full article