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This paper proposes a solution for recognizing human faces under mask-wearing. The lower part of human face is occluded and could not be used in the learning process of face recognition. So, the proposed solution is developed to recognize human faces on any available facial components which could be varied depending on wearing or not wearing a mask.

The proposed solution is developed based on the FaceNet framework, aiming to modify the existing facial recognition model to improve the performance of both scenarios of mask-wearing and without mask-wearing. Then, simulated masked-face images are computed on top of the original face images, to be used in the learning process of face recognition. In addition, feature heatmaps are also drawn out to visualize majority of parts of facial images that are significant in recognizing faces under mask-wearing.

The proposed method is validated using several scenarios of experiments. The result shows an outstanding accuracy of 99.2% on a scenario of mask-wearing faces. The feature heatmaps also show that non-occluded components including eyes and nose become more significant for recognizing human faces, when compared with the lower part of human faces which could be occluded under masks.

The convolutional neural network based solution is tuned up for recognizing human faces under a scenario of mask-wearing. The simulated masks on original face images are augmented for training the face recognition model. The heatmaps are then computed to prove that features generated from the top half of face images are correctly chosen for the face recognition.

This paper aims to analyze what Samarco and Brazilian magazines speak and say about Mariana’s environmental crime. Discover their doxa in this subject. Interpret the speakings, sayings and doxas through the theories of the treadmills of production, crime and law.

It is a qualitative and documental research and a narrative analysis. Regarding the documents: 45 were from public authorities, 14 from Samarco Mineração S.A. and 73 from Brazilian magazines. Theoretically, the authors resorted to Bourdieusian sociology (speaking, saying and doxa) and the treadmills of production, crime and law theories.

Samarco: speaking – mission statements; saying – detailed information and economic and financial concerns; doxa – assistance discourse. Brazilian magazines: speaking – external agents; saying – agreements; doxa – attribution, aggravations, historical facts, impacts and protests.

The absence of discussions that addressed this fatality, with its respective consequences, from an agenda that exposed and denounced how it exacerbated race, class and gender inequalities.

Regarding Mariana’s environmental crime: Samarco Mineração S.A. speaks and says through the treadmill of production theory and supports its doxa through the treadmill of crime theory, and Brazilian magazines speak and say through the treadmill of law theory and support their doxa through the treadmill of crime theory.

To provoke reflections on the relationship between the mining companies and the communities where they settle to develop their productive activities.

Concerning environmental crime in perspective, submit it to a theoretical interpretation based on sociological references, approach it in a debate linked to environmental criminology, and describe it through narratives exposed by the guilty company and by Brazilian magazines with high circulation.

Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.

This study aims to examine the diametrically opposite effects of probabilistic (risk) and nonprobabilistic uncertainty (ambiguity) on accounting conservatism.

This study uses panel regression models with year and industry-fixed effects. It uses financial and market data from the communication and energy sectors of 24 countries, encompassing 1,946 firms and 5,838 firm-year observations.

The study reveals that conservatism is a rational response to risk. However, in the presence of higher ambiguity where uncertainty exceeds firm control and outcomes become unpredictable, management reduces conservative accounting practices. Robustness tests support the validity of these findings across different institutional frameworks, agency risks, sample selection and heterogeneity.

This study contributes to the existing literature by exploring the contrasting effects of risk and ambiguity on accounting conservatism. It enhances the understanding of how various institutional factors influence the asymmetric recognition of bad news compared to good news under conditions of uncertainty.

By understanding the role of accounting conservatism in responding to uncertainties, regulators can develop more informed and effective policies that align with the dynamic nature of business environments.

This research provides novel and original ideas suggesting that the change in accounting conservatism is contingent upon the firms’ ambiguity or risk.

The purpose of this paper is to study the following two issues regarding blockchain crowdsourcing. First, to design smart contracts with lower consumption to meet the needs of blockchain crowdsourcing services and also need to design better interaction modes to further reduce the cost of blockchain crowdsourcing services. Second, to design an effective privacy protection mechanism to protect user privacy while still providing high-quality crowdsourcing services for location-sensitive multiskilled mobile space crowdsourcing scenarios and blockchain exposure issues.

This paper proposes a blockchain-based privacy-preserving crowdsourcing model for multiskill mobile spaces. The model in this paper uses the zero-knowledge proof method to make the requester believe that the user is within a certain location without the user providing specific location information, thereby protecting the user’s location information and other privacy. In addition, through off-chain calculation and on-chain verification methods, gas consumption is also optimized.

This study deployed the model on Ethereum for testing. This study found that the privacy protection is feasible and the gas optimization is obvious.

This study designed a mobile space crowdsourcing based on a zero-knowledge proof privacy protection mechanism and optimized gas consumption.

Based on the perspectives of social capital, image motivation and motivation affordances, this paper explores the direct and moderation effects of different kinds of motivations (i.e. relationship-based motivation, community-based motivation and individual-based motivation) on users' continuous knowledge contributions in social question and answer (Q&A) communities.

The authors collect the panel data of 10,193 users from a popular social Q&A community in China. Then, a negative binomial regression model is adopted to analyze the collected data.

The paper demonstrates that social learning, peer recognition and knowledge seeking positively affect users' continuous contribution behaviors. However, the results also show that social exposure has the opposite effect. In addition, self-presentation is found to moderate the influence of social factors on users' continuous use behaviors, while the moderation effect of motivation affordances has no significance.

First, this study develops a comprehensive motivation framework that helps gain deeper insights into the underlying mechanism of knowledge contribution in social Q&A communities. Second, this study conducts panel data analysis to capture the impacts of motivations over time, rather than intentions at a fixed time point. Third, the findings can help operators of social Q&A communities to optimize community norms and incentive mechanisms.

This paper aims to elucidate the responsiveness of China’s judicial system in addressing the challenges of identifying online illegal fund-raising crimes that have emerged in recent years. This study systematically evaluates the efficacy and potential pitfalls of legal guidelines contained in judicial interpretations, such as holistic determination, sampling verification and presumption of the nature of funds. In addition, the research endeavors to propose pertinent recommendations for refining the existing judicial rules.

This research mainly uses a doctrinal methodology, focusing on the principal judicial interpretations formulated by the Supreme People’s Court and other central judicial entities in China. The scope encompasses the realm of online illegal fund-raising crimes as well as other cybercrimes. The analytical framework involves a comprehensive examination of these authoritative judicial documents, coupled with a theoretical and critical analysis of relevant academic materials.

This research underscores that while judicial interpretations serve as an effective legal strategy to confront the challenges posed by online illegal fund-raising crimes, their implementation introduces a nuanced landscape. These legal guidelines, often emanating from diverse judicial departments and tackling specific issues, carry the inherent risk of giving rise to new complexities and fostering inconsistency. Judicial authorities shall exercise prudence in both the formulation and application of these guidelines, ensuring their harmonization with existing legal norms and fundamental legal principles.

This research constitutes a critical and comprehensive examination of judicial interpretations in China pertaining to online illegal fund-raising crimes. It offers valuable insights into the country’s judicial interpretation system and its legal responses to financial crimes. The paper serves as a valuable resource for academics, law enforcement professionals, policymakers, legislators and researchers.

This paper investigates the semantic association mechanisms of weak demand signals that facilitate innovative product development in terms of conceptual and temporal precedence, despite their inherent ambiguity and uncertainty.

To address this challenge, a domain ontology approach is proposed to construct a customer demand scenario-based framework that eliminates the blind spots in weak demand signal identification. The framework provides a basis for identifying such signals and introduces evaluation indices, such as depth, novelty and association, which are integrated to propose a three-dimensional weak signal recognition model based on domain ontology that outperforms existing research.

Empirical analysis is carried out based on customer comments of new energy vehicles on car platform such as “Auto Home” and “Bitauto”. Results demonstrate that in terms of recognition quantity, the three-dimensional weak demand signal recognition model, based on domain ontology, can accurately identify six demand weak signals. Conversely, the keyword analysis method exhibits a recognition quantity of four weak signals; in terms of recognition quality, the three-dimensional weak demand signal recognition model based on domain ontology can exclude non-demand signals such as “charging technology”, while keyword analysis methods cannot. Overall, the model proposed in this paper has higher sensitivity.

This paper proposes a novel method for identifying weak demand signals that considers the frequency of the signal's novelty, depth and relevance to the target demand. To verify its effectiveness, customer review data for new energy vehicles is used. The results provide a theoretical reference for formulating government policies and identifying weak demand signals for businesses.

This study aims to investigate the effect of accounting conservatism and business strategies as mitigating tools for bankruptcy risk. It determines the association among these factors and provides insights into the effectiveness of accounting discretion and business strategies in decision-making.

The study uses a sample of 83 nonfinancial listed firms in ASE for the period from 2013 to 2019. Bankruptcy risk is measured using the Altman Z-score (1968). Accounting conservatism is measured using the accrual-based approach, and optimal business strategies are identified through cluster analysis.

The results indicate that accounting conservatism has a significant negative effect on bankruptcy risk. Increased application of accounting conservatism practices leads to a decrease in the level of bankruptcy risk. However, the type of business strategy adopted by firms does not have a significant impact on bankruptcy risk, suggesting that firms are not effectively implementing their strategies to mitigate this risk.

This study focuses on nonfinancial listed firms in the ASE, limiting the generalizability of the findings to other contexts. The study's findings contribute to the understanding of the role of accounting conservatism in reducing bankruptcy risk but highlight the need for further research on the effectiveness of business strategies in mitigating this risk.

This study lies in understanding of the role of accounting discretion in financial evaluations and emphasizes the importance of accounting conservatism as a tool for mitigating bankruptcy risk. The study's insights provide valuable guidance to practitioners, regulators and researchers in this field.

The library and information science profession finds itself grappling with substantial difficulties and hurdles when addressing the trustworthiness and accuracy of information disseminated through social media platforms. This study aims to highlight the educational authority of librarians and propose a framework for librarians to establish their identity, understand the meaning behind their practice and integrate their expertise through knowledge practices, ensuring their relevance and effectiveness in the social media environment.

This study delves into a conceptual framework rooted in philosophical inquiry, seeking to establish a harmonious connection between interrelated concepts of civic roles, professional identity and knowledge practices. It draws upon both original research findings and a review of existing literature in the field.

Civic responsibilities reflect the professional identities of librarians. Evidence of knowledge practices collected from scientific literature emerged to be the important characterization of how librarians uphold their image as educational authorities. It describes the meaning of civic roles and professional practice.

The study sheds light on how librarians maintain their reputation as educators and the knowledge practices that underpin their civic responsibilities amidst the pervasiveness of information disorders.

The framework presented in the study offers a timely and relevant contribution to the complex realm of social media information disorders, a challenge that librarians grapple with regularly. It highlights the emerging role of librarians in society to assert their identity and recognize their civic responsibility in addressing this pressing issue that society faces.