Knowledge science, engineering and management: 8th international conference, ksem 2015 Chongqing, China, october 28-30, 2015 proceedings

Knowledge science, engineering and management: 8th international conference, ksem 2015 Chongqing, China, october 28-30, 2015 proceeding

Have we found a solution for health misinformation? A ten-year systematic review of health misinformation literature 2013-2022

Background Health misinformation (HM) has emerged as a prominent social issue in recent years, driven by declining public trust, popularisation of digital media platforms and escalating public health crisis. Since the Covid-19 pandemic, HM has raised critical concerns due to its significant impacts on both individuals and society as a whole. A comprehensive understanding of HM and HM-related studies would be instrumental in identifying possible solutions to address HM and the associated challenges. Methods Following the PRISMA procedure, 11,739 papers published from January 2013 to December 2022 were retrieved from five electronic databases, and 813 papers matching the inclusion criteria were retained for further analysis. This article critically reviewed HM-related studies, detailing the factors facilitating HM creation and dissemination, negative impacts of HM, solutions to HM, and research methods employed in those studies. Results A growing number of studies have focused on HM since 2013. Results of this study highlight that trust plays a significant while latent role in the circuits of HM, facilitating the creation and dissemination of HM, exacerbating the negative impacts of HM and amplifying the difficulty in addressing HM. Conclusion For health authorities and governmental institutions, it is essential to systematically build public trust in order to reduce the probability of individuals acceptation of HM and to improve the effectiveness of misinformation correction. Future studies should pay more attention to the role of trust in how to address HM. Have we found a solution for health misinformation? A ten-year systematic review of health misinformation literature 2013–2022.</p

Factors influencing workplace accident costs of building projects

Workplace accidents would incur various losses to the injured workers and their families, employers and society. This study aims to investigate the magnitude of workplace accident costs to building contractors and identify factors influencing workplace accident costs of building projects. Data were collected using multiple techniques (structured interviews, archival records and questionnaires) from 47 completed building projects in Singapore. Data were analyzed using bivariate correlation analysis and moderated regression analysis. It is found that the average direct accident costs, indirect accident costs and total accident costs of building projects account for 0.165%, 0.086% and 0.25% of contract sum, respectively. It is concluded that workplace accident costs of building projects are influenced by accident rates, project hazard level, project size, company size and the involvement of sub-contractors. The findings of this study may enhance decision makers' understanding of financial implications of workplace accidents in their building projects and motivate them to undertake accident prevention initiatives voluntarily

Why people accept mental health-related misinformation: role of social media metrics in users’ information processing

Drawing on dual-process theories, this study aims to investigate the factors associated with social media users’ acceptance of mental health-related misinformation (MHRM). We conducted a case study of Chinese microblogging Weibo on conversations that emerged following a publicised celebrity suicide of South Korean superstar Sulli. This incident sparked an extensive discussion on mental health issues as Sulli was reported having suffered from depression prior her death. Whilst previous studies on users’ information acceptance mainly adopted survey methods, our study employs a mix-methods approach (i.e., computational data collection method, content analysis and statistical analysis), which opens up new directions to utilise secondary social media data. We identified MHRM from the discussions on Weibo and labelled the responses to the misinformation as whether they indicate an acceptance of the MHRM. Binary logistic regression was used to examine the associations of receivers’ acceptance of MHRM with its information features (e.g., number of likes) and information sources (e.g., gender). Inconsistent with previous studies, our findings suggest that MHRM is less likely to be accepted when published by male users, underscoring the context-specific nature of heuristic cues. This study also revealed some novel findings, such as MHRM with more pictures or with more words is less likely to be accepted. A theoretical model was proposed based on the findings, which highlights the importance of heuristic cues and individuals’ pre-existing knowledge in information processing. </p

Workplace safety implications of cultural diversity on Australian construction sites : a pilot study

Cultural diversity has become a distinctive feature of Australia's construction workforce. There is a need for a systematic investigation into the cultural divergences among workers in difference ethnic groups and their implications for the workplace health and safety management in the construction industry. This research was proposed to examine the workplace safety implications of cultural diversity issues on construction sites. As the pilot study of a 3-year research program, this paper aims to identify potential workplace safety issues caused by cultural diversity on construction sites. Data were collected through semi-structured interviews with 10 safety professionals. The results of the semi-structured interviews indicate that cultural diversity has an influential impact on many aspects of safety practices on construction sites, e.g., management commitment; communication; workers’ involvement; supportive environment; supervisory environment; personal risk appreciation; work pressure; training and education; and rules and procedures. The issues that were identified from the semi-structured interviews will be used to develop a quantitative data collection instrument which aims to develop a framework for managing construction safety in a multicultural workforce (the second stage of this research)

Improving wheat yield estimates using data augmentation models and remotely sensed biophysical indices within deep neural networks in the Guanzhong Plain, PR China

Crop yield estimation and prediction constitutes a key issue in agricultural management, particularly under the context of demographic pressure and climate change. Currently, the main challenge in estimating crop yields based on remotely sensed data and data-driven methods is how to cope with small datasets and the limited amount of annotated samples. In order to cope with small datasets and the limited amount of annotated samples and improve the accuracy of winter wheat yield estimation in the Guanzhong Plain, PR China, this study proposed a method of combining generative adversarial networks (GANs) and convolutional neural network (CNN) for comprehensive growth monitoring of winter wheat, in which the remotely sensed leaf area index (LAI), vegetation temperature condition index (VTCI) and meteorological data at four growth stages of winter wheat during 2012–2017 were generated as the inputs of multi-layer convolutional neural networks (CNNs), and GAN was employed to artificially increase the number of training samples. Then, a linear regression model between the simulated comprehensive growth monitoring (I) and the measured yields was established to estimate yields of winter wheat in the Guanzhong Plain pixel by pixel. The final results showed when GAN was used to double the size of the training samples, and the simulation values obtained by CNN based on augmented samples using GAN provided a better training (R2 = 0.95, RMSE = 0.05), validation (R2 = 0.54, RMSE = 0.16) and testing (R2 = 0.50, RMSE = 0.14) performance than that just using the original samples. The achieved best pixel-scale yield estimation accuracy of winter wheat (R2 = 0.50, RMSE = 591.46 kg/ha) in the Guanzhong Plain. These results showed that small samples can be enlarged by GAN, thus, more important features for reflecting the growth conditions and yields of winter wheat from the remotely sensed indices and meteorological indices can be extracted, and indicated that CNN accompanied with GAN could contribute a lot to the comprehensive growth monitoring and yield estimation of winter wheat and data augmentation methods are extremely useful for the application of small samples in deep learning

Modality-Correlation-Aware Sparse Representation for RGB-Infrared Object Tracking

To intelligently analyze and understand video content, a key step is to accurately perceive the motion of the interested objects in videos. To this end, the task of object tracking, which aims to determine the position and status of the interested object in consecutive video frames, is very important, and has received great research interest in the last decade. Although numerous algorithms have been proposed for object tracking in RGB videos, most of them may fail to track the object when the information from the RGB video is not reliable (e.g. in dim environment or large illumination change). To address this issue, with the popularity of dual-camera systems for capturing RGB and infrared videos, this paper presents a feature representation and fusion model to combine the feature representation of the object in RGB and infrared modalities for object tracking. Specifically, this proposed model is able to (1) perform feature representation of objects in different modalities by employing the robustness of sparse representation, and (2) combine the representation by exploiting the modality correlation. Extensive experiments demonstrate the effectiveness of the proposed method

Attention Guided Domain Alignment for Conditional Face Image Generation

Despite the great success of Generative Adversarial Network (GAN) in face image generation, it is still a challenge to generate a faithful yet high-fidelity face image given an exemplar image and a conditional input from a distinct domain (e.g., a semantic segmentation mask or an edge map). Existing methods learn image-level features to align distinct domains in an intermediate domain, which ignore the spatial relationship of the facial semantic parts and therefore lead to semantic mismatching problems. In addition, it is computationally expensive to establish dense correspondences in the intermediate domain, especially for high-resolution face images. To address these problems, we propose a novel attention guided domain alignment method for conditional face image generation, which aligns two domains directly under the guidance of the local attention learned from semantically similar face parts. In particular, we assign a dedicated index for each feature block and adopt a top-k ranking operation to correspond block-wise features in two distinct domains, which exploits the spatial relationship of facial parts and preserves the texture structure during alignment. The local attention is then learned from the retrieved blocks, which reduces computation complexity substantially and therefore enables to build alignment in a high resolution. The aligned features are finally fused with adaptive weights learned from their long-range correlation coefficients, which capture the semantic coherence of the style features between the two domains. Extensive experimental results on the CelebAMask-HQ dataset demonstrate that the proposed method is superior to the state-of-the-art methods.</p

Integrating an attention-based deep learning framework and the SAFY-V model for winter wheat yield estimation using time series SAR and optical data

Information on the spatial distribution of yields can be obtained over a large area by using remote sensing (RS) data. Combining Synthetic Aperture Radar (SAR), being sensitive to above ground biomass and soil moisture in all weather conditions, and optical data can improve the usability of RS data and provide a basis for pixel-based crop yield estimation (YE). In this study, an Upscaled Convolutional Gated Recurrent Unit model incorporated an attention mechanism (UpSc-AConvGRU model) was proposed to improve the estimation accuracy of the winter wheat growth parameter, Leaf Area Index (LAI). Gap filling the time series of optical data was done with backscatter coefficients, local incidence angles and polarimetric decomposition information from Sentinel-1 SAR imagery. The time series LAI estimated by the UpSc-AConvGRU model and Vegetation Temperature Condition Index (VTCI) retrieved from Sentinel-3 optical imagery were then used as state variables of the SAFY-V model to estimate winter wheat yield. The results showed that the proposed UpSc-AConvGRU model incorporated the Convolutional Block Attention Module (CBAM) can effectively improve the accuracy of LAI estimation, with RMSEs ranging from 0.413 to 0.699 m2 m2 for LAI estimated within main growth stages (MGSs) of winter wheat. The correlation between estimated LAI and Sentinel-3 retrieved LAI was generally higher at irrigated farmland compared to rain-fed farmland. The estimated LAI was closest to Sentinel-3 retrieved LAI at the green-up and late heading-filling stage of winter wheat, followed by the jointing and early heading-filling stage, and finally the milk maturity stage. There was good agreement between the SAFY-V model estimated and field measured winter wheat yields (R2 = 0.546, RMSE = 0.757 t ha−1), and the estimated yields at the pixel scale in the Guanzhong Plain, PR China were satisfactory. This study combined deep learning and crop growth modeling, proposed a new pixel scale winter wheat YE method.</p

Learning on rework management of construction projects: A case study

Effective rework management (RM) not only increases profitability but also enhances management competence of contractors. However, little research has been conducted to explore the learning nature of RM from the perspective of contractors. Based on a longitudinal case study of three construction projects, this paper aims to present a holistic analysis on the RM learning process from the experience of a small-to-medium (SM) general contractor (GC) in China to explore the underlying facts determining the effectiveness of RM practices. It was found that a variety of causes led to rework in the three projects, which highlighted the importance to enhance GC's learning capacity to effectively reduce rework. A conceptual RM learning framework (CRMLF), which consists of people, approach, process, tool and project environment, was developed based on the analysis of influential factors of successful learning within the RM domain. This paper is valuable for practitioners and academics to understand the inherent nature of RM to continuously improve the project performance