Reviewerly/exHarmony huggingface dataset:
Adoption of blockchain technology enabled healthcare sustainable supply chain to improve healthcare supply chain performance

Purpose The COVID-19 pandemic has had a significant and worldwide influence on healthcare delivery, and it has significantly increased the pace at which digital technology is being used. Blockchain, one of these developing digital technologies, is distinguished by a number of properties. This study focuses on a blockchain-enabled healthcare supply chain. The purpose of this work is to investigate how blockchain technology (BCT) benefits the performance of healthcare supply chain management (HSCM). Design/methodology/approach The present study is based on the empirical research. Blockchain Technology (BCT), Healthcare Sustainable Supply Chain Practices (HSSCP), Healthcare Supply Chain Performance (HSCP) and Stakeholders’ Involvement (SI) practices are identified from the literature review and hypotheses are framed to check their interrelationship. For testing of hypothesis, a questionnaire was developed. Data collection was done by healthcare professionals via Google docs. The IBM SPSS version 22.0 was used to analyze the data and IBM SPSS AMOS 22.0 software was used for the development of structural modal. The data was collected through the Google form from the stakeholders of healthcare sector and analyzed through Structural Equation Modelling. Findings This research is focused on adoption of BCT enabled Healthcare Sustainable Supply Chain to improve HSCP. From the result, it had been found that BCT is positively effecting the stakeholder's involvement (SI) and HSSCP practices. Cumulatively, they positively impact the performance of HSCP. From this study, it is found that adoption of BCT enabled Healthcare Sustainable Supply Chain succours to combat COVID-19 situation. Originality/value This study attempts to show the potential benefits of the adoption of BCT enabled HSSCP to improve HSCP.
Overview and Efficiency of Decoder-Side Depth Estimation in MPEG Immersive Video

This paper presents the overview and rationale behind the Decoder-Side Depth Estimation (DSDE) mode of the MPEG Immersive Video (MIV) standard, using the Geometry Absent profile, for efficient compression of immersive multiview video. A MIV bitstream generated by an encoder operating in the DSDE mode does not include depth maps. It only contains the information required to reconstruct them in the client or in the cloud: decoded views and metadata. The paper explains the technical details and techniques supported by this novel MIV DSDE mode. The description additionally includes the specification on Geometry Assistance Supplemental Enhancement Information which helps to reduce the complexity of depth estimation, when performed in the cloud or at the decoder side. The depth estimation in MIV is a non-normative part of the decoding process, therefore, any method can be used to compute the depth maps. This paper lists a set of requirements for depth estimation, induced by the specific characteristics of the DSDE. The depth estimation reference software, continuously and collaboratively developed with MIV to meet these requirements, is presented in this paper. Several original experimental results are presented. The efficiency of the DSDE is compared to two MIV profiles. The combined non-transmission of depth maps and efficient coding of textures enabled by the DSDE leads to efficient compression and rendering quality improvement compared to the usual encoder-side depth estimation. Moreover, results of the first evaluation of state-of-the-art multiview depth estimators in the DSDE context, including machine learning techniques, are presented.
A Version of NL-SHADE-RSP Algorithm with Midpoint for CEC 2022 Single Objective Bound Constrained Problems

This paper presents an enhanced version of NL-SHADE-RSP, which won CEC'2021 competition on single objective bound-constrained numerical optimization for shifted and rotated shifted functions. The proposed version uses the midpoint of the population to estimate the optimum. The midpoint fitness is also used to introduce a restart trigger. For large populations, the midpoint is calculated after splitting the population into two parts by the k-means algorithm. Other introduced modifications include changing the bound constrain handling method and reducing population size. The performance of the proposed approach is evaluated on the CEC 2022 benchmark for single objective bound-constrained numerical optimization. The results confirm that each proposed modification gradually improves the algorithm's ranking on the benchmark.
Named entity recognition of building construction defect information from text with linguistic noise

Neither traditional rule-based named entity recognition (NER) nor the latest language models perform well in information extraction from noisy text—the text that contains linguistic errors, slang, loanwords, and jargon. Building defect complaints filed by residents via online systems is a representative example of such noisy text. This paper proposes an NER method for automatically extracting defect information from noisy text using a defect thesaurus and transfer learning. The thesaurus built herein included 1097 defect named entities in 23 categories. The NER performance was tested using 69,750 defect complaints through transfer learning of three representative pre-trained language models: Multilingual Bidirectional Encoder Representations from Transformers (BERT), Korean BERT (KoBERT), and Korean Efficiently Learning an Encoder that Classifies Token Replacements Accurately (KoELECTRA). The proposed method achieved an average F1 score of 91.0% using KoBERT. This NER performance was higher than that of the open benchmark NER performance for clean text (86.1%).
Reinforced Swin-Convs Transformer for Simultaneous Underwater Sensing Scene Image Enhancement and Super-resolution

Underwater image enhancement (UIE) technology aims to tackle the challenge of restoring the degraded underwater images due to light absorption and scattering. Meanwhile, the ever-increasing requirement for higher resolution images from a lower resolution in the underwater domain cannot be overlooked. To address these problems, a novel U-Net-based reinforced Swin-Convs Transformer for simultaneous enhancement and superresolution (URSCT-SESR) method is proposed. Specifically, with the deficiency of U-Net based on pure convolutions, the Swin Transformer is embedded into U-Net for improving the ability to capture the global dependence. Then, given the inadequacy of the Swin Transformer capturing the local attention, the reintroduction of convolutions may capture more local attention. Thus, an ingenious manner is presented for the fusion of convolutions and the core attention mechanism to build a reinforced Swin-Convs Transformer block (RSCTB) for capturing more local attention, which is reinforced in the channel and the spatial attention of the Swin Transformer. Finally, experimental results on available datasets demonstrate that the proposed URSCT-SESR achieves the state-of-the-art performance compared with other methods in terms of both subjective and objective evaluations. The code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/TingdiRen/URSCT-SESR</uri> .
SaberNet: Self-attention based effective relation network for few-shot learning

Few-shot learning is an essential and challenging field in machine learning since the agent needs to learn novel concepts with a few data. Recent methods aim to learn comparison or relation between query and support samples to tackle few-shot tasks but have not exceeded human performance and made full use of relations in few-shot tasks. Humans can recognize multiple variants of objects located anywhere in images and compare the relation among learned instances. Inspired by the human learning mechanism, we explore the definition of relations in relation networks and propose self-attention relation modules for feature and learning ability. First, we introduce vision self-attention to generate and purify features in few-shot learning. The comparison of different patches leads the backbone to infer relations between local features, which enforces feature extraction focus on more details. Second, we propose task-specific feature augmentation modules to infer relations and weight different contributions of components in few-shot tasks. The proposed SaberNet is conceptually simple and empirically powerful. Its performance surpasses the baseline a great margin, including pushing 5-way 1-shot CUB accuracy to 89.75% (12.73% absolute improvement), Cars to 76.71% (12.99% absolute improvement) and Flowers to 84.33% (7.67% absolute improvement).
CCNet: CNN model with channel attention and convolutional pooling mechanism for spatial image steganalysis

Image steganalysis based on convolutional neural networks(CNN) has attracted great attention. However, existing networks lack attention to regional features with complex texture, which makes the ability of discrimination learning miss in network. In this paper, we described a new CNN designed to focus on useful features and improve detection accuracy for spatial-domain steganalysis. The proposed model consists of three modules: noise extraction module, noise analysis module and classification module. A channel attention mechanism is used in the noise extraction module and analysis module, which is realized by embedding the SE(Squeeze-and-Excitation) module into the residual block. Then, we use convolutional pooling instead of average pooling to aggregate features. The experimental results show that detection accuracy of the proposed model is significantly better than those of the existing models such as SRNet, Zhu-Net and GBRAS-Net. Compared with these models, our model has better generalization ability, which is critical for practical application. • An end-to-end spatial steganalysis network CCNet is proposed. • Channel attention mechanism is introduced to focus on the region suitable for embedding secret information. • Convolution is used instead of average pooling to avoid the loss of steganographic information. • The proposed model is verified to have higher accuracy through comparative experiments.
A Combined Deep CNN: LSTM with a Random Forest Approach for Breast Cancer Diagnosis

The most predominant kind of disease that is normal among ladies is breast cancer. It is one of the significant reasons among ladies, regardless of huge endeavors to stay away from it through screening developers. An automatic detection system for disease helps doctors to identify and provide accurate results, thereby minimizing the death rate. Computer-aided diagnosis (CAD) has minimum intervention of humans and produces more accurate results than humans. It will be a difficult and long task that depends on the expertise of pathologists. Deep learning methods proved to give better outcomes when correlated with ML and extricate the best highlights of the images. The main objective of this paper is to propose a deep learning technique in combination with a convolution neural network (CNN) and long short-term memory (LSTM) with a random forest algorithm to diagnose breast cancer. Here, CNN is used for feature extraction, and LSTM is used for extracted feature detection. The experimental results show that the proposed system accomplishes 100% of accuracy, a sensitivity of 99%, recall of 99%, and an F1-score of 98% compared to other traditional models. As the system achieved correct results, it can help doctors to investigate breast cancer easily.
Single image super‐resolution based on progressive fusion of orientation‐aware features

Single image super-resolution (SISR) is an active research topic in the fields of image processing, computer vision and pattern recognition, restoring high-frequency details and textures based on the low-resolution input image. In this paper, we aim to build more accurate and faster SISR models via developing better-performing feature extraction and fusion techniques. Firstly, we proposed a novel Orientation-Aware feature extraction/selection Module (OAM), which contains a mixture of 1D and 2D convolutional kernels (i.e., 3×1, 1×3, and 3×3) for extracting orientation-aware features. The channel attention mechanism is deployed within each OAM, performing scene-specific selection of informative outputs of the orientation-dependent kernels (e.g., horizontal, vertical, and diagonal). Secondly, we present an effective fusion architecture to progressively integrate multi-scale features extracted in different convolutional stages. Instead of directly combining low-level and high-level features, similar outputs of adjacent feature extraction modules are grouped and further compressed to generate a more concise representation of a specific convolutional stage for high-accuracy SISR task. Based on the above two important improvements, we present a compact but effective CNN-based model for high-quality SISR via Progressive Fusion of Orientation-Aware features (SISR-PF-OA). Extensive experimental results verify the superiority of the proposed SISR-PF-OA model, performing favorably against the state-of-the-art models in terms of both restoration accuracy and computational efficiency (e.g., SISR-PF-OA outperforms RCAN model, achieving higher PSNR 31.25 dB vs. 31.21 dB and using fewer FLOPs 764.41 G vs. 1020.28 G on the Manga109 dataset for scale factor ×4 SISR task.). The source codes will be made publicly available.
No-reference quality index of tone-mapped images based on authenticity, preservation, and scene expressiveness

As a future-proof technology, high dynamic range (HDR) has the superiority in scene reappearance. Tone mapping operator (TMO) renders the dynamic effect of HDR images in a standard dynamic range (SDR) display at the expense of some insignificant information. However, it is difficult for existing TMOs to consistently achieve high-quality conversion of all HDR images, and the loss of converted images is inevitable. Therefore, accurate prediction of tone-mapped image (TMI) quality is of considerable significance. In this paper, we propose the reappearance effect of the TMIs index (RETI). According to the characteristics of primeval HDR images, the quality of TMIs is considered from three important elements: authenticity, the preservation of energy and information, and scene expressiveness. The feature vectors originated from three elements are combined with subjective quality to train prediction model. Experiments are executed on tow mainstream TMI quality assessment databases and the results show that the proposed method has a good prediction ability and generalization ability in comparison to some state-of-the-art methods. Our MATLAB source code will be released at https://github.com/niannianyouyu/RETI.
The New Method for Analyzing Technology Trends of Smart Energy Asset Performance Management

The development of emerging technologies not only has recently affected current industrial production but also has generated promising manufacturing opportunities that impact significantly on social and economic factors. Exploring upcoming renovation tendencies of technologies prematurely is essential for governments, research and development institutes, and industrial companies in managing strategies to achieve dominant advantages in business competitiveness. Additionally, the prospective changes, the scientific research directions, and the focus of technologies are crucial factors in predicting promising technologies. On the other hand, Industry 4.0 revolutionizes standards and models by accompanying significant technology developments in numerous sectors, including the sector of Smart energy. Moreover, asset performance management is always a prominent topic that has attained prevalence over the last decade because numerous challenges force all industrial companies to optimize their asset usability. However, to the best of our knowledge, no study reported an analysis of technology trends of asset performance management in the Smart energy sector by using proper data mining methods. Hence, this paper aims to fill in this gap and provide an analysis of technology trends of asset performance management in the Smart energy sector by structuring and exploring research subjects, considering problems, and solving methods with numerous experiments on scientific papers and patent data.
Similarity attributed knowledge graph embedding enhancement for item recommendation

Knowledge Graph Embedding (KGE)-enhanced recommender systems are effective in providing accurate and personalized recommendations in diverse application scenarios. However, such techniques that exploit entire embedded Knowledge Graph (KG) without data relevance approval constraints fail to stop noise penetration into the data. Additionally, approaches that pay no heed to tackle semantic relations among entities remain unable to effectively capture semantical structure of Heterogeneous Information Graph (HIG). Therefore, in this paper, we propose Similarity Attributed Graph-embedding Enhancement (SAGE) approach to model similarity-aware semantic connections among entities according to their triplets’ granularity. SAGE is a novel Knowledge Graph Embedding Enhancement (KGEE) method that constructs Entity-relevance-based Similarity-attributed Subgraph (ESS) to remove noise from the underlying data. It propagates interactions-enhanced knowledge over ESS to learn higher-order semantic connections among entities; and simultaneously utilizes feedbacks to enhance the interactions and regularize the model to highlight influential targets (nodes). Further, it samples influential targets in KG, independently move their preferences to the Local Central Nodes (LCN) of current influential areas, and streamline the collected information from all LCN to the main unit. Finally, a prediction module is used to determine generalized preferences for recommendation. We performed extensive experiments on benchmark datasets to evaluate the performance of SAGE where it outperformed the state-of-the-art methods with significant improvements in effectively providing the desired explainable recommendations.
DEFNet: Dual-Branch Enhanced Feature Fusion Network for RGB-T Crowd Counting

Most existing crowd counting approaches use limited information of RGB (red–green–blue) images and fail to suitably extract potential pedestrians in unconstrained scenarios. Moreover, complementary depth maps do not provide information of locations where people are more likely to be present. However, by incorporating optical and thermal information, the recognition of pedestrians may be enhanced considerably. In fact, thermal imaging information is robust to weather and lighting scenarios, and information from targets can be extracted even at nighttime. By combining RGB and thermal imaging information, we propose a dual-branch enhanced feature fusion network (DEFNet) for RGB-T (RGB and thermal) crowd counting. In DEFNet, an intensive data-enhancement module fuses complementary features of the same sizes from the RGB and thermal modalities, thus combining various rich receptive fields and generating powerful fused RGB-T features. These features describe both spatial structures and appearance details, highlighting information of crowd location. Then, an efficient dilation fusion module applies convolutions to the RGB -T features to obtain flexible and specific features, effectively eliminating the influence of background on the crowd information for density map prediction. Finally, high- and low-level features are used to efficiently obtain density maps through a fusion decoding module. Experimental results on an RGB-T crowd counting dataset indicate that the proposed DEFNet outperforms existing approaches. Furthermore, DEFNet can be generalized to handle RGB and depth data.
Latency and geofence testing of wireless emergency alerts intended for the ShakeAlert® earthquake early warning system for the West Coast of the United States of America

ShakeAlert, the earthquake early warning (EEW) system for the West Coast of the United States, attempts to provides crucial warnings before strong shaking occurs. However, because the alerts are triggered only when an earthquake is already in progress, and the alert latencies and delivery times are platform dependent, the time between these warnings and the arrival of shaking is variable. The ShakeAlert system uses, among other public alerting platforms like a mobile phone operating system, smartphone apps, and the Federal Emergency Management Agency Integrated Public Alert & Warning System (IPAWS). IPAWS sends Wireless Emergency Alerts (WEAs) informing people via their smartphones and other mobile devices about various events, such as natural hazards, child abductions, or public health information about COVID-19. However, little is known about the IPAWS delivery latencies. Given that people may have only a few seconds of notice after they receive an alert to take a protective action before they feel earthquake shaking, quantifying latencies is critical to understanding whether the IPAWS system is useful for EEW. In this study, we developed new methods to test the IPAWS distribution system's performance, both with devices in a controlled environment and as well as with a 2019 community-based feedback form, in Oakland and San Diego County, California, respectively. The controlled environment test used mobile phones (including smart and non-smart phones) and associated devices to determine alert receipt times; the community research form had participants self-report their receipt times. By triangulating the data between the controlled test environment and the community research, we determined the latency statistics as well as whether the geofence (the geographic area where the alert was intended to be sent) held broadly. We found that the latencies were similar between the two tests despite the large differences in population sizes. WEA messages were received within a median time frame of 6–12 s, and the geofence held with only a few exceptions. We use this latency to assess how the system would have performed in two large earthquakes, the 1989 M6.9 Loma Prieta and 2019 M7.1 Ridgecrest earthquakes, which both occurred near our WEA test locations. Our analysis revealed that had IPAWS been available during those earthquakes, particularly Loma Prieta, it would have provided crucial seconds of notice that damaging shaking was imminent in some locations relatively far from the epicenter. Further, we find affordable non-smart phones can receive WEAs as fast as smartphones. Finally, our new method can be used for latency and geospatial testing going forward for IPAWS and other similar alerting systems.
English teachers’ integration of digital technologies in the classroom

This systematic review set out to advance understanding of practices of digital technologies integration among English teachers and the factors influencing this process. A total number of 20 empirical studies, drawn from peer-reviewed journals, were selected based on several inclusion criteria before being carefully synthesized and evaluated. The main findings derived from these studies were divided into two parts in response to the two research questions. First, in terms of implementation, digital technologies were revealed to be predominantly utilized for teacher-centered purposes. Teachers also adopted digital technologies to address different content-specific areas as well as their and students’ needs. Second, several contributing factors in digital technologies adoption were found to include teachers’ pedagogical beliefs, competence and confidence in digital technologies integration, availability of resources, professional development and socio-cultural context. Based on the recurring findings of this paper, recommendations are provided in terms of research, practice, and policymaking.