Reviewerly/exHarmony huggingface dataset:
Deep collaborative graph hashing for discriminative image retrieval

The most striking success of deep hashing for large-scale image retrieval benefits from its powerful discriminative representation of deep learning and the attractive computational efficiency of compact hash code learning. Most existing deep semantic-preserving hashing regard the available semantic labels as the ground truth for classification or transform them into prevalent pairwise similarities. However, such strategies fail to capture the interactive correlations between the visual semantics embedded in images and the given category-level labels. Moreover, they utilize the fixed piecewise or pairwise semantics as the optimization objectives, which suffers from the limited flexibility on semantic representation and adaptive knowledge communication in hash code learning. In this paper, we propose a novel Deep Collaborative Graph Hashing (DCGH), which collectively considers multi-level semantic embeddings, latent common space construction, and intrinsic structure mining in discriminative hash codes learning, for large-scale image retrieval. To the best of our knowledge, this is the first collaborative graph hashing for image retrieval. Specifically, instead of using the conventional single-flow visual network architecture, we design a dual-stream feature encoding network to jointly explore the multi-level semantic information across visual and semantic features. Moreover, a well-established shared latent space is constructed based on space reconstruction to explore the concurrent information and bridge the semantic gap between visual and semantic space. Furthermore, a graph convolutional network is introduced to preserve the latent structural relations in the optimal pairwise similarity-preserving hash codes. The whole learning framework is optimized in an end-to-end fashion. Extensive experiments on different datasets demonstrate that our DCGH can achieve superb image retrieval performance against state-of-the-art supervised hashing methods. The source codes of the proposed DCGH are available at https://github.com/JalinWang/DCGH .
Emerging dimensions of blockchain application in tourism and hospitality sector: a systematic literature review

ABSTRACTABSTRACTOwing to its unparalleled transparency and disintermediation characteristics, blockchain technology has a huge potential to transform the tourism and hospitality industry. Airlines and hotel companies are now turning to blockchain for its multifaceted applications, yet its holistic value realization in the sector is far from reality. The existing literature in terms of theoretical and methodological underpinnings concerning blockchain technology integrated Smart Tourism 4.0 remains limited and fragmented across its lifecycle. With a structured, systematic literature review approach, the study aims to conduct an in-depth review of 56 selected papers obtained from a specific search criterion employed across widespread repositories of Scopus, Web of Science, and EBSCOhost databases, capturing the period from Jan 2012 to May 2022. The study identifies critical research gaps in technology awareness, particularly around the maturity index for a BCT-integrated digitized business model. The emerging themes based on the primary focus areas are summarized and extended further to provide future research directions on the basis of theory, context, and method (TCM) framework. To advance the readers' understanding to the next level, a few case examples of linked theory, context and methodology are also provided.由于其无与伦比的透明度和非中介化特征,区块链技术具有巨大的潜力来改变旅游业和酒店业. 航空公司和酒店公司现在正转向区块链, 以实现其多方面的应用, 但其在该领域的整体价值实现远未实现. 关于区块链技术集成智能旅游4.0的理论和方法基础方面的现有文献在其整个生命周期中仍然有限且支离破碎. 该研究采用结构化、系统的文献综述方法, 旨在对从Scopus、科学之网和EBSCO宿主数据库的广泛存储库中采用的特定搜索标准获得的56篇论文进行深入审查, 涵盖2012年1月至2022年5月这段时间. 该研究确定了技术意识方面的关键研究差距, 特别是围绕BCT集成数字化业务模型的成熟度指数. 基于主要关注领域的新兴主题被进一步总结和扩展, 以在理论、背景和方法(TCM)框架的基础上提供未来的研究方向. 为了将读者的理解提升到一个新的层次, 还提供了一些相关理论、背景和方法的案例. KEYWORDS: Blockchaindigital ledgerdistributed ledgercryptocurrencytourismhospitality AcknowledgmentsAuthors would like to express sincere thanks to the Editor and reviewers for providing constructive suggestions to improve quality of the manuscript.Disclosure statementNo potential conflict of interest was reported by the authors.Supplementary dataSupplemental data for this article can be accessed online at https://doi.org/10.1080/19368623.2023.2184440
Current Status and Prospects of Research on Sensor Fault Diagnosis of Agricultural Internet of Things

Sensors have been used in various agricultural production scenarios due to significant advances in the Agricultural Internet of Things (Ag-IoT), leading to smart agriculture. Intelligent control or monitoring systems rely heavily on trustworthy sensor systems. Nonetheless, sensor failures are likely due to various factors, including key equipment malfunction or human error. A faulty sensor can produce corrupted measurements, resulting in incorrect decisions. Early detection of potential faults is crucial, and fault diagnosis techniques have been proposed. The purpose of sensor fault diagnosis is to detect faulty data in the sensor and recover or isolate the faulty sensors so that the sensor can finally provide correct data to the user. Current fault diagnosis technologies are based mainly on statistical models, artificial intelligence, deep learning, etc. The further development of fault diagnosis technology is also conducive to reducing the loss caused by sensor failures.
A scoping review to assess the effects of virtual reality in medical education and clinical care

Due to the challenges and restrictions posed by COVID-19 pandemic, technology and digital solutions played an important role in the rendering of necessary healthcare services, notably in medical education and clinical care. The aim of this scoping review was to analyze and sum up the most recent developments in Virtual Reality (VR) use for therapeutic care and medical education, with a focus on training medical students and patients. We identified 3743 studies, of which 28 were ultimately selected for the review. The search strategy followed the most recent Preferred Reporting Items for Systematic Reviews and Meta-Analysis for scoping review (PRISMA-ScR) guidelines. 11 studies (39.3%) in the field of medical education assessed different domains, such as knowledge, skills, attitudes, confidence, self-efficacy, and empathy. 17 studies (60.7%) focused on clinical care, particularly in the areas of mental health, and rehabilitation. Among these, 13 studies also investigated user experiences and feasibility in addition to clinical outcomes. Overall, the findings of our review reported considerable improvements in terms of medical education and clinical care. VR systems were also found to be safe, engaging, and beneficial by the studies' participants. There were huge variations in studies with respect to the study designs, VR contents, devices, evaluation methods, and treatment periods. In the future, studies may focus on creating definitive guidelines that can help in improving patient care further. Hence, there is an urgent need for researchers to collaborate with the VR industry and healthcare professionals to foster a better understanding of contents and simulation development.
A novel deep policy gradient action quantization for trusted collaborative computation in intelligent vehicle networks

The openness of the intelligent vehicle network makes it easy for selfish or untrustworthy vehicles to maliciously occupy limited resources in the mobile edge network or spread malicious information. However, most of the existing trust models rely on evaluating vehicles or data at the application level. For selfish or forgery attacks in intelligent vehicle networks, we propose a trusted deep reinforcement learning (DRL) cybersecurity approach for computation offloading to evaluate the safety and reliability performance in IoT edge networks, including our intelligent system model and a Deep Policy Gradient Action Quantization (DPGAQ) scheme. By introducing a reputation record table and designing a highly decisive communication trusted computing mode, we can accurately predict the untrusted selfish attack of vehicle in the task offloading of the Internet of things. Furthermore, in the multi-vehicle scenario, because the trusted offloading decision is a mixed integer programming problem, which leads to the dimension explosion of channel state and space, we propose a joint action-value quantization with attention mechanism to approximate the continuous actions values to a limited number of discrete values. Because it is not only inefficient but also unnecessary to generate high-dimensional decision actions in each time frame, we prune the infeasible action decisions by order preserving pruning to reduce the computational complexity of training and achieve efficient training on the premise of ensuring accuracy. To verify the feasibility and effectiveness of our proposed algorithm, millions of channels of edge vehicle networks are used as the input data. The simulation results show that compared with the benchmark trust model, DPGAQ achieves more than 72% reputation level, and improves 11%, 10% and 11% respectively in precision, recall and F-score.
Signgraph: An Efficient and Accurate Pose-Based Graph Convolution Approach Toward Sign Language Recognition

Sign language recognition (SLR) enables the deaf and speech-impaired community to integrate and communicate effectively with the rest of society. Word level or isolated SLR is a fundamental yet complex task with the main objective of using models to correctly recognize signed words. Sign language consists of very fast and complex hand, body, face movements, and mouthing cues that make the task very challenging. Several input modalities; RGB, optical Flow, RGB-D, and pose/skeleton have been proposed for SLR. However, the complexity of these modalities and the state-of-the-art (SOTA) methodologies tend to be exceedingly sophisticated and over-parametrized. In this paper, our focus is to use the hands and body poses as an input modality. One major problem in pose-based SLR is extracting the most valuable and distinctive features for all skeleton joints. In this regard, we propose an accurate, efficient, and lightweight pose-based pipeline leveraging a graph convolution network (GCN) along with residual connections and a bottleneck structure. The proposed architecture not only facilitates efficient learning during model training providing significantly improved accuracy scores but also alleviates computational complexity. With the proposed architecture in place, we are able to achieve improved accuracies on three different subsets of the WLASL dataset and the LSA-64 dataset. Our proposed model outperforms previous SOTA pose-based methods by providing a relative improvement of 8.91%, 27.62%, and 26.97% for WLASL-100, WLASL-300, and WLASL-1000 subsets. Moreover, our proposed model also outperforms previous SOTA appearance-based methods by providing a relative improvement of 2.65% and 5.15% for WLASL-300 and WLASL-1000 subsets. For the LSA-64 dataset, our model is able to achieve 100% test recognition accuracy. We are able to achieve this improved performance with far less computational cost as compared to existing appearance-based methods.
Exploring the Capabilities of a Lightweight CNN Model in Accurately Identifying Renal Abnormalities: Cysts, Stones, and Tumors, Using LIME and SHAP

Kidney abnormality is one of the major concerns in modern society, and it affects millions of people around the world. To diagnose different abnormalities in human kidneys, a narrow-beam x-ray imaging procedure, computed tomography, is used, which creates cross-sectional slices of the kidneys. Several deep-learning models have been successfully applied to computer tomography images for classification and segmentation purposes. However, it has been difficult for clinicians to interpret the model’s specific decisions and, thus, creating a “black box” system. Additionally, it has been difficult to integrate complex deep-learning models for internet-of-medical-things devices due to demanding training parameters and memory-resource cost. To overcome these issues, this study proposed (1) a lightweight customized convolutional neural network to detect kidney cysts, stones, and tumors and (2) understandable AI Shapely values based on the Shapley additive explanation and predictive results based on the local interpretable model-agnostic explanations to illustrate the deep-learning model. The proposed CNN model performed better than other state-of-the-art methods and obtained an accuracy of 99.52 ± 0.84% for K = 10-fold of stratified sampling. With improved results and better interpretive power, the proposed work provides clinicians with conclusive and understandable results.
A survey on machine learning from few samples

The capability of learning and generalizing from very few samples successfully is a noticeable demarcation separating artificial intelligence and human intelligence. Despite the long history dated back to the early 2000s and the widespread attention in recent years with booming deep learning, few surveys for few sample learning (FSL) are available. We extensively study almost all papers of FSL spanning from the 2000s to now and provide a timely and comprehensive survey for FSL. In this survey, we review the evolution history and current progress on FSL, categorize FSL approaches into the generative model based and discriminative model based kinds in principle, and emphasize particularly on the meta learning based FSL approaches. We also summarize several recently emerging extensional topics of FSL and review their latest advances. Furthermore, we highlight the important FSL applications covering many research hotspots in computer vision, natural language processing, audio and speech, reinforcement learning and robotic, data analysis, etc. Finally, we conclude the survey with a discussion on promising trends in the hope of providing guidance and insights to follow-up researches.
A Scoping Review of the Metaverse for Software Engineering Education: Overview, Challenges, and Opportunities

Abstract In the Software Engineering Education (SEE) context, virtual worlds have been used in order to improve learning outcomes. However, there is a gap in the literature in order to characterize the use of the Metaverse for SEE. The objective of this work is to characterize the state of the art of virtual worlds in SEE and provide research opportunities and challenges to fill the limitations found. We conducted a systematic literature review, and we established 8 research questions that guided the study, as well as performed data extraction. We report on 17 primary studies that deal mostly with immersive experiences in SEE. The results show some limitations: few Software Engineering (SE) topics are covered; most applications simulate environments and do not explore new ways of viewing and interacting; there is no interoperability between virtual worlds; learning analysis techniques are not applied; and biometric data are not considered in the validations of the studies. Although there are virtual worlds for SEE, the results indicate the need to develop mechanisms in order to support the integration between virtual worlds. Therefore, based on the findings of the review, we established a set of components grouped by 5 layers to enable the Metaverse for SEE through fundamental requirements. We hope that this work can motivate promising research in order to foster immersive learning experiences in SE through the Metaverse.
A robust collision prediction and detection method based on neural network for autonomous delivery robots

Abstract For safe last‐mile autonomous robot delivery services in complex environments, rapid and accurate collision prediction and detection is vital. This study proposes a suitable neural network model that relies on multiple navigation sensors. A light detection and ranging technique is used to measure the relative distances to potential collision obstacles along the robot's path of motion, and an accelerometer is used to detect impacts. The proposed method tightly couples relative distance and acceleration time‐series data in a complementary fashion to minimize errors. A long short‐term memory, fully connected layer, and SoftMax function are integrated to train and classify the rapidly changing collision countermeasure state during robot motion. Simulation results show that the proposed method effectively performs collision prediction and detection for various obstacles.
Dashed expectations in service experiences. Effects of robots human-likeness on customers’ responses

Purpose There is growing interest in the use of human-like social robots, able to undertake complex tasks whilst building consumer engagement. However, further exploration is needed on the optimal level of humanoid appearance for service robots. In particular, the literature is limited with respect to mitigating disconfirmed expectations for robots high in human-likeness. This paper aims to address this gap by testing the effect of robot appearance, disconfirmed expectations and warmth (vs competence) on customers’ responses. Design/methodology/approach The study adopts a mixed-method design by presenting a focus group (Study 1) that guides two laboratory experiments (Studies 2 and 3). Studies 2 and 3 test for the moderating effect of warmth (vs competence) and the mediating roles of perceived eeriness and disconfirmed expectations. Findings The findings show that a robot high (vs low) in human-likeness leads to higher negative customers’ responses, which is explained by disconfirmed expectations rather than perceived eeriness. However, when customers interact with a warm (vs competent) robot high in human-likeness, this negative effect vanishes. Research limitations/implications The paper investigates boundary conditions and underlying mechanisms that affect customers’ experiences. Although the study adopts high realistic experiments, a limitation lies in not measuring customers’ actual behaviours in the field. Practical implications This study provides new insights on how the appearance and characteristics of social robots influence the consumers’ experience. By doing so, this study offers managers actionable insights (i.e. enhancing warmth) to lessen the risk of disconfirmed expectations. Originality/value The paper offers new explanations as to why human-like robots can generate negative responses from customers. Moving beyond the “uncanny valley” hypothesis, this study shows the key role of disconfirmed expectations in explaining consumers’ negative responses towards humanoid robots. Moreover, it sheds light on the moderating role of warmth (vs competence), which can mitigate such negative effects.
Unsupervised Cross-Modal Hashing With Modality-Interaction

Recently, numerous unsupervised cross-modal hashing methods have been proposed to deal the image-text retrieval tasks for the unlabeled cross-modal data. However, when these methods learn to generate hash codes, almost all of them lack modality-interaction in the following two aspects: 1) The instance similarity matrix used to guide the hashing networks training is constructed without image-text interaction, which fails to capture the fine-grained cross-modal cues to elaborately characterize the intrinsic semantic similarity among the datapoints. 2) The binary codes used for quantization loss are inferior because they are generated by directly quantizing a simple combination of continuous hash codes from different modalities without the interaction among these continuous hash codes. Such problems will cause the generated hash codes to be of poor quality and degrade the retrieval performance. Hence, in this paper, we propose a novel Unsupervised Cross-modal Hashing with Modality-interaction, termed UCHM. Specifically, by optimizing a novel hash-similarity-friendly loss, a modality-interaction-enabled (MIE) similarity generator is first trained to generate a superior MIE similarity matrix for the training set. Then, the generated MIE similarity matrix is utilized as guiding information to train the deep hashing networks. Furthermore, during the process of training the hashing networks, a novel bit-selection module is proposed to generate high-quality unified binary codes for the quantization loss with the interaction among continuous codes from different modalities, thereby further enhancing the retrieval performance. Extensive experiments on two widely used datasets show that the proposed UCHM outperforms state-of-the-art techniques on cross-modal retrieval tasks.
Missing Value Imputation Methods for Electronic Health Records

Electronic health records (EHR) are patient-level information, e.g., laboratory tests and questionnaires, stored in electronic format.Compared to physical records, the EHR alternative allows patients to access their data easily and helps staff with management procedural tasks such as information sharing across different organizations.Moreover, this type of data is commonly used by researchers for predictive and classification purposes, employing statistical and machine learning methods.However, missingness is a phenomenon that is observed very frequently for such measurements.Even though this missingness is often significant, it is usually treated poorly with either case deletion or simple methods, resulting in suboptimal and/or inaccurate predictive results.This happens because the simple methods, e.g., k-nearest neighbors (kNN) and mean/mode imputation, fail in most cases to incorporate the complex relationships that define these medical datasets.To address these limitations, in this paper we test and improve state-of-the-art missing data imputation models and practices.We propose a new missing value imputation method based on denoising autoencoders (DAE) with kNN for the pre-imputation task.We optimize the training methodology by re-applying kNN to the missing data every N epochs using a different value for the variable k each time to yield more accurate results.We also revise a state-of-the-art missing data imputation approach based on a generative adversarial network (GAN).Using this as a baseline, we introduce improvements regarding both the architecture and the training procedure.These models are compared with the ones usually employed within clinical research studies for both the task of imputation and post-imputation prediction.Results show that our proposed deep learning approaches outperform the standard baselines, yielding better imputation and predictive results.
Attention-Guided Pyramid Context Network for Polyp Segmentation in Colonoscopy Images

Recently, deep convolutional neural networks (C-NNs) have provided us an effective tool for automated polyp segmentation in colonoscopy images. However, most CNN-based methods do not fully consider the feature interaction among different layers and often cannot provide satisfactory segmentation performance. In this paper, a novel attention-guided pyramid context network (APCNet) is proposed for accurate and robust polyp segmentation in colonoscopy images. Specifically, considering that different network layers represent the polyp in different aspects, APCNet first extracts multi-layer features in a pyramid structure, then utilizes an attention-guided multi-layer aggregation strategy to refine the context features of each layer by utilizing the complementary information of different layers. To obtain abundant context features, APCNet employs a context extraction module that explores the context information of each layer via local information retainment and global information compaction. Through the top-down deep supervision, our APCNet implements a coarse-to-fine polyp segmentation and finally localizes the polyp region precisely. Extensive experiments on two in-domain and four out-of-domain experiments show that APCNet is comparable to 19 state-of-the-art methods. Moreover, it holds a more appropriate trade-off between effectiveness and computational complexity than these competing methods.