Reviewerly/exHarmony huggingface dataset:
Information disclosure and blockchain technology adoption strategy for competing platforms

A key strategic decision for platforms such as Amazon, JD.com, and eBay is the extent to which they provide information to consumers because product information plays an important role in consumers’ purchasing decisions. Considering that consumers may doubt the disclosed information, these platforms may have the incentive to implement blockchain technology to authenticate their disclosed information and, therefore, eliminate consumer distrust. In this paper, we develop an analytical model to investigate the optimal information disclosure and equilibrium blockchain adoption strategy for competing platforms and analyse how the consumers’ beliefs and blockchain cost will affect the equilibrium results. Our results show that the equilibrium blockchain adoption strategy is a threshold-structured policy. To be specific, there are two critical values of blockchain cost. If the blockchain cost is larger than the upper threshold, then neither of the platforms has the incentive to apply blockchain technology, and the market is full of unverified information; however, if the blockchain cost is less than the lower threshold, both platforms prefer to apply blockchain technology, and the amount of authenticated information is larger than that of unverified information in the nonblockchain case. Finally, if the blockchain cost is between the two bounds, then a mixed strategy becomes the equilibrium; that is, one platform adopts the blockchain technology, while the other does not. In this case, the amount of authenticated information may be larger than that of unverified information, but the platform that ignores using blockchains is also better off with this new technology. We make several extensions, and the results show that our main conclusions remain unchanged, which indicates that the conclusions can be generalized.
LLA: Loss-aware label assignment for dense pedestrian detection

Label assignment has been widely studied in general object detection because of its great impact on detectors’ performance. In the field of dense pedestrian detection, human bodies are often heavily entangled, making label assignment more important. However, none of the existing label assignment method focuses on crowd scenarios. Motivated by this, we propose Loss-aware Label Assignment (LLA) to boost the performance of pedestrian detectors in crowd scenarios. Concretely, LLA first calculates classification (cls) and regression (reg) losses between each anchor and ground-truth (GT) pair. A joint loss is then defined as the weighted summation of cls and reg losses as the assigning indicator. Finally, anchors with top K minimum joint losses for a certain GT box are assigned as its positive anchors. Anchors that are not assigned to any GT box are considered negative. LLA is simple but effective. Experiments on CrowdHuman and CityPersons show that such a simple label assigning strategy can boost MR by 9.53% and 5.47% on two famous one-stage detectors – RetinaNet and FCOS, becoming the first one-stage detector that surpasses Faster R-CNN in crowd scenarios.
Quaternion discrete fractional Krawtchouk transform and its application in color image encryption and watermarking

• The quaternion discrete fractional Krawtchouk transform (QDFrKT) is proposed for the purpose of color image processing. • The implementation of QDFrKT is given from two points of view, i.e. direct computation and calculation with quaternion symplectic form. • The detailed mathematical derivation of QDFrKT, together with some definition and properties are provided. • Two applications with QDFrKT are investigated, including color image encryption and color image watermarking, so as to evaluate the applicability of the proposed QDFrKT method. • The promising experiment results demonstrate the effectiveness and efficiency of our proposed QDFrKT based color image encryption and watermarking techniques. This paper extends the traditional discrete fractional Krawtchouk transform (DFrKT) with quaternion theory to quaternion discrete fractional Krawtchouk transform (QDFrKT) for the purpose of color image processing. Also, the implementation of QDFrKT is given from two points of view, i.e. direct computation and calculation with quaternion symplectic form. The detailed mathematical derivation together with definition and some properties of quaternion algebra are provided. In order to evaluate the applicability of the proposed QDFrKT method, two applications are investigated, including color image encryption and color image watermarking. For color image encryption, experiments are conducted on key sensitivity, histogram analysis, and noise attacks; while for color image watermarking, experiments are conducted on watermarking invisibility and the robustness on attacks of media filter, average filter, Gaussian filter, and geometric attacks. Moreover, with the imperfect decryption property of the encryption algorithm, a potential application of the encryption strategy for designing watermarking algorithm is discussed. The promising experiment results demonstrate the effectiveness and efficiency of our proposed QDFrKT based color image encryption and watermarking techniques.
Density peak clustering using global and local consistency adjustable manifold distance

A novel density-based clustering algorithm, called Density Peak Clustering (DPC), has recently received great attention due to its efficiency in clustering performance and simplicity in implementation. However, empirical studies have demonstrated that the commonly used distance measures in DPC cannot simultaneously consider global and local consistency, which can cause the estimated local densities based on it incapable of capturing the ground-truth data structure and thus produce poor clustering results, especially when the clusters existing in datasets exhibit multi-density manifold structures characteristics with different sizes. In order to address those limitations, we propose a novel density peak clustering algorithm using global and local consistency adjustable manifold distance in this paper. In the proposed algorithm, a novel manifold distance with exponential term and scaling factor is introduced to estimate local densities of all data points. By modifying its exponential term and scaling factor, we can flexibly adjust the ratio of the distance between the data within the same manifold to the distance between the data across different manifolds. This flexible adjustment is beneficial to the estimated local densities more accurately reflecting the global and local consistency of data structures. In addition, to effectively deal with clusters with different densities and sizes, a compensation strategy for distance from nearest point with larger density, called local-scale tuning distance, is developed for our proposed approach. By the developed local-scale tuning distance, underlying cluster centers of clusters with different densities and sizes, especially the clusters with low densities or small sizes can remarkably stand out from the decision graph so that the proposed method can accurately identify the number of underlying clusters in the decision graph and thus obtain satisfactory clustering results. In the experimental part, the effect of the scaling factor on the performance of the proposed technique is discussed and some suggestions about the determination of the parameters are given. Theoretical analysis and experimental results on several synthetic datasets and read-world datasets demonstrate that the proposed approach is superior to other existing clustering techniques in terms of three evaluation metrics with statistical significance.
Re-evaluation of the healthcare service quality criteria for the Covid-19 pandemic: Z-number fuzzy cognitive map

Hospitals as healthcare centers have faced many challenges with the Covid-19 spread, which results in a decline in the quality of health care. Because the number of patients referred to hospitals increases dramatically during the pandemic, providing high-quality services and satisfying them is more important than ever to maintain community health and create loyal customers in the future. However, health care quality standards are generally designed for normal circumstances. The SERVPERF standard, which measures customer perceptions of service quality, has also been adjusted for hospital service quality measurement. In this study, the SERVPERF standard criteria for health services are evaluated in the Covid-19 pandemic. For this purpose, by considering the causal relationships between the criteria and using Z-Number theory and Fuzzy Cognitive Maps (FCMs), the importance of these criteria in the prevalence of infectious diseases was analyzed. According to the results, hospital reliability, hospital hygiene, and completeness of the hospital with ratios 0.9559, 0.9305, and 0.9268 are respectively the most influential criteria in improving the quality of health services in the spread of infectious diseases circumstances such as the Covid-19 pandemic. A review of the literature shows that in previous studies, comprehensive research has not been done on prioritizing the criteria for measuring the quality of health services in the context of the spread of infectious diseases.
Blockchain-Based Secure and Lightweight Authentication for Internet of Things

Over the past decade, the Internet of Things (IoT) is widely adopted in various domains, including education, commerce, government, and healthcare. There are also many IoT-based applications drawn significant attentions in recent years. With the increasing numbers of the connected devices in the IoT system, one of the challenging tasks is to ensure devices’ authenticity, which allows users to have a high confidence in the decision. In addition, due to the heterogeneity of the IoT system and the resource-constrained devices, how to efficiently manage such system and guarantee the security and privacy for devices is concerned. In this article, we proposed a new blockchain-based authentication scheme to meet the challenges. Our proposed framework combines the blockchain technique and the modular square root algorithm to achieve an effective authentication process. Besides, we demonstrate the security and utility of the proposed scheme by providing the security analysis and the detailed experiment.
New perspectives on covariant quantum error correction

Covariant codes are quantum codes such that a symmetry transformation on the logical system could be realized by a symmetry transformation on the physical system, usually with limited capability of performing quantum error correction (an important case being the Eastin–Knill theorem). The need for understanding the limits of covariant quantum error correction arises in various realms of physics including fault-tolerant quantum computation, condensed matter physics and quantum gravity. Here, we explore covariant quantum error correction with respect to continuous symmetries from the perspectives of quantum metrology and quantum resource theory, establishing solid connections between these formerly disparate fields. We prove new and powerful lower bounds on the infidelity of covariant quantum error correction, which not only extend the scope of previous no-go results but also provide a substantial improvement over existing bounds. Explicit lower bounds are derived for both erasure and depolarizing noises. We also present a type of covariant codes which nearly saturates these lower bounds.
Beginning teaching during COVID-19: newly qualified Hong Kong teachers’ preparedness for online teaching

Beginning teaching is challenging during normal circumstances. Yet, in the summer of 2020, the newly qualified teachers (NQTs) from initial teacher education (ITE) programmes in Hong Kong, as well as many other global contexts, entered schools that were severely disrupted by COVID-19. This qualitative study, through the use of a qualitative online survey, explored the sense of preparedness and first-semester experiences of one cohort of NQTs who graduated from an ITE programme in Hong Kong in the summer of 2020. Findings suggest that the disruptions to their programme and teaching role fundamentally affected their sense of preparedness and defined their first-year experience. Specifically, the NQTs did not feel practically or psychologically prepared to teach online using digital technologies. However, some NQTs reported positive school-based practices that aided their transition. Based on the study's findings, professional digital competence beginning teachers require for online teaching environments, as well as recommendations for ways ITE programmes can help develop this competence, are provided.
Depth Completion using Plane-Residual Representation

The basic framework of depth completion is to predict a pixel-wise dense depth map using very sparse input data. In this paper, we try to solve this problem in a more effective way, by reformulating the regression-based depth estimation problem into a combination of depth plane classification and residual regression. Our proposed approach is to initially densify sparse depth information by figuring out which plane a pixel should lie among a number of discretized depth planes, and then calculate the final depth value by predicting the distance from the specified plane. This will help the network to lessen the burden of directly regressing the absolute depth information from none, and to effectively obtain more accurate depth prediction result with less computation power and inference time. To do so, we firstly introduce a novel way of interpreting depth information with the closest depth plane label p and a residual value r, as we call it, Plane-Residual (PR) representation. We also propose a depth completion network utilizing PR representation consisting of a shared encoder and two decoders, where one classifies the pixel’s depth plane label, while the other one regresses the normalized distance from the classified depth plane. By interpreting depth information in PR representation and using our corresponding depth completion network, we were able to acquire improved depth completion performance with faster computation, compared to previous approaches.
Effects of Online Learning on Nursing Students in South Korea during COVID-19

Daily life has changed due to COVID-19. This has affected nursing education and caused a shift in online learning. This study examined the effects of online learning on nursing students’ knowledge, self-regulation, and learning flow. We used a quasi-experimental design on a sample comprising 164 senior nursing students. We compared pre- and post-test scores to examine the educational effects. The pre-test was conducted a week before the educational intervention, and the post-test was conducted a week after it. We found a significant increase in knowledge (t = −14.85, p &lt; 0.001) and learning flow (t = −2.15, p = 0.033) in the post-test. We also found an increase in self-regulation (t = −1.57, p = 0.119) from the pre- to the post-test that was not statistically significant. The results could help instructors to provide additional information in online learning. They highlight the need to assess learners’ readiness for online learning and to prepare the learning environment with systematic educational planning, design, development, and evaluation for improving the effectiveness of online learning outcomes.
Chatbot service usage during a pandemic: fear and social distancing

Health organizations have relied heavily on social distancing to limit the spread of the COVID-19 pandemic. The purpose of this research is to examine what factors can influence customers’ evaluations of social distancing as well as how and when these evaluations drive their usage of chatbot services. Using structural equation modeling to analyze the experimental data from 200 U.S. consumers, we found that when the service situation is utilitarian (hedonic) in nature, customers’ contamination fear influences their chatbot usage during service encounters through their social distancing attitudes (subjective norms) and then perceived usefulness of chatbots. Our findings provide meaningful theoretical contributions and practical implications.
PolyGraph: Exposing the Value of Flexibility for Graph Processing Accelerators

Because of the importance of graph workloads and the limitations of CPUs/GPUs, many graph processing accelerators have been proposed. The basic approach of prior accelerators is to focus on a single graph algorithm variant (eg. bulk-synchronous + slicing). While helpful for specialization, this leaves performance potential from flexibility on the table and also complicates understanding the relationship between graph types, workloads, algorithms, and specialization.In this work, we explore the value of flexibility in graph processing accelerators. First, we identify a taxonomy of key algorithm variants. Then we develop a template architecture (PolyGraph) that is flexible across these variants while being able to modularly integrate specialization features for each.Overall we find that flexibility in graph acceleration is critical. If only one variant can be supported, asynchronous-updates/priority-vertex-scheduling/graph-slicing is the best design, achieving 1.93× speedup over the best-performing accelerator, GraphPulse. However, static flexibility per-workload can further improve performance by 2.71×. With dynamic flexibility per-phase, performance further improves by up to 50%.
Beyond the Block: A Novel Blockchain-Based Technical Model for Long-Term Care Insurance

The insurance business is characterized by complicated transactional interrelationships among various stakeholders involved in insurance-related activities. Given this unique nature, the century-old challenge in the insurance industry is to effectively reduce transaction costs among the stakeholders while maintaining business privacy and trust. Although blockchain is a promising technology to mitigate this challenge, two technical issues, namely (1) inefficiency in data auditing and (2) difficulty in verifying encrypted data, are of strategic importance when applying blockchain to the insurance industry. To address these technical challenges, we propose an innovative blockchain-based technical model, InsurModel, in the context of newly initiated long-term care insurance in China. Specifically, we utilize cryptographical methods including “zero-knowledge-proof” to 1) represent business interdependence and 2) verify confidential business information without disclosure of specifics. We demonstrate the scalability and applicability of InsurModel and explore its strategic implications in constraining adverse behaviors of the stakeholders.
Future tourism in a robot-based economy: a perspective article

Purpose Future tourism will take place in a robonomic economy (aka robonomics). The massive introduction of robots, artificial intelligence and automation technologies which will lead to the advent of an economy that will be qualitatively different from the current economy. The robonomic economy will have profound implications on the nature of work, level and sources of incomes, leisure time, politics, international trade and relations, ownership rights, etc., hence leading to major social, economic and political challenges and tension. The purpose of this paper is to elaborate on how tourism will be in a robonomic society. Design/methodology/approach This is a perspective paper that shows how tourism will be in a robonomic society. This is a conceptual perspective article that shows how tourism will be in a robonomic society. Findings This paper elaborates on the tourism/hospitality implications of robonomics, the positive and negative impacts of robonomics on tourism and vice versa. Originality/value This is one of the first papers to discuss tourism implications of a future automated society.
Grayscale Image Colorization Methods: Overview and Evaluation

Colorization is a process of converting grayscale images into visually acceptable color images. The main goal is to convince the viewer of the authenticity of the result. Grayscale images that need to be colorized are, in most cases, images with natural scenes. Over the last 20 years a wide range of colorization methods has been developed – from algorithmically simple, yet time- and energy-consuming because of unavoidable human intervention to more complicated, but simultaneously more automated methods. Automatic conversion has become a challenging area that combines machine learning and deep learning with art. This paper presents an overview and evaluation of grayscale image colorization methods and techniques applied to natural images. The paper provides a classification of existing colorization methods, explains the principles on which they are based, and highlights their advantages and disadvantages. Special attention is paid to deep learning methods. Relevant methods are compared in terms of image quality and processing time. Different metrics for color image quality assessment are used. Measuring the perceived quality of a color image is challenging due to the complexity of the human visual system. Multiple metrics used to evaluate colorization methods provide results by determining the difference between the predicted color value and the ground truth, which in several cases is not in coherence with image plausibility. The results show that user-guided neural networks are the most promising category for colorization because they successfully combine human intervention and neural network automation.
Target Adaptive Context Aggregation for Video Scene Graph Generation

This paper deals with a challenging task of video scene graph generation (VidSGG), which could serve as a structured video representation for high-level understanding tasks. We present a new detect-to-track paradigm for this task by decoupling the context modeling for relation prediction from the complicated low-level entity tracking. Specifically, we design an efficient method for frame-level VidSGG, termed as Target Adaptive Context Aggregation Network (TRACE), with a focus on capturing spatio-temporal context information for relation recognition. Our TRACE framework streamlines the VidSGG pipeline with a modular design, and presents two unique blocks of Hierarchical Relation Tree (HRTree) construction and Target-adaptive Context Aggregation. More specific, our HRTree first provides an adpative structure for organizing possible relation candidates efficiently, and guides context aggregation module to effectively capture spatio-temporal structure information. Then, we obtain a contextualized feature representation for each relation candidate and build a classification head to recognize its relation category. Finally, we provide a simple temporal association strategy to track TRACE detected results to yield the video-level VidSGG. We perform experiments on two VidSGG benchmarks: ImageNet-VidVRD and Action Genome, and the results demonstrate that our TRACE achieves the state-of-the-art performance. The code and models are made available at https://github.com/MCG-NJU/TRACE.