Reviewerly/exHarmony huggingface dataset:
Analysis of MOOC Quality Requirements for Landscape Architecture Based on the KANO Model in the Context of the COVID-19 Epidemic

COVID-19 has had a severe impact on higher education worldwide, and Massive Open Online Courses (MOOCs) have become the best solution to reduce the impact of the COVID-19 on student learning. In order to improve the quality of MOOCs for Landscape Architecture, it is essential to fully understand the psychological needs of students learning online. A total of 119 undergraduates and postgraduates majoring in landscape architecture were selected as the research subjects, and 18 indicators falling into 5 functions, including course organization, course resources, learning environment, learning experience, and learning support were screened. Questionnaires based on the KANO model were prepared at wjx.cn for investigation through WeChat. Attributes were classified according to the traditional KANO model and the KANO model based on Better-Worse coefficients. The research showed that based on the classification results of the traditional KANO model, 17 of the 18 indicators were of the attractive quality factor and the rest were of the must-be quality factor. After reclassification using the KANO model based on Better-Worse coefficients, 4 of the 18 indicators were must-be quality factors, 6 were one-dimensional quality factors, 4 were attractive quality factors, and the rest 4 were indifferent quality factors. Compared to the traditional KANO model, the KANO model based on Better-Worse coefficients has better quality element classification discrimination. According to the KANO-based analysis, appropriate strategies for indicators shall be adopted for MOOC development according to the four types of quality requirements. The research can provide a basis for the development and optimization of MOOCs for landscape architecture so as to better meet the learning needs of students and achieve better learning effects.
The Relationship between the Use of Non-Verbal Information in Communication and Student Connectedness and Engagement in Online Design Education

The COVID-19 pandemic poses a threat to the sustainability of higher education. Connectedness and engagement, two characteristics crucial to design education, have weakened significantly in online courses. However, limited research has been conducted on online design education than on STEM fields. Based on the dual coding theory, the purpose of this study was to use non-verbal tools to enhance design student connectedness and engagement in online class communication. In a quasi-experiment, 122 design students from a Chinese university were questioned and analyzed. They were randomly assigned to four different teaching situations and the effectiveness of two non-verbal tools was tested: emoticons and shared whiteboards. The ANOVA revealed a positive correlation between the use of non-verbal information in online class communication and the connectedness and engagement of design students. Moreover, the students in the group who used plentiful personified-form emoticons gave feedback and reported a stronger sense of connectedness and engagement. The whiteboard group’s data did not significantly differ from the control group, unlike the STEM discipline. To better develop the sustainability of design education, we provide recommendations for the design of online-education software and the method of online design instruction.
Blockchain empowers supply chains: challenges, opportunities and prospects

Purpose The advantages of blockchain technology are being widely discussed by academics, the business community and government, because blockchain can promote data sharing, optimise business processes, reduce operation costs, improve collaborative efficiency and build credible systems. The supply chain is becoming a key area for the application of blockchain technology. However, few studies have discussed the effect of such emerging technologies on the supply chain in depth. Therefore, this paper aims to analyse how blockchain empowers supply chain and promotes supply chain management. Design/methodology/approach Based on a review of relevant literature and blockchain applications in practice, this paper analyses the development and research status of blockchain technologies. In addition, considering the different operational processes within the supply chain, the authors discuss the opportunities and challenges of blockchain technologies, such as the transparency of supply, intelligent manufacturing, the security of logistics, the platformisation of sales and the ecology of governance. Findings The authors find that information sharing, information traceability and trust establishment are the key categories of research achievements and applications of supply chain management. The central issues for blockchain researchers are the authenticity of transaction data, the traceability of long supply chains and the establishment of trust for all participants. Originality/value From the practical and theoretical perspectives, this paper shows the development of blockchain technologies to clarify the challenges, opportunities and prospects. This paper elucidates and facilitates the development of emerging interdisciplinary research and the practice of supply chain.
Simulated automated facial recognition systems as decision-aids in forensic face matching tasks.

Automated Facial Recognition Systems (AFRS) are used by governments, law enforcement agencies, and private businesses to verify the identity of individuals. Although previous research has compared the performance of AFRS and humans on tasks of one-to-one face matching, little is known about how effectively human operators can use these AFRS as decision-aids. Our aim was to investigate how the prior decision from an AFRS affects human performance on a face matching task, and to establish whether human oversight of AFRS decisions can lead to collaborative performance gains for the human-algorithm team. The identification decisions from our simulated AFRS were informed by the performance of a real, state-of-the-art, Deep Convolutional Neural Network (DCNN) AFRS on the same task. Across five pre-registered experiments, human operators used the decisions from highly accurate AFRS (> 90%) to improve their own face matching performance compared with baseline (sensitivity gain: Cohen's d = 0.71-1.28; overall accuracy gain: d = 0.73-1.46). Yet, despite this improvement, AFRS-aided human performance consistently failed to reach the level that the AFRS achieved alone. Even when the AFRS erred only on the face pairs with the highest human accuracy (> 89%), participants often failed to correct the system's errors, while also overruling many correct decisions, raising questions about the conditions under which human oversight might enhance AFRS operation. Overall, these data demonstrate that the human operator is a limiting factor in this simple model of human-AFRS teaming. These findings have implications for the "human-in-the-loop" approach to AFRS oversight in forensic face matching scenarios. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Transfer Learning Across Arabic Dialects for Offensive Language Detection

The Arabic language is spoken by a wide range of countries across Asia, however, it is a low-resource language that has a minimal number of linguistic resources. Moreover, the large spread of Arabic speakers spans several countries and cultures, which creates a complex variation in its dialectal form. This variation makes it very challenging to analyze online Arabic content, particularly for offensive language detection. We propose a transfer learning approach for dialectal Arabic offensives language detection based on the BERT model. The results demonstrate the effectiveness of the proposed system in improving the performance of some Arabic dialects, such as the Tunisian and the Egyptian.
Cultural tourist and user experience with artificial intelligence: a holistic perspective from the Industry 5.0 approach

Purpose The purpose of this paper is to analyse the future of the implementation of artificial intelligence (AI) technologies in services experience provided by cultural institutions (e.g. museums, exhibition halls and cultural centres) from experts’, cultural tourists’ and users’ point of view under the Industry 5.0 approach. Design/methodology/approach The research was conducted using a qualitative approach, which was based on the analysis of the contents obtained from two roundtable discussions with experts and cultural tourists and users. A thematic analysis using NVivo was done to the data obtained. Findings From a futuristic Industry 5.0 approach, AI is considered to be more than a tool – it as an integral part of the entire experience. AI aids in connecting cultural institutions with users and is beneficial since it allows the institutions to get to know the users better and provide a more integrated and immersive experience. Furthermore, AI is critical in establishing a community and nurturing it daily. Originality/value The most important contribution of this research is the theoretical model focused on the user experience and AI application in services experiences of museums and cultural institutions from an Industry 5.0 approach. This model includes the visitors’ and managers’ points of view through the following dimensions: the pre-experience, experience and post-experience. This model is focused on human–AI coworking (HAIC) in museums and cultural institutions.
Dark path holonomic qudit computation

Nonadiabatic holonomic quantum computation is a method used to implement high-speed quantum gates with non-Abelian geometric phases associated with paths in state space. Due to their noise tolerance, these phases can be used to construct error resilient quantum gates. We extend the holonomic dark path qubit scheme in [M.-Z. Ai et al., Fundam. Res. 2, 661 (2022)] to qudits. Specifically, we demonstrate one- and two-qudit universality by using the dark path technique. Explicit qutrit ($d=3$) gates are demonstrated and the scaling of the number of loops with the dimension $d$ is addressed. This scaling is linear and we show how any diagonal qudit gate can be implemented efficiently in any dimension.
CARNet: Context-Aware Residual Learning for JPEG-LS Compressed Remote Sensing Image Restoration

JPEG-LS (a lossless (LS) compression standard developed by the Joint Photographic Expert Group) compressed image restoration is a significant problem in remote sensing applications. It faces the following two challenges: first, bridging small pixel-value gaps from wide numerical ranges; and second, removing banding artifacts in the condition of lacking available context information. As far as we know, there is currently no research dealing with the above issues. Hence, we develop this initial line of work on JPEG-LS compressed remote sensing image restoration. We propose a novel CNN model called CARNet. Its core idea is a context-aware residual learning mechanism. Specifically, it realizes residual learning for accurate restoration by adopting a scale-invariant baseline. It enables large receptive fields for banding artifact removal through a context-aware scheme. Additionally, it eases the information flow among stages by utilizing a prior-guided feature-fusion mechanism. Alternatively, we design novel R IQA models to provide a better restoration performance assessment for our study by utilizing gradient priors of JPEG-LS banding artifacts. Furthermore, we prepare a new dataset of JPEG-LS compressed remote sensing images to supplement existing benchmark data. Experiments show that our method sets the state-of-the-art for JPEG-LS compressed remote sensing image restoration.
Smart Chatbot for User Authentication

Despite being the most widely used authentication mechanism, password-based authentication is not very secure, being easily guessed or brute-forced. To address this, many systems which especially value security adopt Multi-Factor Authentication (MFA), in which multiple different authentication mechanisms are used concurrently. JitHDA (Just-in-time human dynamics based authentication engine) is a new authentication mechanism which can add another option to MFA capabilities. JitHDA observes human behaviour and human dynamics to gather up to date information on the user from which authentication questions can be dynamically generated. This paper proposes a system that implements JitHDA, which we call Autonomous Inquiry-based Authentication Chatbot (AIAC). AIAC uses anomalous events gathered from a user’s recent activity to create personalized questions for the user to answer, and is designed to improve its own capabilities over time using neural networks trained on data gathered during authentication sessions. Due to using the user’s recent activity, they will be easy for the authentic user to answer and hard for a fraudulent user to guess, and as the user’s recent history updates between authentication sessions new questions will be dynamically generated to replace old ones. We intend to show in this paper that AIAC is a viable implementation of JitHDA.
3D Segmentation with Fully Trainable Gabor Kernels and Pearson’s Correlation Coefficient

The convolutional layer and loss function are two fundamental components in deep learning. Because of the success of conventional deep learning kernels, the less versatile Gabor kernels become less popular despite the fact that they can provide abundant features at different frequencies, orientations, and scales with much fewer parameters. For existing loss functions for multi-class image segmentation, there is usually a tradeoff among accuracy, robustness to hyperparameters, and manual weight selections for combining different losses. Therefore, to gain the benefits of using Gabor kernels while keeping the advantage of automatic feature generation in deep learning, we propose a fully trainable Gabor-based convolutional layer where all Gabor parameters are trainable through backpropagation. Furthermore, we propose a loss function based on the Pearson’s correlation coefficient, which is accurate, robust to learning rates, and does not require manual weight selections. Experiments on 43 3D brain magnetic resonance images with 19 anatomical structures show that, using the proposed loss function with a proper combination of conventional and Gabor-based kernels, we can train a network with only 1.6 million parameters to achieve an average Dice coefficient of 83%. This size is 44 times smaller than the original V-Net which has 71 million parameters. This paper demonstrates the potentials of using learnable parametric kernels in deep learning for 3D segmentation.
Coal Mine Belt Conveyor Foreign Objects Recognition Method of Improved YOLOv5 Algorithm with Defogging and Deblurring

The belt conveyor is the main equipment for underground coal transportation. Its coal flow is mixed with large coal, gangue, anchor rods, wooden strips, and other foreign objects, which easily causes failure of the conveyor belt, such as scratching, tearing, and even broken belts. Aiming at the problem that it was difficult to accurately identify the foreign objects of underground belt conveyors due to the influence of fog, high-speed operation, and obscuration, the coal mine belt conveyor foreign object recognition method of improved YOLOv5 algorithm with defogging and deblurring was proposed. In order to improve the clarity of the monitoring video of the belt conveyor, the dark channel priori defogging algorithm is applied to reduce the impact of fog on the clarity of the monitoring video, and the image is sharpened by user-defined convolution method to reduce the blurring effect on the image in high-speed operation condition. In order to improve the precision of foreign object identification, the convolution block attention module is used to improve the feature expression ability of the foreign object in the complex background. Through adaptive spatial feature fusion, the multi-layer feature information of the foreign object image is more fully fused so as to achieve the goal of accurate recognition of foreign objects. In order to verify the recognition effect of the improved YOLOv5 algorithm, a comparative test is conducted with self-built data set and a public data set. The results show that the performance of the improved YOLOv5 algorithm is better than SSD, YOLOv3, and YOLOv5. The belt conveyor monitoring video of resolution for 1920 × 1080 in Huangling Coal Mine is used for identification verification, the recognition accuracy can reach 95.09%, and the recognition frame rate is 56.50 FPS. The improved YOLOv5 algorithm can provide a reference for the accurate recognition of targets in a complex underground environment.
Wearable Inertial Sensor-Based Hand-Guiding Gestures Recognition Method Robust to Significant Changes in the Body-Alignment of Subject

The accuracy of the wearable inertia-measurement-unit (IMU)-sensor-based gesture recognition may be significantly affected by undesired changes in the body-fixed frame and the sensor-fixed frame according to the change in the subject and the sensor attachment. In this study, we proposed a novel wearable IMU-sensor-based hand-guiding gesture recognition method robust to significant changes in the subject’s body alignment based on the floating body-fixed frame method and the bi-directional long short-term memory (bi-LSTM). Through comparative experimental studies with the other two methods, it was confirmed that aligning the sensor-fixed frame with the reference frame of the human body and updating the reference frame according to the change in the subject’s body-heading direction helped improve the generalization performance of the gesture recognition model. As a result, the proposed floating body-fixed frame method showed a 91.7% test accuracy, confirming that it was appropriate for gesture recognition under significant changes in the subject’s body alignment during gestures.
Employee responses to information security related stress: Coping and violation intention

Abstract Studies on employee responses to the information security policy (ISP) demands to show that employees who experience stress over the demands would resort to emotion‐focused coping to alleviate the stress and subsequently violate the ISP. However, their intent to engage in problem‐focused coping to meet the ISP demands and possibly reduce ISP violations has yet to be analysed. We argue that both types of coping responses coexist in employee responses to ISP demands and they together influence ISP violation intention. Drawing upon the Transactional Model of Stress and Coping, we examine how security‐related stress (SRS) triggers inward and outward emotion‐focused coping, and problem‐focused coping to the ISP demands, which together influence employee ISP violations. We also examine how ISP‐related self‐efficacy and organisational support moderate the effects of SRS on coping responses. We surveyed 200 employees in the United States to test our model. The results indicate that SRS triggers all three coping responses, and ISP‐related self‐efficacy and organisational support reduce the effects of SRS on inward and outward emotion‐focused coping. Problem‐focused coping then decreases ISP violation intention, whereas inward and outward emotion‐focused coping increases it. The model was further verified with ISP compliance as the outcome construct, which yielded consistent results. Understanding various coping responses to SRS and the factors that facilitate or inhibit the responses can assist managers in effectively designing and implementing the ISP to reduce employee ISP violations.
The NFT Purchasing Process and the Challenges to Trust at Each Stage

The unique features of Non-Fungible Tokens (NFT) are becoming increasingly appealing as we spend more of our time online. This increased popularity is nevertheless not free of controversies, and there is a lack of clarity over the final form this digital asset will take. While there are some early adopters, the whole NFT ecosystem will have to be clarified for wider adoption, particularly the purchasing process. This research evaluates a model of the purchasing process of NFTs and the role of trust in this process. The validated model identified that the purchasing process of NFTs has four stages and each stage is affected by trust: (1) Trust in the cryptocurrency wallet, (2) trust in the cryptocurrency purchase, (3) trust in the NFT marketplace, and (4) trust in aftersales services.
Antibunched two-mode two-photon bundles via atomic coherence

In this paper we propose an efficient scheme to continuously generate two-mode two-photon bundles by using a three-level atomic system, in which two correlated channels are created for the bundle emission in different modes. It is shown that the antibunching effect not only happens between the same modes but also between two different modes. While the former is due to the dominance of cavity decay over atomic decay, the latter is based on the atomic coherence between the two final states of bundle emission. The antibunched two-mode two-photon bundles are achievable in a wide range of parameters and robust against environmental noise. This scheme provides a kind of photon sources for two-signal-based applications such as quantum information processing, high-precision metrology, and ultrasensitive biosensing.
Toward a Real-Time Index of Pupillary Activity as an Indicator of Cognitive Load

The Low/High Index of Pupillary Activity (LHIPA), an eye-tracked measure of pupil diameter oscillation, is redesigned and implemented to function in real-time. The novel Real-time IPA (RIPA) is shown to discriminate cognitive load in re-streamed data from earlier experiments. Rationale for the RIPA is tied to the functioning of the human autonomic nervous system yielding a hybrid measure based on the ratio of Low/High frequencies of pupil oscillation. The paper's contribution is drawn from provision of documentation of the calculation of the RIPA. As with the LHIPA, it is possible for researchers to apply this metric to their own experiments where a measure of cognitive load is of interest.
Towards Sparsification of Graph Neural Networks

As real-world graphs expand in size, larger GNN models with billions of parameters are deployed. High parameter count in such models makes training and inference on graphs expensive and challenging. To reduce the computational and memory costs of GNNs, optimization methods such as pruning the redundant nodes and edges in input graphs have been commonly adopted. However, model compression, which directly targets the sparsification of model layers, has been mostly limited to traditional Deep Neural Networks (DNNs) used for tasks such as image classification and object detection. In this paper, we utilize two state-of-the-art model compression methods (1) train and prune and (2) sparse training for the sparsification of weight layers in GNNs. We evaluate and compare the efficiency of both methods in terms of accuracy, training sparsity, and training FLOPs on real-world graphs. Our experimental results show that on the ia-email, wiki-talk, and stackoverflow datasets for link prediction, sparse training with much lower training FLOPs achieves a comparable accuracy with the train and prune method. On the brain dataset for node classification, sparse training uses a lower number FLOPs Oess than 1/7 FLOPs of train and prune method) and preserves a much better accuracy performance under extreme model sparsity. Our model sparsification code is publicly available on GitHubl <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .