Reviewerly/exHarmony huggingface dataset:
Scalable Electronic Health Record Management System Using a Dual-Channel Blockchain Hyperledger Fabric

Communication and information technologies have accelerated the implementation of electronic medical records, but at the same time, have put patient privacy, information security and health data at risk. An alternative to address the problem of security and privacy of medical data is the use of blockchain. Scalability has become one of the biggest challenges facing the development of blockchain-based electronic health records (EHRs). The purpose of this article is to implement and test a scalable blockchain-based EHR management system. For this reason, we present a scalable blockchain-based EHR management architecture. In this paper, we propose an EHR management model based on entities and user roles, adapt, and then implement with Hyperledger Fabric in a two-channel configuration. We develop a prototype in Fabric using a one-and two-channel configuration. We then designed and conducted an experiment to verify the performance of the proposed scheme in terms of scalability improvement. This scalable blockchain-based EHR management solution, such as the Hyperledger Fabric platform, offers a viable alternative to address scalability issues, as well as to protect patient’s privacy and the security of their medical data.
A filter feature selection for high-dimensional data

In a classification problem, before building a prediction model, it is very important to identify informative features rather than using tens or thousands which may penalize some learning methods and increase the risk of over-fitting. To overcome these problems, the best solution is to use feature selection. In this article, we propose a new filter method for feature selection, by combining the Relief filter algorithm and the multi-criteria decision-making method called TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), we modeled the feature selection task as a multi-criteria decision problem. Exploiting the Relief methodology, a decision matrix is computed and delivered to Technique for Order Preference by Similarity to Ideal Solution in order to rank the features. The proposed method ends up giving a ranking to the features from the best to the mediocre. To evaluate the performances of the suggested approach, a simulation study including a set of experiments and case studies was conducted on three synthetic dataset scenarios. Finally, the obtained results approve the effectiveness of our proposed filter to detect the best informative features.
Customer acceptance of humanoid service robots in hotels: moderating effects of service voluntariness and culture

Purpose This study aims to propose a research model integrating technology acceptance model 3 (TAM3) constructs and human aspects of humanoid service robots (HSRs), measured by the Godspeed questionnaire series and tested across two hotel properties in Japan and the USA. Design/methodology/approach Potential participants were approached randomly by email invitation. A final sample size of 395 across two hotels, one in Japan and the other in the USA, was obtained, and the data were analysed using structural equation modelling. Findings The results confirm that perceived usefulness, driven by subjective norms and output quality, and perceived ease of use, driven by perceived enjoyment and absence of anxiety, are the immediate direct determinants of users’ re-patronage intentions for HSRs. Results also showed that users prefer anthropomorphism, perceived intelligence and the safety of an HSR for reusing it. Practical implications The findings have practical implications for the hospitality industry, suggesting multiple attributes of an HSRs that managers need to consider before deploying them in their properties. Originality/value The current study proposes an integrated model determining factors that affect the re-patronage of HSRs in hotels.
Improving blended learning in higher technical education institutions with mobile and cloud-based ICTs

This research aims to refine the model of blended learning in higher technical education institutions using cloud-based information and communication technologies (ICTs). The study objectives include determining the role of cloud technologies among contemporary ICTs, analyzing the utilization of cloud technologies in traditional, online, and blended learning, and augmenting the blended learning model with mobile and cloud technologies. The research focuses on the implementation of ICTs in the higher education system of Ukraine. The subject of investigation encompasses the use of cloud technologies and other ICT learning tools in traditional, online, and blended learning. The research methods employed involve analyzing educational programs, monographs, dissertations, articles, conference materials, and online resources. Additionally, targeted pedagogical observations, interviews with educators and students, and analysis of teachers' practical experiences are conducted. The findings emphasize that among various ICTs available for instructional purposes, cloud technologies stand out as facilitators of harmonious integration between the components of traditional education and technology-mediated learning. A promising approach to organizing the learning process, referred to as blended learning, involves the integration of traditional, distance, electronic, mobile, and cloud technologies. The study explores the model of blended learning in higher mathematics education for students in technical institutions, leveraging innovative ICTs. The primary conclusions and recommendations highlight the prevailing trend of transitioning towards blended learning in higher education and the need for further development of cloud technologies in instructional settings. Cloud technologies are particularly valuable for fostering student autonomy, enabling distance learning, and facilitating collaborative research, thereby supporting ongoing monitoring of learning outcomes and timely adjustments to optimize learning experiences.
Logistic Regression Matching Pursuit algorithm for text classification

Text classification is a challenging problem due to the high dimensionality of the text, which can limit classification performance. The orthogonal matching pursuit (OMP) algorithm is one of the most popular sparse recovery algorithms. An OMP based text classification algorithm, called the Logistic-OMP algorithm, was recently proposed by Skianis et al.. Simulation tests indicate that Logistic-OMP has excellent performance in text dimensionality reduction. This paper optimizes the Logistic-OMP algorithm, and proposes a new text classification algorithm called the Logistic Regression Matching Pursuit (LRMP) algorithm. The LRMP algorithm defines a new loss function and residual update function. It requires only one iteration to solve the negative log likelihood minimization problem, and its classification performance is guaranteed by the strong Wolfe condition, which makes it optimizes the classification accuracy while significantly speeding up the training speed. Simulation tests on topic classification and sentiment analysis from 20Newsgroups, Amazon product reviews, and movie reviews datasets show that the LRMP algorithm has a shorter computation time of a single iteration than the Logistic-OMP algorithm, with a total training time of 8.08%–21.16% shorter than that of the Logistic-OMP algorithm, and the memory usage is 3.20%–6.21% lower than that of the Logistic-OMP algorithm. Furthermore, the average Accuracy and F1-Score of the LRMP algorithm are improved by 1.61%–26.55% and 1.83%–25.46%, respectively, compared with the benchmark classifier. Compared with the advanced classifiers (including Logistic-OMP), the average Accuracy and F1-Score of the LRMP algorithm are improved by 0.46%–8.97% and 0.57%–9.40%, respectively.
Autonomous perception and adaptive standardization for few-shot learning

Identifying unseen classes with limited labeled data for reference is a challenging task, which is also known as few-shot learning. Generally, a knowledge-rich model is more robust than a knowledge-poor model when facing novel situations, and an intuitive way to enrich knowledge is to find additional training data, but this is not compatible with the principle of few-shot learning which aims to reduce reliance on big data. In contrast, improving the utilization of existing data is a more attractive option. In this paper, we propose a batch perception distillation approach, which improves the utilization of existing data by guiding individual classification with the intermixed information across a batch. In addition to data utilization, obtaining robust feature representation is also a concern. Specifically, the widely adopted metric-based few-shot classification approach classifies unseen testing classes by comparing the extracted features of different novel samples, which requires that the extracted features can accurately represent the class-related clues of the input images. In this paper, we propose a salience perception attention that enables the model to focus more easily on key clues in images, which helps to reduce the interference of irrelevant factors during classification. To overcome the distribution gap between the training classes and the unseen testing classes, we propose a weighted centering post-processing that standardizes the testing data according to the similarity between the training and testing classes. By combining the three proposed components, our method achieves superior performance on four widely used few-shot image classification datasets.
Herding AI Cats: Lessons from Designing a Chatbot by Prompting GPT-3

Prompting Large Language Models (LLMs) is an exciting new approach to designing chatbots. But can it improve LLM's user experience (UX) reliably enough to power chatbot products? Our attempt to design a robust chatbot by prompting GPT-3/4 alone suggests: not yet. Prompts made achieving "80%" UX goals easy, but not the remaining 20%. Fixing the few remaining interaction breakdowns resembled herding cats: We could not address one UX issue or test one design solution at a time; instead, we had to handle everything everywhere all at once. Moreover, because no prompt could make GPT reliably say "I don't know" when it should, the user-GPT conversations had no guardrails after a breakdown occurred, often leading to UX downward spirals. These risks incentivized us to design highly prescriptive prompts and scripted bots, counter to the promises of LLM-powered chatbots. This paper describes this case study, unpacks prompting's fickleness and its impact on UX design processes, and discusses implications for LLM-based design methods and tools.
Design Resources in Movement-based Design Methods: a Practice-based Characterization

Movement-based design methods are increasingly adopted to help design rich embodied experiences. While there are well-known methods in the field, there is no systematic overview to help designers choose among them, adapt them, or create their own. We collected 41 methods used by movement design researchers and employed a practice-based, bottom-up approach to analyze and characterize their properties. We found 17 categories and arranged them into five main groups: Design Resources, Activities, Delivery, Framing, and Context. In this paper, we describe these groups in general and then focus on Design Resources containing the categories of Movement, Space, and Objects. We ground the characterization with examples from empirical material provided by the design researchers and references to previous work. Additionally, we share recommendations and action points to bring these into practice. This work can help novice and seasoned design researchers who want to employ movement-based design methods in their practice.
Designing a Sustainable Material for 3D Printing with Spent Coffee Grounds

The widespread adoption of 3D printers exacerbates existing environmental challenges as these machines increase energy consumption, waste output, and the use of plastics. Material choice for 3D printing is tightly connected to these challenges, and as such researchers and designers are exploring sustainable alternatives. Building on these efforts, this work explores using spent coffee grounds as a sustainable material for prototyping with 3D printing. This material, in addition to being compostable and recyclable, can be easily made and printed at home. We describe the material in detail, including the process of making it from readily available ingredients, its material characteristics and its printing parameters. We then explore how it can support sustainable prototyping practices as well as HCI applications. In reflecting on our design process, we discuss challenges and opportunities for the HCI community to support sustainable prototyping and personal fabrication. We conclude with a set of design considerations for others to weigh when exploring sustainable materials for 3D printing and prototyping.
Quantum simulation of exact electron dynamics can be more efficient than classical mean-field methods

Abstract Quantum algorithms for simulating electronic ground states are slower than popular classical mean-field algorithms such as Hartree–Fock and density functional theory but offer higher accuracy. Accordingly, quantum computers have been predominantly regarded as competitors to only the most accurate and costly classical methods for treating electron correlation. However, here we tighten bounds showing that certain first-quantized quantum algorithms enable exact time evolution of electronic systems with exponentially less space and polynomially fewer operations in basis set size than conventional real-time time-dependent Hartree–Fock and density functional theory. Although the need to sample observables in the quantum algorithm reduces the speedup, we show that one can estimate all elements of the k -particle reduced density matrix with a number of samples scaling only polylogarithmically in basis set size. We also introduce a more efficient quantum algorithm for first-quantized mean-field state preparation that is likely cheaper than the cost of time evolution. We conclude that quantum speedup is most pronounced for finite-temperature simulations and suggest several practically important electron dynamics problems with potential quantum advantage.
Vision transformer architecture and applications in digital health: a tutorial and survey

The vision transformer (ViT) is a state-of-the-art architecture for image recognition tasks that plays an important role in digital health applications. Medical images account for 90% of the data in digital medicine applications. This article discusses the core foundations of the ViT architecture and its digital health applications. These applications include image segmentation, classification, detection, prediction, reconstruction, synthesis, and telehealth such as report generation and security. This article also presents a roadmap for implementing the ViT in digital health systems and discusses its limitations and challenges.
Neighbor group structure preserving based consensus graph learning for incomplete multi-view clustering

In the area of clustering, multi-view clustering has drawn a lot of research attention by making full use of information from different views. In many practical applications, collecting complete multi-view data without missing views is sometimes expensive and impossible. Therefore, the study in incomplete multi-view clustering has become a trend in the field of clustering analysis. Graph learning-based approach is one of the most effective tools. The essence of graph learning is how to construct the affinity graph or similarity matrix, whose elements depict the similarity of the corresponding sample pairs. In this paper, we propose a new method, called Neighbor Group Structure Preserving-based Consensus Graph Learning (NGSP_CGL), to learn a high-quality consensus graph for incomplete multi-view clustering. Different from the existing graph learning-based works which only focus on the relationship between isolated sample pairs, NGSP_CGL seeks to explore the neighbor group structure corresponding to the nearest neighbor sets of sample pairs and designs a novel but simple nearest neighbor group structure embedding constraint so as to enhance the quality of consensus graph. The experimental results on several datasets demonstrate the effectiveness of NGSP_CGL.
Immune deep reinforcement learning-based path planning for mobile robot in unknown environment

A new deep deterministic policy gradient (DDPG) integrating kinematics analysis and immune optimization (KAI-DDPG) is proposed to address the drawbacks of DDPG in path planning. An orientation angle reward component, linear velocity reward factor, and safety performance reward factor are added to the DDPG reward function based on kinematic modeling and analysis of mobile robots. A multi-objective performance index turns the path planning problem into one of multi-objective optimization. We propose KA-DDPG, which uses the orientation angle, linear speed, and safety degree as evaluation indices, and information entropy to alter the influence coefficient of the multi-objective function in the reward function. KAI-DDPG is proposed to address the low learning and training efficiency of KA-DDPG, using immune optimization to optimize the experience samples in the experience buffer pool. Performance indices of traditional path planning and the proposed techniques are compared on a gazebo simulation platform, and the results suggest that KAI-DDPG can mitigate the drawbacks of DDPG, such as a protracted training cycle and poor path planning technique, and can broaden the range of application.
Multi-scale feature fusion pedestrian detection algorithm based on Transformer

In this paper, a multi-scale feature fusion pedestrian detection network based on Transformer is designed for small-scale pedestrians and pedestrians disturbed by light and shadow. In the feature extraction stage, the network suppresses the interference of irrelevant features through gating mechanism and feature enhancement, enhances the discrimination of pedestrian features at different scales, dynamically controls the fusion weight of feature maps, and realizes the adaptive fusion of feature maps. In the detection stage, Transformer can capture global information and effectively solve the long-distance dependence mechanism between image pixels to improve the pedestrian detection effect. Finally, compared with the existing methods on the general pedestrian detection dataset, the average accuracy of the proposed method is 6.8 % higher than that of the YOLOv5 model, the false detection rate is reduced by 2.7 %, and the missed detection rate is reduced by 3.1 %. And through the subjective evaluation of the pedestrian detection heat map, this method can detect the human body more comprehensively in the pedestrian detection task, rather than focusing on one point alone. In summary, this method can effectively improve the detection accuracy, reduce the false detection rate and missed detection rate, and improve the pedestrian detection task.
CNN Learning Strategy for Recognizing Facial Expressions

The ability to recognize facial expressions using computer vision is a crucial task, with numerous potential applications.Although deep neural networks have achieved high performance, it is still difficult to use them in the recognition of facial expressions.This is because different facial expressions have varying degrees of similarities among themselves, and numerous variations cause diversity in the same facial images.In this study, we propose a novel divide-and-conquer-based learning strategy to improve the performance of facial expression recognition (FER).The face area in an image was detected using MobileNet, and a ResNet-18 model was used as a backbone deep neural network for recognizing facial expressions.Thereafter, groups that included similar facial expressions were categorized by analyzing the confusion matrix, which is the inference result of the trained ResNet-18 model, and these similar facial expression groups were utilized to re-train the deep learning model.In the experiments, the proposed method was evaluated using two thermal (Tufts and RWTH) and two RGB (RAF and FER2013) datasets, and higher FER performances, which were 97.75% for Tufts, 86.11% for RWTH, 90.81% for RAF, and 77.83% for FER2013, were achieved.As such, the proposed method can accurately classify large amounts of facial expression data.
Optimal feature selection and invasive weed tunicate swarm algorithm-based hierarchical attention network for text classification

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Dual-model: Revised imaging network and visual perception correction for underwater image enhancement

Considering the limitation of a single physics-based or deep learning-based method, this paper proposes an effective Dual-model methodology for underwater image enhancement. The Dual-model mainly consists of two branches: the Revised Imaging Network-model (RIN-model) and the Visual Perception Correction-model (VPC-model). Specifically, instead of directly learning an image-to-image mapping strategy, the RIN-model constructs a compact network to estimate the ambient-light and direct-transmission parameters, which are used to reconstruct the preliminary enhanced image based on the revised underwater image formation model. Based on the pre-trained RIN-model, the ambient-light and direct-transmission parameters estimated using only a single input image may be biased, especially for these severely degraded underwater images. To further correct the visual perception of the preliminary enhanced image, the VPC-model incorporates the adaptive-stretching histogram and relative-stretching histogram for contrast enhancement and color restoration, respectively. We also present an adaptive color balance compensation method to estimate the maximum attenuation channel and equalize the information distribution of the three channels in the preliminary enhanced image, which acts as a transition procedure to prevent color distortion or detail destruction caused by excessive stretching in the VPC-model. Comprehensive qualitative and quantitative evaluations have revealed that our Dual-model outperforms seven state-of-the-art underwater image enhancement methods, including the MLLE, Haze-Lines, HLRP, ACDC, UWCNN, FUnIE-GAN, and Water-Net. In the UIEB and EUVP datasets, our methodology achieves relatively better performance than other methods on both full-reference and no-reference evaluation metrics. We also validate that the proposed methodology facilitates the advanced image applications of local feature matching and edge detection.
Ethics of Quantum Computing: an Outline

Abstract This paper intends to contribute to the emerging literature on the ethical problems posed by quantum computing and quantum technologies in general. The key ethical questions are as follows: Does quantum computing pose new ethical problems, or are those raised by quantum computing just a different version of the same ethical problems raised by other technologies, such as nanotechnologies, nuclear plants, or cloud computing? In other words, what is new in quantum computing from an ethical point of view? The paper aims to answer these two questions by (a) developing an analysis of the existing literature on the ethical and social aspects of quantum computing and (b) identifying and analyzing the main ethical problems posed by quantum computing. The conclusion is that quantum computing poses completely new ethical issues that require new conceptual tools and methods.