Reviewerly/exHarmony huggingface dataset:
Smart contract design and process optimization of carbon trading based on blockchain: The case of China's electric power sector

Carbon trading is an important market instrument and policy tool to promote China's practice of low-carbon energy conservation and strive to achieve carbon peaking by 2030 and carbon neutrality by 2060. To improve market flow and respond to the national strategy to support the development of carbon trading in China, a smart carbon trading process optimization framework based on blockchain technology is developed in this article. First, the paper analyzes the current state of carbon trading in China's power sector and identifies the future development trend and barriers to carbon trading in China. Second, a framework for a smart carbon trading system based on the structure and traits of blockchain technology is created to optimize the carbon trading process. Third, the smart contract for the smart carbon trading system is built (including execution logic, transaction matching, contract fulfillment, and other elements). This mechanism measures the reputation of trading users. Finally, policy suggestions are provided to solve the current issues and challenges. According to the research conducted for this paper on “blockchain + carbon trading in China,” carbon trading supported by blockchain technology can guarantee transaction security and efficiency, accurately preserve transaction records, and offer a high level of automated settlement.
Review on security of federated learning and its application in healthcare

Artificial intelligence (AI) has led to a high rate of development in healthcare, and good progress has been made on many complex medical problems. However, there is a lack of patient electronic medical records standards and legal and ethical requirements for patient information privacy. The fragmentation of medical data has hindered the widespread use of AI in healthcare. Federated learning has emerged to address the problem of data fragmentation, and in combination with privacy-preserving algorithms can go a long way to address privacy concerns. Federated learning can also be combined with blockchain, edge computing, and other technologies to improve security and computational efficiency. The paper combs through recent papers on federated learning and medical industry applications, outline several architectures and classification models of federated learning, researches the application of federated learning in healthcare, and analyses the advantages of federated learning in medical applications. The paper also analyzes the security of federated learning and medical applications and analyses the various risks and attacks faced by the applications. Several standard privacy protection methods are introduced, and the current stage of the application of federated learning and healthcare is discussed. Finally, a summary and outlook on the application and security of federated learning in healthcare are presented.
A Fast and Accurate Real-Time Vehicle Detection Method Using Deep Learning for Unconstrained Environments

Deep learning-based classification and detection algorithms have emerged as a powerful tool for vehicle detection in intelligent transportation systems. The limitations of the number of high-quality labeled training samples makes the single vehicle detection methods incapable of accomplishing acceptable accuracy in road vehicle detection. This paper presents detection and classification of vehicles on publicly available datasets by utilizing the YOLO-v5 architecture. This paper’s findings utilize the concept of transfer learning through fine tuning the weights of the pre-trained YOLO-v5 architecture. To employ the concept of transfer learning, extensive data sets of images and videos of the congested traffic patterns were collected by the authors. These datasets were made more comprehensive by pointing various attributes, for instance high- and low-density traffic patterns, occlusions, and different weather circumstances. All of these gathered datasets were manually annotated. Ultimately, the improved YOLO-v5 structure becomes accustomed to any difficult traffic patterns. By fine-tuning the pre-trained network through our datasets, our proposed YOLO-v5 has exceeded several other traditional vehicle detection methods in terms of detection accuracy and execution time. Detailed simulations performed on the PKU, COCO, and DAWN datasets demonstrate the effectiveness of the proposed method in various challenging situations.
A Comprehensive Survey on Federated Learning Techniques for Healthcare Informatics

Healthcare is predominantly regarded as a crucial consideration in promoting the general physical and mental health and well-being of people around the world. The amount of data generated by healthcare systems is enormous, making it challenging to manage. Many machine learning (ML) approaches were implemented to develop dependable and robust solutions to handle the data. ML cannot fully utilize data due to privacy concerns. This primarily happens in the case of medical data. Due to a lack of precise clinical data, the application of ML for the same is challenging and may not yield desired results. Federated learning (FL), which is a recent development in ML where the computation is offloaded to the source of data, appears to be a promising solution to this problem. In this study, we present a detailed survey of applications of FL for healthcare informatics. We initiate a discussion on the need for FL in the healthcare domain, followed by a review of recent review papers. We focus on the fundamentals of FL and the major motivations behind FL for healthcare applications. We then present the applications of FL along with recent state of the art in several verticals of healthcare. Then, lessons learned, open issues, and challenges that are yet to be solved are also highlighted. This is followed by future directions that give directions to the prospective researchers willing to do their research in this domain.
Hierarchical Hashing Learning for Image Set Classification

With the development of video network, image set classification (ISC) has received a lot of attention and can be used for various practical applications, such as video based recognition, action recognition, and so on. Although the existing ISC methods have obtained promising performance, they often have extreme high complexity. Due to the superiority in storage space and complexity cost, learning to hash becomes a powerful solution scheme. However, existing hashing methods often ignore complex structural information and hierarchical semantics of the original features. They usually adopt a single-layer hashing strategy to transform high-dimensional data into short-length binary codes in one step. This sudden drop of dimension could result in the loss of advantageous discriminative information. In addition, they do not take full advantage of intrinsic semantic knowledge from whole gallery sets. To tackle these problems, in this paper, we propose a novel Hierarchical Hashing Learning (HHL) for ISC. Specifically, a coarse-to-fine hierarchical hashing scheme is proposed that utilizes a two-layer hash function to gradually refine the beneficial discriminative information in a layer-wise fashion. Besides, to alleviate the effects of redundant and corrupted features, we impose the ℓ2,1 norm on the layer-wise hash function. Moreover, we adopt a bidirectional semantic representation with the orthogonal constraint to keep intrinsic semantic information of all samples in whole image sets adequately. Comprehensive experiments demonstrate HHL acquires significant improvements in accuracy and running time. We will release the demo code on https://github.com/sunyuan-cs.
Psychometric properties of an instrument to assess the level of knowledge about artificial intelligence in university professors

Introduction: Knowledge about AI in university professors allows them to integrate these technological tools into their teaching strategies and improve the quality of learning. Objective: To determine the sustainable factorial structure of the relationship dimension of an instrument to evaluate the level of knowledge about artificial intelligence in university professors. Methods: A cross-sectional metric validation study was conducted. A sample of 83 university professors was selected. An instrument on artificial intelligence for university professors was applied, consisting of 15 questions divided into three sections. Psychometric analysis was carried out to evaluate its validity and reliability. Results: The results show that Part 1 has an alpha coefficient of 0.77, Part 2 has an alpha coefficient of 0.65, and Part 3 has an alpha coefficient of 0.83. The alpha coefficients for each subscale (0.77 for Part 1, 0.65 for Part 2, and 0.83 for Part 3) indicate that the instrument has good internal consistency and that the questions within each subscale are related to each other. The χ2/gl ratio of 2.1 indicates a good fit of the model, and the GFI, NFI, and CFI values are close to 1, indicating a good fit of the model. Conclusions: The results of the present study support the validity, reliability, and sustainable factorial structure of the instrument on artificial intelligence for university professors, making it an appropriate tool to evaluate the level of knowledge about AI in university professors.
Model Compression for Deep Neural Networks: A Survey

Currently, with the rapid development of deep learning, deep neural networks (DNNs) have been widely applied in various computer vision tasks. However, in the pursuit of performance, advanced DNN models have become more complex, which has led to a large memory footprint and high computation demands. As a result, the models are difficult to apply in real time. To address these issues, model compression has become a focus of research. Furthermore, model compression techniques play an important role in deploying models on edge devices. This study analyzed various model compression methods to assist researchers in reducing device storage space, speeding up model inference, reducing model complexity and training costs, and improving model deployment. Hence, this paper summarized the state-of-the-art techniques for model compression, including model pruning, parameter quantization, low-rank decomposition, knowledge distillation, and lightweight model design. In addition, this paper discusses research challenges and directions for future work.
Trajectory planning for a 6-axis robotic arm with particle swarm optimization algorithm

Robotic arms, which are favored for usage in both large- and small-scale industrial regions, run into issues with numerous limits between the starting and ending locations in the working space when attempting to complete a particular task. With the solution to one of these problems, trajectory planning, the robotic arm manipulator can move from the starting point to the target point vibration-free, without hitting obstacles, and by choosing the shortest way. In this study, a robotic arm with 6 degrees of freedom, which is in the Mechatronics Engineering laboratory of Isparta University of Applied Sciences and whose prototype was realized by Acrome company, was used. In the study, the trajectory planning of the robotic arm was carried out using the MATLAB program and particle swarm optimization (PSO). Trajectory planning is developed using the PSO algorithm to determine the position of the robot at each point as it moves from its starting point to its target. Thus, time optimization was achieved by choosing the shortest path between the two points. Trajectory planning in joint space is aimed to ensure that the position, speed, and acceleration between the starting and ending points are continuous by using the fifth-order polynomial. The instant values of the joint variables used to determine the points followed by the manipulator were obtained by forward kinematics through the MATLAB program. Using forward kinematics, the position information of the manipulator was obtained by providing a transition from joint space to Cartesian space.
Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks

Imbalanced Data (ID) is a problem that deters Machine Learning (ML) models from achieving satisfactory results. ID is the occurrence of a situation where the quantity of the samples belonging to one class outnumbers that of the other by a wide margin, making such models’ learning process biased towards the majority class. In recent years, to address this issue, several solutions have been put forward, which opt for either synthetically generating new data for the minority class or reducing the number of majority classes to balance the data. Hence, in this paper, we investigate the effectiveness of methods based on Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) mixed with a variety of well-known imbalanced data solutions meaning oversampling and undersampling. Then, we propose a CNN-based model in combination with SMOTE to effectively handle imbalanced data. To evaluate our methods, we have used KEEL, breast cancer, and Z-Alizadeh Sani datasets. In order to achieve reliable results, we conducted our experiments 100 times with randomly shuffled data distributions. The classification results demonstrate that the mixed Synthetic Minority Oversampling Technique (SMOTE)-Normalization-CNN outperforms different methodologies achieving 99.08% accuracy on the 24 imbalanced datasets. Therefore, the proposed mixed model can be applied to imbalanced binary classification problems on other real datasets.
I, chatbot! the impact of anthropomorphism and gaze direction on willingness to disclose personal information and behavioral intentions

Abstract The present research focuses on the interplay between two common features of the customer service chatbot experience: gaze direction and anthropomorphism. Although the dominant approach in marketing theory and practice is to make chatbots as human‐like as possible, the current study, built on the humanness‐value‐loyalty model, addresses the chain of effects through which chatbots' nonverbal behaviors affect customers' willingness to disclose personal information and purchase intentions. By means of two experiments that adopt a real chatbot in a simulated shopping environment (i.e., car rental and travel insurance), the present work allows us to understand how to reduce individuals' tendency to see conversational agents as less knowledgeable and empathetic compared with humans. The results show that warmth perceptions are affected by gaze direction, whereas competence perceptions are affected by anthropomorphism. Warmth and competence perceptions are found to be key drivers of consumers’ skepticism toward the chatbot, which, in turn, affects consumers’ trust toward the service provider hosting the chatbot, ultimately leading consumers to be more willing to disclose their personal information and to repatronize the e‐tailer in the future. Building on the Theory of Mind, our results show that perceiving competence from a chatbot makes individuals less skeptical as long as they feel they are good at detecting others’ ultimate intentions.
Cross-level Feature Aggregation Network for Polyp Segmentation

Accurate segmentation of polyps from colonoscopy images plays a critical role in the diagnosis and cure of colorectal cancer. Although effectiveness has been achieved in the field of polyp segmentation, there are still several challenges. Polyps often have a diversity of size and shape and have no sharp boundary between polyps and their surrounding. To address these challenges, we propose a novel Cross-level Feature Aggregation Network (CFA-Net) for polyp segmentation. Specifically, we first propose a boundary prediction network to generate boundary-aware features, which are incorporated into the segmentation network using a layer-wise strategy. In particular, we design a two-stream structure based segmentation network, to exploit hierarchical semantic information from cross-level features. Furthermore, a Cross-level Feature Fusion (CFF) module is proposed to integrate the adjacent features from different levels, which can characterize the cross-level and multi-scale information to handle scale variations of polyps. Further, a Boundary Aggregated Module (BAM) is proposed to incorporate boundary information into the segmentation network, which enhances these hierarchical features to generate finer segmentation maps. Quantitative and qualitative experiments on five public datasets demonstrate the effectiveness of our CFA-Net against other state-of-the-art polyp segmentation methods. The source code and segmentation maps will be released at https://github.com/taozh2017/CFANet.
CPS-GUARD: Intrusion detection for cyber-physical systems and IoT devices using outlier-aware deep autoencoders

Detecting attacks to Cyber-Physical Systems (CPSs) is of utmost importance, due to their increasingly frequent use in many critical assets. Intrusion detection in CPSs and other domains, such as the Internet of Things, is often addressed through machine and deep learning. However, many existing proposals tend to favor the application of complex detection models over the usability in real-world operations. This paper presents CPS-GUARD, a novel intrusion detection approach based on a single semi-supervised autoencoder and a technique to set the threshold used to discriminate normal operations from attacks. The technique is outlier-aware, in that it relies on outlier detection to mitigate inherent imperfections of the training data. CPS-GUARD is evaluated by means of direct experiments with normal and intrusion data points pertaining to individual sensing devices, an HTTP server and four full-fledged systems, including CPSs. Experiments are based on a wide spectrum of attacks available in six state-of-the-art datasets. The intrusion detection results of CPS-GUARD are within 0.949-1.000 recall, 0.961-0.999 precision and 0.006-0.027 false positive rate depending on the specific system. The results are competitive with other existing intrusion detection methods. The evaluation is complemented by a comparative study on alternative threshold selection and outlier detection techniques.
Current Perspective of Metaverse Application in Medical Education, Research and Patient Care

As virtual and augmented reality simulation technologies advance, the use of such technologies in medicine is widespread. The advanced virtual and augmented systems coupled with a complex interactive, immersive environment create a metaverse. The metaverse enables us to connect with others in a virtual world free of spatial restrictions and time constraints. In the educational aspect, it allows collaboration among peers and educators in an immersive 3D environment that can imitate the actual classroom setting with learning tools. Metaverse technology enables visualization of virtual 3D structures, facilitates collaboration and small group activities, improves mentor–mentee interactions, provides opportunities for self-directed learning experiences, and helps develop teamwork skills. The metaverse will be adapted rapidly in healthcare, boost digitalization, and grow in use in surgical procedures and medical education. The potential advantages of using the metaverse in diagnosing and treating patients are tremendous. This perspective paper provides the current state of technology in the medical field and proposes potential research directions to harness the benefits of the metaverse in medical education, research, and patient care. It aims to spark interest and discussion in the application of metaverse technology in healthcare and inspire further research in this area.
Programmable surface plasmonic neural networks for microwave detection and processing

A range of alternative approaches to traditional digital hardware have been explored for the implementation of artificial neural networks, including optical neural networks and diffractive deep neural networks. Spoof surface plasmon polariton waveguides, which operate at microwave and terahertz frequencies, can offer low crosstalk, low radiation loss and easy integration, and are of potential use in the development of an alternative technology for artificial neural networks. Here, we report a programmable surface plasmonic neural network that is based on a spoof surface plasmon polariton platform and can detect and process microwaves. The approach uses a parallel coupled spoof surface plasmon polariton cell integrated with varactors. The weight coefficients of the cell can be adjusted by tuning the voltages of the varactors, and the activation function of the neural network can be programmed by detecting the input intensity and feeding back the threshold to an amplifier. We show that a two-layer fully connected surface plasmonic neural network consisting of four input cells and four output cells can perform a vector classification task. The surface plasmonic neural network can also be used to create a wireless communication system to decode and recover images. In addition, we show that partially connected surface plasmonic neural networks can classify handwritten digits with high accuracy. A spoof surface plasmon polariton platform can be used to create a surface plasmonic neural network with programmable weights and activation functions.
Healthcare Internet of Things (H-IoT): Current Trends, Future Prospects, Applications, Challenges, and Security Issues

Advancements in Healthcare Internet of Things (H-IoT) systems have created new opportunities and solutions for healthcare services, including the remote treatment and monitoring of patients. In addition, the security and privacy of personal health data must be ensured during data transfer. Security breaches in H-IoT can have serious safety and legal implications. This comprehensive review provides insights about secured data accession by employing cryptographic platforms such as H-IoT in big data, H-IoT in blockchain, H-IoT in machine learning and deep learning, H-IoT in edge computing, and H-IoT in software-defined networks. With this information, this paper reveals solutions to mitigate threats caused by different kinds of attacks. The prevailing challenges in H-IoT systems, including security and scalability challenges, real-time operating challenges, resource constraints, latency, and power consumption challenges are also addressed. We also discuss in detail the current trends in H-IoT, such as remote patient monitoring and predictive analytics. Additionally, we have explored future prospects, such as leveraging health data for informed strategic planning. A critical analysis performed by highlighting the prevailing limitations in H-IoT systems is also presented. This paper will hopefully provide future researchers with in-depth insights into the selection of appropriate cryptographic measures to adopt an energy-efficient and resource-optimized healthcare system.