Reviewerly/exHarmony huggingface dataset:
Designing Blockchain Based Consensus Mechanism for Smart Healthcare IoT

In today's world, healthcare system is improving day by day by using smart IoT based system. Blockchain based system is totally a decentralized system due to which attackers are unable to access the confidential records easily. Blockchain play an important role in controlling and management of sensitive information in healthcare system and insurance sector. Consensus mechanisms used in blockchain technology to validate whichever block is going to be added next in the blockchain network. In this paper we proposed a hybrid based system which solves trust issues arises in the blockchain. Hybrid consensus mechanism provides reputation according to the activities of blocks in a blockchain network. Four layer architecture is designed in Hybrid model. Five algorithms are used to create, validate, fork handling, reward and punishment scheme and Markle tree is constructed respectively. This model is implemented on Ethereum based platform which shows that HCM model is better than a PoXR model as it consume less memory (50 percent) and consume less CPU(below 2 percent).
Self-Attention Guidance and Multiscale Feature Fusion-Based UAV Image Object Detection

Object detection on UAV images is a recent research hotspot. Existing object detection methods have achieved good results on general scenes, but there are inherent challenges with UAV images. The detection accuracy of UAV images is limited by complex backgrounds, significant scale differences, and densely arranged small objects. To solve these problems, we propose a UAV image object detection network based on Self-attention Guidance and Multi-scale Feature fusion (SGMFNet). Firstly, we design a Global-Local Feature Guidance module (GLFG). This module can effectively combine local information and global information, which makes the model focus on the object area and reduces the impact of complex background. Secondly, an improved Parallel Sampling Feature Fusion module (PSFF) is designed to efficiently fuse multi-scale features. Thirdly, we design an Inverse-residual Feature Enhancement module (IFE), which is embedded in the front of the newly added detection head to enhance feature extraction on small objects. Finally, we conduct a large number of experiments on the VisDrone2019 dataset. The results show that the proposed SGMFNet outperforms other popular methods, and has achieved good results in many scenarios.
Semi-Supervised Principal Neighbourhood Aggregation Model For Sar Image Classification

Graph neural networks (GNNs) are researched extensively as effective models for representing data in a graph structure and extracting important neighbourhood features. This paper explored a GNN model named Principal Neighbourhood Aggregation (PNA) for Synthetic-aperture radar (SAR) image classification. Given the low availability of SAR image data, specially labelled ones, we incorporate a semi-supervised learning strategy named MixMatch with PNA architecture. Furthermore, we propose an augmentation technique specific to SAR images that exploit the physical properties of image content and significantly improve the performance of both supervised and semi-supervised training paradigm. Additionally, we tested the algorithm on benchmark image classification datasets, including CIFAR, MNIST, STL10 and achieved promising results.
Integration of Self-Adaptive Physical-Layer Key Distribution and Encryption in Optical Coherent Communication

We propose and experimentally demonstrate a compatible physical-layer secure optical communication (PLSOC) system that integrates self-adaptive physical-layer key distribution (PLKD) and encryption (PLE) in optical coherent communication.Based on bit error rate difference of QAM signals mapped by asymmetric basis state Y-00 protocol, the secret key can be secretly exchanged over public fiber links without the pre-shared keys.Moreover, we perform a parameter self-adaptive strategy for practical and dynamic PLKD.The security of the key is evaluated in the case of a fiber-tapping attack.A secure hash algorithm, SHA3-512, is used to perform privacy amplification to obtain the virtually secure key.An error-free PLKD rate reaches 39.3 Kbits/s over 300km ultra-low loss fiber.We experimentally enable the integration of the proposed PLKD scheme and quantum noise stream cipher (QNSC) with a single wavelength, same system.Q factor penalty of the integration system compared to the QNSC system is 3.7dB (optical back-to-back) and 4.8dB (300km) respectively.By exploiting a common hardware platform, with the same wavelength, the proposed PLSOC system addresses the problem that PLKD and PLE are separately performed through independent optical fiber links or wavelengths.Since only digital signal processing is used, the scheme does not require extra hardware.
Detection Threshold for Correlated Erdős-Rényi Graphs via Densest Subgraph

The problem of detecting edge correlation between two Erdős-Rényi random graphs on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> unlabeled nodes can be formulated as a hypothesis testing problem: under the null hypothesis, the two graphs are sampled independently; under the alternative, the two graphs are independently sub-sampled from a parent graph which is Erdős-Rényi <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {G}(n, p)$ </tex-math></inline-formula> (so that their marginal distributions are the same as the null). We establish a sharp information-theoretic threshold when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p = n^{-\alpha +o({1)}}$ </tex-math></inline-formula> for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha \in (0, 1]$ </tex-math></inline-formula> which sharpens a constant factor in a recent work by Wu, Xu and Yu. A key novelty in our work is an interesting connection between the detection problem and the densest subgraph of an Erdős-Rényi graph.
Dimensions of artificial intelligence techniques, blockchain, and cyber security in the Internet of medical things: Opportunities, challenges, and future directions

Abstract The Internet of medical things (IoMT) is a modern technology that is increasingly being used to provide good healthcare services. As IoMT devices are vulnerable to cyberattacks, healthcare centers and patients face privacy and security challenges. A safe IoMT environment has been used by combining blockchain (BC) technology with artificial intelligence (AI). However, the services of the systems are costly and suffer from security and privacy problems. This study aims to summarize previous research in the IoMT and discusses the roles of AI, BC, and cybersecurity in the IoMT, as well as the problems, opportunities, and directions of research in this field based on a comprehensive literature review. This review describes the integration schemes of AI, BC, and cybersecurity technologies, which can support the development of new systems based on a decentralized approach, especially in healthcare applications. This study also identifies the strengths and weaknesses of these technologies, as well as the datasets they use.
Temporal Pixel-Level Semantic Understanding Through the VSPW Dataset

Scene understanding through pixel-level semantic parsing is one of the main problems in computer vision. Till now, image-based methods and datasets for scene parsing have been well explored. However, the real world is naturally dynamic instead of a static state. Thus, learning to perform video scene parsing is more practical for real-world applications. Considering that few datasets cover an extensive range of scenes and object categories with temporal pixel-level annotations, in this work, we present a large-scale video scene parsing dataset, namely VSPW (Video Scene Parsing in the Wild). To be specific, there are a total of 251,633 frames from 3,536 videos with densely pixel-wise annotations in VSPW, including a large variety of 231 scenes and 124 object categories. Besides, VSPW is densely annotated with a high frame rate of 15 f/s, and over 96% of videos from VSPW have high spatial resolutions from 720P to 4 K. To the best of our knowledge, VSPW is the first attempt to address the challenging video scene parsing task in the wild by considering diverse scenes. Based on our VSPW, we further propose Temporal Attention Blending (TAB) Networks to harness temporal context information for better pixel-level semantic understanding of videos. Extensive experiments on VSPW well demonstrate the superiority of the proposed TAB over other baseline approaches. We hope the new proposed dataset and the explorations in this work can help advance the challenging yet practical video scene parsing task in the future. Both the dataset and the code are available at www.vspwdataset.com.
Trust intermediary in a cryptomarket for illegal drugs

Abstract Cooperation without third-party enforcement is particularly puzzling in illicit online markets given the anonymity of online exchanges in the ‘dark web’ and the asymmetry of information between buyers and sellers. Most of the literature investigates the effects of reputation systems on sales. Less is known about the role of (semi)institutionalized solutions to trust problems, such as the escrow service, which deposits payments for online purchases with the market platform and releases them only upon confirmation of the item delivery by a customer. We study the effect of such a trust intermediary on sales in a cryptomarket for illegal drugs. Using a large dataset of illegal online transactions, we estimate two sets of fixed effects models predicting the sellers’ choice to offer the trust intermediary and examine the effects of such a choice on sales. Our results indicate that the trust intermediary reduces online drug sales. We explain this finding by showing suggestive evidence that escrow may crowd out traders’ trust and reciprocity. Our findings have implications for theories of the role of institutions in online markets and offer policy recommendations for law enforcement agencies.
3D-CDRNet: Retrieval-based dense point cloud reconstruction from a single image under complex background

3D reconstruction technique based on deep learning is gaining increasing attention from researchers. The majority of current 3D reconstruction techniques require a simple background, which limit their applications on complex background image. Extracting point cloud features comprehensively is also extremely difficult. This paper design a novel 3D reconstruction network to overcome these limitations. Firstly, we get the image and the retrieved point cloud that is the most similar to the input image. Secondly, to learn the features of the retrieved point cloud, the network encodes and decodes the single image and the retrieved point cloud to generate sparse point cloud. Finally, the proposed dense module densifies the sparse point cloud into the dense point cloud. We use single image of complex background and public dataset to evaluate our network. The reconstruction results indicate that the network surpasses previous reconstruction networks.
INS-GNN: Improving graph imbalance learning with self-supervision

Graph Neural Networks (GNNs) have achieved tremendous success in various applications, such as node classification, link prediction and graph classification. However, graph-structured data is usually imbalanced in many real-world scenarios. When trained on an imbalanced dataset, the performance of GNNs is distant from satisfactory for nodes of minority classes. Due to the small population, these minority nodes have less engagement in the objective function of training and the message passing mechanism behind GNNs exacerbates this problem further, as the information from minority nodes can be overwhelmed by majority nodes in the process of information propagation. To tackle the problems of imbalanced node classification based on GNNs, the most effective way is to promote the engagement of minority nodes during propagation. Therefore, inspired by self-supervised learning on exploring useful information from unlabeled data samples, we propose a novel model-agnostic framework in this paper, named INS-GNN, which tackles the imbalanced node classification problem. Specifically, self-supervised pre-training is firstly used as a pretext task to pre-train the model without label information, which can effectively alleviate the phenomenon that imbalanced labels naturally impose label bias during learning. Then, self-training is exploited to assign pseudo-labels to unlabeled nodes and conduct self-supervised learning to help model training. It can effectively alleviate the problem that obtaining numerous annotation nodes is time-consuming and resource-costly. Finally, self-supervised edge augmentation is utilized to change the structure information of the minority nodes for enhancing the contribution of minority nodes. Experimental results on various real-world datasets demonstrate INS-GNN is able to achieve state-of-the-art performance on solving the imbalanced node classification problem.
AdaBoost Algorithm Could Lead to Weak Results for Data with Certain Characteristics

There are many state-of-the-art algorithms presented in the literature that perform very well on some evaluation data but are not studied with the data properties on which they are applied; therefore, they could have low performance on data with other characteristics. In this paper, the results of comprehensive research regarding the prediction with the frequently applied AdaBoost algorithm on real-world sensor data are presented. The chosen dataset has some specific characteristics, and it contains error and failure data of several machines and their components. The research aims to investigate whether the AdaBoost algorithm has the capability of predicting failures, thus providing the necessary information for monitoring and condition-based maintenance (CBM). The dataset is analyzed, and the principal characteristics are presented. Performance evaluations of the AdaBoost algorithm that we present show a prediction capability below expectations for this algorithm. The specificity of this study is that it indicates the limitation of the AdaBoost algorithm, which could perform very well on some data, but not so well on others. Based on this research and some others that we performed, and actual research from worldwide studies, we must outline that the mathematical analysis of the data is especially important to develop or adapt algorithms to be very efficient.
A compatible detector based on improved YOLOv5 for hydropower device detection in AR inspection system

Recent advancements in computer vision and augmented reality (AR) technology have created a pathway for enhanced human–computer interaction. Despite the potential benefits, the integration of these technologies is not yet common in the industry. Object detection is the foundation of AR systems for inspection, and an efficient object detection model will enable accurate object localization in AR devices. However, the limited computing capabilities and memory usage of wearable AR devices pose a significant challenge to the deployment of state-of-the-art detectors on these devices in real work scenarios. Moreover, as the person wearing the AR device keeps moving, the constantly changing scale of the captured image often leads to significant information loss when feature maps of different scales are fused. To address these challenges, this paper proposes a new compatible detector YOLO-Master based on improved YOLOv5 through deep learning and AR. The paper introduces several improvements, such as using MobileNetv3 as the backbone to reduce memory overhead and computational costs and designing an attention module MCA based on coordinate to reduce information loss. Besides, 20 hyperparameters of YOLOv5 are optimized by the genetic algorithm(GA) before model training, which can help reduces the loss value generated. YOLO-Master was trained and tested on hydropower device images collected from a power plant in the China Southern Power Grid, and the experiments on a self-made dataset demonstrated that the mAP of the proposed method can reach 70.1%, which is 2.9% higher than the baseline of 67.2%. Additionally, the number of parameters of the model was reduced by 66.7%, enabling the model to ensure accuracy while being compatible with real-time performance, meeting the urgent needs of engineering deployment.
University teachers’ profiles based on digital innovativeness and instructional adaptation to COVID-19: Association with learning patterns and teacher demographics

The purpose of this study was to understand the factors behind university teachers' ability to implement instructional changes during the COVID-19 pandemic. An online questionnaire comprised of open-ended and Likert-scale questions was administered to teachers at a Finnish university in April 2020. The sample consisted of 378 university teachers who were categorised into four groups based on their digital innovativeness and the extent to which they implemented changes to adapt their teaching practices to COVID-19 restrictions: Avoider Survival Adapters, Avoider Ambitious Adapters, Embracer Survival Adapters, and Embracer Ambitious Adapters. We examined the association between the teacher groups and their learning patterns and background characteristics. The findings showed that Embracer Ambitious Adapters have significantly more meaning-oriented and application-oriented learning patterns than Embracer Survival Adapters, though Avoider Survival Adapters have more problematic learning patterns. Furthermore, the results indicated that pedagogical training and having more teaching experience helped innovative teachers embrace more changes in their teaching practices during the COVID-19 pandemic. In terms of discipline, the results showed that teachers working in hard disciplines (e.g., physics) were more likely to belong to the Embracer Survival Adapters group, while teachers working in soft disciplines (e.g., history) were more likely to belong to the Embracer Ambitious Adapters group. Possible interpretations of these findings and perspectives for further research are discussed.
A novel facial expression recognition algorithm using geometry β –skeleton in fusion based on deep CNN

Facial expression recognition (FER) methods based on single-source facial data often suffer from reduced accuracy or unpredictability due to facial occlusion or illumination changes. To address this, a new technique called Fusion-CNN is proposed. It improves accuracy by extracting hybrid features using a β-skeleton undirected graph and an ellipse with parameters trained using a 1D-CNN. In addition, a 2D-CNN is trained on the same image. The outputs from these two subnetworks are fused, and their features are concatenated to create a feature vector for classification in a deep neural network. The proposed method is evaluated on four public face datasets: the extended Cohn-Kanade (CK +) dataset, the Japanese Female Facial Expression (JAFFE) dataset, Karolinska Directed Emotional Faces (KDEF), and Oulu-CASIA. The experimental results show that Fusion-CNN outperforms other algorithms, achieving recognition accuracy of 98.22%, 93.07%, 90.30%, and 90.13% for the CK +, JAFFE, KDEF, and Oulu-CASIA datasets, respectively.
Re-evaluation on fuzzy logic controlled system by optimizing the membership functions

Heat exchange among two fluids at different temperatures is achieved by temperature gradient. Heat exchange process modelling and control is complex because of its non-linearity, large inertia, and time variability. Its control effect is often not satisfactory because of these disadvantages which are difficult to overcome. Therefore, it is very important to identify an effective and accurate temperature control scheme for heat exchange system. A fuzzy logic control (FLC) system can be used for controlling this process. FLC system works with fuzzy set theories which are characterized via membership functions (MFs) i.e. fuzzy sets are constructed by their MFs. Their simulation studies can be performed with different shapes such as triangular, trapezoidal, Gaussian, etc. There are different responses to different shapes of MFs of a fuzzy inference system. The membership function must have its values in the range [0,1], which is the only constraint it must satisfy. This paper evaluates the detailed design method of a fuzzy logic control (FLC) system. The study considers the plant function of a heat exchange system and presents the controller response of this system using various membership functions. The study also details out comparison of three different types of membership functions in a fuzzy logic controller. The plant model [2] is used as a test base to confirm the efficacy of this design technique, with modeling and simulations performed using MATLAB Simulink software. Through the results of the simulation, the study, justifies the control strategy which has a significant robustness, quicker response, and better temperature control of the plant system.
Secure Shapley Value for Cross-Silo Federated Learning

The Shapley value (SV) is a fair and principled metric for contribution evaluation in cross-silo federated learning (cross-silo FL), wherein organizations, i.e., clients, collaboratively train prediction models with the coordination of a parameter server. However, existing SV calculation methods for FL assume that the server can access the raw FL models and public test data. This may not be a valid assumption in practice considering the emerging privacy attacks on FL models and the fact that test data might be clients' private assets. Hence, we investigate the problem of secure SV calculation for cross-silo FL. We first propose HESV, a one-server solution based solely on homomorphic encryption (HE) for privacy protection, which has limitations in efficiency. To overcome these limitations, we propose SecSV, an efficient two-server protocol with the following novel features. First, SecSV utilizes a hybrid privacy protection scheme to avoid ciphertext--ciphertext multiplications between test data and models, which are extremely expensive under HE. Second, an efficient secure matrix multiplication method is proposed for SecSV. Third, SecSV strategically identifies and skips some test samples without significantly affecting the evaluation accuracy. Our experiments demonstrate that SecSV is 7.2-36.6 times as fast as HESV, with a limited loss in the accuracy of calculated SVs.