Reviewerly/exHarmony huggingface dataset:
SemanticGraph2Vec: Semantic graph embedding for text representation

Graph embedding is an important representational technique that aims to maintain the structure of a graph while learning low-dimensional representations of its vertices. Semantic relationships between vertices contain essential information regarding the meaning of the represented graph. However, most graph embedding methods do not consider the semantic relationships during the learning process. In this paper, we propose a novel semantic graph embedding approach, called SemanticGraph2Vec. SemanticGraph2Vec learns mappings of vertices into low-dimensional feature spaces that consider the most important semantic relationships between graph vertices. The proposed approach extends and enhances prior work based on a set of random walks of graph vertices by using semantic walks instead of random walks which provides more useful embeddings for text graphs. A set of experiments are conducted to evaluate the performance of SemanticGraph2Vec. SemanticGraph2Vec is employed on a part-of-speech tagging task. Experimental results demonstrate that SemanticGraph2Vec outperforms two state-of-the-art baselines methods in terms of precision and F1 score.
IdenMultiSig: Identity-Based Decentralized Multi-Signature in Internet of Things

Most devices in the Internet of Things (IoT) work on unsafe networks and are constrained by limited computing, power, and storage resources. Since the existing centralized signature schemes cannot address the challenges to security and efficiency in IoT identification, this article proposes IdenMultiSig, a decentralized multi-signature protocol that combines identity-based signature (IBS) with Schnorr scheme under discrete logarithms on elliptic curves. First, to solve the problem of offline or faulty devices under unstable networks, we introduce a novel improvement of the existing Schnorr scheme by introducing a threshold Merkle tree for the verification with only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> valid signatures among <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> participants ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> – <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> tree), while hiding the real identity to protect the data security and privacy of IoT nodes. Furthermore, to prevent dishonest or malicious behavior of the private key generator (PKG), a consortium blockchain is innovatively applied to replace the traditional PKG as a decentralized and trusted private key issuer. Finally, the proposed scheme is proven to be unforgeable against forgery signature attacks in the random oracle model (ROM) under the elliptic curve discrete logarithm (ECDL) assumption. Theoretical analysis and experimental results show that our scheme matches or outperforms existing research studies in privacy protection, offline device support, decentralized PKG, and provable security.
Towards efficient communications in federated learning: A contemporary survey

In the traditional distributed machine learning scenario, the user's private data is transmitted between clients and a central server, which results in significant potential privacy risks. In order to balance the issues of data privacy and joint training of models, federated learning (FL) is proposed as a particular distributed machine learning procedure with privacy protection mechanisms, which can achieve multi-party collaborative computing without revealing the original data. However, in practice, FL faces a variety of challenging communication problems. This review seeks to elucidate the relationship between these communication issues by methodically assessing the development of FL communication research from three perspectives: communication efficiency, communication environment, and communication resource allocation. Firstly, we sort out the current challenges existing in the communications of FL. Second, we have collated FL communications-related papers and described the overall development trend of the field based on their logical relationship. Ultimately, we discuss the future directions of research for communications in FL.
Semi-Supervised and Long-Tailed Object Detection with CascadeMatch

This paper focuses on long-tailed object detection in the semi-supervised learning setting, which poses realistic challenges, but has rarely been studied in the literature. We propose a novel pseudo-labeling-based detector called CascadeMatch. Our detector features a cascade network architecture, which has multi-stage detection heads with progressive confidence thresholds. To avoid manually tuning the thresholds, we design a new adaptive pseudo-label mining mechanism to automatically identify suitable values from data . To mitigate confirmation bias, where a model is negatively reinforced by incorrect pseudo-labels produced by itself, each detection head is trained by the ensemble pseudo-labels of all detection heads. Experiments on two long-tailed datasets, i.e., LVIS and COCO-LT, demonstrate that CascadeMatch surpasses existing state-of-the-art semi-supervised approaches—across a wide range of detection architectures—in handling long-tailed object detection. For instance, CascadeMatch outperforms Unbiased Teacher by 1.9 $$\hbox {AP}^{{\text {Fix}}}$$ on LVIS when using a ResNet50-based Cascade R-CNN structure, and by 1.7 $$\hbox {AP}^{{\text {Fix}}}$$ when using Sparse R-CNN with a Transformer encoder. We also show that CascadeMatch can even handle the challenging sparsely annotated object detection problem. Code: https://github.com/yuhangzang/CascadeMatch .
An access control model for data security sharing cross‐domain in consortium blockchain

Abstract With the rapid increment of the demand for data sharing among parties, data is considered a cornerstone component to provide value in the big data environment. Concerns regarding sharing data security have impeded the development of cross‐domain data interaction. Therefore, an access control model for data security sharing cross‐domain is proposed, Fabric‐ABAC, that is based on Hyperledger Fabric and Attribute‐based Access Control (ABAC). In order to solve the data security challenges caused by a trusted central organization implementation, a distributed environment is constructed that consists of stakeholders among parties. The unified attribute model is designed for multi‐environment combined with smart contracts. Fabric‐ABAC realizes multi‐level, fine‐grained, and auditable access control, enabling data security through automatic permission verification. Considering the ledger is visible to all participants in consortium blockchain, it is necessary to protect the confidentiality of sensitive data. Thus, Proxy Re‐Encryption (PRE), which is implemented by smart contracts, is adopted in the scheme to realize the ciphertext interaction without the third party. The security of PRE and the access control model used in Fabric‐ABAC is discussed to show that a secure environment for data sharing is provided. Moreover, the completeness of the implementation and effectiveness of the system performance in the multi‐domain environment is demonstrated in the experimental results.
An improved arithmetic optimization algorithm for training feedforward neural networks under dynamic environments

This paper proposes an improved Arithmetic Optimization Algorithm (AOA) to train artificial neural networks (ANNs) under dynamic environments. Despite many successful applications of metaheuristic training of ANNs, these studies assume static environments, which might not be realistic in real-world nonstationary processes. In this study, the training of ANNs is modeled as a dynamic optimization problem, and the proposed AOA is used to optimize connection weights and biases of the ANN under the presence of concept drift. The proposed method is designed to work for classification tasks. The performance of the proposed algorithm has been tested on twelve dynamic classification problems. Comparative analysis with state-of-the-art metaheuristic optimization algorithms has been provided. The superiority of the compared algorithms has been verified using nonparametric statistical tests. The results show that the improved AOA outperforms compared algorithms in training ANNs under dynamic environments. The findings demonstrate the potential of improved AOA for dynamic data-driven applications.
Machine Learning-Based Detection of Dengue from Blood Smear Images Utilizing Platelet and Lymphocyte Characteristics

Dengue fever, also known as break-bone fever, can be life-threatening. Caused by DENV, an RNA virus from the Flaviviridae family, dengue is currently a globally important public health problem. The clinical methods available for dengue diagnosis require skilled supervision. They are manual, time-consuming, labor-intensive, and not affordable to common people. This paper describes a method that can support clinicians during dengue diagnosis. It is proposed to automate the peripheral blood smear (PBS) examination using Artificial Intelligence (AI) to aid dengue diagnosis. Nowadays, AI, especially Machine Learning (ML), is increasingly being explored for successful analyses in the biomedical field. Digital pathology coupled with AI holds great potential in developing healthcare services. The automation system developed incorporates a blob detection method to detect platelets and thrombocytopenia from the PBS images. The results achieved are clinically acceptable. Moreover, an ML-based technique is proposed to detect dengue from the images of PBS based on the lymphocyte nucleus. Ten features are extracted, including six morphological and four Gray Level Spatial Dependance Matrix (GLSDM) features, out of the lymphocyte nucleus of normal and dengue cases. Features are then subjected to various popular supervised classifiers built using a ten-fold cross-validation policy for automated dengue detection. Among all the classifiers, the best performance was achieved by Support Vector Machine (SVM) and Decision Tree (DT), each with an accuracy of 93.62%. Furthermore, 1000 deep features extracted using pre-trained MobileNetV2 and 177 textural features extracted using Local binary pattern (LBP) from the lymphocyte nucleus are subjected to feature selection. The ReliefF selected 100 most significant features are then fed to the classifiers. The best performance was attained using an SVM classifier with 95.74% accuracy. With the obtained results, it is evident that this proposed approach can efficiently contribute as an adjuvant tool for diagnosing dengue from the digital microscopic images of PBS.
Radar specific emitter identification based on open-selective kernel residual network

Radar specific emitter identification (SEI) plays important roles in spectrum resource management and Internet of Things (IoT) security. This term refers to the identification of different individual devices of the same type by analyzing the unintentional modulation features caused by hardware imperfections (i.e., fingerprint features). Increasingly many researchers are using deep learning (DL)-based methods for SEI. However, DL-based methods still have many problems, including model generalization, recognition efficiency, and adaptability in open electromagnetic environments. This paper proposes a one-dimensional convolutional neural network (1D CNN)—called a selective kernel residual network (SKResNet)—capable of autonomously adjusting the receptive field, to improve the feature extraction capability. The transient part is cut efficiently from a radar signal sequence as the input of SKResNet using a universal preprocessing method. In addition, considering that most existing methods obtain incorrect results while identifying new emitters without any prior knowledge, SKResNet was improved using OpenMax—an open-set recognition method—to develop Open-SKResNet. As this network could identify unknown emitters effectively, generating reliable classification results of known emitters simultaneously, it could be of great importance and have broad application prospects.
Active federated transfer algorithm based on broad learning for fault diagnosis

Federated learning (FL) guaranteeing data privacy is of great interest in decentralized fault diagnosis. However, limited research attention has been paid to the dynamic domain-shift issue due to varying working conditions. This paper proposes an active federated transfer algorithm based on broad learning to address the domain shift issue in FL. First, a central server dispatches a global model to the source clients for collaborative modeling. Subsequently, the global model is initialized with a federated averaging strategy. Next, the initialized global model is used to annotate emerging signals from the target clients based on an active sampling strategy proposed. Finally, an asynchronous update scheme is designed to adapt the global model to the target domain. The performance of the AFTBL algorithm is validated with three datasets, including 24 centralized- and decentralized-modeling tasks. The computational results indicate that the proposed algorithm is more accurate and efficient than the prevalent algorithms.
PEDNet: A Lightweight Detection Network of Power Equipment in Infrared Image Based on YOLOv4-Tiny

As a promising and noncontact detection technique, machine vision has been widely used in fault diagnosis of substation equipment. The rapid and accurate detection of substation equipment in infrared images is one of the key steps for automatic fault diagnosis. However, the complexity of image background, the low contrast of infrared images, and the rotational targets in infrared images pose a great challenge to detection task. This study aims to improve the detection accuracy of the model while having real-time detection speed and propose a lightweight power equipment detection network (PEDNet) based on You Only Look Once (YOLOv4)-tiny. First, a novel global information aggregation module (GIAM) is constructed to guide the network to focus on the salient regions where the target equipment is located. Second, an improved spatial transformer network (ISTN) is introduced to reduce the impact of rotational targets on detection accuracy. Finally, a feature enhanced fusion network (FEFN) is designed through the use of a multiscale feature cross-fusion structure. It can fully fuse the feature information of the salient region, the rotational targets, and the strong semantic information. The experimental results show that the proposed PEDNet can reach 92.66% detection accuracy and 107.07 frames/s real-time detection speed on the testing datasets. Compared with YOLOv4-tiny, there is a small sacrifice in detection speed, but the detection accuracy is improved and significantly higher than the existing state-of-the-art (SOTA) object detection models.
ICT interaction with trade, FDI and financial inclusion on inclusive growth in top African nations ranked by ICT development

The significance of ICT (information and communication technology) framework in efforts to attract foreign investment (FDI), sustain commerce, and boost financial inclusion for enhanced inclusive growth has revolutionized the global system recently. Furthermore, earlier researches on the connection between ICT and growth concentrated more on general expansion than on the kind of growth that is evenly distributed and has broader societal implications than merely growing the size of the overall economic pie. This study critically analyzes the connection between ICT diffusion and inclusive growth, considering the interactive impacts of trade openness, FDI, and financial inclusion on inclusive growth in top African nations ranked in terms of ICT development, so as to close the existing literature gap. An improved-GMM (generalized method of moments) technique, founded on panel data-set spanning the years 2000–2020, is used in the study. By employing PCA (principal component analysis) to measure inclusive growth, ICT diffusion, and financial inclusion indices, the study contributes to the body of prior research. The model also accounts for the heterogeneous impact of the interactive term between ICT diffusions and trade openness, FDI, and financial inclusion on inclusive growth. Overall, results indicate that while ICT diffusion, trade openness, FDI, and financial inclusion significantly and positively enhance inclusive growth at both the individual and interaction levels, inflation hinders it. The study suggests that policymakers should establish measures to advance ICT development, encourage trade openness, attract foreign direct investment, and improve financial inclusion because these measures have tendencies to support inclusive growth by opening up a number of opportunities.
Quantifying the Vulnerability of Attributes for Effective Privacy Preservation Using Machine Learning

Personal data have been increasingly used in data-driven applications to improve quality of life. However, privacy preservation of personal data while sharing it with analysts/ researchers has become an essential requirement to be met by data owners (hospitals, banks, insurance companies, etc.). The existing literature on privacy preservation does not precisely quantify the vulnerability of each item among user attributes, thereby leading to explicit privacy disclosures and poor data utility during published data analytics. In this work, we propose and implement an automated way of quantifying the vulnerability of each item among the attributes by using a machine learning (ML) technique to significantly preserve the privacy of users without degrading data utility. Our work can solve four technical problems in the privacy preservation field: optimization of the privacy-utility trade-off, privacy guarantees (i.e., safeguard against identity and sensitive information disclosures) in imbalanced data (or clusters), over-anonymization issues, and rectifying or enabling the applicability of prior privacy models when data have skewed distributions. The experiments were performed on two real-world benchmark datasets to prove the feasibility of the concept in practical scenarios. Compared with state-of-the-art (SOTA) methods, the proposed method effectively preserves the equilibrium between utility and privacy in the anonymized data. Furthermore, our method can significantly contribute towards responsible data science (extracting enclosed knowledge from data without violating subjects’ privacy) by controlling higher changes in data during its anonymization.
FL-Incentivizer: FL-NFT and FL-Tokens for Federated Learning Model Trading and Training

Federated learning (FL) is an on-device distributed learning scheme that does not require training devices to transfer their data to a centralized facility. The goal of federated learning is to learn a global model over several iterations. It is challenging to claim ownership rights and commercialize the global model efficiently and transparently. Additionally, incentives need to be provided to ensure that devices participate in the FL process. In this paper, we propose a smart contract-based framework called FL-Incentivizer, which relies on custom smart contracts to maintain flow governance of the FL process in a transparent and immutable manner. FL-Incentivizer commercializes and tokenizes the global model using FL-NFT (FL Non-Fungible Token) based on the ERC-721 standard. FL-Incentivizer uses ERC-20 compliant FL-Tokens to incentivize devices participating in FL. We present the system design and operational sequence of the FL-Incentivizer. We provide implementation and deployment details, complete smart contract codes, and qualitative evaluation of the FL-Incentivizer. After implementing FL-Incentivizer for a global iteration of a Federated learning task, we showed the FL-NFT on OpenSea and an FL-Token for a learner on MetaMask. FL-NFTs can be traded on markets such as OpenSea like other NFTs. While FL-Tokens can be transferred in the same manner as other ERC-20-based tokens.
Quantum Multiagent Actor–Critic Neural Networks for Internet-Connected Multirobot Coordination in Smart Factory Management

As one of the latest fields of interest in both academia and industry, quantum computing has garnered significant attention. Among various topics in quantum computing, variational quantum circuits (VQCs) have been noticed for their ability to carry out quantum deep reinforcement learning (QRL). This article verifies the potential of QRL, which will be further realized by implementing quantum multiagent reinforcement learning (QMARL) from QRL, especially for Internet-connected autonomous multirobot control and coordination in smart factory applications. However, the extension is not straightforward due to the nonstationarity of classical MARL. To cope with this, the centralized training and decentralized execution (CTDE) QMARL framework is proposed under the Internet connection. A smart factory environment with the Internet of Things (IoT)-based multiple agents is used to show the efficacy of the proposed algorithm. The simulation corroborates that the proposed QMARL-based autonomous multirobot control and coordination performs better than the other frameworks.
Active Learning With Co-Auxiliary Learning and Multi-Level Diversity for Image Classification

Due to the fact that it is expensive and time-consuming to annotate a large amount of data, the available labeled data to train a deep neural network is usually scarce, resulting in the poor performance of the deep classification network in most cases. In this paper, we propose a novel active learning method to train a competitive deep classification neural network by labeling a limited amount of diverse images. The proposed method advances most of the active learning methods in two aspects. First, active learning is enhanced with a co-auxiliary learning strategy. We use an auxiliary network to provide diverse pseudo-labels for the primary network. The auxiliary network is also adopted to remove a part of redundancy information from the candidate pool of active learning when image predictions of the primary network and the auxiliary network are the same. Meanwhile, the primary network can also provide pseudo-labels to improve the performance of the auxiliary network. Second, we further remove the redundancy within the query samples with multi-level diversity selection. The multi-level diversity strategy not only considers the feature-level diversity but also the class-level diversity with the predictions of the primary network, and it can select both representative and uncertain samples effectively and efficiently from the large-scale data. The extensive experiments on four large-scale datasets show that the proposed method outperforms the state-of-the-art methods.